Chin-Der Wann

Hybrid TDOA/AOA Indoor Positioning and Tracking Using Extended Kalman Filters

A hybrid time-difference-of-arrival/angle-of-arrival (TDOA/AOA) positioning technique for indoor ultra wideband (UWB) systems is presented in this paper. The non line-of-sight (NLOS) propagation error is considered one of the major error sources in location systems; therefore, NLOS identification and mitigation technique with Kalman filters are utilized to reduce the NLOS time-of-arrival (TOA) errors in indoor UWB environments. To deal with the effects of inaccurate NLOS AOA data, an AOA selection process is included. An adjustable extended Kalman filter (EKF) structure is used to process the formulated TDOA and selected AOA measurements for mobile positioning and tracking. The simulation results show that the proposed hybrid scheme can effectively respond to the NLOS/LOS changes in the UWB environment, and improve the position accuracy

NLOS mitigation with biased Kalman filters for range estimation in UWB systems

In this paper, a non-line of sight (NLOS) error identification and mitigation technique utilizing modified biased Kalman filter in ultra-wideband (UWB) systems is proposed. NLOS error is considered one of the major error sources in range estimation and wireless location systems. To improve time-based location accuracy in UWB location systems, an NLOS identification and mitigation technique is derived. Kalman filters (KFs) are used for smoothing range data and mitigating the NLOS errors. We combine the modified biased Kalman filter and a sliding window to identify and mitigate different degree of NLOS errors. The simulation results show that the change of NLOS/LOS status can be identified accordingly and the NLOS errors can be mitigated effectively by modified biased Kalman filter. The technique achieves high accuracy in range estimation for UWB positioning and tracking.

Hybrid TOA/RSSI Wireless Location with Unconstrained Nonlinear Optimization for Indoor UWB Channels

In this paper, a hybrid TOA/RSSI wireless positioning technique is proposed for gaining favorable position accuracy in indoor UWB systems. One major problem encountered in indoor wireless location is the effects caused by non-line of sight (NLOS) propagation. To mitigate the NLOS effects, an unconstrained nonlinear optimization approach is utilized to process time-of-arrival (TOA) and received signal strength (RSSI) in the location system. Path loss models and geometrical cell layout based on the TOAs are used in formulating the LOS/NLOS decision boundary. The weighting factors, used in solving the objective function in the unconstrained nonlinear optimization approach, are related to the credibility of the TOA measurements, and are determined from the LOS/NLOS identification. Computer simulations are conducted to investigate the performance of the proposed technique in different scenarios of the NLOS errors. The results show that the proposed technique outperforms other algorithms and achieves improved location accuracy under severe NLOS conditions.

Data fusion methods for accuracy improvement in wireless location systems

Wireless location methods in wireless communications networks have received significant attention in recent years. Among the network-based approaches, time of arrival (TOA) and time difference of arrival (TDOA) are two major time-based techniques used for location estimation. It is known that the accuracy of location estimation may suffer from poor geometric dilution of precision (GDOP) situation and non-line of sight (NLOS) propagation effect. To improve the accuracy of time-based location techniques in wireless communications networks, data fusion methods using fuzzy logic and Bayes rules are presented. Based on the statistical properties of the raw location estimates, data fusion methods integrate the data obtained from the TOA and TDOA techniques. Computer simulations are conducted to investigate the wireless location problem affected by GDOP and NLOS. Simulation results show that the overall accuracy of location estimation in wireless location systems can be significantly improved when the LOS reconstruction and the data fusion methods are used.

Estimation and analysis of signal arrival time for UWB systems

In this paper, an improved MUSIC algorithm is presented for estimation of signal arrival time in impulse radio UWB systems. An accurate estimate of signal arrival time is considered essential in time-based wireless and indoor location systems. The fine resolution of UWB signals provides potentially accurate ranging for indoor geolocation. However, the ambiguity caused by the irresolvable first arrival path may still yield an error in determining the true signal arrival time. To use the improved MUSIC technique in estimation of the shortest signal arrival time for UWB radio link, an experimental criterion of path resolvability is defined. For a two-multipath case, simulations are conducted. The results show that the improved MUSIC algorithm achieves better probability of multipath resolvability, and smaller variance of estimation errors than other MUSIC methods.

Mobile location tracking with NLOS error mitigation

We present a mobile location tracking method with non-line-of-sight (NLOS) error mitigation for network-based wireless location systems. Based on the reconstructed LOS time of arrival (TOA) results from NLOS propagation identification and correction, geometric location techniques are used to calculate the location estimate of a mobile station. General TOA geometric cases and positioning solutions are analyzed. The time sequence of location samples is processed by Kalman filtering for position tracking and velocity estimation. A post-Kalman filter smoothing method is also applied to improve the velocity estimation. It is shown that the location tracking error can be reduced by processing the reconstructed line-of-sight TOA measurements with Kalman filtering.

Position tracking and velocity estimation for mobile positioning systems

A method of mobile position tracking and velocity estimation for network-based mobile positioning systems is presented. Kalman filtering is used to process the sequences of mobile location measures obtained from time-of-arrival (TOA) geometric location techniques. Two-dimensional position tracking and velocity estimation are obtained. Simulation results show that carefully adjusting the processing rate of Kalman filtering can reduce the location tracking error. One post-Kalman-filter smoothing method is also proposed for better performance of velocity estimation.

Mobile location tracking with velocity estimation

In this paper, we develop a mobile positioning method for mobile position tracking and velocity estimation in a network-based wireless location system. The time of arrival (TOA) information obtained at multiple base stations are used in geometric location techniques for estimating the location of a mobile station. The time sequence of location measures are then processed by Kalman filters for position tracking and velocity estimation. It is shown that the location tracking error can be reduced by adjusting the processing rate of Kalman filtering. Two post-Kalman filter smoothing methods are also proposed to improve performance of velocity estimation.

Location estimation with data fusion for wireless location systems

Wireless location in wireless communications networks has been studied extensively in recent years. Among the network-based approaches, time of arrival (TOA) and time difference of arrival (TDOA) are two time-based techniques used in location estimation. It is known that the accuracy of location estimation may suffer from poor geometric dilution of precision (GDOP) situation and non-line of sight (NLOS) propagation effect. To provide more accurate measures for time-based location techniques under the effects of GDOP in wireless communications networks, a data fusion with fuzzy rules is presented. Based on the GDOP statistical properties of the raw location measures, the data fusion integrates data obtained from the TOA and TDOA techniques. Simulation results show that the proposed fuzzy logic-based data fusion effectively improves the overall accuracy of location estimation in wireless location systems.

Non-line of Sight Error Mitigation in Ultra-wideband Ranging Systems Using Biased Kalman Filtering

In this paper, a non-line of sight (NLOS) error mitigation method based on biased Kalman filtering for ultra-wideband (UWB) ranging is proposed. The NLOS effect on the measures of signal arrival time is considered one of the major error sources in range estimation and time-based wireless location systems. An improved biased Kalman filtering system, incorporated with sliding-window data smoothing and hypothesis test, is used for NLOS identification and error mitigation. Based on the results of hypothesis test, the estimated ranges are either calculated by smoothing the measured range when line of sight (LOS) status is detected, or obtained by conducting error mitigation on the NLOS corrupted measured range when NLOS status is detected. The effectiveness of the proposed scheme in mitigating errors during the LOS-to-NLOS and NLOS-to-LOS transitions is discussed. Improved NLOS identification and mitigation during the NLOS/LOS variations of channel status are attained by an adaptive variance-adjusting scheme in the biased filter. Simulation results show that the UWB channel status and the transition between NLOS and LOS can be identified promptly by the proposed scheme. The estimated time-based location metrics can be used for achieving higher accuracy in location estimation and target tracking.