Research On The Multi-target Track Initiation Under Heavy Clutter

In the early stage of target tracking,the measurement cycles of the detection system are limited.Track initiation is to select the stable and reliable tracks from the limited measurement cycles.As the first step in target tracking,the quality of track initiation affects all subsequent stages of target tracking.In the heavy clutter and multi-target environment,the information capacity is large,the relationships among information are complicate,the number of the detected targets is unknown,and track initiation often has a high false alarm rate or missing alarm rate.Obviously,it is still a difficult task to get a high-quality Track Initiation in the limited measurement cycles.Therefore,multi-target track initiation under heavy clutter is a challenging and significant task.The dissertation researches the theory and key technologies of clutter filter,track initiation and missing alarm detection in the heavy clutter,and gives the corresponding solutions to these problems.It has three aspects as follows.Firstly,a clutter filter algorithm based on the relaxed logic is presented.In the algorithm,the raw measurement is filtered by using the relaxed logic method.The algorithm not only designs a kind of incremental and adaptive filtering gate,but also adds the angle extrapolation on the basis of polynomial extrapolation.The algorithm is to eliminate most of the clutter and to obtain the environment with high detection rate and less clutter.Secondly,a track initiation algorithm based on fuzzy sequential Hough transform is proposed.The algorithm establishes a new meshing rule according to system noise to balance the relationship between granularity of grid and the quality of track initiation.And a flexible superposition matrix based on the fuzzy clustering is constructed,which avoids the transformation error caused by 0-1 voting method in traditional Hough transform.In addition,the algorithm allows the superposition matrixes of nonadjacent cycles to be associated to overcome the shortcoming that the track can't be initiated timely when the measurement appear in an intermittent way.And a slope verification method is introduced to detect formation-intensive serial tracks.Last,the sliding window method is used to feedback the track initiation results in time and confirm the track.Finally,a missing alarm detection algorithm based on fuzzy clustering is introduced.The algorithm first restores the peak points to the real tracks,then sets the membership degree as the correlation weight,and calculates the membership degree between the missing points and the tracks by using fuzzy clustering.Last,a verification method is given to mesure the quality of missing alarm detection.The algorithm can detect most of missing points,reduce missing alarm rate,and improve the success rate of track initiation effectively.Simulation results verify that the proposed algorithms can initiate the tracks accurately and solve the following key problems effectively: clutter filter,track initiation,and missing alarm detection in the heavy clutter and multi-target environment.