Target track optimization method based on information entropy weight and nearest neighbor data association

Abstract

The invention discloses a target track optimization method based on information entropy weight and nearest neighbor data association. The method comprises the following steps: obtaining measurement vectors of a measurement point at the first two moments after track initiation, and calculating an initial state vector of a target; carrying out initialization on a Kalman filter; calculating a state estimation vector, an estimation error covariance matrix, a prediction vector, an innovation covariance matrix and Kalman gain of the target at the moment k through an iteration method; obtaining candidate measurement points falling into the center of a tracking gate at the moment k; and when the number of the measurement points at the moment k is larger than 1, optimizing and updating the target track at the moment k through an entropy weight method. Through deep mining of known measurement information, and on the basis of not changing the priori information, the method makes full use of the information carried by the target measurement point, so that the method has lower transformation cost and better applicability, and meanwhile, improves accuracy of radar track tracking.

Description

technical field [0001] The invention belongs to the technical field of radar, and in particular relates to a target track optimization method based on information entropy weight and nearest neighbor data association. Background technique [0002] At present, with the continuous emergence of various new system radars, the characteristics that can be used in the field of multi-target tracking are becoming more and more diversified, and data association technology is still the key technology in the field of multi-target tracking. Data association is based on the target and its quantity. A mapping relationship between measurements is established according to a specific data association algorithm, and data association can be used to determine which measurement the target is associated with. Therefore, the accuracy of data association seriously affects the radar's target tracking performance. [0003] Typical data association target tracking algorithms include nearest neighbor al...

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Problems solved by technology

[0004] However, the existing nearest neighbor algorithm still has the problems of low accuracy rate of data association, inaccurate filtering results and easy to generate wrong association when multi-target tracking; at the same time, since all attributes participate in the calculation equally in the process of data association, there is no Highlight the importance of attributes, the results are easily affected by a single attribute

[0005] There are different types of improved methods for the above problems, such as the method of using the actual distance between the estimated position and the measured position at the next moment instead of the statistical distance, by introducing radial Doppler velocity information and by The method of introducing the probability of association between measurement values ​​and map features, etc., has a limited improvement in the accuracy of data association, and the above methods are to improve the nearest neighbor algorithm by introducing various new feature information, and the introduction of new feature information will Increase the difficulty of engineering realization and the cost of transformation, which limits the scope of promotion and application of the improved method

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