Rapid and refined processing method for radar maneuvering targets based on hierarchical accumulation detection

Abstract

The invention relates to a radar maneuvering target fast fine processing method based on graded accumulative detection, and belongs to the technical field of the radar signal processing and detection.The method comprises the following steps: firstly performing Fourier transform, comparing with a primary threshold under a high-false alarm probability condition, and then performing fractional Fourier transform and fractional ambiguity function operation on a distance unit echo after exceeding the threshold in parallel, selecting the optimal transform domain corresponding to the large output signal to clutter ratio to form the distance-optimal transform domain two-dimension data; regarding the amplitude thereof as the detection statistics, comparing the detection statistics with the primarythreshold under the low-false-alarm probability condition and judging, thereby accomplishing the maneuvering target detection. The processing is performed only in a few distance units exceeding the primary threshold, thereby reducing the operation burden while guaranteeing high detection performance; multiple movement parameters of the maneuvering target can be accurately estimated, such as speed,acceleration and jerk, and the fast fine processing of the maneuvering target is realized.

Description

technical field [0001] The invention belongs to the technical field of radar signal processing and detection. More specifically, the invention relates to a method for rapid and refined processing of radar maneuvering targets based on hierarchical accumulation detection, which can be used for radar detection and estimation of maneuvering targets. Background technique [0002] As the main means of target detection and surveillance, radar is widely used in public and national defense security fields such as air and sea target surveillance and early warning detection. Affected by the clutter environment and the complex motion characteristics of the target, the radar echo of the moving target is extremely weak, the characteristics are complex, and it has low observability, which makes the detection performance of the radar for the moving target, especially the maneuvering target, difficult to meet the actual needs. Reliable and fast detection and estimation of maneuvering targets...

AI Technical Summary

Problems solved by technology

[0003] 1) The signal-to-clutter ratio (SCR) of maneuvering targets is usually low, and has time-varying and non-stationary characteristics. The echo has high-order phase and high-order frequency modulation characteristics. Traditional filter-based The moving target detection (MTD) method of group processing is suitable for the analysis of uniform moving targets. For the detection of maneuvering targets, the accumulated echo spectrum will span multiple Doppler units, and the energy divergence is difficult to detect in a single Doppler unit. The Le channel forms a peak, and the detection performance decreases

[0004] 2) Extend the one-dimensional frequency domain processing to time-frequency two-dimensional processing, that is, the radar moving target detection method based on time-frequency analysis can reflect the change of signal Doppler over time, which is a two-dimensional extension of the MTD method, such as short-term Fourier transform (Short-Time Fourier Transform, STFT), Wigner-Vill distribution (Wigner-Vill Distribution, WVD), etc., have been used in feature extraction, target imaging and recognition, but this type of method still has a degree of time-frequency aggregation Low, limited resolution, partially affected by cross effects, etc., it is difficult to meet the actual requirements

In addition, most of these methods are signal matching enhancement methods, and the time-frequency transformation needs to match the motion characteristics of the target. However, in practice, the moving target signal is complex and the cumulative gain decreases.

[0005] 3) Generally, the accumulation time can be extended, more echo pulses can be obtained, the energy of the target can be increased, and the ability to finely describe the moving target can be improved, but the observation of long accumulation time and high sampling frequency greatly increase the number of echo pulses. This increases the computational load of the algorithm, consumes a large amount of radar signal processing resources, degrades the processing performance, and makes it difficult to balance high detection performance and computational efficiency.

[0007] The traditional moving target detection method can be realized based on Fast Fourier Transform (FFT), which has certain advantages in computing efficiency, but it is difficult to apply to the maneuvering target echo with time-varying non-stationary characteristics. Le broadening, detection performance degrades

The fractional transform method uses LFM as the basis function, and any fractional domain representation between the time domain and the frequency domain can reflect the change law of Doppler, and is very suitable for dealing with time-varying non-stationary signals without cross-terms. Interference, such as fractional Fourier transform (Fractional FT, FRFT) and fractional ambiguity function (Fractional Ambiguity Function, FRAF), but due to the need for two-dimensional parameter search, it is difficult to apply to large-scale radar echo processing (multi-range unit )

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