A Fusion Recognition Method Based on Multi-scale Sparse Preserving Projection One-dimensional Range Image
A technology that integrates recognition and maintains projections. It is applied in character and pattern recognition, instruments, computing, etc., and can solve problems such as insufficient information utilization, difficulty in breaking through recognition bottlenecks, and limited effects.
Active Publication Date: 2020-12-01
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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However, although a single principal component extraction can reduce the feature dimension to a certain extent and reduce the influence of noise, the effect is limited, and the utilization of information is not sufficient, so it is difficult to break through its identification bottleneck.
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[0068] The present invention will be further described below in conjunction with the accompanying drawings.
[0069] The present invention provides a one-dimensional distance image fusion recognition method based on multi-scale sparsity-preserving projection. The general flow chart is as follows figure 1 shown. The existing one-dimensional range image echo data of an aircraft are as follows: figure 2 As shown, in the actual situation, the echoes of different types of aircraft are different, and the echo signals of the same type of aircraft at different angles are also different. The main purpose of the present invention is to complete the category judgment of such echo signals. In order to solve the recognition and classification problem of this kind of one-dimensional echo signal. The present invention comprises the following steps:
[0070] Training phase:
[0071] Step 1: For the training sample set X=[x 1 ,x 2 ,...,x n ], extracting its normalized spectrum magnitud...
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The invention provides a one-dimensional range-image fusion recognition method based on multi-scale sparse-preserving projection. Firstly, extract the normalized amplitude features of the measured one-dimensional range image signal samples and perform translation alignment preprocessing; then, use the one-dimensional Laplacian of Gaussian operator for multi-scale space mapping; then, use the sparse-preserving projection method to The multi-scale spatial signal is sparsely represented to obtain the multi-scale spatial sparse feature vector; finally, the multi-scale decision fusion classifier is used to classify and identify the target sparse feature vector, and the classification result is output. Based on multi-scale spatial analysis, sparse-preserving projection and decision-making fusion classifier, the present invention extracts multi-scale effective sparse features for one-dimensional range image and fuses and recognizes the results through decision-making fusion classifier, which improves the one-dimensional range image to a certain extent. Signal recognition efficiency and anti-interference performance have also been significantly enhanced.
Description
technical field [0001] The invention relates to a method for identifying a radar one-dimensional signal, in particular to a technology for accurately identifying a target radar one-dimensional range image in an interference environment. Background technique [0002] Radar Automatic Target Recognition (RATR) is based on the echo signal of the target and the environment, and the radar extracts the target feature information based on the electromagnetic scattering mechanism to realize the automatic determination of the target attribute category. Radar high-resolution one-dimensional range profile (HRRP) reflects the distribution of target scattering points along the radar line-of-sight direction, which contains rich target structural features, and its inherent advantages are easy acquisition and small data volume. Therefore, HRRP-based target recognition has always been a research hotspot in the field of radar automatic target recognition, and it is also the radar target recogn...
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IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/12G06F18/2136G06F18/2411G06F18/256
Inventor 戴为龙刘文波张弓
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS