An Efficient Particle Filter Track-Before-Detect Algorithm with Road Constraints

The standard multiple model(MM) particle filter(PF) track-before-detect(TBD) algorithm(MM-PFTBD) with a fixed structure and number of movement models has been become a popular and efficient approach to detect and track the maneuverable weak target in the much lower signal-to-noise ratio(SNR).But the performance of the detection and tracking is degraded owing to the competition among the movement models when the number of movement models is great.Then a novel TBD algorithm with variable structure MM-PF approach(VS-MM-PFTBD) is presented by utilizing the road information.The model set not only depending on the target state but also relating to the road information available is updated and varies adaptively so as to choose the effective model set and reduce the number of the movement models.In addition,target velocity constraints can be applied effectively along the directions of the different roads.Finally,the simulation results show that the VS-MM-PFTBD outperforms the standard MM-PFTBD using a fixed model structure without the road information.