High-precision tracking method applied to non-engine maneuvering target

Abstract

The invention relates to a high-precision tracking method applied to a non-engine maneuvering target. According to the method, firstly, a target motion model containing system adaptive parameters is built according to the non-zero mean time relevant random process characteristic met by the accelerated speed in the actual target motion; secondly, the target motion characteristic is predicated according to the built target motion model; then, the current state estimation value of the target is calculated through utilizing the target state predicating value and the radar observation data value; and finally, the system adaptive parameters are corrected according to the accelerated speed estimation value of the target, the corrected system adaptive parameters are utilized for updating the target motion model, and the updated target motion model is utilized for carrying out next predication and estimation. The method has the advantages that the target with the non-engine maneuvering random character realizes the online estimation, the accuracy of a maneuvering target model for describing the actual motion characteristics is improved, and the tracking precision of the non-engine maneuvering target is improved.

Description

technical field [0001] The invention relates to the technical field of target tracking, in particular to a high-precision tracking method for non-engine maneuvering targets by radar. Background technique [0002] Target tracking technology includes target modeling and estimation using measurement data. Traditional maneuvering target models, such as constant velocity model, constant acceleration model, Singer model, Jerk model and "current statistical model", all model the target's motion characteristics based on prior knowledge. The following patents all use one of the above models: weak target tracking method based on particle filter and track management (201210106282), a high maneuvering target tracking method (201210138397), a target based on multi-scale dimension decomposition Tracking method (201110361072.5), a three-dimensional target tracking method using forward scattering radar (201110247074), a maneuvering target tracking method combining Kalman filtering and empi...

AI Technical Summary

Problems solved by technology

However, for non-engine-driven targets such as people and animals, the acceleration maneuver frequency and its random characteristics are very complex and various. At present, there is no accurate description data of the acceleration maneuver frequency and its random characteristics of each movement mode, which leads to these methods. Non-engine maneuvering targets cannot achieve good tracking results

[0003] Adaptive Filtering Target Tracking and Positioning Method Based on Embedded Platform (201110404800) discloses a method for adaptive filtering target tracking and positioning based on embedded platform, which involves expanding geographical The position observation data is modeled, the error value output by the motion model modeling module is processed by the residual data, the key parameters are estimated by using the noise space, and the parameters of the gain matrix are optimized, but it has the disadvantage that the observation data is uncertain

[0004] To sum up, the current methods for modeling and tracking targets do not model in real time based on the motion characteristics of non-engine targets, and cannot achieve good tracking results when tracking such targets

Images