Design And Implementation Of Target Detection And Tracking System Based On Multi-platform Network

Target detection and tracking has always been a research hotspot in the field of monitoring.At the same time,target detection equipment is various,including active radar,passive radar network,binocular vision and so on.Therefore,can a variety of detection platforms be used to monitor the area at the same time in order to get a better detection effect?The purpose of this project is to design a processing framework so that the data from various testing platforms can be processed uniformly.Although there are many algorithms for each detection platform and data fusion processing,the detection effect of target trajectory in the unified processing framework is still not satisfactory.There is room for improvement in both the detection method of a single platform and the target tracking algorithm of data fusion.In view of the shortcomings of each detection platform and data fusion method,a series of improved algorithms are proposed.1.Aiming at the problems of detection performance,false alarm rate,real-time processing and storage space limitation in active radar target tracking.Firstly,a novel detection method which bases its principle on sampling and spatio-temporal detection is proposed.The method consists of two stages,coarse detection and fine detection.Sampling based coarse detection is designed to guarantee the real-time processing,low memory requirement by locating the area where targets may exist in advance.In the stage of fine detection,the suspected target area is divided into three categories:single-target,multi-target and clutter area,and different models are used for estimation.Secondly,the proposed target detection algorithm based on multi-contour tracking is used to accurately detect the single target region,and target likelihood estimation is carried out for the suspected target region through the target contour under multiple detection threshold and the target region derived from the growth of the region.Because of the use of multiple detection thresholds,this method has a good detection effect in detecting targets in different noise bases.2.Aiming at the problem that the detection probability is low in the passive radar network with the traditional processing method and the computation is large when the number of targets is large,a target imaging algorithm based on waveform voting is proposed.At each sampling time,the echo can be obtained at each node of the radar network.Then the passive radar network imaging algorithm is used to get the image of the surveillance area.Compared with the traditional algorithm that USES CFAR for target detection of a single node,this method retains more original information and ensures the system's ability to detect dim small targets.Secondly,the proposed image segmentation algorithm based on Rolle's theorem is used to detect the image of the obtained monitoring area,and the area where the suspected target exists is obtained.The target likelihood probability of each suspected area is calculated,and a low detection threshold is used for detection,so as to improve the detection ability of dim small targets.3.In binocular vision system,the existing stereo matching algorithms all use the pixel itself and its surrounding texture features to match,the algorithm computation is large,and the effect is not ideal,a superpixel based stereo matching method is proposed.The similar pixels in the local area are clustered as a superpixel.Then the internal similarity and external similarity of the superpixels are used to match the superpixels in different images.This method can obtain lower region matching error rate with lower computation effort.After the point cloud of the multiplex binocular vision system is obtained,the target of the search area can be detected by the multiplex point cloud.A target detection algorithm based on three-dimensional region growth is proposed to divide the points of the same target into a cluster.At the same time,the target likelihood probability of each cluster cloud is calculated as the weight of the cluster cloud.4.For the traditional multi-sensor target tracking system,the tracking effect is poor and there are many false alarm points when the target echoes are weak.By comprehensively considering the problems of target expansion,target maneuver,dense target,weak target and strong clutter background,a multi-platform target tracking system framework is proposed based on the idea of pre-detection tracking.This framework can effectively detect dense,mobile and small targets in dense clutter by using plots obtained from multiple platforms.In this method,the idea of TBD is used to realize the joint processing of plots on multiple platforms.A 3-dimensional projection based TBD algorithm is proposed to solve the problems of target expansion,small and weak target density and clutter.The points of each platform are put together and divided into time sub-windows according to the time.Some sub-windows cross to form the time window for detection.The 3-dimensional projection based TBD algorithm is used to obtain the track segment in each time window,and the result of this stage is the track segment in each time window.The trajectory segment correlation algorithm based on the breeding model of lions is proposed to solve the tracking problem of dense targets and maneuvering targets.In the algorithm iteration,the ordinary track segment is only associated with the track segment with higher score,so as to reduce the number of times of calculating the distance between the two track segments.The complete target trajectory can be obtained at a lower computational cost,and the better trajectory can be obtained at a constant computational cost.