Autonomous path planning for radar and sonar sensing faces significant challenges arising from dynamic targets, obstacle occlusions, and low signal-to-noise (SNR) conditions. We propose a hierarchical sensor scheduling framework that combines a long-horizon strategic planner, based on the Rapidly-exploring Random Tree star (RRT*) algorithm, with a fast-adapting tactical planner. Efficient coordination of the two planners is achieved via a novel message passing mechanism, enabling guidance of the sensor out of complex environments while maintaining effective target tracking. Additionally, we introduce an RRT* rerooting strategy that significantly reduces computation time and so expedites the update of the strategic plan. Extensive simulation results demonstrate that our proposed fusion approach outperforms conventional stand-alone short-term and long-term planners in challenging scenarios and low-SNR regimes,

We present a sensor scheduling algorithm to plan the motion of multiple autonomous platforms for cooperative tracking of targets within a region that contains obstacles and occlusions. The platforms have kinematic constraints and their sensors have restricted field of view and range. The proposed algorithm is a variant of the Rapidly exploring Random Tree star algorithm (RRT*) that has been adapted to the problem of determining paths for multiple independent kinematically constrained platforms to optimise their tracking performance. To guide the scheduling algorithm, we define a tracking cost based on the Posterior Cramér Rao Bound (PCRB) derived from the predicted positions of the platforms and targets. Through simulations of generated paths, we show that the algorithm generates rational plans for tracking targets and that the tracking cost accurately predicts the realised performance of the platforms.

Sonobuoy fields, comprising a network of sonar transmitters and receivers, are used to find and track underwater targets. For a given environment and sonobuoy field layout, the performance of such a field depends on the scheduling, that is, deciding which source should transmit, and which waveform should be transmitted at any given time. In this paper, we explore the choice of cost function used in myopic scheduling and its effect on tracking performance. Specifically, we consider 5 different cost functions derived from the predicted error covariance matrix of the track. Importantly, our cost functions combine both positional and velocity covariance information to allow the scheduler to choose the optimum source-waveform action. Using realistic multistatic sonobuoy simulations, we demonstrate that each cost function results in a different choice of source-waveform actions, which in turn affects the performance of the scheduler. In particular, we show there is a trade-off between position and velocity error performance such that no one cost function is superior in both.