  We investigate the challenging problem of integrating detection, signal
processing, target tracking, and adaptive waveform scheduling with lookahead in
urban terrain. We propose a closed-loop active sensing system to address this
problem by exploiting three distinct levels of diversity: (1) spatial diversity
through the use of coordinated multistatic radars; (2) waveform diversity by
adaptively scheduling the transmitted waveform; and (3) motion model diversity
by using a bank of parallel filters matched to different motion models.
Specifically, at every radar scan, the waveform that yields the minimum trace
of the one-step-ahead error covariance matrix is transmitted; the received
signal goes through a matched-filter, and curve fitting is used to extract
range and range-rate measurements that feed the LMIPDA-VSIMM algorithm for data
association and filtering. Monte Carlo simulations demonstrate the
effectiveness of the proposed system in an urban scenario contaminated by dense
and uneven clutter, strong multipath, and limited line-of-sight.
