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In this paper the tracking problem of multi-agent systems, in a particularscenario where a segment of agents entering a sensing-denied environment orbehaving as non-cooperative targets, is considered. The focus is on determiningthe optimal sensor precisions while simultaneously promoting sparseness in thesensor measurements to guarantee a specified estimation performance. Theproblem is formulated in the discrete-time centralized Kalman filteringframework. A semi-definite program subject to linear matrix inequalities issolved to minimize the trace of precision matrix which is defined to be theinverse of sensor noise covariance matrix. Simulation results expose atrade-off between sensor precisions and sensing frequency.

Sparse Sensing Architectures with Optimal Precision for Tracking Multi-agent Systems in Sensing-denied Environments