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  • Distributed Particle Filter With Online Model Learning for Localization Using Time-Difference-of-Arrival (TDOA) Measurements
Title: Distributed Particle Filter With Online Model Learning for Localization Using Time-Difference-of-Arrival (TDOA) Measurements
Abstract The problem of localizing a moving target arises in various forms in wireless sensor networks. Deploying multiple sensing receivers and using the time-difference-of-arrival (TDOA) of the target’s emitted signal is widely considered an effective localization technique. Traditionally, TDOA-based algorithms adopt a centralized approach where all measurements are sent to a predefined reference node for position estimation. More recently, distributed TDOA-based localization algorithms have been shown to improve the robustness of these estimates. For target models governed by highly stochastic processes, the method of nonlinear filtering and state estimation must be carefully considered. In this work, a distributed TDOA-based particle filter algorithm is proposed for localizing a moving target modeled by a discrete-time correlated random walk (DCRW). We present a method for using data collected by the particle filter to estimate the unknown probability distributions of the target’s movement model, and then apply the distribution estimates to recursively update the particle filter’s propagation model. The performance of the distributed approach is evaluated through numerical simulation, and we show the benefit of using a particle filter with online model learning by comparing it with the non-adaptive approach.  more » « less
Award ID(s):
1848945 1734272
PAR ID:
10216297
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of ASME 2020 Dynamic Systems and Control Conference
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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