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.

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.

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.

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.

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.

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.

BACKGROUND: Since the function of a protein is largely dictated by its three dimensional configuration, determining a protein's structure is of fundamental importance to biology. Here we report on a novel approach to determining the one dimensional secondary structure of proteins (distinguishing alpha-helices, beta-strands, and non-regular structures) from primary sequence data which makes use of Parallel Cascade Identification (PCI), a powerful technique from the field of nonlinear system identification. RESULTS: Using PSI-BLAST divergent evolutionary profiles as input data, dynamic nonlinear systems are built through a black-box approach to model the process of protein folding. Genetic algorithms (GAs) are applied in order to optimize the architectural parameters of the PCI models. The three-state prediction problem is broken down into a combination of three binary sub-problems and protein structure classifiers are built using 2 layers of PCI classifiers. Careful construction of the optimization, training, and test datasets ensures that no homology exists between any training and testing data. A detailed comparison between PCI and 9 contemporary methods is provided over a set of 125 new protein chains guaranteed to be dissimilar to all training data. Unlike other secondary structure prediction methods, here a web service is developed to provide both human- and machine-readable interfaces to PCI-based protein secondary structure prediction. This server, called PCI-SS, is available at http://bioinf.sce.carleton.ca/PCISS. In addition to a dynamic PHP-generated web interface for humans, a Simple Object Access Protocol (SOAP) interface is added to permit invocation of the PCI-SS service remotely. This machine-readable interface facilitates incorporation of PCI-SS into multi-faceted systems biology analysis pipelines requiring protein secondary structure information, and greatly simplifies high-throughput analyses. XML is used to represent the input protein sequence data and also to encode the resulting structure prediction in a machine-readable format. To our knowledge, this represents the only publicly available SOAP-interface for a protein secondary structure prediction service with published WSDL interface definition. CONCLUSION: Relative to the 9 contemporary methods included in the comparison cascaded PCI classifiers perform well, however PCI finds greatest application as a consensus classifier. When PCI is used to combine a sequence-to-structure PCI-based classifier with the current leading ANN-based method, PSIPRED, the overall error rate (Q3) is maintained while the rate of occurrence of a particularly detrimental error is reduced by up to 25%. This improvement in BAD score, combined with the machine-readable SOAP web service interface makes PCI-SS particularly useful for inclusion in a tertiary structure prediction pipeline.

A paper by Marmarelis and Zhao (1997) describes the use of what the authors call a "separable Volterra network" for modeling high-order Volterra systems. This model is identical to a parallel cascade of dynamic linear/polynomial static nonlinear elements, which has been extensively studied since 1982 for the same purpose.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

In classifier design, the squared error criterion is often used as an approximation to more relevant cost functions based on the number of classification errors, due to the relative computational ease of least-squares methods. This practice results in decision boundaries which are sub-optimal in terms of classifier accuracy, often failing to separate even linearly separable classes. We describe a method for choosing target values in such a way as to decrease the undesirable effects of the sum of squared errors criterion. The proposed technique may be used with any least-squares or penalized least-squares training method. We demonstrate its use with linear least-squares classifiers, and give a bound on the number of iterations required for the special case of linearly separable classes.

Orthogonal search techniques are often used in training generalized single-layer networks (GSLNs) such as the radial basis function (RBF) network. Care must be taken with these techniques in order to avoid ill-conditioning of the required data matrix. The usual approach is to impose an arbitrary lower limit, say d/sub min/, on the norms of the orthogonal expansion terms, or equivalently on the diagonal values in the Cholesky decomposition matrices, which are calculated by the algorithms in question. In the paper, a bound on the condition number of the data matrix in terms of these qualities is given, and is used to derive a model-dependent guideline for d/sub min/.

Neural networks are applied to the problem of predicting patient requirements for mechanical ventilation in an intensive care unit (ICU). Two classes of neural network are considered: generalized single-layer networks (GSLNs) trained using a technique known as iterative fast orthogonal search with dynamic model resizing (IFOS-DMR), and feedforward sigmoid-activated multilayer perceptrons (MLPs) trained using backpropagation with weight elimination (BP-WE). It is found that (1) The GSLNs and MLPs implemented have similar correct classification rates on the test data, and (2) in contrast with BP-WE, IFOS-DMR accomplishes automatic determination of an appropriate model structure without reference to the test data. This work is undertaken as part of the Medical Intelligent Decision Aid Systems (Medical IDEAS) project.

The generalized single-layer network (GSLN) architecture, which implements a sum of arbitrary basis functions defined on its inputs, is potentially a flexible and efficient structure for approximating arbitrary nonlinear functions. A drawback of GSLNs is that a large number of weights and basis functions may be required to provide satisfactory approximations. In this paper, we present a new approach in which an algorithm known as iterative fast orthogonal search (IFOS) is coupled with the minimum description length (MDL) criterion to provide automatic structure selection and parameter estimation for GSLNs. The resulting algorithm, dubbed IFOS with dynamic model resizing (IFOS-DMR) performs both network growth and pruning to construct sparse GSLNs from potentially large spaces of candidate basis functions.

The fast orthogonal search method is illustrated for carrying out both system identification and time-series analysis of biological processes. It is first shown how the method can be used to rapidly obtain concise and accurate difference equation models of nonlinear dynamic systems. Then it is considered how the fast orthogonal algorithm enables accurate identification of cascades of alternating dynamic linear and static nonlinear sub-systems from short data records. Finally, it is illustrated how the method achieves accurate, parsimonious sinusoidal series representations of time-series data. It is shown that the method is capable of precise detection of component frequencies in time-series heavily corrupted with noise, demonstrating finer frequency resolution than a conventional Fourier series analysis.>