Sparse Sensing Architectures with Optimal Precision for Tracking Multi-agent Systems in Sensing-denied Environments
Sparse Sensing Architectures with Optimal Precision for Tracking Multi-agent Systems in Sensing-denied Environments
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DOI：
10.23919/acc50511.2021.9483377
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Vedang M. Deshpande;R. Bhattacharya
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文献类型：
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作者：
Vedang M. Deshpande;R. Bhattacharya
在本文中，跟踪问题的多智能体系统，在一个特定的场景中，一段代理进入感知拒绝环境或行为作为非合作目标，被认为是。重点是确定最佳的传感器精度，同时促进稀疏的传感器测量，以保证指定的估计性能。该问题是制定在离散时间集中卡尔曼滤波框架。通过求解线性矩阵不等式约束下的半定规划，使精度矩阵的迹最小，精度矩阵定义为传感器噪声协方差矩阵的逆矩阵。仿真结果揭示了传感器精度和感测频率之间的权衡。
In this paper the tracking problem of multi-agent systems, in a particular scenario where a segment of agents entering a sensing-denied environment or behaving as noncooperative targets, is considered. The focus is on determining the optimal sensor precisions while simultaneously promoting sparseness in the sensor measurements to guarantee a specified estimation performance. The problem is formulated in the discrete-time centralized Kalman filtering framework. A semidefinite program subject to linear matrix inequalities is solved to minimize the trace of precision matrix which is defined to be the inverse of sensor noise covariance matrix. Simulation results expose a trade-off between sensor precisions and sensing frequency.