A Message Passing based Adaptive PDA Algorithm for Robust Radio-based Localization and Tracking

Abstract

We present a message passing algorithm for localization and tracking in multipath-prone environments that implicitly considers obstructed line-of-sight situations. The proposed adaptive probabilistic data association algorithm infers the position of a mobile agent using multiple anchors by utilizing delay and amplitude of the multipath components (MPCs) as well as their respective uncertainties. By employing a nonuniform clutter model, we enable the algorithm to facilitate the position information contained in the MPCs to support the estimation of the agent position without exact knowledge about the environment geometry. Our algorithm adapts in an online manner to both, the time-varying signal-to-noise-ratio and line-of-sight (LOS) existence probability of each anchor. In a numerical analysis we show that the algorithm is able to operate reliably in environments characterized by strong multipath propagation, even if a temporary obstruction of all anchors occurs simultaneously.

A Graph-based Algorithm for Robust Sequential Localization Exploiting Multipath for Obstructed-LOS-Bias Mitigation

“…• We analyze the influence of the individual features of our algorithm and compare it to a particle-based variant of the multiple-sensors AIPDA algorithm and to the posterior Cramér-Rao lower bound (PCRLB) [44]. This work advances over the preliminary account of our conference publication [24] (and that of the related work [23]) by (i) applying an accurate model for the joint distribution of delay and amplitude measurements instead of using heuristical models, (ii) sequentially inferring all parameters of the NLOS model together with the agent instead of using predetermined constants, (iii) improving the convergence behavior using a modified, "decoupled" SPA, (iv) demonstrating the performance of the proposed algorithm using simulated radio signals as well as real radio measurements obtained by (v) applying a real CEDA and (vi) comparing to the PCRLB.…”

“…Other methods exploit cooperation among individual agents [4], [17], [18], or perform robust signal processing against multipath propagation and clutter measurements in general. The latter comprise heuristics [8], machine learning-based approaches [10], [19]- [21] as well as Bayesian methods [22]- [24], and hybrids thereof [25], [26]. Heuristic methods, such as searching for the first amplitude to exceed a threshold value, are fast and easily implementable but suffer from low accuracy as well as a high probability of outage in low signal-to-noise-ratio (SNR) regions [8].…”

“…This is why recent algorithms facilitate deep learning and auto-encoding based methods to directly operate on the received radio signal and reduce the dependence on training data [21], [27], [28]. Multipath-based localization [12], [13], [16], [23], [24], [29], [30], multiobject-tracking [31]- [33], and parametric channel tracking [34], [35] pose common challenges -uncertainties beyond Gaussian noise, like missed detections and clutter, an uncertain origin of measurements, and unknown and time-varying number of objects to be localized and tracked -that can be well addressed by Bayesian inference that leverages graphical models, performing joint detection and estimation. Similarly, the probabilistic data association (PDA) algorithm [31], [36] represents a low-complexity Bayesian method for robust localization and tracking with extension to multiple-sensors PDA [37] and amplitude-information probabilistic data association (AIPDA) [30], [38].…”

Pairwise Node Localization From Differences in Their UWB Channels to Observer Nodes

“…These applications concern dense and dynamic propagation environments, characterized by time-variant channels with frequent line-of-sight (LOS) obstruction [9] and rich multipath propagation. This poses a great challenge to wireless localization and ranging.…”

“…Other promising approaches are soft information processing [23], [24] and temporal filtering [2], [25], [26] with the incorporation of inertial measurements [2], [27]. Furthermore, various recent work considers multipath as opportunity rather than interference [3], [9], [15], [26], [28]- [31]. Multipath-assisted localization yields improved accuracy and robustness if knowledge about the propagation environment is either available a-priori [29] or obtained with mapping [26], [31].…”