New fuzzy algorithm for fusing data from active/passive radars

The full paper starts with a discussion of the merits and shortcomings of existing methods for tracking enemy targets. Then we propose a new fuzzy algorithm for fusing data from both active and passive radars that we believe can retain the merits and reduce the shortcomings as much as possible. In the full paper, we explain our new fuzzy algorithm in much detail, here we just list the two topics discussed; (1) the iterative process of target tracking system based on Kalman filter, each iteration includes six steps; (2) data fusion based on fuzzy logic, including four subtopics - (a) how to obtain the input data of the system for fuzzy deduction, (b) the fuzzification of input data, (c) the defuzzification of output data, (d) the rules of fuzzy deduction. As a result of the above-mentioned two topics of discussion, we can accomplish three things; (1) the measurement vector of each radar is time calibrated, (2) the calibrated measurement is estimated by Kalman filter, (3) the estimated data of the target state is fused. We give a numerical example. We give just the errors of the X-axis simulation results of the target state; X components of position error, velocity error and acceleration error. These errors are different for four different methods; (1) active radar only, (2) passive radar only, (3) traditional data fussion, (4) our fuzzy method of data fusion. We compare the errors of the four methods for our numerical example. The errors of X-components of position are (1) 25.9765 m, (2) 102.2131 m, (3) 25.4538 m, (4) 21.4965 m. The errors of X-components of velocity are (1) 25.4997 m/s, (2) 83.9500 m/s, (3) 24.6031 m/s, (4) 22.4838 m/s. The errors of X-components of acceleration are (1) 15.4763 m/s², (2) 34.8053 m/s², (3) 13.8955 m/s², (4) 13.4363 m/s². The simulation results show preliminarily that the algorithm proposed by us is effective.