Radar multi-target tracking optimization method based on joint probability data association algorithm

Abstract

The invention discloses a radar multi-target tracking optimization method based on a joint probability data association algorithm. The method comprises the following steps: determining the total number T' of targets tracked by radar and the number of measurements nk corresponding to a time k; calculating the state one-step prediction of a t(th) target at the time k, the measurement prediction of the t(th) target at the time k, the measurement prediction innovation of a j(th) measurement to the t(th) target at the time k, a one-step prediction error covariance matrix of the t(th) target at the time k, an innovation covariance matrix of the t(th) target at the time k, and the Kalman gain of the t(th) target at the time k; calculating an nk*T'-dimension measurement-target association matrix at the time k, a measurement-target interconnection probability matrix at the time k and a measurement-target confirmation matrix at the time k; getting zeta(k) joint events of association between the nk measurements and the T' targets at the time k and the probability of each of the zeta(k) joint events; and calculating an exact probability matrix of interconnection between the nk measurements and the T' targets at the time k, a state equation of the t(th) target at the time k, and an error covariance matrix of the t(th) target at the time k.

Description

technical field [0001] The invention belongs to the technical field of radar, and in particular relates to a radar multi-target tracking optimization method based on a joint probability data association algorithm, which is suitable for real-time tracking of multiple targets by a radar in a clutter environment. Background technique [0002] In recent years, with the complex and changeable application environment, radars are required to have multi-target tracking capabilities and can simultaneously realize multi-target tracking; the basic concept of multi-target tracking was proposed by Wax in an article in the Journal of Applied Physics in 1955. After that, in 1964, Steer published a paper entitled "The Optimal Data Association Problem in Surveillance Theory" on IEEE, which became the forerunner of multi-target tracking, but at that time Kalman filtering was not yet widely used, Steer The track bifurcation method is used to solve the data association problem; in the early 197...

AI Technical Summary

Problems solved by technology

[0002] In recent years, with the complex and changeable application environment, radars are required to have multi-target tracking capabilities and can simultaneously realize multi-target tracking; the basic concept of multi-target tracking was proposed by Wax in an article in the Journal of Applied Physics in 1955. After that, in 1964, Steer published a paper entitled "The Optimal Data Association Problem in Surveillance Theory" on IEEE, which became the forerunner of multi-target tracking, but at that time Kalman filtering was not yet widely used, Steer The track bifurcation method is used to solve the data association problem; in the early 1970s, in the presence of false alarms, the Kalman filter method (Kalman) is used to systematically track and process multiple targets; in 1971, Singer proposed the nearest neighbor The method is the simplest method to solve data association, but the correct association rate of the nearest neighbor method in the clutter environment is low; The probabilistic data association algorithm (PDA) for tracking a single target in a clutter environment effectively solves the problem of single target tracking in a clutter environment; T.E.Formann and Y.Bar-Shalom et al. proposed a joint probabilistic data association algorithm (JPDA) , JPDA arranges and combines all targets and measurements, and selects a reasonable joint event to calculate the joint probability. JPDA considers the possibility that multiple measurements from other targets are in the same target interconnection domain, which can solve the problem well The problem of multi-target measurement in an interconnected domain in a clutter environment; but at the same time, JPDA is more complicated, with a large amount of calculation, and with the increase of the number of targets, the split of the confirmation matrix will cause a combinatorial explosion; therefore, JPDA It is more difficult to implement in engineering

Images