Maneuvering Target Tracking Method with Doppler Measurement in Polar Coordinate System

Abstract

The invention belongs to the field of Doppler radar target tracking, in particular to a target tracking system and method using Doppler measurement information. The invention introduces the conversion measurement Kalman filter method with radial velocity into the interactive multi-model method, and provides a Doppler radar maneuvering target tracking method capable of accurately tracking the maneuvering target with Doppler measurement. When solving the correlation algorithm, first calculate the initial mixture probability and input state estimation interaction through the traditional interactive multi-model algorithm; then obtain the unbiased measurement value in the Cartesian coordinate system through unbiased measurement conversion, and obtain the target state based on the model probability The weighted result of one-step prediction; then the statistical characteristics of the measurement error are calculated based on the prediction information; finally, information Kalman filtering is performed on each model and the model probability is updated to obtain the final state estimate.

Description

technical field [0001] The invention belongs to the field of Doppler radar target tracking, and particularly relates to a target tracking system and method utilizing Doppler measurement information. Background technique [0002] In radar target tracking, the state equation of the target is generally established in the Cartesian coordinate system, and the measured value is generally obtained in the polar coordinate system. Some radars can obtain Doppler measurements in addition to position measurement information, and there is a nonlinear relationship between the target's position and Doppler measurements and its motion state. In order to solve this nonlinear relationship, various filtering methods have been proposed, including the following: Measurement Conversion Kalman Filter Based On Sequential Filter (SQ-MCKF) , Measurement Conversion Kalman Filter Based On Statistical Fusion (SF-MCKF) based on static fusion, Unbiased Converted Measurement Kalman Filter with Range Rate ...

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Problems solved by technology

Literature (S.M.Aly, R.E.Fouly and H.Braka, "Extended Kalman filtering and Interacting Multiple Model for tracking maneuvering targets in sensor netwotrks, "2009Seventh Workshop on Intelligent solutions in Embedded Systems, Ancona, 2009, pp.149-156.) traditionally The Extended Kalman Filter method (Extended Kalman Filter) and the Interacting Multiple Model (Interacting Multiple Model) are combined to obtain a maneuvering target tracking algorithm that can handle nonlinear measurements, and its performance is far superior to the single-model nonlinear filtering algorithm, but When the degree of nonlinearity is high, there is a problem of poor tracking accuracy or tracking divergence; literature (D.Xiaolong and Z.Pingfang, "A New Interacting MultipleModel Algorithm Based on the Unscented Particle Filter," 2009 Fifth International Conference on Information Assurance and Security, Xi ’an, 2009, pp.419-422.) By combining the unscented particle filter method and the interactive multi-model algorithm, the maneuvering target tracking based on nonlinear measurement can be realized, but the unscented transformation cannot completely eliminate the conversion deviation, resulting in filtering The estimated results are still not ideal

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