Multi-sensor Fusion Tracking Based on Particle Filtering and Detection Information

A multi-sensor fusion algorithm under low detection probability and low signal-to-noise ratio environment is discussed in this paper. First, detection probability of sensor is modeled. Then, mixed likelihood of detection response and measurement information is calculated. A nonlinear target multi-sensor multi-resource fusion algorithm based on particle filter is established within Bayesian framework. The proposed algorithm utilizes detection responses and measurement information of sensors, and the tracking accuracy is enhanced. The effectiveness of the proposed method is shown by Monte Carlo simulation results.