NLOS Detection and Mitigation Based on Confidence Metric and Adapted EKF

Kurzfassung

In global navigation satellite systems, the mobile location is calculated by measuring the line-of-sight (LOS) signal propagation time between the mobile terminal and at least 4 satellites. However, in an urban area, the direct path may be blocked. Thus, the satellite signals propagate an additional distance due to reflection and diffraction. This effect, where no direct path is available, is called non-line-of-sight (NLOS) propagation and adds a positive bias to the geometric LOS (GLOS) propagation time. This can cause an estimation error in the order of hundreds of meters [1]. If enough measurements to identify the position are available, it is possible to discard the NLOS signals or weight the signal with a lower priority. However it poses a great challenge to mitigate the NLOS impact through processing at the receiver. To handle the NLOS propagation various NLOS mitigation algorithms and methods have been proposed, cf. [1], [2]. The authors of [3] summarize for example the NLOS detection algorithms for ultra wide–band (UWB) systems and compare them to each other. The main contribution of this paper is the detection and mitigation of NLOS scenarios. Most mitigation algorithms try to detect and mitigate the NLOS effects at the same time. Therefore they try to estimate the NLOS error. This paper separates the NLOS detection and mitigation into two parts. The first part describes an algorithm to detect NLOS signals. A lot of approaches have been published in the recent years to detect NLOS paths for example by the received signal strengths (RSS), cf. [1], [2]. These approaches are usable, but the accuracy depends directly on the noise and the fading effects. Therefore we analyze a more robust algorithm to detect such signals. The authors of [3] proposed an algorithm for UWB called confidence metric which considers the multipath propagation effects. Therefore it detects NLOS signals by comparing the power of the first incoming path to the multipath with the highest power under consideration of the each propagation time. This approach is useful for small distance propagation. In case of GNSS this method is not applicable because of the long propagation time and thus almost equivalent measurements. Thus we adapt this algorithm to the case of GNSS. Instead of considering the whole propagation time, we consider only the additionally propagation time of the first incoming signal power and the signal with the highest power. This adapted algorithm is more stable against varying signal to noise ratio and outputs the probability whether the received signal is LOS or NLOS. If we detect a NLOS signal and more than 3 satellites are LOS, we can easily extract the NLOS signals. But for example in urban scenarios, not always 4 LOS GNSS satellite signals are available. Thus we have to use the NLOS signals, mitigate this error or use other sensors. Therefore the second part of this paper adapts the extended Kalman filter by using the NLOS probability information. The EKF is a well-known algorithm for positioning and tracking. This adapted EKF (AEKF) estimates the position by using the probability information of the previous algorithm. For low LOS probabilities, the AEKF either extracts the signals or estimates the position with the highest probability. Additionally we consider in the further simulation the TDOA measurements of the LTE (long time evolution) network. If more than 3 LOS satellite signals are available, the LTE measurements generally cannot improve our position estimate. But if less than 4 LOS satellites are available, the AEKF uses this information to mitigate the NLOS effect. To verify the derived NLOS detection algorithm and the AEKF, we consider a typical urban scenario with high buildings and street canyons. We analyzed this scenario with a raytracing tool for a fixed satellite constellation and fixed LTE base stations. The simulation results compare the position estimation of the AEKF to the EKF and the static method with Gauss-Newton. Additionally we consider different scenarios with GPS only, GPS + Galileo and GPS + Galileo + LTE. Especially the simulations show that if more than 3 LOS GNSS satellite signals are available, we cannot improve the positioning error with additionally using the LTE signals. But in scenarios where less than 4 satellites are LOS we can improve the position estimation. Additionally the simulations show the improvement of the described NLOS detection algorithm compared to general detection algorithms. With a varying signal to noise ratio, we are still able to detect more than 90% of the LOS signals correct. To conclude, this paper shows an algorithm for NLOS detection and mitigation. The NLOS detection algorithm is based on the idea of [3] where the multipath power is compared to the propagation time. Especially this algorithm provides probability information if a received signal is LOS or NLOS. The proximate adapted EKF uses this information, mitigates the NLOS effect or uses the additional LTE network. With these algorithms we are able to obtain a precise position fix even if less than 4 LOS satellites signals are available. In the final paper, we will present the derivation of the NLOS detection algorithm and the AEKF. Additionally we provide information about the false alarm probability, detection probability of NLOS signals versus signal to noise ratio. Furthermore we provide additional the simulation results which show the advantage of using these algorithms compared to EKF and the static method with Gauss-Newton. [1] J. a. Figueiras and S. Frattasi, Mobile positioning and tracking: from conventional to cooperative techniques, ser. New ecologies for the twenty-first century. Wiley, 2010. [2] K. Yu, I. Sharp, and Guo. (2009) Groundbased wireless positioning [3] J. Schroeder, S. Galler, K. Kyamakya, and K. Jobmann, "NLOS detection algorithms for ultra-wideband localization," in Workshop on Positioning, Navigation and Commun. (WPNC), Mar. 2007