Research On Multi-target&ESM Passive Location And Tracking

Electronic Support Measure(ESM) system is widely used in military and civilianfields,because it’s hard to be detected. ESM locating system has gradually receivedextensive attention for the advantages of its strong antijamming ability and longerdetecting range. Constructing the ESM system is significant for passive target locatingand tracking. The paper mainly considered the following three systems of passivelocating and tracking: double sensors and single target systems, multi-sensor andmulti-target systems, mobile multi-sensor and multi-target.This paper has carried out the following research work:First, Cross-location through double sensors has a relatively low precisiongeometrically. Thus, we proposed a passive locating algorithm based on theUnscented Kalman Filter (UKF) under the double sensors and single target. Bychanging the traditional angle measurement function, we got the target positionalmeasurement function.With the positional measurement function being nonlinear, weadopted the UKF algorithm to estimate the states of targets, so as to reduce thepositioning accuracy and the lost rate of targets.Second, for the ghost problem in multi-sensor and multi-target passive locating,we proposed a locating algorithm based on predicted judgement and the UKFalgorithm. We modeled the orienting functionof sensor-to-target and compared it tothe smallest distance between the predicted position and the orienting function. Thentreated the points of the orienting function as target measurement states. Last, weobtained the target states through the UKF algorithm, so as to reduce thecomputational load and improve the accuracy.Third, aiming at multi-target information fusion problem in mobile ESM locatingsystem, we proposed an estimation fusion algorithm based on supporting degree. Thescheme first changes the angle measurement function to positional measurementfunction. Then we adopted unequal-weighted fusion rule based on supporting degree,the distance between target estimates and orienting function have an inverserelationship with supporting degree. Finally, we got the target states through the UKFalgorithm and achieved the target locating quickly and effectively for the mobilemulti-target and multi-sensor.