Bayesian Joint Localization and Tracking Algorithm Using Multiple-Input Multiple-Output Radar

被引：0

作者：

Westerkam, Anders Malthe ^{[1
]}

Manchon, Carles Navarro ^{[1
]}

Mogensen, Preben ^{[1
]}

Pedersen, Troels ^{[1
]}

机构：

[1] Aalborg Univ, Aalborg, Denmark

来源：

2023 IEEE 9TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING, CAMSAP | 2023年

关键词：

Bayesian Learning; localization and Tracking; Active Sensing; MIMO-radar; BEFORE-DETECT; PARTICLE FILTER;

D O I：

10.1109/CAMSAP58249.2023.10403518

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We present a novel joint localization and tracking algorithm for multiple-input multiple-output active radars. The proposed algorithm, which we dub Bayesian localization and tracking (BLaT), relies on approximate Bayesian inference using the mean field approach and processes all available received data to jointly estimate the target's track and location. This approach makes it possible to take advantage of the inherent synergy between the tracking and localization tasks. BLaT is shown by simulation to outperform a classical sequential processing baseline in terms of its ability to track targets in low signal-to-noise ratio conditions as well as superior tracking of manoeuvring targets.

引用

页码：316 / 320

页数：5

共 26 条

[1] Multiple model efficient particle filter based track-before-detect for maneuvering weak targets [J].