Target classification with low-resolution radar based on dispersion situations of eigenvalue spectra

Abstract

Most low-resolution radar systems, especially ground surveillance radar systems, work at relatively low pulse repeat frequency (PRF) and with short time-on-target (TOT) (duration in scanning). Low PRF leads to Doppler ambiguity and short TOT results in low Doppler resolution, which poses a problem to target classification with low-resolution radar based on the jet engine modulation (JEM) characteristic of radar echo. From the pattern classification viewpoint, we propose a method of using dispersion situations of JEM eigenvalue spectra to categorize aeroplanes into three kinds, namely turbojet aircraft, prop aircraft and helicopter. We analyze the mathematical model of JEM echoes consisting of a series of line spectra and regard them as a sum of several series of harmonious waves. Classification features can be extracted based on the harmonious wave sum model. Some schemes for extracting features from echoes within or between pulses are proposed. Low-dimensional features are extracted to reduce computation burden. Our methods do not compensate for the fuselage echoes and are insensitive to the variation of fuselage Doppler. The feasibility of our methods is demonstrated by simulation experiment.

Classification of Aircraft Targets With Surveillance Radars Based on Fuzzy Fractal Features

“…Secondly, on basis of the foregoing introduction, it analyzes the fuzzy fractal characteristics of return signals from aircraft targets, and puts forward a fuzzy-fractalfeature-based classification method for aircrafts. Finally, it does classification experiments with the real recorded echo data, and takes the classification methods proposed in [16,19] as the contrast to analyze the classification performance of the proposed method. The experimental results show that in the conventional low-resolution radar system, the fuzzy-fractal-feature-based SVM classifier can classify different types of aircraft targets effectively and has an excellent classification performance in condition of no compensation for airframe echo components.…”

“…On basis of analyzing the performance of methods using some typical low-resolution radar target classification features [15,[24][25][26][27][28][29][30][31][32][33][34], [16] indicates that the classification method based on dispersion situations of eigenvalue spectra (CMDSES) outgoes other methods remarkably.…”

“…JEM features lie on the leaf number and rotary speed of the rotating parts of a target, and are independent with the target attitude if no LOS-sheltering, i.e., the rotating parts can be seen by the radar. Currently, the related research work is mostly concentrated on the extraction of JEM features, and proposed methods mainly contain the complex cepstrum method, self-correlation method, autoregressive (AR) model power spectrum method, singular value decomposition (SVD) eigenvalue decomposition method, etc., however most of these methods have high computational complexity, and often demand a higher pulse repetition frequency (PRF) as well as a longer observation time, therefore it is difficult to put them to use [16].…”

Target Classification With Low-Resolution Surveillance Radars Based on Multifractal Features

“…However, due to the low-resolution radar system, such as low pulse repetition frequency (abbr. PRF), narrow signal band, short irradiation time, automatic target classification and recognition with low-resolution radars also becomes a research difficulty [1][2][3].…”

“…JEM modulation features are determined by the leaf number and rotary speed of the rotating parts of a target and independent with the target attitude angle if no LOS-sheltering, i.e., the rotating parts can be seen by the radar. Now proposed extraction methods for JEM features mainly contain the complex cepstrum method, self-correlation method, AR model power spectrum method, SVD eigenvalue decomposition method, etc., but most of these methods have high computational complexit, and often demand a higher PRF and longer observation time, so it is difficult to apply them to engineering [3].…”