JIPDA Filtering with Information Diffusion

The paper introduces a novel collaborative method for multi-target tracking in cluttered environments. Based on the information diffusion framework, the proposed approach involves agents sharing observations and/or posterior estimates with neighboring agents within one hop distance. No intermediate iterations are employed to achieve global agreement, as in consensus algorithms, nor is there a single point of failure, as in algorithms with a fusion center. The tracking method is based on the joint integrated probabilistic data association (JIPDA) filter, which is adapted to accurately incorporate neighbors' observations and optimally fuse available estimates. The ultimate goal is to improve the tracking performance by overcoming the intrinsic uncertainty of target-measurement association and object detection. The method is computationally cheap and does not require any numerical optimization. Simulation results clearly demonstrate the performance of the proposed method.