A dynamic adaptive deviation registration algorithm for heterogeneous sensors

Deviation registration algorithm of homogeneous sensors has grown mature, but heterogeneous sensors deviation registration algorithm still faces the problems like strong nonlinearity and low precision, which needs urgent solutions. To eliminate the difficulties in deviation registration of heterogeneous sensors, especially the different dimensional sensors, the heterogeneous sensors deviation registration model and dynamic adaptive particle swarm optimized particle filter are proposed. In this paper, dynamic adaptive particle swarm optimized particle filter and Kalman filter is federated to carry out the real-time calibration for the system deviation of radar and infrared sensor. With particle neighborhood information taken into account, this algorithm conducts a self-adaptive adjustment over the number of particle's neighborhood particles by means of diversity factor, neighborhood extension factor, and neighborhood restriction factor. The adjustment is intended to control the influence of particle on neighborhood and reduce its local optimum so as to attain the optimal balance between convergence rate and optimization ability. The experimental result shows that the improved heterogeneous sensors deviation registration algorithm has upgraded the accuracy and speed of deviation registration; therefore, this algorithm is of high application value in the deviation registration of three-dimensional radar and two-dimensional infrared sensor.