PCRLB-Based Radar Resource Management for Multiple Target Tracking

Abstract

As a crucial factor in improving radar performance for multiple target tracking (MTT), resource management problems are analyzed in this thesis with regard to sensor platform path planning, beam scheduling, and burst parameter design. This thesis addresses problems to deploy or adapt radar configurations for multisensor-multitarget tracking, including 1) the path planning of movable receivers and power allocation of transmitted signals, 2) the optimal beam steering of high-precision pencil beams, and 3) the pulsed repetition frequency (PRF) set selection and waveform design.

Firstly, the coordinated sensor management on the ends of both receivers and transmitters for a multistatic radar is studied. A multistatic radar system consists of fixed transmitters and movable receivers. To form better transmitter-target-receiver geometry and to establish an effective power allocation scheme to illuminate targets with different priorities, a joint path planning and power allocation problems, which determines the moving trajectories of receivers mounted on unmanned airborne vehicles (UAVs) and the power allocation scheme of transmitted signals over a limited time horizon, is formulated as a weighted-sum optimization. The problem is solved with a genetic algorithm (GA) with a novel pre-selection operator. The pre-selection operator, which takes advantage of the receding horizon control (RHC) framework to improve population structures prior to the next generation, can accelerate the convergence of GA.

Secondly, the beam steering strategies for a cooperative phased array radar system with high-precision beams are developed. Pencil beams with narrow beamwidth, which are designated to track targets for a phased array radar, offer efficient performance in an energy-saving design, but can cause partial observations. The novel concept of expected Cramér-Rao lower bound (EPCRLB) is proposed to model partial observations. A formulation based on PCRLB is given and solved with a hierarchical genetic algorithm (HGA). An optimal strategy based on EPCRLB, which is effective in performance and efficient in time, is proposed.

Finally, a joint pulsed repetition frequency (PRF) set selection and waveform design is studied. The problem tries to improve blind zone maps while preventing targets from falling into blind zones. Waveform parameters are then optimized for the system to provide better tracking accuracy. The problem is first formulated as a bi-objective optimization problem and solved with a multiple-objective genetic algorithm. Then, a two-step strategy that prioritizes the visibility of targets is developed. Numerical results demonstrate the effectiveness of proposed strategies over simple approaches.