Combined penalized weights based GM-PHD for point target tracking in starry-sky background

Abstract

It is well known that the point targets observed by the space-based platform are extremely difficult to track due to the interference of a large number of stellar targets and background noise. The traditional GM-PHD, due to its probability accumulation, is easy to mistakenly identify the star as a derived new target when the real target passes closely to the star. In order to achieve precise tracking of multiple moving point targets for space-based observations in a complex background environment such as a starry sky background, this paper proposes a simple and effective combined penalized weights based GM-PHD for filtering. First, a unique label is given for each target to determine which real target the estimate came from. Next, the moving region of target is discretized into 10 irregular intervals to construct ten different motion templates, numbered one by one, for discretization of the direction of the target motion. Next, the true target at time k − 1 and all the estimated values having the same label at time k are used to calculate the direction penalty factor and the speed penalty factor to obtain the penalty strength matrix and penalized weight matrix, respectively. Finally, the penalized weight matrix is used to output the target with the highest weight in each label as the real target at time k. And a given penalty threshold is used to remove some fuzzy weights with greater penalty in the penalty strength matrix, so as to selectively filter out some less relevant targets to further avoid the stars participating in the next iteration. In order to verify the effectiveness of the proposed algorithm, we used the Tycho-2 catalog to simulate four kinds of starry-sky data sets with different complexity and conducted comparative experiments on each data set. The experimental results show that the proposed algorithm can effectively track multiple space-based infrared weak point targets in complex starry-sky scenarios.