Two kinds of sensor data fusion in target tracking

Describes an algorithm for fusion of tracks created by two kinds of sensor (radar and IR), these measurement data are obtained with different dimensions and different sample rates. By means of the time matching technique, two asynchronous data are fused and then the filter is updated according to the fused information. The rotation Kalman filter algorithm for data fusion is discussed; this approach can effectively solve the problem of nonlinear measurement and reduce the load of calculation.