Po-Hsuan Tseng

Wireless Location Tracking Algorithms for Environments with Insufficient Signal Sources

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.

Gaussian message passing-based cooperative localization on factor graph in wireless networks

Location information has become attractive for a variety of applications in wireless networks. Cooperative localization was proposed to improve the performance in harsh environment where conventional localization methods failed due to the insufficient number of anchors. In this paper, distributed cooperative localization is studied based on message passing on factor graph. The joint a posteriori distribution of nodes? positions is represented by factor graph. Due to the nonlinearity between the positions and range measurement, the expressions of messages cannot be obtained in closed form by directly applying sum-product algorithm. Most existing methods resort to particle-based representation, which leads to both high computational complexity and large communication overhead. We propose to replace the factor node of the likelihood function by a linear Gaussian model. Accordingly, we are able to derive Gaussian messages on the revised factor graph, which only requires to update the mean vectors and the covariance matrices of multivariate Gaussian distributions. Simulation results show that the proposed method significantly outperforms the distributed maximum likelihood estimator and the extended Kalman filter, and performs very close to or even better than SPAWN estimator with much lower communication overhead and computational complexity in both static and mobile wireless networks. HighlightsA distributed cooperative localization based on message passing is proposed in the paper.Gaussian parametric messages are utilized to represent the messages on factor graph to reduce the communication overhead.Relying on the linearization on the nonlinear observation functions, messages are derived with Gaussian forms, which leads to efficient computations.To further reduce the communication overhead, a broadcast message scheme is considered.The proposed algorithm reaches superior performance compared to the SPAWN, EKF and ML.

Wireless Location Estimation With the Assistance of Virtual Base Stations

In recent years, wireless location estimation has attracted a significant amount of attention in different areas. Various types of radio signals are applied for the development of location-estimation algorithms. In this paper, the range measurements acquired from the received time-based information are adopted, and the modified least square (LS) method is utilized to process the raw data and to finally locate the target object. Practical issues, such as the nonline-of-sight (NLOS) errors and the geometric dilution of precision (GDOP) effect, are of concern. The NLOS error will cause a large nonnegative bias while measuring the propagation delay, which will lead to an unreliable result for location estimation. On the other hand, a large GDOP value corresponds to a poor geometric topology, which will result in inferior performance by adopting most of the existing location algorithms. The proposed location-estimation algorithms with virtual base stations (VBSs) will both mitigate the influence from the NLOS errors by imposing the geometric constraints and reduce the GDOP effect by incorporating the assisted VBSs. Two iterative schemes are proposed, including the center-of-gravity-based VBS (VBS-CG) and the minimal GDOP-based VBS (VBS-MG) algorithms, to determine the required number and the locations of the assisted VBSs. The proposed VBS algorithms are compared with other existing location-estimation schemes via simulations. The performance of the VBS-MG algorithm is observed to outperform the other schemes, particularly under the environments with larger NLOS errors and poor geometric layouts.

Hybrid Network/Satellite-Based Location Estimation and Tracking Systems for Wireless Networks

Mobile location estimation has attracted much attention in recent years. Location algorithms for mobile stations (MSs) can generally be categorized into network- and satellite-based systems. Both types of systems have their advantages and limitations under different environments (i.e., urban or rural area). In this paper, hybrid location estimation schemes, which combine both the satellite- and the network-based signals, are proposed to provide adaptation to various scenarios for location estimation. By exploiting the fusion algorithm, the proposed fusion-based hybrid (FH) architecture integrates the estimation results that are acquired from both the satellite- and the network-based systems. Two different types of signal selection schemes are adopted within the FH architecture: 1) the fixed set of signal inputs approach and 2) the selective set of signal inputs approach. On the other hand, the unified hybrid architecture employs the proposed hybrid signal-selection scheme and the hybrid least square estimator, which can conduct location estimation within a selected set of signal sources from the heterogeneous networks. The Kalman filtering technique is exploited in the proposed algorithms to both eliminate the measurement noises and to track the trajectories of the MSs. Numerical results demonstrate that the proposed hybrid location schemes can provide accurate location estimation by adapting themselves to different environments.

A Predictive Movement Based Handover Algorithm for Broadband Wireless Networks

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.

Cooperative Self-Navigation in a Mixed LOS and NLOS Environment

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.

Hybrid Unified Kalman Tracking Algorithms for Heterogeneous Wireless Location Systems

Location estimation and tracking for mobile stations have attracted a significant amount of attention in recent years. Different types of signal sources are considered available to provide measurement inputs for location estimation and tracking in heterogeneous wireless networks. Various techniques have been studied and combined for location tracking, e.g., the least square methods for location estimation associated with the Kalman filters for location tracking. In this paper, the hybrid unified Kalman tracking (HUKT) technique is proposed to provide an integrated algorithm for precise location tracking based on both 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 to consider the nonlinear behavior in the measurement update process. The relationship between this new variable and the desired location estimate is applied in the state update process of the Kalman filter. Three different designs of hybrid factor are proposed to adaptively adjust the weighting value between the TOA and TDOA measurements. Moreover, similar concepts are also utilized in the design of unified Kalman tracking schemes for pure TOA and TDOA measurement inputs in this paper. Compared with existing schemes, numerical results illustrate that the proposed HUKT algorithm can achieve enhanced accuracy for mobile location tracking, particularly under environments with an insufficient number of measurements in one of the signal paths.

Location tracking assisted handover algorithms for broadband wireless networks

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 MSpsilas moving speeds. With the requirements of providing location-based services as stated in the standard, it becomes feasible to utilize the MSpsilas 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 MSpsilas location while the non-serving BSs are unavailable. Furthermore, the geometry-assisted tracking (GAT) scheme utilizes the geometric constraints for the prediction of the MSpsilas position. Numerical results show that the proposed GAT algorithm can effectively reduce the handover number under different environments.

GDOP-Assisted Location Estimation Algorithms in Wireless Location Systems

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.

Maximal power path detection for OFDM timing-advanced synchronization schemes

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.