Low and slow small-target track filtering method and device thereof

Abstract

The invention discloses a low and slow small-target track filtering method based on interactive multi-model-Kalman filtering. The method comprises the steps of 1, obtaining a target initial state estimated value X0 and an initial state estimation covariance matrix P0 through a track initial algorithm; 2, setting a target motion model set, and obtaining a corresponding state transfer matrix and a radar measuring matrix according to a motion characteristic; 3, calculating a model predication probability and mixing probability which correspond with a time point k; 4, calculating the mixing stateand the mixing covariance at the time point k; 5, calculating the one-step predication value and the predicated covariance matrix of each model mixing state at a time point k+1 and the measured one-step predication value and the measured predicated variance matrix; and 6, calculating the target state estimated value and the estimated covariance matrix at the time point k+1, and calculating a likelihood function and a model probability; adding one to the value of k, and executing the step 3 again. The method improves point trace association success rate and finally improves radar detecting precision.

Description

technical field [0001] The invention belongs to the technical field of radar, and in particular relates to a low-slow and small-target trajectory filtering method and device based on interactive multi-model-Kalman filtering. Background technique [0002] Traditional radar usually detects high-altitude fast and large targets (referred to as high-large-fast targets), but cannot effectively detect low-altitude and slow-moving small targets (referred to as low-slow and small targets). It is impossible to effectively prevent low-altitude safety. In view of this, the low-slow and small-target detection radar came into being, which can detect low-slow and small flying targets in a certain range in time and take corresponding measures. However, when detecting UAVs at low altitudes, the target association results will be affected by various clutter and interference, especially when detecting targets close to the ground, due to the detection error of the radar itself and the strong g...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the existing technology has the following disadvantages: traditional radars cannot effectively detect low-altitude, slow, and small targets, and the existing low, slow, and small target detection radars have poor detection and tracking accuracy

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