Due to the availability of cheap passive sensors, it is possible to deploy a large number of them for tracking
purposes in anti-submarine warfare (ASW). However, modern submarines are quiet and difficult to track with
passive sensors alone. Multistatic sensor networks, which have few transmitters (e.g., dipping sonars) in addition
to passive receivers, have the potential to improve the tracking performance. We can improve the performance
further by moving the transmitters according to existing target states and any possible new targets. Even
though a large number of passive sensors are available, due to frequency, processing power and other physical
limitations, only a few of them can be used at any one time. Then the problems are to decide the path of the
transmitters and select a subset from the available passive sensors in order to optimize tracking performance.
In this paper, the PCRLB, which gives a lower bound on estimation uncertainty, is used as the performance
measure. We present an algorithm to decide jointly the optimal path of the movable transmitters, by considering
their operational constraints, and the optimal subset of passive sensors that should be used at each time steps for
tracking multiple, possibly time-varying, number of targets. Finding the optimal solution in real time is difficult
for large scale problems, and we propose a genetic algorithm based suboptimal solution technique. Simulation
results illustrating the performance of the proposed algorithm are also presented.