It is difficult to distinguish the intensity between dim moving targets and background noise in the case of extremely Low Signal-to-Noise Ratio (LSNR). In order to solve the problem, a new extremely dim target search algorithm based on detection and tracking mutual iteration was proposed with a new strategy for combining and iterating the process of temporal domain detection and spatial domain tracking. Firstly, the difference between the signal segment in the detection window and the extracted background estimated feature was calculated during the detection process. Then, the dynamic programming algorithm was adopted to remain the trajectories with the largest trajectory energy accumulation in the tracking process. Finally, the threshold parameters of the detector of the remained trajectory were adaptively adjusted in the next detection process, so that the pixels in this trajectory were able to be retained to the next detection and tracking stage with a more tolerant strategy. Experimental results show that, the dim moving targets with SNR as low as 0 dB can be detected by the proposed algorithm, false alarm rate of 1% - 2% and detection rate of about 70%. It can be seen that the detection ability for dim targets with extremely LSNR can be improved effectively by the proposed algorithm.

In the real world, multi-label text has a wider application scenario than single-label text. At the same time, due to its huge output space, it brings a lot of challenges to the classification task. The multi-label text classification problem was regarded as label sequence generation problem, and the Sequence Generation Model (SGM) was applied to the multi-label text classification field. Aiming at the problems such as that the sequence structure of the model is easy to produce the cumulative error, an SGM based on Dynamic Routing (DR-SGM) was proposed. The model was based on Encoder-Decoder mode. In the Encoder layer, Bi-directional Long Short-Term Memory (Bi-LSTM) neural network+Attention was used to encode the semantic information. In the Decoder layer, a decoder structure with the dynamic routing aggregation layer was designed which reduces the influence of the cumulative error added behind the hidden layer. At the same time, the part-part and part-glob position information in the text was captured by dynamic routing. And by optimizing the dynamic routing algorithm, the semantic clustering effect was further improved. DR-SGM was applied to the classification of multi-label texts. The experimental results show that DR-SGM improves multi-label text classification results on the RCV1-V2, AAPD and Slashdot datasets.