ISAR (Inverse Synthetic Aperture Radar) imaging method for complex moving target

Abstract

The invention provides an ISAR (Inverse Synthetic Aperture Radar) imaging method for a complex moving target. Through polynomial phase optimization estimation on each dominant scatterer distance unitecho signal after translational compensation and polynomial phase signal time frequency decomposition, each signal component obtained by decomposition is a single component only corresponding to one frequency point at any time, the defect that cross terms exist in a non-signal component corresponding to multiple frequency points at one time in the traditional time frequency transform is overcome,each dominant scatterer distance unit echo signal has no any cross term interference and building of time frequency distribution with good time frequency joint resolution is realized finally, and range-instantaneous Doppler imaging is thus obtained. The principle is simple, the operation is convenient, bad influences of cross term interference in the classical time frequency analysis method and losses of the time frequency joint resolution are overcome effectively, the quality and the benefits of nonstationary polynomial phase signal time frequency analysis are effectively enhanced, and a target image with good quality and good resolution is obtained.

Description

technical field [0001] The invention belongs to the field of signal processing, in particular to an ISAR imaging method of a complex moving target. Background technique [0002] Inverse Synthetic Aperture Radar (Inverse Synthetic Aperture Radar, ISAR) is an important tool for acquiring target characteristics. It can perform high-resolution two-dimensional imaging of moving targets such as aircraft, ships, and space debris, and has become a research tool in the field of target detection imaging and recognition. Highlights and hotspots. ISAR imaging requires motion compensation processing of target echoes, and there are two important steps: envelope alignment and initial phase correction. Each has a different implementation method, the former has the correlation neighborhood method, the minimum entropy method, etc., and the latter has the phase gradient autofocus method, Doppler center tracking method, etc. Such processing methods regard the target as a whole and consider th...

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Problems solved by technology

However, the cubic phase function still has false peaks in the analysis of multi-component signals, which also causes the identification problem of multi-component signals

[0012] To sum up, the sequential processing methods of multidimensional search for dimensionality reduction all have more or less error transfer effects, and each has its own characteristics or shortcomings: the PPT method is fast and simple, but there is a high signal-to-noise ratio threshold. In the case of the above low SNR, the estimation performance needs to be improved, and there may be recognition problems in the multi-component case; ML-HAF, PHAF, IGAF, etc. can effectively solve the recognition problem in multi-signal processing, but the amount of calculation is generally relatively large. Large; the cubic phase function method has a low SNR threshold, and has a strong processing capability for low SNR, but has a large amount of calculation, and there are identification problems in the case of multiple signals

[0013] The time-frequency decomposition method applied to polynomial phase signals not only requires good time-frequency joint resolution, but also requires little or no cross-term interference; existing processing technologies are capable of reducing cross-terms and maintaining high time-frequency resolution However, how to improve the time-frequency joint resolution ability while reducing cross-term interference has become a major practical problem in the time-frequency decomposition and time-frequency analysis of polynomial phase signals

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