  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 non-cooperative 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 semi-definite 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.
