首页|Wideband Sensor Resource Allocation for Extended Target Tracking and Classification

Wideband Sensor Resource Allocation for Extended Target Tracking and Classification

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  • NETL
  • NSTL
  • IEEE
Communication base stations can achieve high-precision tracking and accurate classification for multiple extended targets in the context of integrated communication and sensing by transmitting wideband signal. However, the time resources of the base stations are often limited. In the time-division operation mode, part of the time resources must be reserved to guarantee communication performance, while the rest of the resources must be properly allocated for better multi-target sensing performance. To deal with this, we develop a sensing task-oriented resource allocation (RA) scheme for wideband sensors. We first derive the Cramér–Rao lower bound for the estimation errors of position and shape parameters of the extended targets, and analyze their inside relations w.r.t. the resource vectors. Based on this, we construct the evaluation metric of tracking and classification performance, and subsequently build a non-smooth mathematical resource optimization model to maximize the target capacity within predetermined tracking and classification requirements. To solve this RA model, we then design an efficient two-step solution technique that incorporates dual transformation and discrete search. Finally, simulation results demonstrate that the proposed RA scheme can greatly increase the number of the well sensed targets within a limited sensing resource budget.

SensorsTarget trackingBase stationsWidebandShapeResource managementRadar trackingVectorsRadarOptimization

Hao Jiao、Junkun Yan、Wenqiang Pu、Yijun Chen、Hongwei Liu、Maria Sabrina Greco

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National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China

National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China|Hangzhou Institute of Technology, Xidian University, Hangzhou, China

Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, China|Hangzhou Institute of Technology, Xidian University, Hangzhou, China

Department of Information Engineering, University of Pisa, Pisa, Italy

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2025

10.1109/TSP.2024.3512615

IEEE transactions on signal processing

IEEE transactions on signal processing

ISSN：
年,卷(期)：2025.73(1)
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