Autonomous driving technology relies heavily on sensor data for environment perception. Heterogeneous sensors such as lidar, radar, and camera have their own strengths and limitations. Therefore, relying on any single sensor would restrict the effectiveness of autonomous driving technology. However, integrating data from such heterogeneous sensors poses challenges due to differences in their representations. This article outlines a deep learning network aimed at designing modality-agnostic multi-modal fusion architecture. We study sensor data from different modalities and learn fine-grained representations using modality-specific feature encoders independently. Then, a multimodal attention-based network (MMA-Net) is proposed to fuse the data from heterogeneous modalities. The proposed MMA-Net fuses multi-modal sensor data by jointly exploiting the inter-modality and intra-modality relationships among camera, lidar, and radar sensors. The effectiveness of the proposed multi-modal fusion architecture is demonstrated using 2-D object detection metrics through extensive experiments on a dataset generated using the CARLA simulator.

This paper proposes a contrastive learning-based pretraining method for using millimeter-wave radar signals for pose estimation. By integrating the camera and radar data in the model pretraining, we first utilize K-means clustering algorithm to divide the data into distinct clusters from the spatial distribution. We propose a contrastive camera-radar-images pertaining (CCRP) technique by conducting contrastive learning on camera-captured coordinates and radar signal heatmaps of the associated cluster. The trained radar heatmap encoder network is based on a vision transformer capable of extracting highly distinctive features, reduces the training difficulty of the subsequent pose estimation network, and improves performance with fine-tuning. Based on the HIBER dataset, the proposed CCRP achieved leading results a marked performance improvement by 31% compared to other unsupervised pertaining methods.

Although sequential Coronary Angiograms (CAG) are often used in cardiac catheterizations, most vessel segmentation techniques are developed just for single images, owing to the expense of collecting and labeling huge amounts of sequential X-Ray images. The CAG segmentation using single images could cause the overlook of critical temporal information among images. Existing video segmentation methods, while they achieve high accuracy for daily life videos, cannot be used directly for sequential CAG, because they are not trained to capture the special structure of coronary articles. In this work, we investigate data manipulation strategies for training sequential CAG segmentation without labeled video data. We leveraged a general-purpose video segmentation model, XMem, for sequential CAG segmentation. We have proposed three data manipulation strategies for model training, including (1) sequence reversal for CAG data; (2) data hybridization for model training; (3) pseudo labels for sequential CAG data. The experimental results show that with good training strategies, one can use general-purpose video segmentation networks for sequential CAG data without explicitly labeled data. The best result of our trained network can achieve an average F1 score of 85.04% and an average region similarity of 73.97%, both of which are higher than the state-of-the-art results.

As frequency modulated continuous wave (FMCW) radars are becoming a key component of autonomous driving technology, mutual interference among automotive FMCW radars is a significant challenge. In this work, we propose a novel signal processing solution to effectively mitigate mutual interference among FMCW radars. The proposed framework distinguishes the target echoes and interference using their characteristics in the time-frequency spectrum, both of which appear as lines but with different slope characteristics. An interference map is generated from the interference contaminated time-frequency spectrum by employing the Hough transform along with slope filtering. The interference map is then used as a mask for interference mitigation. The interference-mitigated signal is reconstructed by applying time-frequency ridge-based mapping to subjugate the power loss associated with the target signals. We carry out extensive simulations to demonstrate the interference mitigation capabilities of the proposed Hough trans-form and time-frequency ridge (HTFR)-based scheme in terms of the signal-to-interference plus noise ratio (SINR), correlation coefficient, and computational efficiency.

In recent years, pedestrian flow analysis has gained popularity in public area such as shopping malls, hospitals or public facilities. Also, as the location-based service (LBS) become prevalent, more indoor environments have provided wireless positioning system which can record user's location and generate user's trajectory database. In this paper, a pedestrian flow analysis scheme is proposed on the basis of recorded location sequences provided by indoor wireless positioning system. To consider different application scenarios for pedestrian flow and with the existence of positioning errors, we proposed a trajectory regularization method to normalize the location sequences in a suitable format. Furthermore, to analysis the pedestrian flow, a trajectory consecutive pattern mining method which considers the sequential continuity of the trajectories is proposed based on the properties and proofs of consecutiveness of frequent patterns. Simulation results show that our proposed scheme can provide effective pedestrian flow analysis for both route and hotspot scenarios with lowered computational complexity.

Human presence detection via camera-based monitoring systems has been well-adopted in various applications including smart homes, factories, and hospitals. However, its privacy concerns have been raised in many occasions such as daycare centers and homes with elderly living alone. In recent years, literatures adopting wireless signals were proposed to resolve privacy issues for presence detection; nevertheless, existing works can only be applied in a single room scenario. In this paper, we are the first work to propose a device-free multi-room human presence detection system based on efficient star network topology. Our proposed conditional recurrent architecture-based multi-room presence detection (C-MuRP) system extracts both spatial and temporal features from the Wi-Fi channel state information (CSI). Associated with a voting scheme, the proposed novel deep-learning architecture classifies the states of multi-room with the condition on current waveform to emphasize present feature states. Real-time experimental results showed that our proposed C-MuRP system can achieve higher accuracy for multi-room presence detection compared to existing methods.

Visual acuity (VA) measurement is utilized to test a subject's acuteness of vision. Conventional VA measurement requires a physician's assistance to ask a subject to speak out or wave a hand in response to the direction of an optotype. To avoid this repetitive testing procedure, different types of automatic VA tests have been developed in recent years by adopting contact-based responses, such as pushing buttons or keyboards on a device. However, contact-based testing is not as intuitive as speaking or waving hands, and it may distract the subjects from concentrating on the VA test. Moreover, problems related to hygiene may arise if all the subjects operate on the same testing device. To overcome these problems, we propose an intelligent VA estimation (iVAE) system for automatic VA measurements that assists the subject to respond in an intuitive, noncontact manner. VA estimation algorithms using maximum likelihood (VAML) are developed to automatically estimate the subject's vision by compromising between a prespecified logistic function and a machine-learning technique. The neural-network model adapts human learning behavior to consider the accuracy of recognizing the optotype as well as the reaction time of the subject. Furthermore, a velocity-based hand motion recognition algorithm is adopted to classify hand motion data, collected by a sensing device, into one of the four optotype directions. Realistic experiments show that the proposed iVAE system outperforms the conventional line-by-line testing method as it is approximately ten times faster in testing trials while achieving a logarithm of the minimum angle of resolution error of less than 0.2. We believe that our proposed system provides a method for accurate and fast noncontact automatic VA testing.

The radar sensing on fine human-motion/hand-gesture provides further human-computer interaction (HCI) experience. Most of the studies about gesture recognition with mmWave frequency modulated continuous wave (FMCW) radar adopts the range and the velocity estimated from the raw data, such as the time-frequency spectrogram, micro-doppler spectrogram, or range-Doppler image (RDI). Besides, the angle estimated using multiple receive antennas also contains rich information of gesture, especially in the discrimination among the horizontal movement of the gesture. Thus, we propose to use the range-angle image (RAI) as the input and train a model consisting of the convolutional neural network and long short term memory that is capable of recognizing hand gestures. We validate the proposed scheme based on the collection of hand gestures by several subjects in different classrooms using 77 - 81GHz mmWave radar of Texas Instrument. Based on the configuration of one transmit antenna and four receive antennas, we show that the hand gesture recognition using RAI outperforms that using RDI. Also, we adopt the fusion strategy to consider both RDI and RAI to further improve accuracy.

Present detection can remotely detect whether a person appears in specific scene. Channel state information (CSI) can provide high precision environment detection by its characteristic, and this technology can often be adopted for indoor localization or presence detection nowadays. We propose a device-free multiple presence detection system by using particular preprocessing method for our system, and a two-stage learning system combining convolutional denoising autoencoder (CDAE) and neural network (NN) to classify different cases of presence detection. The first stage contains several one-dimensional convolutional hidden layers which can denoise and reduce the dimension of data. The second stage classifies the cases for the purpose of presence detection. The proposed system can detect the presence of multiple persons at hotspots with high estimation accuracy.

Unstable drone landing scenarios have caused severe damage to expensive drone-based devices. It is required to provide precision altitude information of the drone during its landing maneuvers. In this paper, we propose a device-free wireless localization algorithm, by adopting channel state information (CSI) to assist drone landing. By adopting our proposed system, the drone will not need to carry any additional sensing devices for precise positioning. The commodity Wi-Fi access points only need to be installed near the landing nest of the drone, which means that multiple drones can use the same set of landing systems. Experimental results show that our proposed system can provide centimeter localization precision for drone landing applications. With the device-free feature, system resources can be effectively utilized and costs are greatly reduced.

The channel impulse response (CIR), which characterizes the multipath channel between a transmitter and a receiver, can serve as a received position signature for indoor position fingerprinting (FP). Since it takes large system bandwidth to distinguish individual paths along which the signal waves travel in an indoor environment, a small bandwidth may yield an unsatisfactory performance of FP based on mere CIR. In this paper, we apply the multiple signal classification (MUSIC) algorithm, a super-resolution method, to unveil the path-delay signatures covered by bandwidth-limited CIRs. With the pseudospectrum evaluated with MUSIC, we resolve and identify the arrival times of the individual paths at a sub-sample precision. We further propose a super-resolution-aided fingerprinting (SFP) algorithm to estimate the receiver's position by taking the averaged positions of the reference points (RPs) of similar FP signatures with weights evaluated by the difference in pseudospectrum and received power. Experiments in an indoor environment show that SFP reduces the positioning error compared to the FP based on conventional channel state information (CSI), and that demands fewer infrastructures and less protocol complexity than CIR-based FP does to achieve similar performance.

Wireless indoor localization technique has attracted wide attention recently. Fingerprint (FP) based method with received signal strength indicator (RSSI) is a popular approach due to easy implementation and robustness. Nowadays, fine-grained indoor spot localization resort to channel state information (CSI) owing to rich information property of CSI. However, due to a higher dimension of CSI compared to RSSI, CSI-based FP method requires higher storage and communication overhead, which is not suitable for most scenarios. In this paper, we propose a novel refined autoencoder-based CSI hidden feature extraction for indoor spot localization (RACHEL). Based on the concept of FP, we first introduce an autoencoder (AE) for the dimension reduction and feature discrimination. A low dimensional hidden feature of trained AE model is saved as FP database in the off-line stage. For indoor spot localization problems, users position is assumed to be close to one of the reference points. Therefore, CSI transforms to hidden feature space and users location is estimated by the nearest-neighbor algorithm in the on-line stage. Furthermore, to enhance the performance, instinctive AE is modified by considering corruption from time-varying environment and sensitivity between the hidden layer and input layer. Performance evaluations demonstrate that the proposed RACHEL can achieve 97.8% spot classification accuracy and yield a spaced savings of 94.3%.

Map information can assist indoor localization to avoid improbable cases and achieve accurate location estimation. In this paper, we proposed a automatic method to extract useful information from indoor map as spatial skeleton database (SSD). Based on conventional probabilistic fingerprinting technique and particle filter tracking algorithm, we also proposed spatial skeleton-based dynamic probabilistic fingerprinting database (S-DFD) to filter out reference points (RPs) in fingerprinting database according to the previous target location and the walking distance between RPs. Finally, we proposed a spatial skeleton-based particle filter tracking (S-PT) which use SSD to construct realistic transition model. According to the experiment result, the whole system consists of SSD, S-DFD and S-PT called spatial skeleton-enhanced location tracking for indoor localization (SELT) can achieve accurate location estimation.

Location estimation has received wide attention due to the emerging demand for location-based services (LBSs) in indoor environments. Although positioning algorithms have been rapidly developed, the positioning accuracy has not reached the requirement of indoor LBSs. Indoor positioning methods based on the existing communication systems such as Wi-Fi or Bluetooth have the advantage of lower cost and higher penetration rates, which can provide a sufficient number of signal sources. Unlike the global positioning system where the satellites are well- deployed to provide four or more signal sources and well-conditioned geometric for outdoor devices, the critical limits of indoor positioning are the insufficient signal sources and disunified deployment for access points (APs). To address the problem, we propose an automatic hybrid AP deployment (AHAD) algorithm to provide optimal locations and required numbers of both WiFi APs and BLE APs for achieving higher location estimation accuracy. With the adoption of genetic algorithm, the AHAD scheme can maintain satisfactory Wi-Fi communication quality and fulfill user's budget for AP deployment. Furthermore, a hybrid indoor positioning (HIP) scheme is also proposed based on the combination of Wi-Fi fingerprinting and BLE proximity. Experimental results show that the proposed AHAD algorithm can provide better location estimation accuracy compared to conventional AP deployment based on user instinct.

Position fingerprinting (FP), in which the signature of a position is captured from the radio frequency signal, is one of the most efficient indoor positioning algorithms. Besides the received signal strength (RSS), the channel impulse response (CIR) is regarded as a linear temporal filter, which characterizes the multipath channel of the operating environment. Since the CIR requires a larger system bandwidth to distinguishing individual paths along which the signal waves travel, a smaller bandwidth may limit the performance of CIR- based fingerprinting. In this paper, we utilize a super-resolution method, i.e. the multiple signal classification (MUSIC) algorithm, to obtain the pseudo-spectrum for the enhanced resolution of the arriving paths. We create an offline database by the implementation of an OFDM-based channel sounder and obtain the cumulative pseudo-spectrum based on the alignment of the maximal power path. Based on the online#x002F;offline measurements with enhancing resolutions, we propose a super-resolution-aided fingerprinting (S- FP) to estimate the position by finding the reference points (RPs) with the highest similarity of the cumulative pseudo-spectrum. The experimental results show that S-FP reduces the localization error compared with the conventional CIR FP.

With the widespread of smartphones, people can easily figure out where they are and enjoy other advanced services like searching nearby restaurant information or checking bus arrival time. Indoor positioning becomes a popular issue for location- based services used in shopping malls, hospitals, or largescale buildings. Contrast with spacious surroundings of outdoor, indoor environment is filled with obstacles and moving people, which impose great challenges to provide precise estimation of indoor positioning. This paper proposes Wi-Fi fingerprinting technique using received signal strength with consideration of map information to effectively eliminate unreasonable estimation outcomes. The proposed area estimation (AE) algorithms calculate the similarity of each area in the entire region to increase accuracy of distinguishing which area the user locates. Moreover, the shortest path with adjacent recognition (SPAR) algorithm further utilizes the concept of Dijkstra's shortest path algorithm and the previous location information to predict user's position. Experimental results show that the proposed AE with SPAR algorithms can provide better area estimation compared to conventional scheme.

Cooperative localization (CL) is an efficient way to enhance the position estimate by cooperating among agents, especially under an insufficient signal source situation. To realize CL, a suitable network architecture has to be designed. For the centralized CL, all the information is transmitted to a data center to find a global optimal solution for all the agents. Note that the centralized network is not scalable in communication aspect since all the information has to be collected before estimation, which requires complex scheduling for range measurement and measurement report. On the other hand, an agent estimates its own position and exchanges with other agents locally to solve the scalability issue in the distributed CL. However, measurement information collected locally in the distributed scheme is less than measurement information in the centralized scheme, which might degrade the position estimate and cause divergence by propagating position estimate iteratively. In order to solve the scalability issue of centralized CL and the divergence issue of distributed CL, cluster network is adopted in the paper. After cluster formation, an agent estimates its initial position based on intra-cluster measurement in a cluster to obtain a reliable initial position. To further cooperate with the agent of other clusters, the cluster gateway iteratively refines its position estimate with other cluster gateways based on inter-cluster measurement. Simulation results validate that the proposed cluster-based CL outperforms distributed CL.

Due to the popularity of location-based services in environment with weak GPS signals, indoor location estimation problem have attracted more and more attention in recent years. Among several distance-related measurements, channel impulse response (CIR) reflects multi-path situation between the transmitter and receiver pair and is suitable to describe the characteristic of different positions. Note that CIR, which can be obtained from the inverse Fourier transform of channel frequency response in broadband wireless networks, is supported in most of the commercial standards. In this paper, a novel compressive sensing based location estimation using CIR measurements (CS-CIR) is proposed as well as fingerprinting algorithm. CIR information is collected from each reference point (RP) to access point (AP) and stored in the database. During the on-line stage, the mobile user measures CIR from the AP and compares measured CIR with those CIR values in the database. Note that user position is close to one of the RPs and user position vector is represented as a sparse vector. By applying compressive sensing theory, user position can be recovered by solving l1-minimization problem. Simulation result validate that the CS-CIR outperforms the K-nearest neighbour method using CIR measurements and conventional received signal strength based methods.

Visual acuity (VA) measurement is for a subject to test his/her acuteness of vision. Several kinds of automatic VA test are gradually developed and used in recent years. Without experimenter, the traditional way for a subject to speak out or wave a hand in response to the direction of optotype is then replaced mostly by the contact based response such as pushing buttons or keyboards on a device nowadays. However, the contact based response is not intuitive as speaking or waving hands, and it may distract subjects from concentrating on the test. To overcome these problems, we propose a hand motion recognition based visual acuity (HMRVA) measurement which keeps the advantage of automatic VA measurement, and also allows subject to respond in an intuitive contactless way. A velocity based hand motion recognition (V-HMR) algorithm is used to classify hand motion data collected by a sensing device into one of the four directions of optotypes. Based on the V-HMR scheme, a maximum likelihood based visual acuity (ML-VA) estimation algorithm is developed for VA estimation and is implemented on a tablet. According to the experimental results, we can conclude that the proposed HMRVA system achieve our goals to provide accurate and efficient automatic VA tests.

Augmented reality (AR) technology is mainly composed of feature extraction, feature points matching and 3D object drawing to impose virtual information onto the real camera. Hand tracking method is designed to realize interactive AR system to further enhance user experience (UX). However, feature extraction, feature points matching, and hand tracking all require high computational cost. To achieve real-time implementation on mobile, we proposed a Mobile Augmented ReAlity Interactive System based on cloud computing (MARAIS). Cloud side is responsible for heavy tasks that need sufficient computational capabilities, and device side performs sensing, detecting and displaying. Device/cloud architecture has been designed to maintain tolerable processing delay with minimum communication overhead. To validate the suitability for real-time interaction, MARAIS is combined with a picture book to realize a digital learning system. Implementation of interactive AR system based on cloud computing eases the requirement of data storage, memory, and computational power at device side.

We investigate the problem of cooperative self-navigation (CSN) for multiple mobile sensors in the mixed line-of-sight (LOS) and nonline-of-sight (NLOS) environment based on measuring time-of-arrival (TOA) from the cooperative sensing. We first derive an optimized recursive Bayesian solution by adopting a multiple model sampling-based importance resampling particle filter for the development of CSN. It can accommodate nonlinear signal model and non-Gaussian position movement under different levels of channel knowledge. We also utilize a Rao-Blackwellization particle filter to split the original problem by tracking the channel condition with a grid-based filter and estimating the position with a particle filter. The CSN with position and channel tracking exhibits advantage over the noncooperative methods by utilizing additional cooperative measurements. It also shows improvement over the methods without channel tracking. Simulation results validate that both schemes can take the advantage of cooperative sensing and channel condition tracking in mixed LOS/NLOS environments, which motivates future research of cooperative gain for navigation and localization in a more general environment.

The limitation on battery lifetime has been a critical issue for the advancement of mobile computing. Different types of power-saving techniques have been proposed in various fields. In order to provide feasible energy-conserving mechanisms for the mobile subscriber stations (MSSs), three power-saving types have been proposed for the IEEE 802.16e broadband wireless networks. However, these power-saving types are primarily targeting for the cases with a single connection between the base station (BS) and the MSS. With the existence of multiple connections, the power efficiency obtained by adopting the conventional scheduling algorithm can be severely degraded. In this paper, with the consideration of multiple connections and their quality-of-service (QoS) constraints, a frame aggregation-based power-saving scheduling (FAPS) algorithm is proposed to enhance the power efficiency by aggregating multiple under-utilized frames into fully-utilized ones. The optimality on the minimum number of listen frames in the proposed FAPS algorithm is also provided, and is further validated via the correctness proofs. Performance evaluation of proposed FAPS scheme is conducted and compared via simulations. Simulation results show that the power efficiency of FAPS algorithm outperforms the other existing protocols with tolerable frame delay.

Fine timing estimation in timing synchronization scheme of orthogonal frequency division multiplexing systems gives an estimate of symbol starting time index corresponding to the path with maximal power within an interval suggested by coarse timing stage. The actual starting index fed to the following stages is brought forward by an amount that should be adaptive to the estimated index to optimize system performance. In this paper, a method of detecting the estimated starting time index of path with maximal power in channel impulse response is proposed based on conventional preamble with repetitive structure. To deal with the adverse effect of fractional timing offset on the detection metric, we propose a preamble composed of cyclic-shifted parts and the accompanying fine timing and detection scheme. Simulation with time-varying wireless channel shows the detection methods makes use of the time diversity provided by time-varying paths and has good error performance. The scheme with proposed preamble further reduces probability of error detection with the diversity in fractional timing offset provided inherently in the parts of preamble.

Location estimation and tracking for mobile stations (MSs) have attracted a significant amount of attention in recent years. In indoor environment where the signals from global positioning system (GPS) are either weak or blocked, signal sources from long term evolution advanced (LTE-A) system can be adopted to provide location estimation for MS. Based on the range signals from both macro base station (mBS) and femto BS (fBS) in LTE-A heterogeneous networks (HetNet), we propose femto-assisted location estimation (FALE) schemes to estimate MS's position especially for indoor environments with insufficient signal inputs from mBSs. Since fBSs are usually deployed by users in their residential or business buildings, the locations of these fBSs generally cannot be known exactly. We depict the imprecise fBSs' positions as belief information to investigate a Bayesian estimation method based on the time difference of arrival (TDOA) measurement, and utilize particle filtering technique to develop the FALE algorithm. Moreover, a simplified FALE (FALE-S) scheme is proposed to reduce the computation cost resulting from the particle filter in original FALE method. Performance evaluation is conducted based on the LTE-A HetNet environments. Compared to conventional scheme, simulation results show that the proposed FALE algorithms can provide better location estimation of MS through the assistance of fBSs.

Linear estimators have been extensively utilized for wireless location estimation for their simplicity and closed form property. In the paper, the class of linear estimator by introducing an additional variable, e.g., the well-adopted linear least squares (LLS) estimator, is discussed. There exists information loss from the linearization of location estimator to the nonlinear location estimation, which prevents the linear estimator from approaching the Cramér-Rao lower bound (CRLB). The linearized location estimation problem-based CRLB (L-CRLB) is derived in this paper to provide a portrayal that can fully characterize the behavior for this type of linearized location estimator. The relationships between the proposed L-CRLB and the conventional CRLB are obtained and theoretically proven in this paper. As suggested by the L-CRLB, higher estimation accuracy can be achieved if the mobile station (MS) is located inside the convex hull of the base stations (BSs) compared to the case that the MS is situated outside of the geometric layout. This result motivates the proposal of geometry-assisted localization (GAL) algorithm in order to consider the geometric effect associated with the linearization loss. Based on the initial estimation, the GAL algorithm fictitiously moves the BSs based on the L-CRLB criteria. Two different implementations, including the GAL with two-step least squares estimator (GAL-TSLS) and the GAL with Kalman filter (GAL-KF), are proposed to consider the situations with and without the adoption of MS's historical estimation. Simulation results show that the GAL-KF scheme can compensate the linearization loss and improve the performance of conventional location estimators.

The driver assistance system (DAS) is important to assist drivers with driving safety and efficiency. The lane departure warning system (LDWS), being an essential portion within the DAS, aims at providing warning messages under irregular driving behaviors due to distraction, drowsiness, or driver negligence. As the developments of mobile devices are boosted vigorously, it becomes more feasible to integrate sensing, photographing and displaying components within the LDWS. In this paper, the pitch-aided lane tracking (PALT) algorithm is proposed to detect and track the lanes on the roadways base on the particle filter. A pitch model is proposed to track the lane attributes associated with the pitch angle of a mobile device in order to conquer the effects from bumpy road and loosely-equipped of a mobile device. Moreover, the proposed PALT scheme can compensate the influences of insufficient observations from the display. Numerical results show that proposed PALT scheme can provide better lane-tracking performance compared to both original observations and the model without considering pitch angle.

Location estimation and tracking for the mobile stations have attracted a significant amount of attention in recent years. Moreover, different types of signal sources are considered available to provide the measurement inputs for location estimation and tracking. In this paper, a hybrid unified Kalman tracking (HUKT) technique is proposed to provide an integrated algorithm for precise location tracking based on both the time-of-arrival (TOA) and time-difference-of-arrival (TDOA) measurements. A new variable is incorporated as an additional state within the Kalman filtering formulation in order to consider the nonlinear behavior for wireless location estimation. Comparing with existing schemes, numerical results illustrate that the proposed HUKT algorithm can achieve enhanced accuracy for mobile location tracking, especially under the environments with insufficient number of signal sources in a single signal path.

Location estimation and tracking for the mobile devices have attracted a significant amount of attention in recent years. The location estimators associated with the Kalman filtering techniques are exploited to both acquire location estimation and trajectory tracking for the mobile devices. However, most of the existing schemes become inapplicable for location tracking due to the deficiency of signal sources. In this paper, the enhanced predictive location tracking (EPLT) are proposed to alleviate this problem. The EPLT scheme utilizes the predictive information obtained from the Kalman filter in order to provide the additional signal inputs for the location estimator. Furthermore, the EPLT scheme incorporates the geometric dilution of precision (GDOP) information into the algorithm design. Persistent accuracy for location tracking can be achieved by adopting the proposed EPLT scheme, especially with inadequate signal sources. Numerical results demonstrate that the EPLT algorithm can achieve better precision in comparison with other location tracking schemes.

Location estimation and tracking for the mobile devices have attracted a significant amount of attention in recent years. The network-based location estimation schemes have been widely adopted based on the radio signals between the mobile device and the base stations. The location estimators associated with the Kalman filtering techniques are exploited to both acquire location estimation and trajectory tracking for the mobile devices. However, most of the existing schemes become inapplicable for location tracking due to the deficiency of signal sources. In this paper, two predictive location tracking algorithms are proposed to alleviate this problem. The predictive location tracking (PLT) scheme utilizes the predictive information obtained from the Kalman filter in order to provide the additional signal inputs for the location estimator. Furthermore, the geometric-assisted PLT (GPLT) scheme incorporates the geometric dilution of precision (GDOP) information into the algorithm design. Persistent accuracy for location tracking can be achieved by adopting the proposed GPLT scheme, especially with inadequate signal sources. Numerical results demonstrate that the GPLT algorithm can achieve better precision in comparison with other network-based location tracking schemes.

In recent years, wireless location estimation has attracted a significant amount of attention in different areas. The network-based location estimation schemes have been widely adopted based on the radio signals between the mobile station (MS) and the base stations (BSs). The two-step Least Square (LS) method has been studied in related research to provide efficient location estimation of the MS. However, the algorithm results in inaccurate location estimation under the circumstances with poor geometric dilution of precision (GDOP). In this paper, the GDOP- assisted location estimation (GOLE) schemes are proposed by considering the geometric relationships between the MS and its associated BSs. According to the minimal GDOP criterion, the BSs are fictitiously repositioned and are served as a new set of BSs within the formulation of the two-step LS algorithm. The proposed GOLE schemes can both preserve the computational efficiency from the two-step LS method and obtain precise location estimation under poor GDOP environments. Comparing with other existing schemes, numerical results demonstrate that the proposed GOLE algorithms can achieve better accuracy in wireless location estimation.

The design of an efficient handover technique is considered a crucial topic in the cellular-based wireless networks. Feasible handover process can minimize the performance degradation as the mobile station (MS) is moving between different base stations (BSs). Based on the IEEE 802.16e standard, it is required to provide satisfactory handover performance under a wide-range of MS’s moving speeds. With the requirements of providing location-based services as stated in the standard, it becomes feasible to utilize the MS’s estimated location to assist the decision of the handover process. However, in order to achieve a satisfactory performance for location estimation, excessive time is required for synchronizing with multiple non-serving BSs. In this paper, two location tracking based handover schemes are proposed for achieving reduced number of handover without additional connections between the MS and the non-serving BSs. The kinematics-assisted tracking (KAT) algorithm adopts the kinematic relationship to estimate the MS’s location while the non-serving BSs are unavailable. Furthermore, the geometry-assisted tracking (GAT) scheme utilizes the geometric constraints for the prediction of the MS’s position. Numerical results show that the proposed GAT algorithm can effectively reduce the handover number under different environments.

The design of an efficient handover technique is considered as one of the crucial topics in the cellular-based wireless networks. Feasible handover process can minimize the performance degradation as the mobile device is moving between different base stations. Based on the IEEE 802.16e standard, it is required to provide satisfactory handover performance under a wide-range of moving speeds of the mobile device. In this paper, a predictive movement-based handover (PMHO) algorithm is proposed. Without incurring excessive overheads as from the conventional location estimation and tracking techniques, the proposed sideline-traveling scheme within the PMHO algorithm employs the moving state indicator (MSI) to predict the potential moving characteristics of the mobile device. The handover decision of the PMHO approach is determined based on the MSI values associated with the neighbor base stations. Numerical results show that the proposed PMHO algorithm can effectively reduce both the number of handover and the size of the neighbor-scanning set within the mobile device.

The limitation on the battery lifetime has been a critical issue for the advancement of mobile computing. Different types of power-saving techniques have been proposed in various fields. In order to provide feasible energy-conserving mechanism for the mobile subscriber stations (MSSs), three power-saving types have been proposed for the IEEE 802.16e broadband wireless networks. However, these power-saving types are primarily targeting for the cases with a single connection between the base station (BS) and the MSS. With the existence of multiple connections, the power efficiency obtained by adopting the conventional scheduling algorithm can be severely degraded. In this paper, a maximal power-conserving (MPC) scheduling algorithm is proposed to consider the aggregated effect from the multiple connections to the power efficiency. Moreover, the quality-of-service (QoS) constraints from both the downlink and the uplink traffic are employed in the design of the MPC algorithm in order to facilitate the corresponding MSS to fulfill its QoS requirements in both directions. Numerical results show that the proposed MPC scheduling algorithm outperforms the conventional 802.16e power-saving mechanism, especially under the multi-connection scenarios.

location estimation and tracking for the mobile devices have attracted a significant amount of attention in recent years. The network-based location estimation schemes have been widely adopted based on the radio signals between the mobile device and the base stations. The location estimators associated with the Kalman filtering techniques are exploited to both acquire location estimation and trajectory tracking for the mobile devices. However, most of the existing schemes become unapplicable due to the insufficiency of signal sources. In this paper, a predictive location tracking (PLT) algorithm is proposed to alleviate this problem. The predictive information obtained from the Kalman filter is employed to provide the additional signal inputs for the location estimators. The proposed PLT scheme can offer persistent accuracy for location tracking of the mobile devices, especially with inadequate signal sources. Numerical results demonstrate that the proposed PLT algorithm can achieve better precision, comparing with other existing schemes, in mobile location estimation and tracking.