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Deep space exploration activities can effectively promote the development and application of space technology, which holds significant scientific and strategic value. Detectors operating in deep space are of long mission periods and substantial distance from Earth, posing challenges in timely resource replenishment. Moreover, scheduled missions may face external uncertainties, hindering their smooth execution. Therefore, resource constraint processing strategies that possess dynamic adjustment capabilities play a crucial role in ensuring the successful execution of deep space detectors. Existing methods mainly focus on studying fixed mission sequences, and the efficiency of planning methods is not high. Specifically, the single uncertainty is only considered, and there is a lack of detection and processing mechanisms, rendering the requirements of deep space detectors inadequate. In this paper, we propose a reverse chain search algorithm, aiming to efficiently plan missions subject to resource constraints and let the mission sequence to possess a certain degree of dynamic repair capability. The results have demonstrated that the resource allocated to each mission is within the resource value range. Compared with the classic algorithm, the proposed algorithm has fewer iterations and whose maximum flow value is accurate. Meanwhile, the potential resource constraints problem during execution can be alerted with the detection and warning system. The missions that were warned are repaired by a dynamic resource repair system, and the proposed algorithm is of higher efficiency and a certain ability to resist external uncertainties. Overall, this paper lays a solid foundation for the processing of resource constraints for deep space detectors. [ABSTRACT FROM AUTHOR]

A new method for removing salt-and-pepper noise from corrupted images is presented. We employ an efficient impulse noise detector based on the image sparse representation to detect the noisy pixels, and a local median filter to estimate the intensity values of noisy pixels. Experimental results demonstrate that the proposed method obtains better performance in terms of both qualitative and quantitative evaluations than those de-noising techniques such as the median filter, the peak-and-valley filter, the detail preserving filter, and the boundary discriminative noise detection filter, etc. Especially, the proposed method provides high detection rate and preserves the detail very well. [ABSTRACT FROM AUTHOR]

For the extraction and identification of systematic errors model of missile-borne radar, the independent variables are extracted for constructing a basis function, and an energy-based removing method is proposed by considering the physical mechanism and data characteristics. The effects of related factors, such as platform status and target status, on the error measurement are discussed by taking the angle-measurement errors of a missile-borne radar for example, and then a model of angle-measurement errors is build using the proposed methods, which can fit the measured data well. The result shows that the methods can be used to accurately model the systematic angle measurement error of missile-borne radar. [ABSTRACT FROM AUTHOR]