Key Technology Research On Ship Tracking In High Frequency Surface Wave Radar

For its good detection performance in long distance and extensive areas, High Frequency Surface Wave Radar (HFSWR) has been in important application in some areas. However, some kinds of shortcomings, like low detection rate, low detection accuracy in angle, may happen with the different characters of targets or with the difference of surroundings, which makes the target tracking much more difficult.Facing the request to ship target tracking of HFSWR, this paper brings up or improves methods for data processing. The main point is focused on the multi-ships tracking in complex backgrounds and track initiation of targets in long distance. Firstly the paper introduces the basic knowledge about the state estimation in radar target tracking. The classic filtering method in target tracking is Kalman Filter, the paper shows its basic theory and its algorithm process. The traditional filtering method of non-linear filtering is Extend Kalman Filtering (EKF), it extends the Kalman Filter into the non-linear areas through linearization, and the paper shows both the values and the limitations of EKF. When the target is maneuvering, the equation of motion is also non-linear, then the interacting multi-model (IMM) algorithm is one of the most valuable methods, this paper introduces the theory of IMM and brings up a new selection method of transfer probability matrix between multi-models, which improves the performance of the algorithm.In data processing of HFSWR, a target that is further from the radar will lead to bigger tangential location error, which makes the track initiation difficult. What's more, relatively low detection probability degreades the data rate, which further adds the indefinability. Aiming at this problem, based on the theory of information entropy, and taking advantage of the non-correlation between the angle error and the range error, this paper brings up a new track initiation method for the far targets, through research on the radial information (range and radial velocity) and tangential information (angle), the track initiation problem is transferred into a process of entropy decrese. The method also shows the calculation of entrapy and the determinant method of temporary tracks, affirmative tracks and false tracks. In the end, both the imitations and the real datas approve the validity of the method.For the HFSWR, the measurement equation is non-linear and the motion is sometimes non-linear too. Traditional method to solve the non-linear filtering is to approcimate through linearization, while this approcimation will surely bring up the linearization error, and the error accumulation will reduce the performance of filtering and even make the filtering diverge. A substitution of the method is to sampling according to the current state, and the sampling"particles"can show the distribution of current state, there are two kinds of methods based on sampling methods: the confirmed sampling (as the unscented filtering) and the random sampling (as the particle filtering).Unscented filtering (UF) provides a new way to solve the non-linear problem. The paper firstly introduces the basic principle of unscented filtering——unscented transform, and shows the basic algorithm process. Through compared with EKF, UKF costs a little more in calculation, while shows much better performance in precision for the maneuvering target.Particle filtering (PF) achieves the recursive Bayes filtering through Monte-Carlo method, which is applicable to target tracking of non-linear and non-Gaussian background, the essence of PF is to seek random samples that spread in the state space, and the precision of initiation affects the filtering performance a lot. Aiming at the problem of initiation in target tracking, this paper brings up the particle filtering initiation method, which is based on the effective numbers of initial particles, in the new method, the filtering performance is improved for the more precise particles are selected. The most difficult problem of PF is particle degeneracy, two main kinds of methods to deal with degeneracy are: (1) optimize selection method of importance density function; (2) resampling. Although importance density function matters a lot to PF, there is still no perfect selection method in general circumstance. Resampling alleviates particle degeneracy, but it takes much calculation, and can hardly satisfy real-time operation. What's more, resampling decreases diversity of particles hence make algorithm unrobust. Aiming at this problem, the paper brings up a new method: one-step particle filtering method, as well as the resampling method, the new method aims at solving the degeneracy through new sampling method; while the difference is that the new method sample particles not through the particles of prior time, but based on the mean and coviance of prior filtering results. This method shows better performance in both precision and calculation amount.Probability data association (PDA) algorithm, which belongs to Bayes algorithm, is one of the most useful methods in target tracking. The optimal Bayes method need consider all affirmative measurements and calculate the probability of every measurement sequence, while the PDA algorithm only considers the current affirmative measurements, hence it is sub-optimal Bayes algorithm. It has been proved that at the cost of much more calculation, the optimal Bayes method shows better performance than the sub-optimal Bayes algorithm, while it is difficult in engineerring. To increase the performance of PDA at the condition of proper calculation, this paper leads into the velocity estimation and course estimation which is based on the history information, Simulations show that the new PDA algorithm combined with velocity and course information can improve the accuracy of association.