Aiming at the problem of pedestrian behavior recognition in infrared images, a method based on Improved GoogLeNet is proposed. Firstly, by analyzing the application scenarios and the characteristics of common network models, GoogLeNet with better comprehensive performance is selected as the backbone network. Inspired by NIN, a kind of 1*1 convolution kernel structure is introduced to reduce the number of channels and significantly reduce the number of parameters. Then channel padding and resize to adapt to the network requirements for the training set and test set of the infrared image human behavior data set. Next, the fully connected layer and the classification output layer of the network are modified according to the number of behavior types contained in the data set. The convolution kernel and inception parameter in the pre-training network are introduced to accelerate the network training and improve the generalization ability of the network. Finally, the quantitative index is used to analyze the experimental results and judge the recognition performance of the network. Experimental results shows that the Mean Average Precision, Average Recall and F1 score obtained by the proposed algorithm are better than the traditional methods.
The channel estimation of IR-UWB ultra wideband wireless communication system realized by using compressed sensing theory. Firstly, sparse signal, observation matrix and reconstruction algorithm of compressed sensing theory were discussed. Secondly, discussed the composition of IR-UWB wireless communication system. The IEEE802.15.SG3a channel model was adopted for UWB multipath channel. According to the matrix calculation method of cyclic convolution, the compressed sensing model for channel estimation of IR-UWB system was derived, and GOMP algorithm was used to reconstruct the channel parameters of IR-UWB system. With the help of Matlab software, the simulation results showed that GOMP algorithm can reconstruct the channel parameters of IR-UWB system well.
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