In order to reduce complexity of construction of indoor positioning fingerprint database and improve the positioning accuracy, an indoor fingerprint positioning algorithm based on matrix completion under the 5G ultra-dense network is proposed. In the offline database construction stage,the algorithm first uses the K-nearest Neighbor(KNN) interpolation method to complete the matrix of part of the fingerprint database to construct a complete database. Secondly,the sparse auto-encoder is used to extract the sparse features of the fingerprint database, and the high-dimensional received signal strength indication (RSSI) signal is reduced. In the online fingerprint matching stage,the weighted KNN algorithm is used to estimate the coordinates of the point to be located. After experimental simulation,the average relative error of the algorithm to reconstruct the fingerprint database is 0.31% . Compared with that of the traditional KNN fingerprint matching algorithm,the average error is reduced by 24.41% .

Recently, with the development of deep learning, the field of lightweight object detection has witnessed significant progress. However, mainstream lightweight detectors ignore the extraction of multi-scale semantic information. In addition, these approaches ignore the relationship between deep semantic features and shallow detail features. To relieve above shortcomings, a Pyramid Pooling Enhanced Multi-scale Network(PPMENet) is proposed and an Efficient Pyramid Pooling Block (EPPB) is designed to extract multi-scale deep semantic information,strengthening the feature expression ability of the model. On the other hand, a Cross Semantic Level Interaction Attention Module (CSIAM) is designed to enhance information interaction between features at different semantic levels. Experimental results on the MS COCO 2017 test set show that PPMENet gets 28.0% average precision, only with 2.16×10⁶ model size and 0.97GFLOPs,and achieves inference speed of 218 frame/s. Compared with other methods, PPMENet realizes a good balance between detection accuracy and model execution efficiency.

In order to further improve the accuracy of human action recognition and fully explore the spatiotemporal features of action sequences, a graph convolution action recognition method based on spatiotemporal feature fusion and attention mechanism is proposed. The spatial attention map convolution is used to refine the topology to capture the correlation features of the joints under different motion types,and the time convolution structure is extended by the time domain multi-scale convolution module to capture the multi-scale time features. A multi-level feature fusion module is constructed,which takes the initial feature and the convolution output feature of the time-domain multiscale graph as the module input,and uses a two-branch structure to obtain the global and local channel features respectively. On this basis,a limb attention mechanism is proposed to divide the human topological structure and calculate the attention weights in the channel dimension respectively to enhance the model's ability to pay attention to local action features. The experimental results show that the recognition accuracy is 93.0% and 96.9% in CS and CV evaluation mode of NTU RGB+D data set,and 89.8% and 91.1% in X-Sub and X-Set evaluation mode of NTU RGB+D 120 data set,respectively. The recognition accuracy is higher than that of ST-GCN,CTR-GCN and other models.

It's difficult to implement maintainability design and evaluation effectively in the early stages of development of space TT&C ground system. To solve this problem,a maintainability design and evaluation system is constructed based on virtual reality technology,a maintainability design and evaluation workflow is developed,and a comprehensive evaluation criteria for maintainability design is proposed. In the virtual maintenance scenario,virtual maintenance prototypes and virtual maintenance resources are used to verify the overall process,and immersive simulation is conducted to verify the accessibility,visibility,and human body comfort of the maintenance of components of a vehicle mounted space TT&C ground system. Thus the quantitative comprehensive evaluation of virtual maintainability design is achieved. The results show that this method reduces costs by 68% , shortens duration by 61% compared with traditional method. It may serve as a practical reference for visualizing,quantifying carrying out maintainability design and evaluation in the early stages of development of space TT&C ground system.

The traditional antenna array wideband signal synthesis performance evaluation method has the problem of low signal synthesis performance evaluation accuracy due to the limited accuracy of the signal-to-noise ratio(SNR) estimation algorithm in the wideband and low SNR scenarios. For above problem, a wideband signal synthesis performance evaluation method of antenna array using power calculation is proposed. The method first simulates multiple intermediate frequency(IF) signals, applies time and phase delays to simulate the time delay and phase difference of the actual antenna received signals, and adds noise to each signal to simulate a low SNR environment. Then, the original and delayed signals are synchronously compensated until convergence using the antenna grouping algorithm to be evaluated. Finally, the synthesized power of the original signal after compensation is calculated and compared with the synthesized power of the ideal signal to obtain the synthesis loss. Simulation experiments results show that under the conditions of signal bandwidth of 250～500 MHz and SNR of -20～0 dB, the method has an improvement of about 1 dB in evaluation accuracy and 0.1 dB² in evaluation stability compared with the wideband signal synthesis performance evaluation method based on SNR, and the improvement effect is more significant with the decrease of signal bandwidth, and the improvement effect is more significant with the decrease of signal bandwidth.

To reduce the computations of parameters estimation in high-dynamic and long integration global navigation satellite system(GNSS) signal detection applications,the authors propose a low-computation GNSS acquisition method (LGAM) suitable for high-dynamic environment. The goal of LGAM is to apply the synthetic Doppler frequency hypothesis testing (SDHT) method to the acquisition of high dynamic GNSS signals with Doppler rate and bit flipping. Firstly,sparse Doppler frequency(SDF) process is implemented by coarse Doppler estimation,and post-correlation signal model is derived based on SDF structure. Then,in order to improve the detection efficiency of Doppler and Doppler rate, double-FFT based detection is proposed based on the post-correlation signal model for parameters estimation. The results demonstrate that in high dynamic environments, when the signal-to-noise ratio (SNR ) is higher than -43 dB, the computational complexity based on FFT method is 15 times that of LGAM1 and 780 times that of LGAM2.

Multi-dimensional telemetry data pattern mining holds significant importance for satellite status monitoring. However, the sheer volume of telemetry parameters and data poses a challenge in obtaining precise solutions within a short timeframe. To address this issue,the authors propose a matrix profile-based pattern mining approach that employs stochastic principles to search for approximate solutions,which can serve as surrogates for precise solutions within an acceptable error margin. Firstly, spectral analysis is performed on the multi-dimensional telemetry data to determine the template length based on the characteristic frequencies of the patterns. Subsequently,the Mueen's algorithm for similarity search(MASS) is iteratively applied in a stochastic manner to compute elements within the distance matrix. A crucial step involves zeroing out elements near the main diagonal to form the multi-dimensional distance matrix. Finally, the minimum values are extracted from each column to generate the multi-dimensional distance matrix profile(MDMP ) . On this profile, the locations of the maximum and minimum values correspond to the identified rare and frequent patterns, respectively. Experimental analysis indicates that when processing three-dimensional telemetry data containing 150000 sampling points, the proposed method, at a 1% mining depth,is able to constrain the positional error between the approximate and precise solutions within 400 sampling points.