Maneuvering target tracking system capable of processing Doppler measurement on basis of predicted value and maneuvering target tracking method capable of processing Doppler measurement on basis of predicted value

Abstract

The invention discloses a maneuvering target tracking system capable of processing Doppler measurement on the basis of a predicted value and a maneuvering target tracking method capable of processingDoppler measurement on the basis of a predicted value, which belong to the field of target tracking by radars. The invention adopts a measurement conversion technique, and uses a subtractive debiasingmethod for Doppler pseudo-measurement; when statistic characteristics of converted measurement errors are calculated, with a predicted position value under a rectangular coordinate system as a condition, a mean value and covariance of the converted measurement errors are obtained, and the correlation between the converted measurement error covariance and measurement errors is eliminated; then, onthe basis of converted position measurement and Doppler pseudo-measurement, state estimation is respectively carried out on the position state and Doppler pseudo-state of each motion model; a least mean square error criterion is used for merging position and Doppler pseudo-state estimation results of each motion model, so that a state estimation result of the model is obtained; a model probability mean is calculated according to the position and Doppler pseudo-state information of each model and a covariance matrix thereof; and the state estimation result of each model is weighted, so that afinal state estimation result of the target is obtained.

Description

technical field [0001] The invention belongs to the field of radar target tracking, in particular to a system and method for tracking a maneuvering target by using Doppler radar measurement. Background technique [0002] In radar target tracking, the state equation of the target is generally established in the Cartesian coordinate system, and the measured values ​​are generally obtained in the polar coordinate system. In this way, object tracking becomes a nonlinear estimation problem. A common method to solve this problem is the conversion measurement Kalman filter (CMKF) algorithm, which uses the conversion measurement method to express the measurement transformation in the polar (spherical) coordinates into the measurement in the rectangular coordinate system, so that the target tracking becomes a linear estimation problem. However, the traditional conversion measurement method will produce deviations when converting the measurement (see literature: Lerro.D, and Bar-Sha...

AI Technical Summary

Problems solved by technology

However, the algorithm only derives the mean and covariance matrix of the transformed measurement error under the condition of the measured value, resulting in a correlation between the covariance matrix and the measurement error

This correlation causes the Kalman gain to depend on the measurement error, and thus the state estimate is biased

[0005] None of the above algorithms consider the maneuvering characteristics of the target, so the static fusion algorithm based on the predicted value is applied to the field of maneuvering target tracking

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