Scheduling and Power Allocation in a Cognitive Radar Network for Multiple-Target Tracking

TLDR: A hybrid Bayesian filter that operates by partitioning the state space into smaller subspaces and thereby reducing the complexity involved with high-dimensional state space is proposed.

Abstract: We propose a cognitive radar network (CRN) system for the joint estimation of the target state comprising the positions and velocities of multiple targets, and the channel state comprising the propagation conditions of an urban transmission channel. We develop a measurement model for the received signal by considering a finite-dimensional representation of the time-varying system function which characterizes the urban transmission channel. We employ sequential Bayesian filtering at the receiver to estimate the target and the channel state. We propose a hybrid Bayesian filter that operates by partitioning the state space into smaller subspaces and thereby reducing the complexity involved with high-dimensional state space. The feedback loop that embodies the radar environment and the receiver enables the transmitter to employ approximate greedy programming to find a suitable subset of antennas to be employed in each tracking interval, as well as the power transmitted by these antennas. We compute the posterior Cramer-Rao bound (PCRB) on the estimates of the target state and the channel state and use it as an optimization criterion for the antenna selection and power allocation algorithms. We use several numerical examples to demonstrate the performance of the proposed system.