Radar weak target detecting method based on information geometry multiple autoregressive model

Abstract

The invention discloses a radar weak target detecting method based on an information geometry multiple autoregressive model. According to the method, mean value estimation of N covariance matrixes is achieved based on the definition of the Karcher mean value and the geodesic line, the clutter environment around a covariance matrix unit to be detected is iterate-estimated by means of the gradient descent algorithm, the multiple autoregressive model is introduced, a parallel iterative algorithm is defined with a covariance matrix block structure to calculate Siegel measurement, the distinguishable distance between the covariance matrix unit to be detected and the clutter environment around is estimated, the detection threshold is estimated with the Monte-Carlo method, and weak target signals are distinguished from clutters. According to the method, Fourier transform is replaced with the information geometry method, and the problem of Doppler frequency resolution reduction is avoided; meanwhile, windowing does not needed to be conducted on data series, resolution limitation, energy leakage and pollution to a whole filter bank by the clutter spectrum are avoided, and correct and efficient detection of a radar weak target is achieved.

Description

technical field [0001] The invention relates to the radar field, in particular to a radar weak target detection method based on an information geometric complex autoregressive model. Background technique [0002] New requirements for radar target detection: (a) detection of low-altitude targets (small size, stealth, strong maneuverability, asymmetry, etc.); (b) improvement of reaction time for ultra-lethal threat targets. The radar Doppler & array signal processing method has reached its limit, and the traditional constant false alarm (CFAR) faces many shortcomings in the detection of weak targets in dense non-uniform clutter. Especially for the case of clutter transitions, the detection results are sub-optimal directly due to not preserving edges well and considering the statistical properties of clutter. Ridges or complex terrains in exposed areas (threats: missile-capable helicopters, low-altitude cruise missiles, drones, asymmetric threats, etc.) correspond to clutter t...

AI Technical Summary

Problems solved by technology

[0005] (3) The low resolution of the filter bank and the sidebands of the Doppler filter lead to dense non-uniform ground / sea clutter propagation polluting the entire filter bank

[0006] Thus affecting the detection performance of the radar

Images