Localization and tracking of multiple fast moving targets in bistatic MIMO radar

In this paper, we propose a maximum likelihood (ML)-based scheme to accurately localize and track multiple fast-moving targets in multiple-input multiple-output (MIMO) radar systems. For the problem of tracking, the motion of the target will cause a spread spectrum effect, which broadens the beam width and skews the beam steering, rendering accurately localizing and tracking the fast moving targets extremely challenging. To tackle this problem, we use a locally linear model to approximate the real motion states of the target and define a vector to describe its motion state. As such, the motion states of targets at adjacent times can be accurately inferred within a short term based on this approximation model. To incorporate the effect of target motion when performing tracking, we estimate the motion state vector at a reference time from a batch of snapshots by using an ML estimator, instead of estimating the time-varying angular parameters directly as usual. Then the motion state vectors at other times can be inferred based on the estimated one by using the locally linear approximation model. The Cramér-Rao lower bound (CRLB) of the estimated parameters are also derived for performance evaluation. Numerical results validate the performance improvement of the proposed scheme compared to the benchmarks.