Multi-hypothesis testing-based dynamic programming track-before-detect method

Abstract

The present invention discloses a multi-hypothesis testing-based dynamic programming track-before-detect method and belongs to the target detection field. The method includes the following steps that: value functions are calculated through using all echo data in sliding windows, and the value functions are detected, a detection result and target flight path estimation are outputted; and the information of the first frame of data is removed from the value functions sequentially, and the processed value functions are detected, a detection result and flight path estimation are outputted. According to the method, the detection of different sub sliding windows is considered, the calculation of the value functions does not need the re-processing of the data in the sub sliding windows, and therefore, the problem of detection delay of a traditional dynamic programming track-before-detect method can be effectively solved with calculation quantities unchanged nearly, and the detection probability of a target of which the emergence time is later than sliding windows beginning time is improved, and the performance of the algorithm is greatly improved.

Description

technical field [0001] The invention belongs to the field of target detection, in particular to the technical field of weak target detection and tracking. Background technique [0002] Dynamic programming tracking before detection algorithm is an effective method to detect and track weak targets. In recent years, it has received extensive attention and research from scholars at home and abroad, and has been applied to many fields such as radar tracking and image processing. This method is a batch processing method, which jointly processes the latest several frames of echo data at each moment, which is called a sliding window. [0003] In practical applications, due to the limited visual range of the sensor, target splitting, and mutual occlusion of targets, new targets may appear at any time within the detection range of the sensor. For example, in radar detection, the target may enter the radar's monitoring area after the radar starts scanning for a period of time; or in ...

AI Technical Summary

Problems solved by technology

In these cases, the first few frames of data in the sliding window do not contain the information of the newborn target, but the traditional dynamic programming tracking before detection algorithm still jointly processes all the echo data in the sliding window, resulting in the value function of the target state being Noise and clutter interference cannot be detected in this sliding window, and the detection probability of the algorithm is damaged

Although as the sliding window moves, the data that does not contain new target information will be gradually moved out of the sliding window, and the value function can be accumulated correctly, but this will cause the detection delay of the target and bring loss to the system performance

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