Method for extracting space conical reentry target micro-motion features based on empirical mode decomposition

Abstract

The invention discloses a method for extracting space conical reentry target micro-motion features based on empirical mode decomposition, which mainly solves the problem of easiness in failure of feature extracting in the prior art. The method adopts the scheme with the following steps of 1, according to a narrow-band linear frequency modulating signal model, calculating a transmitting signal sequence in a pulse repeating cycle; 2, according to a transmitting signal and a received target echo signal, establishing a pulse compression signal matrix, and establishing a Doppler echo signal of a conical reentry target according to the matrix; 3, according the calculated Doppler echo signal of the conical reentry target, utilizing the empirical mode decomposition to obtain a plurality of feature mode functions; 4, according to the obtained feature mode functions, reestablishing a Doppler signal of a conical reentry target scattering center; 5, according to the reestablished scattering center signal, establishing a time-frequency map of the conical reentry target scattering center; 6, extracting the micro-motion features of the target from the time-frequency map. The method has the advantage that while the micro-motion features are accurately extracted, the feature extracting efficiency is improved, so that the method can be used for identifying the target.

Description

technical field [0001] The invention belongs to the technical field of radar, in particular to a method for extracting micro-movement features of a space cone target, which can be used for target recognition. Background technique [0002] When the space cone target or component moves on its orbit, it is usually accompanied by some small movements such as rotation and vibration. These small movements are called fretting. Moving objects are usually accompanied by micro-movements, such as the swing of the limbs when walking, the chest vibration caused by human breathing and heartbeat, the rotation of the wheel when the vehicle is moving forward, the rotation of the helicopter rotor, the rotation of the antenna, the vibration of the engine when it is working, etc. The fretting phenomenon was first observed in coherent lidar and applied to vibration measurement. In 2000, Professor V.C. Chen of the U.S. Naval Laboratory introduced it into the research of microwave radar, and defi...

AI Technical Summary

Problems solved by technology

The micro-movement feature extraction methods based on time-frequency diagrams all need to solve the problem of frequency point correlation, but there are two shortcomings in frequency point correlation. The extraction of motion features fails; on the other hand, time-frequency correlation will increase the computational complexity and reduce the efficiency of micro-motion feature extraction

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