Robust CPHD algorithm for maneuvering targets tracking via airborne pulsed Doppler radar

A robust cardinalized probability hypothesis density based on interacting multiple model (RCPHD-IMM) is proposed for multiple maneuvering targets tracking under the Doppler blind zone of airborne pulse Doppler radar. This tracking algorithm integrates the advantages of unbiased converted measurements (UCM), interacting multiple model (IMM), and cardinalized probability hypothesis density (CPHD). Under the architecture of the proposed algorithm, an IMM deals with the model switching, and the UCM accounts for nonlinearity in the dynamic system models, whereas the CPHD estimates the number and state of the targets. The gain adjustment matrix is designed to improve the robustness of the proposed algorithm in the blind zone. Furthermore, an adaptive gating strategy is proposed to remove the irrelevant measurements and guarantee the detection of new targets, thereby effectively reduce the computational complexity of the proposed algorithm. Simulation results demonstrate the validity and feasibility of the proposed algorithm. The number and state of the targets in the loss of measurements can be estimated more accurately, and the amount of calculation needed with the RCPHD-IMM algorithm is reduced by 16.03% compared with the traditional CPHD algorithm.