Bernoulli smoothing weak target detection and tracking method under thresholding measuring

Abstract

The invention discloses a Bernoulli smoothing weak target detection and tracking method under thresholding measuring. The method mainly solves the problems that in the prior art, the weak and small target tracking efficiency is low, the tracking performance is not ideal, and the computing computation complexity is high under the condition of the low signal-to-noise ratio. The method includes the steps that firstly, a measuring threshold value is set, indexes of pixel points exceeding the measuring threshold value in grayscale image data are extracted, and an index set serves as a measuring set to be stored; secondly, the set measuring threshold value is utilized, and the detection probability of all the pixel points in the grayscale image is computed; thirdly, the measuring likelihood is computed according to the stored measuring set and the detection probability of the pixel points; lastly, a target is detected and tracked through Bernoulli smoothing. By means of the method, under the condition of the low signal-to-noise ratio, the tracking efficiency and the tracking accuracy of weak and small targets can be improved effectively, and the computation complexity and storage space are lowered.

Description

technical field [0001] The invention belongs to the technical field of guidance, and further relates to a multi-Bernoulli filter weak target detection and tracking method under thresholded measurement in the technical field of target tracking. The present invention calculates and measures the likelihood function after thresholding and adopts the Gaussian particle multi-Bernoulli detection-before-tracking method to realize accurate detection and tracking of multiple weak and small targets of unknown number. Background technique [0002] The detection and tracking of weak and small targets is a key technology in infrared early warning systems, precision guidance systems, and satellite remote sensing systems, and has important application value in the development of modern high-tech. The traditional weak target detection algorithm adopts the detection-then-tracking (DBT) method, which needs to set a fixed threshold or an adaptive threshold for each frame of data. This type of a...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the traditional particle filter has a large capacity of sampling particles and the depletion of particles caused by resampling, which leads to the problem of unsatisfactory target tracking performance in the case of low signal-to-noise ratio.

The disadvantage of this method is that the traditional weak target detection method needs to store and transmit all the data of each frame of image, which has the problems of large amount of calculation, high calculation complexity and high storage space occupation, and radar, sonar and passive infrared sensors are mostly is a threshold sensor, making this method still have flaws in practical applications

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