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Distributed Multi-Sensor Control for Multi-Target Tracking With a Sparsity-Promoting Objective Function
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DOI：10.1109/LSP.2024.3362189.png)
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摘要
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A distributed multi-sensor control method is presented for multi-target tracking. The problem is formulated as auctioned partially observed Markov decision processes (auctioned POMDPs), which is a tractable approach to approximate the solutions in a distributed manner. To ensure adequate coverage of the multi-sensor system, a sparsity-promoting objective function is also designed to reduce overlapping sensing areas, balancing a tradeoff between the control reward and sensor coverage. Simulation results demonstrate that the proposed distributed method achieves comparable tracking performance to the state-of-art centralized approach. Furthermore, the proposed sparsity-promoting objective function outperforms the conventional Cauchy-Schwarz divergence (CSD) in discovery performance.
Keyword:
Sensors
Linear programming
Sensor systems
Markov processes
Target tracking
Radio frequency
Process control
Multi-sensor control
multi-target tracking
partially observed Markov decision process
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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