Abstract
The data fusion can come down to a process that combines the state vectors from different sources to obtain a more accurate result. Compare to the achieved results that depend on single source, the method has gained an improved performance and reduced the computational complexity and bandwidth of transmission as well. This paper makes use of Probabilistic Data Association (PDA) algorithm and Joint Probabilistic Data Association (JPDA) algorithm to track the Multi-target for each local sensor in a clutter environment. Furthermore, a method based on statistical double-threshold association algorithm and covariance-weighted fusion algorithm is proposed in this paper. Meanwhile, the simulation result shows that the performance has been improved significantly in multi-sensor and multi-target tracking progress with the proposed method in the paper.
Keywords: Data fusion, track correlation, multi-sensor, multi-target, double-threshold, synchronous sampling time, Track Fusion, Probabilistic Data Association, Joint Probabilistic Data Association (JPDA)
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