Improved TBD algorithm

Abstract

The invention provides an improved TBD algorithm, which belongs to the technical field of radar target detection. The improved TBD algorithm is divided into four parts that: (1) an algorithm based onCA-CFAR detection causes a false alarm rate at the edge of clutters, and judges a current environment according to data in a reference window; (2) N_cpi points can be sampled in each CPI, and N / M processing can be performed on the plots after the judgment; (3) the preprocessed plot for Radon transform is selected; (4) and angular information of a target and a distance from a central point positioncan be obtained according to the result after Radon transform. The TBD algorithm based on Radon transform is adopted for increasing a signal-to-noise ratio of the target plot, an improved CFAR detection algorithm is adopted to perform confidence level classification on the threshold plot and process the multi-frame plots, and the detection probability of the target plot is increased by effectively suppressing sea clutters and part of interference signals.

Description

【Technical field】 [0001] The invention relates to the technical field of radar target detection, in particular to an improved TBD algorithm. 【Background technique】 [0002] TBD is an effective method for small and weak target detection and tracking. It usually uses two-level thresholds. The first level sets a lower constant false alarm detection threshold to detect the echoes on each wave position during the radar beam scanning process, suppressing most of the echoes. Clutter and noise, and then use the different characteristics of clutter and interference traces and target traces to filter out most of the clutter tracks. Commonly used are TBD algorithms based on dynamic programming, particle filtering and Radon transform. Under the condition of low signal-to-noise ratio, the TBD algorithm based on dynamic programming may not be able to detect the target in a given limited number of frames, and based on dynamic programming and particle The filtered TBD algorithm has disadva...

AI Technical Summary

Problems solved by technology

Commonly used are TBD algorithms based on dynamic programming, particle filter, and Radon transform. Under low signal-to-noise ratio conditions, TBD algorithms based on dynamic programming may not be able to detect targets given a limited number of frames, and based on dynamic programming and particle The filtered TBD algorithm has the disadvantages of large amount of calculation and poor real-time performance, while the TBD algorithm based on Radon transform has more clutter and interference points in the detection results for weak targets, which leads to the inability to detect the weak targets after Radon transform. Effective accumulation of target points

Images