Switching Extensible FIR Filter Bank for Adaptive Horizon State Estimation With Application

Horizon size is an important parameter that affects the estimation performance of finite impulse response (FIR) filters. In this brief, we propose a novel adaptive horizon approach that aims to adapt the horizon size at each time point. The approach suggests providing state estimation using a bank of FIR filters called the switching extensible FIR filter bank (SEFFB), which consists of several FIR filters operating using different horizon sizes. The horizon sizes and the number of FIR filters in the SEFFB are adapted to changes in system characteristics using maximum likelihood. The SEFFB is applied to target tracking using a ground moving target indicator. A significant performance improvement is demonstrated using the SEFFB in comparison with a single FIR filter using constant optimal horizon size.Jung Min Pak, Choon Ki Ahn, Yuriy S. Shmaliy, Peng Shi and Myo Taeg Li

Distributed Hybrid Particle/FIR Filtering for Mitigating NLOS Effects in TOA-Based Localization Using Wireless Sensor Networks

For indoor localization based on wireless sensor networks, the transmission of wireless signals can be disrupted by obstacles and walls. This situation, called non-line-of-sight (NLOS), degrades localization accuracy and may lead to localization failures. This paper proposes a new NLOS identification algorithm based on distributed filtering to mitigate NLOS effects, including localization failures. Rather than processing all measurements via a single filter, the proposed algorithm distributes the measurements among several local filters. Using distributed filtering and data association techniques, abnormal measurements due to NLOS are identified, and negative effects can be prevented. To address cases of localization failures due to NLOS, the hybrid particle finite impulse response filter (HPFF) was adopted. The resulting distributed HPFF can self-recover by detecting failures and resetting the algorithm. Extensive simulations of indoor localization using time of arrival measurements were performed for various NLOS situations to demonstrate the effectiveness of the proposed algorithm.Jung Min Pak, Choon Ki Ahn, Peng Shi, Yuriy S. Shmaliy and Myo Taeg Li