Target state estimation method for multi-radar linear track line

Abstract

The invention belongs to the technical field of multi-sensor target tracking and data fusion, and specifically relates to a target state estimation method for a multi-radar linear track line in the complex data environment. According to the method, vertical distance double weighted estimation relative to the measurement time is carried out on X, Y components of limited observation points from different radars at present by using a characteristic that the target is in a uniform linear motion state in allusion to problems of the traditional multi-radar target state estimation and track fusion method in the complex data environment, and then the final target state (position, velocity, course) estimation result is determined according to obtained component linear track line model parameters. The method not only is easy to implement in engineering, but also acquires a good estimation effect according to a contrast test with the traditional Kalman filtering algorithm in target position, course and velocity estimated values, thereby providing a filtering method which is stable, effective and easy to implement in engineering for carrying out multi-radar target tracking and data fusion in the system.

Description

technical field [0001] The invention belongs to the technical field of multi-sensor target tracking and data fusion. Specifically, it relates to a target state estimation method for multi-radar straight track in complex data environment. Background technique [0002] Multi-sensor target tracking refers to the time-series discrete observation data of moving targets (aircraft, tanks, ships, etc.) obtained by using multiple sensors (radar, sonar, infrared, etc.), ) to estimate and track. Multi-radar straight track target state estimation is the real-time estimation of the target position, velocity and heading using the measurement data of the same target flying along a straight line by multiple radars. Kalman filtering is a classic optimal linear unbiased estimation method in the application of target tracking and data fusion. When the target state space and measurement space are both linear Gaussian systems, it can minimize the variance of the target motion state in the back...

AI Technical Summary

Problems solved by technology

[0003] In the multi-radar network detection and target tracking system (hereinafter referred to as the "system"), radars are used across generations (the measurement accuracy varies greatly), the deployment is scattered and the geographical span is large (causing coordinate conversion errors), microwave, shortwave, optical cable, Multiple data transmission methods such as cables coexist (transmission errors occur), and the detection environment is easily affected by factors such as electromagnetic, meteorological, and seasonal factors (the process noise covariance Q and the measurement noise covariance R are difficult to fix). Therefore, in practical applications, The sampling interval of single-radar measurement data is relatively large, and measurement errors, coordinate conversion errors, and data calibration and transmission errors exist at the same time; the accuracy of multi-radar measurement data is different, and there are relative systematic errors that are difficult to completely eliminate in time and space

[0004] The above complex data environment has caused great trouble to the traditional Kalman filter method for multi-radar target tracking and data fusion

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