A New Particle Filter for Target Tracking in MIMO OFDM Integrated Sensing and Communications
Shixiong Wang, Wei Dai, Geoffrey Ye Li

TL;DR
This paper introduces a novel particle filtering approach for target tracking in MIMO OFDM systems that overcomes key challenges by using a cost function-based evaluation, leading to improved efficiency and accuracy in integrated sensing and communications.
Contribution
It proposes a new particle filtering framework that avoids explicit likelihood models and reduces computational complexity for MIMO radar target tracking.
Findings
Significant reduction in tracking error and computational time.
Enhanced target tracking performance in MIMO OFDM ISAC systems.
Potential for improved integrated sensing and communications capabilities.
Abstract
Particle filtering for target tracking using multi-input multi-output (MIMO) pulse-Doppler radars faces three long-standing obstacles: a) the absence of reliable likelihood models for raw radar data; b) the computational and statistical complications that arise when nuisance parameters (e.g., complex path gains) are augmented into state vectors; and c) the prohibitive computational burden of extracting noisy measurements of range, Doppler, and angles from snapshots. Motivated by an optimization-centric interpretation of Bayes' rule, this article addresses these challenges by proposing a new particle filtering framework that evaluates each hypothesized state using a tailored cost function, rather than relying on an explicit likelihood relation. The framework yields substantial reductions in both running time and tracking error compared to existing schemes. In addition, we examine the…
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Taxonomy
TopicsTarget Tracking and Data Fusion in Sensor Networks · Radar Systems and Signal Processing · Direction-of-Arrival Estimation Techniques
