基于粒子滤波和检测信息的多传感器融合跟踪

从概率角度审视低检测率、低信噪比下的多传感器融合算法.首先建立传感器检测概率模型，然后计算传感器检测响应、量测信息的融合似然度，在贝叶斯框架下建立一种非线性目标基于粒子滤波器的多传感器多源信息融合算法，该算法融合了传感器的量测信息和检测响应，提高了跟踪精度蒙特卡洛仿真结果表明了算法的有效性.

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

A multi-sensor fusion algorithm under low detect io n 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 ta rget 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 simul ation results.