Detection and Tracking Low Maneuvering Target in a High Noise Environments

International audienceDetect and tracking of maneuvering target is a complicated dynamic state estimation problem whose difficulty is increased in case of high noise environments or low signal-to-noise ratio (SNR). In this case, the track-before-detect filter (TBDF) that uses unthresholded measurements considers as an effective method for detecting and tracking a single target under low SNR conditions. Nevertheless, the performance of the algorithm will be affected with severe loss because of the mismatching of target model during maneuver. In this paper, to resolve the target maneuvers, we propose an application of particle filtering which depends on track before detect (PF - TBD) algorithm in order to track the maneuvering target. We employ the Constant Acceleration (CA) model and Coordinate Turn model (CT). Our simulation results show that the detection and tracking of maneuvering target performance of TBD-PF has been improved using the proposed algorithm