Multisensor applications rely on effectively managing sensor resources. In particular, next-generation multifunctional
agile radars demand innovative resource management techniques to achieve a common sensing goal
while satisfying resource constraints. We consider an active sensing platform where multiple waveform-agile
radars scan a hostile surveillance area for targets. A central controller adaptively selects which transmitters
should be active and which waveforms should be transmitted. The controller's goal is to choose the sequence
of (transmitter, waveform) pairs that yields the most accurate tracking estimate. We formulate this problem as
a partially observable Markov decision process (POMDP), and propose a novel "two-level" scheduling scheme
that uses two distinct schedulers: (1) at the lower level, a myopic waveform scheduler; and (2) at the upper
level, a non-myopic transmitter scheduler. Scheduling decisions at these two levels are carried out differently.
While waveforms are updated at every radar scan, a new set of transmitters only becomes active if the overall
tracking accuracy falls below a given threshold, or if the "detection risk" is exceeded, given by a limit on the
number of consecutive scans during which a set of transmitters is active. By simultaneously exploiting myopic
and non-myopic scheduling schemes, we benefit from trading off short-term for long-term performance, while
maintaining low computational costs. Moreover, in certain situations, the myopic scheduling of waveforms at
each radar scan improves on non-myopic actions taken in the past. Monte Carlo simulations are used to evaluate
the performance of the proposed adaptive sensing scheme in a multitarget tracking setting.
When compared to tracking airborne targets, tracking ground targets on urban terrains brings a new set of challenges.
Target mobility is constrained by road networks, and the quality of measurements is affected by dense
clutter, multipath, and limited line-of-sight. We investigate the integration of detection, signal processing, tracking,
and scheduling by exploiting distinct levels of diversity: (1) spatial diversity through the use of coordinated
multistatic radars; (2) waveform diversity by adaptively scheduling the transmitted radar waveform according
to the scene conditions; and (3) motion model diversity by using a bank of parallel filters, each one matched to a
different maneuvering model. Specifically, at each scan, the waveform that yields the minimum one-step-ahead
error covariance matrix determinant is transmitted; the received signal is then matched-filtered, and quadratic
curve fitting is applied to extract range and azimuth measurements that are input to the LMIPDA-VSIMM
algorithm for data association and filtering. Monte Carlo simulations are used to demonstrate the effectiveness
of the proposed system on a realistic urban scenario. A more traditional open-loop system, in which waveforms
are scheduled on a round-robin fashion and with no other modes of diversity available, is used as a baseline for
comparison. Simulation results show that our closed-loop system significantly outperforms the baseline system,
presenting both a reduction on the number of lost tracks, and a reduction on the volume of the estimation
uncertainty ellipse. The interdisciplinary nature of this work highlights the challenges involved in designing a
closed-loop active sensing platform for next-generation urban tracking systems.