Extension factorization space-time two-dimensional self-adaptive processing method based on data fitting

Abstract

The invention discloses an extension factorization space-time two-dimensional self-adaptive processing method based on data fitting and relates to radar technology. The method comprises the specific steps that 1, dimensionality reduction echo data and space-time guide vectors are obtained; 2, a data basis matrix of a distance unit to be detected is constructed; 3, a data basis matrix with targets blocked is obtained; 4, the fitting coefficient of auxiliary echo data is obtained; 5, the minimum fitting error of data of the distance unit to be detected is obtained; 6, unit average constant false alarm rate detection is carried out on the minimum fitting error of the data of the distance unit to be detected. The extension factorization space-time two-dimensional self-adaptive processing method mainly solves the problem that a traditional STAP method is huge in computation burden and strict in sample requirement, and has the good application prospect in measured data processing and STAP performance improvement.

Description

technical field [0001] The invention belongs to the technical field of communication, relates to radar technology, in particular to a data fitting-based expansion factorized space-time two-dimensional self-adaptive processing method for airborne early warning radar signal processing. Background technique [0002] The main task of airborne early warning radar is to detect targets in complex clutter background, and to locate and track them. Effective suppression of clutter is the core means to improve the performance of airborne early warning radar. Space-time adaptive processing (STAP) technology makes full use of airspace and time domain information, while coherently accumulating target signals, and filters ground clutter through space-time adaptive processing to realize airborne early warning radar For the effective detection of targets, such as the E2-D airborne early warning radar of the United States, this technology is adopted. [0003] In practical applications, STAP ...

AI Technical Summary

Problems solved by technology

[0003] In practical applications, STAP technology mainly has the following two problems: on the one hand, in a non-uniform clutter environment, it is necessary to obtain enough independent and identically distributed (IID) training Samples are very difficult; on the other hand, even if the demand for training samples is met, the problem of excessive calculation in all-time processing will make it difficult to guarantee real-time performance

However, this method still has the following main deficiencies: First, the problem of large amount of calculation. The number of overcomplete bases for sparsely representing the clutter spectrum is undetermined, but it is far greater than the system degrees of freedom. In practice, the system degrees of freedom are usually tens of thousands , so the amount of calculation required in the process of reconstructing the covariance matrix of each distance unit sample is very large, which is not conducive to real-time processing, thus affecting the effect in practical engineering applications

The second is the base mismatch problem. The base used in this method is actually a set of interpolated discrete Fourier DFT vectors, but the actual echo data cannot be sparsely represented by the given base vector due to non-ideal factors such as errors; In addition, this method needs to discretize the space-time plane, which will make the signal not located in the center of the discrete grid point leak to all the grid points, thus destroying the sparsity of the echo data

Images