A Target State Estimation Method for Multiple Radar Straight Tracks

Abstract

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. This method aims at the existing problems of the traditional multi-radar target state estimation and track fusion method in the complex data environment, and uses the characteristics of the target in a state of uniform linear motion, respectively, for the X and Y components of the current limited observation points from different radars. The vertical distance is double-weighted estimated relative to the measurement time, and then the final target state (position, speed, heading) estimation result is determined according to the obtained component linear track model parameters. This method is not only easy to implement in engineering, but also shows that the present invention has achieved better estimation results by comparing with the traditional Kalman filter algorithm on target position, course and speed estimation, and is useful for carrying out multi-radar target tracking in this system. Working with data fusion provides a robust, efficient and easy-to-engineer filtering method.

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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