Joint fractal-based method for detecting small target under sea clutter background

Abstract

The invention provides a joint fractal-based method for detecting a small target under a sea clutter background. The joint fractal-based method is higher in detection probability. The detection problem of a non-additive model is transformed into a classification problem, i.e. whether a target exists or not is equivalent to belong to a class in which a pure sea clutter exists, and a characteristic joint detection algorithm is provided. A bilogarithmic graph is established by using a trend fluctuation method through sea clutter data, a slope, namely a Hurst index, is fitted by using a least square method within a scale-free interval, and is used as a characteristic scalar, a nodal increment of a keypoint in the bilogarithmic graph is used as another characteristic scalar, therefore, a double-scalar obtained by each group of sea clutter data corresponds to one point in the bilogarithmic graph, n groups of corresponding points (i=1,...n) of the pure sea clutter data are obtained by using the steps, a space optimal classification line omega is obtained by using a convex hull function, sea clutters of regions in which the target possibly exits are obtained by using the same steps, and finally, by using whether the points exist in the space optimal classification line omega or not as a criterion, when the points exists in the space optimal classification line omega, no target exists, and when the points are outside the space optimal classification line omega, the target exists.

Description

Technical field: [0001] The invention relates to the field of radar data processing, in particular to a radar detection method for a sea target and a sea clutter detection method. Background technique: [0002] Sea clutter is the backscattered echo from a piece of sea surface illuminated by the radar transmission signal. When the radar detects the target above or close to the sea surface, it must overcome the interference of the echo of the sea surface itself. When the radar reflection cross-section (RCS) of a weak target on or near the sea is small, its radar echo is often lost in sea clutter and noise, and sea clutter is also affected by radar polarization, operating frequency, and antenna angle of view. And sea conditions, wind direction and other factors, showing obvious non-stationary, non-Gaussian, making it difficult to describe the sea clutter with a fixed statistical model. [0003] Through the precise analysis of sea clutter in the time domain, explore the essenti...

AI Technical Summary

Problems solved by technology

[0004] The traditional additive model feature detection mainly includes non-linear prediction methods, which is to train the sea clutter samples to produce as accurate a neural network or support vector machine model as possible, and use the prediction error to achieve target detection. The premise of this method is the chaos of sea clutter The characteristics have not been fully verified theoretically, and most of the predicted models trained are not ergodic

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