Radar target adaptive detection method based on linear fusion

Abstract

The invention discloses a radar target adaptive detection method based on linear fusion, which belongs to the field of radar signal processing. Aiming at non-Gaussian degree spatiotemporal variability of clutters in the actual environment, based on an adaptive match filter or a generalized likelihood ratio detection detector in the Gaussian background and adaptive normalized match filter in the non-Gaussian background, through simple and effective linear fusion rules, a suitable adaptive detector structure is designed by comprehensively adopting the optimal or sub-optimal clutter covariance matrix estimation method in the specific clutter background and rationally using clutter non-Gaussian feature information. The detector is simple in structure and has strong generalization ability. The detection performance of the detector, the amount of algorithm calculations and other needs are simultaneously taken into account. The constant false alarm rate robustness of the detector and the adaptability to the clutter environment are enhanced, and the radar target detection performance under unknown clutter environment is improved. The radar target adaptive detection method of the invention has popularization and application values.

Description

technical field [0001] The invention belongs to the field of radar signal processing, and in particular relates to a radar target adaptive detection method based on linear fusion. Background technique [0002] The adaptive detection of radar targets mainly involves two aspects: the accurate estimation of the unknown clutter spectral characteristics and the reasonable construction of the adaptive detector structure; the estimation of the unknown clutter covariance matrix is ​​an important content in the estimation of the clutter spectral characteristics, and the estimation results are good. It directly affects the final detection performance. In practice, the unknown clutter spectral characteristics can be estimated by using the reference data that does not contain the target adjacent to the range unit to be detected. [0003] For low-resolution radar scenes, the clutter distribution satisfies the central limit theorem, and the statistical characteristics of clutter at differ...

AI Technical Summary

Problems solved by technology

Although there are optimal or suboptimal clutter covariance matrix estimation methods and point target detector structures for Gaussian and compound Gaussian clutter backgrounds, in practical environments, the non-Gaussian properties of clutter often vary with the environment in time and space. The optimal or suboptimal clutter covariance matrix estimation methods and point target detectors in the above-mentioned specific clutter background are difficult to adapt to the rapid changes in the clutter environment, resulting in the corresponding deterioration of detection performance and CFAR characteristics

Images