Cooperative perception optimization based on self - checking machine learning

In the process of spectrum perception, in order to realize accurate perception of the channel state, the method of multi-node cooperative perception can usually be used. However, the first problem to be considered is how to complete information fusion and obtain more accurate and reliable judgment results based on multi-node perception results. The ideas put forward in this paper are as follows: firstly, the perceived results of each node are obtained on the premise of limiting detection probability and false alarm probability. Then, on the one hand, the weighted fusion criterion of decision-making weight optimization of each node is realized based on a genetic algorithm, and the useless nodes also can be screened out to reduce energy loss; on the other hand, through the linear fitting ability of RBF neural network, the self-inspection of the perceptive nodes can be realized to ensure the normal operation of the perceptive work of each node. What's more, the real-time training data can be obtained by spectral segmentation technology to ensure the real-time accuracy of the optimization results. Finally, the simulation results show that this method can effectively improve the accuracy and stability of channel perception results, optimize the structure of the cooperative network and reduce energy consumption.