Analytical observation-history-dependent tracking characterization and sequential transmit optimization

Establish an analytical and explicit characterization of wireless radar tracking performance that exploits observation history, together with sequential transmit signal optimization based on that characterization, for multi-antenna radar tracking of moving targets.

Background

The paper considers a MIMO radar tracking system in which a base station tracks a moving target over multiple time slots and adapts its transmit covariance matrix using previously collected echo observations. The relevant performance metric is the conditional posterior Cramér–Rao bound (PCRB), which depends on the predictive distribution of the target state conditioned on the observation history.

Existing approaches generally characterize the conditional PCRB numerically using Monte Carlo methods, particle filtering, or recursive techniques. These methods are computationally expensive for long observation histories and do not provide an explicit relationship between the PCRB and the transmit signals. The stated open problem is therefore to develop analytical, explicit performance characterization and corresponding sequential transmit-signal optimization that jointly exploit observation history.