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.
For the current multi-optical sensor cross-communications algorithm, the main method is the centralized algorithm. However, this algorithm has the characteristics of complex calculation, low effectiveness. Different types of optical sensors detect the target multi-layer attributes, and based on a predetermined priority sequence, they are finally judged by the global data fusion system, and the level of the target is divided. The target response level function is established by fuzzy algorithm to improve the communication efficiency of the sensor network. Based on the established target response level function, this paper proposes a multi-optical sensor cross-communication algorithm based on auction algorithm. The auction algorithm belongs to a distributed algorithm, and simulation experiments show that the auction algorithm proposed comparing with the centralized algorithm, the auction algorithm has the characteristics of small communication network demand, fast calculation speed, and good convergence and stability of the algorithm.
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