A 1D Range Image Adaptive Framing Method Based on Factor Analysis Model

Abstract

The invention discloses a one-dimensional range image adaptive framing method based on a factor analysis model. Firstly, the normalized spectrum amplitude feature is extracted from the measured one-dimensional range image signal sample and the spectrum amplitude feature is evenly divided into small sub-frames; and then , respectively establish the corresponding factor analysis models, use the EM iterative algorithm to calculate the parameters of each model to obtain the probability distribution of each subframe; then, use the JS distance in statistics to measure the similarity between the subframe models, and merge the high similarity Adjacent subframes; finally, the second and third steps are cyclically performed until the JS distance between all subframes is greater than the set threshold, and the frame merging process is ended to output the frame division result. The present invention is based on a factor analysis model and utilizes the JS distance measure to complete frame-to-frame merging. Compared with the traditional uniform framing method and the newer probabilistic principal component analysis model, the present invention has better framing effect and higher recognition accuracy.

Description

technical field [0001] The invention belongs to the technical field of signal recognition, and relates to a method for framing one-dimensional radar signal recognition, in particular to a method for adaptively framing one-dimensional range images based on a factor analysis model, and in particular to a method for framing through a factor analysis model and JS Optimal Framing Method Combining Distance Measures. Background technique [0002] High-resolution one-dimensional range profile (HRRP) is a kind of target reflection signal based on broadband radar detection. It is the projection vector sum of target scattering point echoes in the direction of radar line of sight. It contains important structural information such as the size and shape of the target and Compared with SAR and ISAR images, it is easy to acquire and process, so it is widely used in the field of radar automatic target recognition, and it is the most promising radar target recognition method. [0003] Howeve...

AI Technical Summary

Problems solved by technology

However, this method is unreasonable. In the process of the target attitude changing continuously, the speed of change should directly affect the result of framing, that is to say, the frame size should be adaptively adjusted by the speed of the target attitude change.

For adaptive framing, predecessors also proposed a variety of methods, such as framing based on signal similarity, HRRP framing method based on subspace model, framing method based on probabilistic principal component analysis model and KL distance, etc. These The methods have completed the adaptive adjustment process of frame division with the change of target attitude to a certain extent, and achieved good results in recognition rate, but they all have certain defects, that is, the noise of HRRP is not well considered, and there is no Considering the existence of noise or assuming that the noise variance is fixed, this does not match the reality and affects the accuracy of actual framing

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