Efficient Marginalized Particle Smoother for Indoor CSS–TOF Localization with Non-Gaussian Errors

Yuan Yang,Manyi Wang,Yunxia Qiao,Haoran Yang,Bo Zhang

doi:10.3390/rs12223838

Yuan Yang, Manyi Wang + Show 3 more

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https://doi.org/10.3390/rs12223838

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Abstract

The time-series state and parameter estimations of indoor localization continue to be a topic of growing importance. To deal with the nonlinear and positive skewed non-Gaussian dynamic of indoor CSS–TOF (Chirp-Spread-Spectrum Time-of-Flight) ranging measurements and position estimations, Monte Carlo Bayesian smoothers are promising as involving the past, present, and future observations. However, the main problems are how to derive trackable smoothing recursions and to avoid the degeneracy of particle-based smoothed distributions. To incorporate the backward smoothing density propagation with the forward probability recursion efficiently, we propose a lightweight Marginalized Particle Smoother (MPS) for nonlinear and non-Gaussian errors mitigation. The performance of the position prediction, filtering, and smoothing are investigated in real-world experiments carried out with vehicle on-board sensors. Results demonstrate the proposed smoother enables a great tool by reducing temporal and spatial errors of mobile trajectories, with the cost of a few sequence delay and a small number of particles. Therefore, MPS outperforms the filtering and smoothing methods under weak assumptions, low computation, and memory requirements. In the view that the sampled trajectories stay numerically stable, the MPS form is validated to be applicable for time-series position tracking.

Highlights

Indoor wireless positioning has attracted much attention in recent years, which is the key important issue arising in robotics, advanced signal processing, social networking, or mobile monitoring of indoor environments, just to mention a few
We summarize that: (1) the observation a few time-series later contains a significant amount of information to combat the state uncertainty and the measurement errors; (2) the Marginalized Particle Smoother (MPS) improvement arises from incorporating the smoothing density into the Sequential Monte Carlo (SMC) recursion, differing from the referral smoothing algorithms that only influence the backward density
The positioning performance the algorithms is investigated in terms of accuracy, complexity and trajectory behavior and smoothness

Summary

Introduction

Indoor wireless positioning has attracted much attention in recent years, which is the key important issue arising in robotics, advanced signal processing, social networking, or mobile monitoring of indoor environments, just to mention a few. Most positioning applications provide time-series measurements and demand for continuous estimation of the target’s position [1]. When considering Time-of-Flight (TOF) measurements as ranging measurements, the uncertainty of ranging measurements is coherent with dynamic environments and sensor limitations [2]. The indoor positioning problem is to understand the dynamic of which limited and noisy observations are available. A considerable amount of research has been put into the purpose of sequential estimations of indoor position, known as indoor sequential positioning or position tracking [3,4,5]. Fairly fruitful research has been proposed in sequential positioning algorithms, to achieve accurate indoor position tracking from a multimodal distribution or noisy measurements is still very challenging due to the following difficulties:

Results

Discussion

Conclusion