A low-slow and small-target trajectory filtering method and device

Abstract

The invention discloses a low-slow and small-target trajectory filtering method based on interactive multi-model-Kalman filter, which includes: Step 1: Obtaining the target initial state estimation value X through the trajectory initial algorithm 0 and the initial state estimation covariance matrix P 0 ; Step 2: Set the target motion model set, and obtain the corresponding state transition matrix and radar measurement matrix according to the motion characteristics; Step 3: Calculate the model prediction probability and mixing probability corresponding to the k-th moment; Step 4: Calculate the k-th moment mixing State, mixed covariance; Step 5: Calculate the one-step predicted value and predicted covariance matrix of each model mixed state at the k+1th moment and the one-step predicted value and predicted covariance matrix of the measurement; Step 6: Calculate the k+1th The estimated value of the target state and the estimated covariance matrix at all times, and calculate the likelihood function and model probability; add 1 to the k value, and then perform step 3 again. The invention improves the success rate of dot trace correlation, and finally improves the radar detection 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 radars usually detect high-altitude fast and large targets (referred to as tall and fast targets), but cannot effectively detect low-altitude slow and 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 time within a certain range and take corresponding measures. However, when detecting UAVs at low altitudes, the target correlation 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 ground...

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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