Track Fusion Technology Based On Radar And ESM

Nowadays,UAVs develop at a fast pace and with various types.In cities,they are faced with challenges such as complex electromagnetic environment,interference and densely distributed targets.Therefore,the supervision of UAVs in urban environment has become an urgent problem to be solved.It is difficult for a single sensor to estimate the state of the target to form a flight path,so multiple sensors are used to improve the accuracy.In the multi-sensor environment,due to the constraints of the sensor’s own conditions and the lack of prior knowledge of the environment,it is necessary to conduct track association pairing before multi-sensor track fusion.Because heterogeneous sensors have the advantage of information complementarity,after decades of development,track fusion is no longer limited to similar sensors,but gradually develops to heterogeneous sensors.The fusion of different sensors includes the fusion of radar and Electronic Warfare Support Measure(ESM).ESM uses passive reception of radiation source signals to acquire target information,so it has good concealability and comprehensive radar observation.However,its anti-electromagnetic interference ability is poor,and it is easy to expose its position.In this thesis,ESM and radar are combined to improve system reliability and stability.The main contents are as follows:1.The basic principle of track fusion process,data preprocessing and track fusion algorithm are studied.The performance of different track fusion algorithms for multi-sensor is compared,including convex combination fusion algorithm and Covariance Intersection(CI)fusion algorithm,and the advantages of radar and ESM sensor track fusion are described.2.The Nested probabilistic-numerical linguistic term sets(NPNLTSs)algorithm is studied,and the theoretical basis and basic principle of NPNLTSs are expounded.At the same time,a consensus-based track fusion algorithm of NPNLTSs is studied.The problem of radar and ESM sensor track fusion is brought into the NPNLTSs model to be transformed into a Multiple Attribute Decision Making(MADM)problem,so that the radar and ESM sensor can reach a consensus when evaluating the track,so as to obtain a more accurate and effective fusion track.3.Design and develop the target tracking software platform.The simulation platform can realize the construction of scene,target and sensor,and track the simulation target to generate the track of radar and ESM and conduct track fusion,so as to verify the effectiveness of the above algorithm.At the same time,a hardware experiment platform is designed and built.This hardware platform can receive signals in real scenes,estimate the Angle parameters of the received signals through the combination of adjacent amplitude method and phase method,and transmit the data to the target tracking software platform,which is convenient for the subsequent realization of radar and ESM target tracking track generation.