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

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 Cramér-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. Index Terms-Adaptive power allocation, adaptive scheduling, Bayesian inference, cognitive radar network, complex urban environment, multi-target tracking, sequential Monte Carlo estimation.

Multisource Spatiotemporal Tracking Using Sparse Large Aperture Arrays

“…Note that this is distinctly different to the active tracking ( [17], [18], [19], [20]) or active localisation (e.g. [21]) approaches discussed extensively for example in the "radar" literature.…”

“…[21]) approaches discussed extensively for example in the "radar" literature. For instance, in [17], a multi-target tracking approach is proposed that employs a radar network that uses a new particle-filter tracking method with global feedback. MIMO radar is also an "active" echo tracking system where a number of Tx-antennas transmit independent but known signals and the Rx-antennas receive the various echoes .…”

“…For example, in [18], Bayesian estimators (including Kalman and "particle" filters) are proposed for tracking in MIMO radar. Note that both [17] and [18] use underlying statistical models that are different from the array processing modelling proposed in this paper. Furthermore, in [22] MIMO radar that employs multiple, spatially distributed transmitters and receivers is presented.…”

Novel Power Control Scheme for Target Tracking in Radar Network with Passive Cooperation

“…In addition to aforementioned, considerable research has been conducted relevant to the potential use of radar networks for achieving systems performance improvement correlated to target detection [3][4][5][6][7], target localization [8], [9], target tracking [10][11][12][13], waveform design [14][15][16], sensor selection [17], and information extraction.…”

Array of sensors: A spatiotemporal-state-space model for target trajectory tracking

“…In this paper, in the framework of a state space model, a solution to track multiple mobile sources is presented using a large aperture array of passive sensors with known array geometry connected with fibre, wireline or wireless links. Note that this is distinctly different from the active tracking ( [11], [12], [13], [14]) or active localisation (e.g. [15], [16]) approaches discussed extensively for example in the "radar" literature.…”