As the need for more accurate and reliable positioning systems grows, satellite-based navigation techniques are gaining significant attention, particularly those utilizing Doppler shifts from Low Earth Orbit (LEO) satellites. Traditional Doppler positioning systems often suffer from errors induced by atmospheric disturbances, satellite clock biases, and other signal impairments, especially in dynamic environments. This has motivated the exploration of differential Doppler positioning as a promising solution to mitigate these common-mode errors. This paper explores the theoretical limits of differential Doppler positioning, focusing on Doppler-only methods where position and velocity estimates are derived from Doppler measurements without relying on time-of-arrival (TOA) measurements. By leveraging the Cramér-Rao lower bound (CRLB), we provide a theoretical performance benchmark for the accuracy of position, velocity, and frequency bias estimation. Furthermore, we present a correlation model for atmospheric effects to demonstrate the impact of baseline distance on the estimation performance of differential Doppler positioning. The results show that differential Doppler positioning notably outperforms traditional non-differential Doppler positioning, particularly in low-SNR environments, with substantial gains in frequency bias, 3D velocity, and 3D position estimation accuracy.

Accurate and reliable positioning is essential for Vehicular Internet of Things (IoT) applications, such as autonomous and connected vehicles, to ensure their effective and safe operation. This calls for innovative methods that leverage various sensors and systems to fulfill such demands across diverse environmental and operational conditions. This article presents a multisensor positioning and navigation system that leverages cost-effective commercial-grade sensors for global navigation satellite system (GNSS)-challenging urban and indoor environments. The system integrates the vehicle’s onboard motion sensors (OBMSs) measurements with 3-D point clouds from light detection and ranging (LiDAR) registered to high-accuracy 3-D digital maps for sustained decimeter-level positioning accuracy. Key contributions include accurate LiDAR scan georeferencing with motion compensation, efficient map-to-map registration, and an effective decentralized fusion. Road test experiments on a professional land vehicle setup equipped with a multisensory navigation instrument were performed in downtown and covered parking garage environments with accurate 3-D geodatabase (GDB) available. Results from several road test trajectories demonstrate robust high-precision positioning performance with an average root mean-square error of 20 cm horizontally and 13 cm vertically, as well as position errors of less than 50 cm for 97% of the time and less than 30 cm for 90.7% of the time. The proposed system is a practical option for the positioning and navigation of self-driving cars and has the potential for cooperative mapping and updating 3-D city maps.

5G mmWave offers a high-precision positioning solution, functioning effectively in both line-of-sight (LoS) and operable non-line-of-sight (NLoS) conditions. However, in scenarios with complete signal blockage, integrating with motion-based models becomes crucial. This integration is achieved through Bayesian-based estimators, which entail a prediction and a correction stage weighted by their respective covariance matrices. Although covariance matrices of different prediction models have been extensively studied in the literature, the measurement covariance matrix derived from 5G-based position computations remains largely unexplored. In this paper, we propose a measurement covariance matrix tuning scheme based on a data-driven Cramér-Rao lower bound CRLB model. We validate the proposed algorithm within a simple linear Kalman filter (LKF) positioning framework. The methodology was tested in a controlled simulation scenario using a real 24-minute-long vehicular trajectory in a deep-urban environment. The results demonstrate that the developed data-driven model is reliable, maintaining a standard deviation error of less than 7 cm for 95% of the time and less than 0.5 m for 100% of the time relative to the true computed CRLB. The proposed adaptive KF sustains a position error below 30 cm for 99.3% of the time.

This paper studies the impact of intra- and inter-cell mobility on mmWave networks with a specific focus on beam switching. The paper utilises a geometric model to partition the coverage area of a mmWave gNB cell into radial and angular sectors, thus accounting for the coverage footprints of planar antenna arrays with azimuth-tilt beam orientations (i.e., horizontal and vertical orientations). Using this model, intra-cell beam switching rate is derived analytically. We extrapolate the analysis using stochastic geometry to address inter-cell mobility and system-level beam switching. Our study establishes a relationship between the shape of the antenna array pattern and the beam switching rate. We validate our analysis via extensive Monte Carlo simulations and the results reveal the significant impact of the antenna configuration on beam switching rate. Even when the number of beams remains the same, the beam switching rate can almost double depending on how the antenna array elements are arranged.

5G-based mmWave wireless positioning has emerged as a promising solution for autonomous vehicle (AV) positioning in recent years. Previous studies have highlighted the benefits of fusing line-of-sight (LoS) 5G signals with an Inertial Navigation System (INS) for an improved positioning solution. However, the highly dynamic environment of urban areas, where AVs are expected to operate, poses a challenge, as non-line-of-sight (NLoS) communication can deteriorate the 5G mmWave positioning solution and lead to erroneous corrections to the INS. To address this challenge, we exploit 5G single-bounce reflections (SBRs) and LoS signals to improve positioning performance in dense urban environments. In addition, we integrate the proposed 5G-based positioning with a low-cost inertial measurement unit (IMU) and a wheel encoder. Moreover, the integration is realized using an unscented Kalman filter (UKF) as an alternative to the widely utilized extended Kalman filter (EKF) within the 5G-based positioning research community. We performed two test trajectories in the dense urban environment of downtown Toronto, Canada. For each trajectory, quasi-real 5G measurements were generated using a ray-tracing tool incorporating 3D map scans of real-world buildings, allowing for realistic NLoS and multipath scenarios. For the same trajectories, real motion data were collected from two different low-cost IMUs. Our integrated positioning solution was capable of maintaining a level of accuracy below 30 cm for approximately 97% of the time, which is superior to the accuracy level achieved when SBR signals are not considered, which is only around 92% of the time.

Intelligent Transportation Systems (ITS) rely on connected vehicles to overcome problems such as occlusions and potential accidents, due to non-line-of-sight (NLoS) and other perception challenges. These challenges are magnified when explored in conjunction with communication network limitations, such as limited coverage (e.g., base station limitations) or simple packet collisions. Regardless of the reason behind information loss, the successfully received information should be prioritized to allow successful cooperative perception and accident avoidance. We address these issues by proposing a clustering algorithm that considers information relevance to the receivers and requires no extra communication overhead or network infrastructure. Four different information scoring functions are explored to reorganize data based on its perception relevance in the different clusters, with collision awareness being the focal metric for cluster formation. Our proposed technique achieves the best reduction in the number of packets used compared to existing state-of-the-art: ETSI CPM rules, Look Ahead, and Redundancy Mitigation algorithms. Moreover, thanks to its packet prioritization and reordering, the proposed algorithm outperforms these approaches in terms of the number of packets successfully received by more than 25%. Additionally, it achieves 13.1% and 19.8% enhancement in newly perceived objects, compared to the CPM rules, for the urban and highway scenarios, respectively. Lastly, due to a 6X and 5X improvement in information quality, based on the developed information scoring functions, compared to the baselines for the urban and highway scenarios, respectively.

Achieving the highest levels of autonomy within autonomous vehicles (AV s) requires a precise and dependable positioning solution that is not influenced by the environment. 5G mm Wave signals have been extensively studied in the literature to provide such a positioning solution. Yet, it is evident that 5G alone will not be able to provide uninterrupted positioning services, as outages are inevitable to occur. Towards that end, few works have explored the benefits of integrating mm Wave positioning with onboard motion sensors (OBMS) like inertial measurement units (IMUs) and odometers. Inspired by INS-GNSS integration literature, all methods defaulted to a tightly-coupled (TC) integration scheme, which hinders the potential of such an integration. Additionally, the proposed methods were validated using simulated 5G and INS data with probability-based line-of-sight (LOS) assumptions. Such an experimental setup fails to highlight the true advantages and challenges of 5G-OBMS integration. Therefore, this study first explores a loosely-coupled (LC) 5G-OBMS integration scheme as a viable alternative to TC schemes. Next, it examines the merits and challenges of such an integration in a deep-urban setting using a novel quasi-real simulation setup. The setup comprises quasi-real 5G measurements from the Siradel simulator and real commercial-grade IMU measurements from a challenging one-hour-long trajectory in downtown Toronto. The trajectory featured multiple natural 5G outages which helped with assessing the integration's performance. The proposed LC method achieved a 14-cm level of accuracy for 95% of the time, while significantly limiting positioning errors during natural 5G outages.

It is well-established that mobility is a prominent challenge for beam-based communication. Despite the beam management functions specified by 3GPP to facilitate beam-based communication, its reliability under beam-level mobility remains questionable. Hence, this paper highlights the challenges impeding the reliability of beam-based communication under user mobility and poor propagation conditions. Specifically, this paper investigates beam-switching in mmWave networks and assesses the merits of beam-switching optimization through parametrization. Several parameters, including a Hysteresis margin and a Time-To-Trigger, are investigated with regards to enhancing beam switching. To carry-out the analysis, real beamformed mmWave data is used. The results report key beam switching performance measures and show a critical beam switching optimization trade-off.

Constrained environments, such as indoor and urban settings, present a significant challenge for accurate moving object positioning due to the diminished line-of-sight (LoS) communication with the wireless anchor used for positioning. The 5th generation new radio (5G NR) millimeter wave (mmWave) spectrum promises high multipath resolvability in the time and angle domains, enabling the utilization of multipath signals for such problems rather than mitigating their effects. This paper investigates the benefits of integrating multipath signals into 5G LoS-based positioning systems with onboard motion sensors (OBMS). We provide a comprehensive analysis of the positioning system’s performance in various conditions of erroneous 5G measurements and outage scenarios, which offers insights into the system’s behavior in challenging environments. To validate our approach, we conducted a road test in downtown Toronto, utilizing actual OBMS measurements gathered from sensors installed in the test vehicle. The results indicate that utilization of multipath signals for wireless positioning operating in multipath-rich environments (e.g. urban and indoor) can bridge 5G LoS signal outages, thus enhancing the reliability and accuracy of the positioning solution. The redundant measurements obtained from the multipath signals can enhance the system’s robustness, particularly when low-cost 5G receivers with a limited angle or range measurements are present. This holds true even when only considering the utilization of single-bounce reflections (SBRs).

5G NR mm Wave promises accurate positioning down to the centimeter level. However, mmWave signals endure prismatic propagation, making them prone to signal blockages and non-line of sight (NLoS) communications. To achieve a precise positioning solution, it is rather essential to filter out NLoS gNBs as they yield erroneous pose estimation of IoT vehicular applications. Previous works have attempted to address this issue, however, they are either based on impractical or invalid assumptions about the operation scenario. In this paper, a novel, yet, simple and realistic NLoS detection algorithm is developed. The proposed method measures the discrepancy between Received Signal Strength (RSS)-based and time-based ranges as means to detect NLoS operation. To validate the proposed NLoS detection method, it was incorporated into a Kalman Filter (KF) that fuses the angle of departure (AoD) and round trip time (RTT) measurements from multiple gNBs in a loosely coupled fashion. The proposed method was evaluated on quasi-real measurements acquired from a highly validated 5G simulation tool that simulates the cores of downtown Toronto. The proposed method demonstrates superior results, as it sustains a sub-1m level of accuracy for around 95% of the time, as compared to merely 29% of the time without the NLoS detection.

Cooperative perception is a widely adopted approach to cope with occlusion and non-line-of-sight limitations of the vehicles' local sensors. It enables vehicles to increase their awareness of the environment by sharing their local perception information with others using Vehicle-to-Everything (V2X) technology, thus, avoiding potential accidents. This paper studies the sensor errors and properties and reflects their impact on redundant information shared over communication while arguing for the cases where redundant information could be accepted. Specifically, three perspectives are evaluated: perception issues due to object detection errors, localization errors due to inaccuracy in the onboard navigation system (NS) and the effect of different perception Field of View (FoV). The system is implemented and evaluated using Simulation of Urban MObility (SUMO) traffic simulator and a centralized basestation that coordinates the CV2X communication. Results confirm that 63% of the missed vehicles due to detection error can be retrieved using the suggested Estimated Error Detection (EED) approach. The drawback is increasing the number of duplicate information sent to the receiver. While this exhausts the communication resources, it is still useful for cases where detection is hindered (e.g., by weather conditions). Moreover, our experiments show that the system becomes less reliable when the positioning error is above 1 meter. Lastly, we analyze the effect of the Field of View (FoV) on the centralized basestation objective value, highlighting the importance of 360° perception although it increases duplicate information (51%), pointing to further research required for mitigating duplicate information.

This paper proposes beam failure prediction to recover from inevitable link failures in beam-based mmWave communication proactively. The proposed system consists of two components. First, a prediction engine to foresee future beam failures and their severity. For this purpose, machine learning and deep learning are proposed to perform prediction. The second component is a proactive recovery mechanism, that matches the prediction failure results with a suitable recovery action, with the goal to maintain seamless connectivity and prevent service interruptions. The performance of the proposed system is compared against conventional beam failure detection and recovery. Simulations were carried out using real beamforming data. The results indicate a substantial improvement in the network performance. The improvement is measured in terms of prediction accuracy, beam failure probability and successful beam failure probability. This paper also assesses a drawback of the proposed system, particularly the increase in handover rate, and shows that the achieved gain outweighs this weakness.

5G small cells have the potential to enable sub-meter positioning accuracy in urban canyons and downtown areas, where global navigation satellite system (GNSS) precise point positioning (PPP) suffers the most. As 5G is expected to have a dense deployment of base stations (BSs), it became imperative to utilize the extra information available by means of sensor fusion. Traditionally, an extended Kalman filter (EKF) is used for such a purpose. Yet, one of its main drawbacks is that it requires a linear relationship between the states and the measurements to ensure its optimality. Many papers in the literature perform multi-BS hybrid positioning through the fusion of raw range-based and angle-based measurements via an EKF. Such measurements are inherently highly non-linear with respect to the estimated position state, which leads to high linearization errors. In this paper, we first propose the integration of the available BSs on the positioning level instead of the integration on the raw measurement level to avoid the linearization errors of the EKF. Additionally, we propose a dynamically tuned covariance matrix (DTCM)-KF method, where the BSs are weighted based on their proximity to the UEs, with BSs further away weighted less. The proposed method was tested using a quasi-real setup based on a highway trajectory in Toronto, Canada, along with a ray-tracing-based 5G simulator. The potential of using the proposed 5G positioning as an alternative to GNSS-based positioning in urban canyons is investigated through the comparison with the GPS PPP. The results show that the proposed method outperforms traditional EKF-based measurements level fusion methods. Moreover, it is able to outperform the GPS-only PPP solution. The RMS, maximum, and 95% errors of the proposed method were found to be 0. 39m, 1.4m, and 0. 74m respectively.

Multi-sensor integration is necessary to provide high-precision navigation solutions for autonomous vehicles. Land-vehicles often rely on global navigation satellite systems (GNSS) to acquire its position. However, there are some environments where GNSS signals are unavailable, such as indoor-parking garages, tunnels, and under bridges. Additional sensors are required to allow reliable positioning regardless of location. The vehicle’s on-board low cost inertial sensors (accelerometers and gyroscopes) are used for positioning in GNSS-denied environments. Despite their fidelity in short-term usage, inertial navigation systems (INS) are susceptible to drifts in their positioning solution due to the inherent inertial sensor errors, causing positioning errors over time in prolonged scenarios, such as indoor-parking garages. Modern land-vehicles can be equipped with a diversified set of perception systems (e.g. LiDAR, cameras) to provide information about the surrounding environment. These systems are well-studied in literature to provide accurate positioning. Yet, the performance of these systems may degrade in weather conditions such as heavy snow or rain, and may fail in degraded vision environments. The proposed research addresses some of the limitations of current positioning technologies for land vehicles by integrating low-cost on-board motion sensors with the all-weather electronic scanning radar (ESR) systems presently used in adaptive cruise control. This research employs a method of estimating the vehicle position based on multiple ESR systems. Integration with the on-board motion sensors guarantees continuous positioning estimation for uninterrupted navigation. The multi-sensor system integration utilizes extended Kalman filter (EKF) with a unique dynamic tuning approach for reliable and uninterrupted positioning. The proposed solution was examined in real GNSS-denied scenario of an indoor parking. This research has reached an uninterrupted self-contained EKF-based multi-sensor positioning system providing less than 2m error 90% of the time in a GNSS-denied environment for up to four minutes.

Future autonomous vehicles will rely mainly on global navigation satellite system (GNSS) receivers for positioning services. However, GNSS cannot maintain an accurate, continuous and reliable navigation solution in the presence of jamming. The widespread availability of in-car jammers or personal privacy devices (PPDs) made GNSS receivers an attractive target for signal jamming. Jammers not only jeopardize current positioning and timing services; they can indeed endanger the safety of the evolving intelligent transportation, autonomous road vehicles and critical infrastructure. The disruption of GNSS signal in some applications, especially safety-critical ones can lead to crucial consequences and risks such as loss of time synchronization and potential loss of life, to name a few. Thus, it is essential for future autonomous vehicles and transport service providers to deploy reliable GNSS anti-jamming techniques. This paper introduces a novel anti-jamming technique based on a high-resolution spectral estimation that utilizes fast orthogonal search (FOS) algorithm in which the jamming signal is modeled using a set of candidate functions and then eliminated from the received signal. The performance of the proposed method is assessed using experiments obtained from SpirentTMGSS6700 simulation system. The jamming signal is either obtained from a real jammer or simulated using interference signal generator that is connected to SpirentTMsystem. The results showed that the developed anti-jamming algorithm was able to successfully suppress the continuous wave (CW) interference signal, thus, the performance of the acquisition, tracking and navigation modules within a GPS software receiver were all enhanced.

The last decade has witnessed a growing demand for precise positioning in many applications, including autonomous car navigation. The safety features in autonomous driving and Advanced Driver Assistance Systems (ADAS) require lane-level positioning accuracy. Such accuracy can be obtained from the Global Navigation Satellite Systems (GNSS) through either differential techniques or Precise Point Positioning (PPP). PPP is currently favored over differential GNSS because it provides a global solution without the need for local reference stations. Nevertheless, employing PPP for land vehicles would be challenging due to frequent signal degradation and blockage. Integrating PPP with an Inertial Navigation System (INS) can solve the solution continuity problem; however, the INS solution drifts over time, resulting in losing the desired accuracy. Implementing a reliable PPP/INS system that can preserve the required accuracy is not trivial, especially with financial and computational cost constraints. This article proposes the integration of PPP with the Reduced Inertial Sensor System (RISS) for lane-level car navigation. The high-precision needed in lane-level positioning can be achieved by integrating PPP with high-end INS. Since high-end INS are expensive, this work proposes the use of RISS instead of the traditional INS. RISS uses only one gyroscope and two accelerometers, which can save more than half the high-end INS cost. The proposed PPP/RISS system was tested through three road tests that included highway driving under several overpasses. The system was able to maintain horizontal position errors of less than 50 cm.

Global Navigation Satellite Systems (GNSS) is utilized to provide route guidance information to land and autonomous vehicles. The GNSS-based positioning and navigation (POS/NAV) usually suffer from satellite signal blockage, interference, and multipath in urban areas. Autonomous land vehicles are equipped with cameras, radars, and laser ranging devices. The availability of these systems provides an attractive opportunity to increase the POS/NAV system accuracy. This research focuses on the development of an integrated multi-sensor POS/NAV system capable of offering seamless positioning for autonomous land vehicles. A new multi-sensor POS/NAV module integrating both adaptive cruise control frequency modulated continuous wave (ACC-FMCW) radar (RAD), and magnetometer measurements with the reduced inertial sensor system (RISS) was designed to update the navigation system during GNSS outages. Augmenting RAD/RISS system with the magnetometer measurements produces a robust solution. The designed system is further improved by utilizing fast orthogonal search (FOS) to provide nonlinear error modeling of the residual errors associated with the RAD/RISS positioning solution in order to reduce the error growth overextended and frequent GNSS outages.The proposed systems were evaluated on several real road test trajectories involving different types of land vehicles experiencing different motion dynamics. GNSS outages of up to 10 minutes were intentionally introduced to examine the performance. The results show that the proposed methods have resulted in a significant performance improvement in the positioning accuracy that can reach more than 80% if compared to the present techniques that rely only on integrating the inertial sensor technology with GNSS.

The past decade has witnessed a staggering evolution in cellular networks. Mobile wireless technologies have undergone four distinct generations; from uncomplicated voice calls in the first generation to high-speed, low latency and video streaming in the fourth generation. The numerous services brought to the users by 4G network have caused an increasing load demand. This increasing demand in network usage has proven the necessity of further service enhancements, such as predictive resource allocation techniques and handover analysis. For these techniques to be deployed, network quality and performance analysis must be performed on real-world network data. Since throughput is a major indicator of the network's performance, throughput modelling and prediction can be utilized for analyzing network quality. In this paper, two approaches for throughput analysis are examined: classical machine learning and time series forecasting. For the first approach, various machine learning models were deployed for throughput prediction and our analysis showed that the random forest model achieved the highest prediction performance. For time series forecasting, statistical methods as well as deep learning architectures were used. The evaluation shows that the machine learning models had a higher throughput prediction performance than the time series forecasting techniques.

With the advancements in wireless network technologies over the past few decades and the deployment of 4G LTE networks, the capabilities and services provided to end-users have become seemingly endless. Users of smartphones utilize high-speed network services while commuting on public transit and hope to have a consistent, high-quality connection for the duration of their trip. Due to the massive load demand on cellular networks and frequent changes in the underlying radio channel, users often experience sudden unexpected variations in the connection quality. To overcome such variations and maintain a consistent connection, these variations need to be predicted before they occur. This can be accomplished by the spatio-temporal analysis of the different network quality parameters and the investigation of the main factors that affect the network's performance and QoS. To this end, we conducted a network survey via Kingston Transit in Kingston, Ontario, Canada. We used the Android network monitoring application G-NetTrack Pro to build a dataset of various client-side wireless network quality parameters. The dataset consists of 30 repeated public transit bus trips at three different times of the day, each lasting around one hour. In this paper, we describe the data collection process, present an analysis of the collected data, and investigate the effects of time and location on the network's measured throughput and signal strength. We made the collected data, including more than 190 thousand unique records, publicly available to researchers in a domain where open data is rare.

Automated vehicles (AVs) have gained increasing interest over the past few years. A crucial feature of these vehicles is an accurate and robust positioning system. Global navigation satellite system (GNSS) precise point positioning (PPP) can achieve decimeter-level accuracy without the need for local reference stations. Nevertheless, the solution availability is affected by GNSS signal outages, which frequently occur in AVs driving scenarios. The integration with an inertial navigation system (INS) provides a continuous positioning solution; however, high-end inertial sensors are bulky and expensive. The recent improvements to the low-cost micro-electro-mechanical (MEMS) sensors opened the way to utilize these sensors in high-precision applications. The objective of this work is to investigate the performance of integrating dual-frequency PPP with low-cost MEMS sensors for land vehicles on highways and suburban areas. Furthermore, the Reduced Inertial Sensor System (RISS) is used instead of the traditional INS system. RISS uses two horizontal accelerometers and one vertical gyroscope in addition to the vehicle odometer, eliminating two gyroscopes and one accelerometer compared to the full IMU system. The lower number of sensors contributes to reducing the error growth over time and reducing the system cost and complexity. A road test was performed that included suburban areas and highway driving with multiple overpasses. The result showed that the developed PPP/RISS system was able to achieve decimeter-level rms positioning errors and a maximum of one meter horizontal positioning error.

Global Navigation Satellite System (GNSS) has always been used as the leading positioning systems in land vehicles for decades. The satellite-based localization system provides an accurate solution most of the time except in denied environments like tunnels, urban canyons, and closed parking areas. Such issues were traditionally mitigated by the aid of other sensors. Inertial Navigation Systems (INS) tackle the GNSS drawbacks in challenging environments. However, the INS can't stand for an extended period without substantial drift in the localization output. Despite the availability of advanced inertial solution as Reduced Inertial Sensors Systems (RISS), the GNSS/RISS integration still suffers from the same error sources. Recently, several perceptual sensors are included in most of the cars for autonomy purposes. The proposed solution utilizes electronic scanning radar, which exists in vehicles from level 1 of autonomy and higher. The methodology presented shows the ESR ability to determine the vehicle's forward from static objects. Moreover, the acquired frontal speed is used to produce an unique ESR odometry. The obtained ESR/RISS solution aids the 3D-RISS using an Extended Kalman Filter (EKF) in GNSS outages. The system was examined in real challenging scenarios in Indoor parking and downtown areas in Toronto.

Navigation of land or self-driving vehicles is essential for safe and accurate travel. The global navigation satellite systems (GNSSs), such as global positioning system (GPS) are the primary sources of navigation information for such purpose. However, high-rise buildings in urban canyons block the GPS satellites signals. Alternatively, inertial navigation system (INS) is typically working as a backup. A reduced inertial sensor system (RISS) is used instead of the full INS to achieve the same purpose in land vehicles navigation with fewer sensors and computations. Unfortunately, the RISS solution drifts over time, but this can be mitigated when integrated with the GPS. However, the integration solution drifts in the case of GPS signal loss (outages). Therefore, the position errors grow especially during extended periods of GPS outages. Azimuth/heading angle is critical to keep the vehicle on the route. In this paper, an azimuth update estimated from a calibrated magnetometer is introduced to improve the accuracy of the overall system. A new approach is proposed for pre-processing the magnetometer data utilizing a discrete-cosine-transform (DCT)-based pre-filtering stage. The obtained azimuth is utilized in updating the RISS system during the whole trajectory and mainly during GPS outage periods. The proposed approach significantly decreases both the azimuth error and the position error growth rate when driving in urban canyons where the GPS signals are blocked. Finally, the proposed system was tested on a real road trajectory data for a metropolitan area. The results demonstrate that the accuracy of the whole system improved, especially during the GPS outage periods.

In the era of autonomous cars, accurate vehicular positioning becomes very essential. The global navigation satellite systems (GNSS) suffer from signal blockage and severe multipath in urban canyons, which degrades the positioning accuracy and availability. Therefore, vehicles solely relying on positioning from GNSS receivers have limited performance. In this research, we present a novel unified cooperative positioning solution which enhances positioning accuracy and availability in urban canyons. The proposed system exploits the fact that vehicles have different positioning resources and is based on angle approximation, which artificially generates the hindered pseudorange by sharing angle information between vehicles using dedicated short-range communication. In addition, we propose a system that employs the proposed cooperative technique to assist the loose integration between the inertial navigation system (INS) and the GPS system (using extended Kalman filter) during partial GPS outages. Using raw data from inertial sensors and GPS receivers in the real road trajectories, we implement the cooperative INS/GPS loose integration and show that our cooperative integrated system outperforms the non-cooperative integrated system. The performance metrics used are the 2-D positioning root-mean-square error, the maximum 2-D positioning error, and the positioning accuracy gain (PAG). Specifically, the PAG gain is around 88%, 80%, and 60% when the number of blocked satellites is one, two, and three, respectively.

Today deployments of massive Internet of Things (IoT) applications are expected from 5G networks. A primary challenge however is designing scalable wireless resource management schemes that can adapt to the varying temporal and spatial demand of IoT applications. As such, intelligence-based solutions that are agile to, and are able to exploit IoT traffic patterns are emerging as key enablers for 5G IoT applications. For example, Predictive Resource Allocation (PRA) has been proposed in wireless network literature as a mechanism to provide significant energy-savings and Quality of Experience (QoE) gains by leveraging predictions of the user location. While the results are very promising, further research is needed to 1) model and handle the inherent uncertainty in the predicted rates of PRA, and 2) develop low-complexity solutions for practical adoption. This is the topic of this paper, where we present a credibility-based chance-constrained fuzzy programming solution for PRA that enables the operator to control the energy efficiency-QoE tradeoff for different users and services. We demonstrate the use of a Kalman Filter (KF) to adaptively model rate prediction uncertainty by modifying the limits of the fuzzy membership functions in real-time. Our simulation results indicate that the proposed credibility-based framework provides a low-complexity solution for robust PRA.

Future smart cities are profoundly looking forward to providing services that assure daily competent functionality. Efficient traffic management and related vehicular services are crucial aspects when considering the city's decent operation. The significant presence of the vehicular and smartphone sensing and computing capabilities within and amongst the vehicles open the door towards robust vehicular and road services. The retrofitted present and future vehicles will be able to provide accurate real-time information about the road conditions and hazards, driver behaviour, and traffic. Adequate geo-referencing is remarkably demanded in order to preserve robustness while providing vehicular services. Present and widely spread global positioning systems (GPS) receivers are providing low- resolution position update at 1 Hz, which is not sufficient at high speeds. Also, alternative high data rate geo-referencing technologies may face self-contained or environmental-based performance limitations. In this paper, we propose an adaptive resolution integrated geo-referencing framework that augments GPS and inertial sensors to provide accurate localization and positioning for road information services. Also, we examine the effectiveness of the proposed system in geo- referencing for selected real-life road services.

Owing to the expeditious growth in the information and communication technologies, smart cities have raised the expectations in terms of efficient functioning and management. One key aspect of residents' daily comfort is assured through affording reliable traffic management and route planning. Comprehensively, the majority of the present trip planning applications and service providers are enabling their trip planning recommendations relying on shortest paths and/or fastest routes. However, such suggestions may discount drivers' preferences with respect to safe and less disturbing trips. Road anomalies such as cracks, potholes, and manholes induce risky driving scenarios and can lead to vehicles damages and costly repairs. Accordingly, in this paper, we propose a crowdsensing based dynamic route planning system. Leveraging both the vehicle motion sensors and the inertial sensors within the smart devices, road surface types and anomalies have been detected and categorized. In addition, the monitored events are geo-referenced utilizing GPS receivers on both vehicles and smart devices. Consequently, road segments assessments are conducted using fuzzy system models based on aspects such as the number of anomalies and their severity levels in each road segment. Afterward, another fuzzy model is adopted to recommend the best trip routes based on the road segments quality in each potential route. Extensive road experiments are held to build and show the potential of the proposed system.

Recently, smart cities functionality and management have captured notable consideration. Owing to the rapid development in the information and communication technologies (ICT), various applications and services are highly engaged in the cities' operation. Specifically, intelligent road services as traffic management, driver behavior assessment and crowdsensing based road condition monitoring contribute towards better operability. To sustain decent performance of these applications, accurate and continuous positioning is an essential concern. Generally, Global Navigation Satellite System (GNSS) receivers are vulnerable to partial or complete outages due to multipath or signal blockage. Consequently, inertial navigation systems integrated with GNSS receivers are affected by inertial sensors noises and biases. In this paper, we apply wavelet packet de-nosing to eliminate noises of the Micro-Electro-Mechanical Systems (MEMS) grade inertial sensors. Afterwards, we integrate the de-noised reduced inertial sensor system (RISS) with GNSS receivers in real road experiment to assess the system performance. In addition, we show the significance ofthe proposed integration over the conventional one during multiple GNSS outages under various driving scenarios.

The Internet of Things (IoT) infrastructure, systems, and applications demonstrate potential in serving smart city development. Crowdsensing approaches for road surface conditions monitoring can benefit smart city road information services. Deteriorated roads induce vehicle damage, traffic congestion, and driver discomfort which influence traffic management. In this paper, we propose a framework for monitoring road surface anomalies. We analyze the common road surface types and irregularities as well as their impact on vehicle motion. In addition to the traditional use of sensors available in smart devices, we utilize the vehicle motion sensors (accelerometers and gyroscopes) presently available in most land vehicles. Various land vehicles were used in this paper, spanning different sizes, and year model for extensive road experiments. These trajectories were used to collect and build multiple labeled data sets that were used in the system structure. In order to enhance the performance of the sensor measurements, wavelet packet de-noising is used in this paper to enable efficient classification of road surface anomalies. We adopt statistical, time domain, and frequency domain features to distinguish different road anomalies. The descriptive data sets collected in this paper are used to build, train, and test a system classifier through machine learning techniques to detect and categorize multiple road anomalies with different severity levels. Furthermore, we analyze and assess the capabilities of the smart devices and the other vehicle motion sensors to accurately geo-reference the road surface anomalies. Several road test experiments examine the benefits and assess the performance of the proposed architecture.

Recent research on predictive video delivery promised optimal resource utilization and quality of service (QoS) satisfaction to both dynamic adaptive streaming over HTTP (DASH) providers and mobile users. These gains were attained while presuming an idealistic environment with perfect predictions. Thus, a robust QoS-aware predictive-DASH (P-DASH) is of paramount importance to handling the practical uncertainty implied in predicted information. In this paper, we propose a stochastic QoS-aware robust predictive-DASH (RP-DASH) scheme over future wireless networks that takes into account imperfect rate predictions. The objective is to achieve long-term quality fairness among the DASH users while capping the probability of service degradation by an operator predefined level. A deterministic formulation is then obtained using the scenario approximation, which adopts the probability density function (PDF) of predicted rates. A linear conservative approximation is introduced to provide an NP-complete formulation, which can be optimized by commercial solvers. Since exact PDF might not be available, Gaussian approximation is adopted by the introduced scheme to provide a closed form less complexity formulation. To support real-time implementations, a guided heuristic algorithm is devised to obtain near-optimal resource allocations and quality selections, while satisfying the predefined QoS level. Previous non-robust P-DASH schemes are evaluated in this paper, while considering typical error models in predicted rates. Such schemes resulted in increased QoS and the quality of experience degradations with the network load, which was avoided by the introduced RP-DASH. Results further revealed the ability of RP-DASH to reach optimal and fair QoS satisfactions.

The exploitation of mobility traces and rate predictions has enabled predictive delivery of video content that can achieve optimal resource utilization and long-term Quality of Service (QoS) satisfaction. The network recognizes users moving towards poor radio conditions in order to prioritize them over other users with better future conditions. In this paper, we propose a QoS-aware predictive Dynamic Adaptive Streaming over HTTP (DASH) scheme that leverages future information to select both the resource sharing and video qualities over a time horizon. The scheme minimizes the number of quality switches while achieving a minimal average quality level with no video stops. We firstly define the maximum prediction gains under idealistic conditions by a scheme referred to as Optimal QoS-Aware Predictive-DASH (OQP-DASH). Then, a robust stochastic based formulation is introduced to handle the practical uncertainty in predicted information, where the scheme is denoted by Robust QoS-Aware Predictive-DASH (RQP-DASH). A chance constraint programming model based on Scenario Approximation (SA) is adopted to cap the risk of service degradation while using the Probability Mass Function (PMF) of predicted rates. Under idealistic conditions, OQP-DASH outperforms the non-predictive opportunistic counterpart and results in fewer quality switches. Applying estimation errors, RQP-DASH avoids QoS degradation without compromising the prediction gains which supports the application of predictive DASH in future network.

Road information services (RIS) is a major component of the information and communication technologies with the main purpose of RIS-based systems is to monitor road health conditions, weather information and traffic congestion. Considering the road conditions, there are various kinds of road surface types and anomalies with lack of efficient analysis of their behavior on the vehicle sensor measurements. Consequently, there are difficulties in detecting and categorizing the different road types and anomalies. This paper demonstrates road test results for the measurements of inertial sensors mounted in land vehicles while monitoring various road surface types and anomalies. In addition, a wavelet-based feature extraction together with statistical approach for the road types and anomalies are explored in this study. Two road test experiments on two different vehicles performed in Kingston, ON, Canada together with in-depth analysis are discussed in this paper.

Highly predictable users' location and traffic have enabled a new video delivery paradigm over wireless networks referred to as Predictive Resource Allocation (PRA). Existing research assumes perfect prediction of information in order to derive the performance bounds of PRA and define its gains over conventional Resource Allocation (RA). In this paper we sustain the application of energy-efficient PRA under prediction uncertainties. To that end, we propose a stochastic robust PRA scheme that models the uncertainty in future demands and incorporates them in the mathematical formulation. A linear Recourse Programming (RP) model is adopted in order to represent the trade-off between the energy-savings and the risk of wasting resources while considering the probability of a user terminating or skipping the video session. Thus, avoids prebuffering the video chunks that might be skipped by the user. A low complexity near optimal algorithm is then introduced to provide real-time solutions for the formulated RP model. Simulation results demonstrate the ability of the introduced robust PRA to deliver energy-efficient video streaming with lower resources than the existing PRA while promising QoS satisfaction. These results provide the impetus to implement the robust PRA in future wireless networks.

Recognition of the mode of motion or mode of transit of the user or platform carrying a device is needed in portable navigation, as well as other technological domains. An extensive survey on motion mode recognition approaches is provided in this survey paper. The survey compares and describes motion mode recognition approaches from different viewpoints: usability and convenience, types of devices in terms of setup mounting and data acquisition, various types of sensors used, signal processing methods employed, features extracted, and classification techniques. This paper ends with a quantitative comparison of the performance of motion mode recognition modules developed by researchers in different domains.

Predictive resource allocations (PRAs) have recently gained attention in wireless network literature due to their significant energy-savings and quality of service (QoS) gains. This enhanced performance was primarily demonstrated while assuming the perfect prediction of both mobility traces and anticipated channel rates. While the results are very promising, several technical challenges need to be overcome before PRAs can be practically adopted. Techniques that model the prediction uncertainty and provide probabilistic quality of service (QoS) guarantees are among such challenges. This differs from the traditional robust optimization of wireless resources, as PRAs use a time horizon with predicted demands and anticipated data rates. In this paper, we tackle this problem and present an energy-efficient stochastic PRAs framework that is robust to prediction uncertainty under generic error probability density functions. The framework is applied for video delivery, where the desired video demands are modeled as probabilistic chance constraints over the prediction time horizon, and a deterministic closed form is then derived based on the Bernstein approximation (BA). In addition to handling prediction uncertainty, mechanisms that track the variance of the channel in real-time are practically needed. Towards this end, we demonstrate how a particle filter (PF) can be adopted to effectively achieve this functionality. A low complexity guided heuristic algorithm is also integrated with the BA-based allocations, and particle filter (PF), to provide a real-time solution. Extensive numerical simulations using a standard compliant long term evolution system are then presented to examine the developed solutions under various operating conditions. Results indicate the ability of our framework to significantly reduce base station energy consumption while satisfying users' QoS under practical prediction uncertainty.

Predictive Resource Allocation (PRA) has demonstrated its ability to provide smooth video delivery with minimal and fair interruptions. Recent work on PRA techniques exploited rate predictions to strategically allocate the limited radio resources for delivering video content. However, existing PRA techniques assume perfect prediction of future information in order to define the maximum attainable gains. In this paper, we introduce a probabilistic robust PRA framework that handles prediction errors. By adopting chance constraint programming we were able to define a probabilistic measure on the QoS degradation due to prediction uncertainties. A deterministic non-convex formulation is then obtained using the statistical parameters of predicted rates. Accordingly, we propose a convex approximation to the formulated fair PRA, which can be solved using optimal solvers to obtain a benchmark solution for future robust PRA schemes. We evaluate non-PRA and non-robust PRA schemes considering typical error models of the predicted rates. We found these schemes to result in suboptimal fairness and increased QoS degradations with the network load. Results further reveal the ability of the introduced robust fair PRA to reach the optimal and fair QoS satisfaction levels. Our approach provides a step towards applying PRA in future wireless networks to deliver video streaming content.

In this paper, we propose a new architecture to read the smart meters which are commonly distributed nowadays in smart cities. In this architecture, public transportation vehicles are utilized as Data Collectors (DCs) that reads these smart meters. Moreover, we target the path planning problem for these DCs given that a limited number of vehicles with a specific storage capacity are able to participate in collecting readings from these meters. We optimize the number of DCs while maintaining their minimum travelling distances and satisfied traffic constraints. We propose a Genetic-based Routing (GR) approach for more optimized solutions. Extensive simulation results are performed to confirm the effectiveness of the proposed approach in comparison to other heuristic approaches.

This paper addresses the challenge of vehicle selection in a Vehicular Ad-hoc Network (VANET) used to assist vehicles with limited satellite visibility in urban environments. In [1], we proposed a Non-Range cooperative positioning system which uses pseudoranges from only one assisting vehicle at any given time. However, many vehicles are within the communication zone of the target vehicle especially in dense urban canyons. In this paper, we elevate the performance of our cooperative system by proposing a distributed vehicle selection criterion named Absolute Sum of Single Differencing (ASOSD). To test the viability of the proposed system, we design a cooperative experiment using three NovAtel receivers and show that the Positioning Accuracy Gain (PAG) of our system has increased by 60% compared to a system that averages Artificial Candidate Pseudoranges (ACPs) from two assisting receivers. Moreover, we study the effect of; the number of assisting vehicles, multipath, receiver noise, satellite clock bias, ionospheric and tropospheric errors on the selectivity of the proposed system. We show that as the number of assisting vehicles increase, the Root-Mean-Square-Error (RMSE) of the generated ACP decreases. Moreover, the selectivity of the ASOSD selector is not affected by the common errors in the shared pseudoranges.

Predictive resource allocation (PRA) techniques that exploit knowledge of the future signal strength along roads have recently been recognized as promising approaches to save base station (BS) energy and improve user quality of service (QoS). Recent studies on human mobility patterns and wireless signal strength measurements along buses and trains have indeed supported the practical potential of PRA. An unresolved challenge, however, is modeling the uncertainty in the predictions, and developing real-time robust solutions that incorporate probabilistic QoS guarantees. This is of paramount importance in PRA due to the prediction time horizon that adds considerable complexity and increases the rate uncertainty in the problem. With these developments in mind, this paper addresses energy-efficient PRA applied to stored video streaming using chance constrained programming. The proposed solution incorporates: 1) uncertainty in predicted user rates; 2) a joint level of probabilistic constraint satisfaction over a time horizon; and 3) both optimal gradient-based and real-time guided heuristic solutions. Our framework fundamentally differs from previous PRA work in the literature where nonstochastic approaches with assumptions of perfect prediction were primarily used to demonstrate the potential energy savings and QoS gains. Numerical simulations based on a standard compliant long term evolution (LTE) system are provided to examine and compare the developed solution. Unlike existing energy-efficient PRA, the proposed framework achieves the desired QoS level under imperfect channel predictions. This robustness is attained without compromising the energy-efficiency compared to opportunistic schedulers, and thus supports PRA implementation in practice.

The goal to achieve accurate and ubiquitous localization is the driving force for location based services in vehicular ad hoc networks (VANETs). In urban areas, global positioning system (GPS) and in-vehicle navigation sensors (e.g. odometers) suffer from prolonged outages and unsustainable error accumulation, respectively. The need for precise vehicle localization remains paramount, and cooperative vehicle localization based on ranging techniques are being exploited to this end. This paper presents a novel cooperative localization scheme that utilizes round trip time (RTT) for inter-vehicle distance calculation, integrated with inertial sensor measurements to update the position of not only the vehicle to be localized, but its neighbors as well. We adopted the extended Kalman filter (EKF), to limit the effect of errors in both the sensors and the neighbors' positions, in computing the new location. In comparison to the existing cooperative localization techniques, our proposed cooperative scheme does not depend on GPS updates for the neighbors' positions thus making it far more suitable in urban canyons and tunnels. In addition, our scheme considers updating the neighbors' positions using their current inertial sensor measurements resulting in; better position estimation. The scheme is implemented and tested using the network simulator 3 (ns-3), vehicle traces are generated using SUMO and error models are introduced to the sensors and initial positions for different velocities and densities. Results show that our scheme outperforms the inertial navigation systems (INS) technology typically used in environments where GPS fails.

The promising energy saving and QoS gains of Predictive Resource Allocation (PRA) techniques have recently been recognized in the wireless network research community. These gains were primarily introduced in light of perfect prediction of both mobility traces and anticipated channel rates. However, under real world considerations of prediction errors, the reported gains cannot be guaranteed and further investigation is needed. In this paper, we demonstrate the practical potential of PRA by developing a robust, probabilistic framework that guarantees QoS satisfaction for video streaming under imperfect predictions, without compromising the energy saving gains. The proposed PRA framework uses chance-constrained programming to model video streaming QoS for all users during the foreseen time horizon. Closed form solutions are developed using the Gaussian and Bernstein approximations based on the channel statistical measures. Extensive numerical simulations using a standard compliant Long Term Evolution (LTE) system are presented to examine the developed solutions, for different user mobility scenarios and target QoS levels. The results demonstrate the various design trade-offs involved toward the practical deployment of predictive video streaming in future generation networks.

Unmanned Aerial Vehicles (UAVs) have demonstrated their efficacy in supporting both military and civilian applications, many of which contain tasks that are parallel in nature, and can benefit from cooperation in terms of effectiveness. One of the fundamental challenges of multi-UAV systems is autonomous team coordination. This paper looks at flocking with small fixed-wing UAVs in the context of a model-free reinforcement learning problem. Dyna-Q ( ) with a variable learning rate is employed by the agents to learn a control policy that facilitates flocking in a leader-follower topology while operating in a stochastic environment. Simulation results demonstrate the followers learning and adapting their policies to non-stationary stochastic environments.

Machine vision is widely used in many applications of engineering. In this paper, a new approach of machine vision is proposed for autonomous robotics navigation. The proposed methodology uses a sequence of images to locate homologous points and rebuild the objects in the robot surroundings. Using this approach a robot is able to measure an obstacle in order to avoid a collision as well as measure its velocity in maneuver using only information provided by cameras. Experimental results validate the application of the proposed method for autonomous robots applications.

Location-Based Services (LBS) and Intelligent Transportation Systems (ITS) demand positioning accuracy and availability requirements. In urban canyons, Global Navigation Satellite Systems (GNSS) suffer from signal blockage, jamming , severe multipath and low Carrier-to-Noise (C/No) ratio which degrade location accuracy and availability. Therefore, applications solely relying on GNSS have limited performance. In this paper, we present a novel unified Cooperative Positioning (CP) solution which enhances positioning accuracy and availability in urban canyons. Our proposed approach is named Angle Approximation (AA). AA requires no infrastructure or other aiding sensors, AA is distributed and addresses two core challenges (limited positioning accuracy and availability) in a unified solution. AA artificially generates the hindered pseudorange by sharing angle information between vehicles using Dedicated Short Range Communication (DSRC). To enhance the performance of the AA technique, we propose and analytically derive the Absolute Sum of Double Differencing (ASODD) method which increases the probability of selecting the most accurate generated pseudorange. We experimentally evaluate the performance of the proposed system through real measurements using NovAtel receivers. We also carry out extensive simulations to demonstrate the ability of the proposed system to increase solution availability. Our experimental and simulation results demonstrate that the solution accuracy of our approach is inversely proportional to the distance between vehicles. Specifically, the mean error of the generated pseudorange is limited to 14 percent of the distance between vehicles.

Novel mobility-aware resource allocation schemes have recently been introduced for efficient transmission of stored videos. The essence of such mechanisms is to lookahead at the future rates users will experience, and then strategically buffer content into user devices when they are at peak radio conditions. For example, a user approaching poor coverage will be preallocated additional video segments to ensure smooth streaming. Advances in mobility prediction and real-time radio environment map updates are driving forces for such Predictive Video Streaming (PVS) mechanisms. Although previous efforts have demonstrated the large potential gains of PVS, ideal channel predictions were assumed. This paper addresses the problem of channel uncertainty in PVS, and proposes a robust resource allocation framework that 1) models channel uncertainty, 2) solves the PVS problem with a tunable level of quality of service guarantees, and 3) learns the degree of uncertainty, and adapts the channel model accordingly. Numerical results demonstrate the effectiveness of the proposed approach for PVS under channel variability.

A team of Unmanned Aerial Vehicles (UAVs) is used for the dynamic encirclement of a moving target in simulation. The encirclement tactic is defined for the situation in which a target is isolated and surrounded by a group of UAVs. It may be employed by a team of UAVs to neutralize the target and restrict its movement. A combination of decentralized Linear Model Predictive Control (LMPC) and Feedback Linearization (FL) is implemented on the team of UAVs in order to accomplish dynamic encirclement around a moving target. The main contribution of this paper lays in the application of LMPC and FL to solve the problem of encirclement of a moving target using an autonomous team of UAVs in simulation.

Using absolute navigation systems (such as global navigation satellite systems) has proved to be insufficient for indoor navigation or when navigating in urban canyons due to multipath and obstruction. This opened the gate widely for sensor-based navigation systems to be used, particularly after the development of low-cost microelectromechanical system sensors. Heading determination is one of the most important aspects for navigation solutions. Magnetometer is a low-cost sensor that can provide an absolute heading from magnetic north by sensing the Earth's magnetic field. Magnetometer readings are usually affected by magnetic fields, other than the Earth's magnetic field, and by other error sources; therefore, magnetometer calibration is required. In this paper, a technique is proposed for fast and automatic magnetometer calibration that requires small space coverage. There is no user involvement in the calibration process, and there are no required specific movements. The proposed technique performs 3-D-space magnetometer calibration using 2-D calibration equations with pitch and roll sectors. The 3-D-space is divided into a group of pitch and roll sectors. Inside each sector, 2-D calibration can be performed for the leveled magnetometer readings, which make the calibration process faster and requiring less data. This technique makes the magnetometer useful for determining heading in pseudo-tethered devices, particularly when used while driving. Pseudo-tethered navigation devices are tethered at normal operation, but they can change their orientation according to user needs such as portable vehicle navigation devices, which can be placed on the dashboard of a vehicle or attached to the wind shield.

Integrated navigation systems based on a tightly coupled integration scheme utilize pseudoranges and pseudorange rates from Global Positioning System (GPS) satellites measured by the receiver. The positioning accuracy is highly dependent on the accuracy of the pseudoranges whose residual errors can deteriorate the overall positioning accuracy. The integrated system can be improved by the provision of more accurate pseudoranges through modeling the residual correlated errors. This paper utilizes parallel cascade identification (PCI), which is a nonlinear system identification technique, to model these correlated errors. To address the nonlinear error characteristics in the whole integrated navigation system, a nonlinear filter, i.e., mixture particle filter (M-PF), is employed to perform tightly coupled integration of a 3-D reduced inertial sensor system (RISS) with a GPS. The M-PF can accommodate the PCI models of the pseudorange errors in the measurement model. The results demonstrate the advantages of using M-PF-PCI for correcting the pseudoranges and enhancing the positioning solution as compared with M-PF-only, Kalman filter (KF)-PCI, and KF-only solutions.

Context-awareness and Location-Based-Services are of great importance in mobile computing environments. Although fingerprinting provides accurate indoor positioning in Wireless Local Area Networks (WLAN), difficulty of offline site surveys and the dynamic environment changes prevent it from being practically implemented and commercially adopted. This paper introduces a novel client/server-based system that dynamically estimates and continuously calibrates a fine radio map for indoor positioning without extra network hardware or prior knowledge about the area and without time-consuming offline surveys. A modified Bayesian regression algorithm is introduced to estimate a posterior signal strength probability distribution over all locations based on online observations from WLAN access points (AP) assuming Gaussian prior centered over a logarithmic pass loss mean. To continuously adapt to dynamic changes, Bayesian kernels parameters are continuously updated and optimized genetically based on recent APs observations. The radio map is further optimized by a fast features reduction algorithm to select the most informative APs. Additionally, the system provides reliable integrity monitor (accuracy measure). Two different experiments on IEEE 802.11 networks show that the dynamic radio map provides 2-3m accuracy, which is comparable to results of an up-to-date offline radio map. Also results show the consistency of estimated accuracy measure with actual positioning accuracy.

The problem of multitarget tracking in underwater multistatic active sonobuoy systems is challenging because of the large number of false contacts and multiple reflections that reach the receivers. Targeting a robust solution that can track an unknown time-varying number of multiple targets, while keeping continuous tracks even in scenarios with large number of false contacts per ping, a particle filter (PF)-based technique is proposed in this paper. The PF is a nonlinear filtering technique that can accommodate arbitrary sensor characteristics, motion dynamics, and noise distributions. An enhanced version of the PF called the mixture PF is utilized in this paper. While the sampling/importance resampling PF samples from the prior importance density and weights the particles according to the observation likelihood, the mixture PF samples from both importance densities and weights the different groups of particles respectively. The usage of this mixture of importance densities provides better performance and faster convergence to the true targets locations. In order to track an unknown time-varying number of targets, two mixture PFs are used (one for target detection and the other for tracking multiple targets), and a density-based clustering technique. The first filter starts with random uniformly distributed samples over the surveillance area and resets every five pings. Just before the reset, the clustering technique runs to detect the clusters that corresponds to different targets and passes them to the second filter. The performance of the proposed technique is examined and demonstrated by different simulated scenarios and some real datasets from the SEABAR07 trial by the NATO Underwater Research Center.

Land vehicle positioning relies mostly on satellite navigation systems such as the Global Positioning System (GPS). However, GPS signals may be degraded or suffer from blockage in urban canyons and tunnels, and the positioning information provided is interrupted. One solution for such a problem is to integrate GPS with an inertial measurement unit (IMU) and the navigation solution is achieved using an estimation technique which is traditionally based on a Kalman filter (KF). In order to have a low cost navigation solution for land vehicles, MEMS-based inertial sensors are used. To further reduce the cost a reduced inertial sensor system (RISS) which consists of only one gyroscope and a speed sensor is integrated with GPS. The position and velocity errors can be estimated by a KF relying on RISS dynamic error model and GPS position and velocity updates. However, low-cost MEMS sensors suffer from complex error characteristics, which are difficult to model by the linearized KF models. The positional accuracy of the integrated system can be improved using Parallel Cascade Identification (PCI) that is cascaded with the KF. The proposed augmented KF-PCI method can handle both linear and nonlinear system errors as the linear parts of the errors are modeled inside the KF and the nonlinear residual RISS errors are modeled by PCI. The performance of this method is examined by road test trajectories in a land vehicle and compared to KF.

To have a continuous navigation solution that does not suffer from interruption, GPS is integrated with relative positioning techniques such as odometry and inertial navigation. Targeting a low-cost navigation solution for land vehicles, this paper uses a reduced multisensor system consisting of one microelectromechanical-system (MEMS)-based single-axis gyroscope used together with the vehicle's odometer, and the whole system is integrated with GPS. This system provides a 2-D navigation solution, which is adequate for land vehicles. The traditional technique for this multisensor integration problem is Kalman filtering (KF). Due to the inherent errors of MEMS inertial sensors and their stochastic nature, which is difficult to model, the KF with its linearized models has limited capabilities in providing accurate positioning. Particle filtering (PF) has recently been suggested as a nonlinear filtering technique to accommodate arbitrary inertial sensor characteristics, motion dynamics, and noise distributions. An enhanced version of PF is utilized in this paper and is called the Mixture PF. Since PF can accommodate nonlinear models, this paper uses total-state nonlinear system and measurement models. In addition, sophisticated models are used to model the stochastic drift of the MEMS-based gyroscope. A nonlinear system identification technique based on parallel cascade identification (PCI) is used to model this stochastic gyroscope drift. In this paper, the performance of the PCI model is compared with that of higher order autoregressive (AR) stochastic models. Such higher order models are difficult to use with KF since the size of the dynamic matrix and the error-covariance matrix becomes very large and complicates the KF operation. The performance of the proposed 2-D navigation solution using Mixture PF with both PCI and higher order AR models is examined by road-test trajectories in a land vehicle. The two proposed combinations are compared with four other 2-D solutions: a Mixture PF with the Gauss-Markov (GM) model for the gyro drift, a Mixture PF with only white Gaussian noise (WGN) for stochastic gyro errors, and two different KF solutions with GM model for the gyro drift. The experimental results show that the two proposed solutions outperform all the compared counterparts.

Global Positioning System (GPS) is currently the common solution for land vehicle positioning. However, GPS signals may suffer from blockage in urban canyons and tunnels, and the positioning information provided is interrupted. One solution to have continuous vehicle positioning is to integrate GPS with an inertial measurement unit (IMU) and the navigation solution is achieved using an estimation technique which is traditionally based on Kalman filter (KF). In order to have a low cost navigation solution for land vehicles, MEMS-based inertial sensors are used. To achieve a better performance during GPS outages, the speed derived from the vehicle odometer is used as a measurement update. To improve the positioning accuracy of the MEMS-based INS/Odometer/GPS integration, particle filtering (PF) is used as a nonlinear filtering technique, which does not need to linearize the models as in Extended KF (EKF). Because of PF ability to deal with nonlinear models, it can accommodate arbitrary sensor characteristics and motion dynamics. An enhanced version of PF is used which is called Mixture PF. While the Sampling/Importance Resampling (SIR) PF samples from the prior importance density and the Likelihood PF samples from the observation likelihood, the Mixture PF samples from both densities, then appropriate weighting is achieved followed by resampling. This mixture of importance densities leads to a better performance. The performance of this method is examined by road test trajectories in a land vehicle and compared to KF.

Kalman filter (KF) is the most commonly used estimation technique for integrating signals from short-term high performance systems, like inertial navigation systems (INSs), with reference systems exhibiting long-term stability, like the global positioning system (GPS). However, KF only works well under appropriately predefined linear dynamic error models and input data that fit this model. The latter condition is rather difficult to be fulfilled by a low-cost inertial measurement unit (IMU) utilizing microelectromechanical system (MEMS) sensors due to the significance of their long- and short-term errors that are mixed with the motion dynamics. As a result, if the reference GPS signals are absent or the Kalman filter is working for a long time in prediction mode, the corresponding state estimate will quickly drift with time causing a dramatic degradation in the overall accuracy of the integrated system. An auxiliary fuzzy-based model for predicting the KF positioning error states during GPS signal outages is presented in this paper. The initial parameters of this model is developed through an offline fuzzy orthogonal-least-squares (OLS) training while the adaptive neuro-fuzzy inference system (ANFIS) is implemented for online adaptation of these initial parameters. Performance of the proposed model has been experimentally verified using low-cost inertial data collected in a land vehicle navigation test and by simulating a number of GPS signal outages. The test results indicate that the proposed fuzzy-based model can efficiently provide corrections to the standalone IMU predicted navigation states particularly position.

Land vehicles rely mainly on global positioning system (GPS) to provide their position with consistent accuracy. However, GPS receivers may encounter frequent GPS outages within urban areas where satellite signals are blocked. In order to overcome this problem, GPS is usually combined with inertial sensors mounted inside the vehicle to obtain a reliable navigation solution, especially during GPS outages. This letter proposes a data fusion technique based on radial basis function neural network (RBFNN) that integrates GPS with inertial sensors in real time. A field test data was used to examine the performance of the proposed data fusion module and the results discuss the merits and the limitations of the proposed technique.