Aiming at the threat assessment of unmanned aerial vehicle (UAV) cluster in formation flying stage, a threat assessment method of UAV cluster based on fuzzy analytic hierarchy process (FAHP) is proposed. Firstly, according to the characteristics of UAV cluster in formation flight stage, four suitable evaluation indexes are proposed; then, a threat assessment model of UAV cluster based on fuzzy analytic hierarchy process is established. Finally, an application example is given to verify the effectiveness of the proposed method.
Aiming at infrared dim small target detection, this paper proposed a spatial and temporal fusion detection algorithm based on mathematical morphology. Firstly, the obtained infrared image is filtered in spatial domain by morphological Top-hat filtering, so that most of the background and clutter in the original infrared image are suppressed. In time domain, some fixed background is removed and the target is enhanced by three frame difference filtering, and the corresponding rules are used for fusion. The fused image is segmented by adaptive threshold to obtain the potential target points. Finally, according to the characteristics that the real target has continuity and regularity in time domain and the noise points are randomly distributed, the pipeline filtering algorithm is used to further filter the false alarm points and detect the motion trajectory of the target. Simulation results show that comparing with the Top-hat filtering and the time domain three frame filtering algorithm, the proposed fusion algorithm can effectively detect the target and accurately detect the false alarm target correctly.
This work presents a spectral matching algorithm of missile plume detection that based on neural network. The radiation value of the characteristic spectrum of the missile tail flame is taken as the input of the network. The network’s structure including the number of nodes and layers is determined according to the number of characteristic spectral bands and missile types. We can get the network weight matrixes and threshold vectors through training the network using training samples, and we can determine the performance of the network through testing the network using the test samples. A small amount of data cause the network has the advantages of simple structure and practicality. Network structure composed of weight matrix and threshold vector can complete task of spectrum matching without large database support. Network can achieve real-time requirements with a small quantity of data. Experiment results show that the algorithm has the ability to match the precise spectrum and strong robustness.
Although sparse representation based classification (SRC) has gained great success, doubts on the necessity of sparse constraint come in recent years. And collaborative representation based classification (CRC) has attracted much attention from researchers in fields of signal processing, image processing and pattern recognition. In this paper, an algorithm called collaborative dictionary learning with structured incoherence (CDLSI) is proposed for collaborative representation based detection (CRD), which can be viewed as a binary classification problem, in hyperspectral imagery (HSI). An inter-class incoherence term is added to make sub-dictionaries to be as independent as possible. During the optimizing procedure, sub-dictionaries are updated atoms-by-atoms with metaface method. Specifically, considering the non-sparse representation of CRC, the coefficients are iteratively optimized with l2 -norm regularization during the coding procedure in CDLSI. Once the sub-dictionaries are obtained, the collaborative representation based technique is then used for detection. The proposed algorithm is applied to several real hyperspectral images for detection. Experimental results confirm the effectiveness of the proposed approach, and prove the superiority to the traditional algorithms.
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