Fast acquisition method using modified PCA with a sparse factor for burst DS spread-spectrum transmission

To improve the acquisition speed and inbound capacity of the ground station in a burst direct-sequence (DS) spread-spectrum transmission system, an acquisition method based on a modified parallel code-phase acquisition (PCA) scheme is proposed. By taking advantage of the sparsity of the acquisition result with PCA in the time domain, we introduce the sparse factor to handle the signals via sparsification and apply the sparse recovery algorithm to search and estimate the acquisition result. The computational complexity, mean acquisition time, and relationship between the inbound capacity and acquisition performance are provided. We theoretically analyse the effect of the sparse factor on the acquisition performance. The estimation errors verify our analysis, and simulations show that the acquisition time of our proposed method outperforms that of advanced PCA by 1.2–4.0 times; additionally, the inbound capacity increases by 6.2–36.7%

Evaluation Model for Protocol Conformance of BDS D1 Navigation Messages

Navigation messages are the basic data for navigation and positioning calculations in satellite navigation systems. Nowadays, there is a growing need to evaluate the protocol conformance of navigation messages. In this paper, the evaluation model and the calculation method for protocol conformance of BDS D1 navigation messages are proposed, and the example analysis of actual BDS D1 navigation messages is presented. The evaluation model is described from three aspects, including formatting conformance, verification conformance and quantization unit conformance. Every aspect is considered carefully with calculation and analysis. The results show that the model can evaluate the protocol conformance of BDS D1 navigation messages reasonably

Fast GPU based acquisition with minimum loss and downsampling techniques for weak BeiDou SBAS signals

Abstract For a receiver of the BeiDou Satellite Based Augmentation System (BDSBAS), the acquisition aims to coarsely estimate the pseudo‐code delays and carrier Doppler shifts of the received signals. The use of downsampling techniques to accelerate this acquisition process for weak signals has been an emerging topic. However, the acquisition probabilities of conventional algorithms with downsampling are inadequate and need to be enhanced to prevent excessive false alarms due to degradation of the peak signal‐to‐noise ratios (PSNRs) during acquisition. To improve signal acquisition performance, we introduce an enhancer function before downsampling to achieve the minimum PSNR loss during acquisition. Then, we use graphic processing unit (GPU) functions to speed up the processing loop, matrix multiplication and fast Fourier transform operations. We thus present a fast GPU‐based acquisition method by using minimum loss and downsampling techniques. Our experimental results show the remarkable effectiveness of our method for receiving weak BDSBAS signals as compared to legacy downsampling algorithms. This work seeks not only to solve the current issue of achieving fast acquisition of weak BDSBAS signals but also to inspire creative new ideas that spur further advances in this promising field in the future

A Recognition Method of Ancient Architectures Based on the Improved Inception V3 Model

Traditional ancient architecture is a symbolic product of cultural development and inheritance, with high social and cultural value. An automatic recognition model of ancient building types is one possible application of asymmetric systems, and it will be of great significance to be able to identify ancient building types via machine vision. In the context of Chinese traditional ancient buildings, this paper proposes a recognition method of ancient buildings, based on the improved asymmetric Inception V3 model. Firstly, the improved Inception V3 model adds a dropout layer between the global average pooling layer and the SoftMax classification layer to solve the overfitting problem caused by the small sample size of the ancient building data set. Secondly, migration learning and the ImageNet dataset are integrated into model training, which improves the speed of network training while solving the problems of the small scale of the ancient building dataset and insufficient model training. Thirdly, through ablation experiments, the effects of different data preprocessing methods and different dropout rates on the accuracy of model recognition were compared, to obtain the optimized model parameters. To verify the effectiveness of the model, this paper takes the ancient building dataset that was independently constructed by the South China University of Technology team as the experimental data and compares the recognition effect of the improved Inception V3 model proposed in this paper with several classical models. The experimental results show that when the data preprocessing method is based on filling and the dropout rate is 0.3, the recognition accuracy of the model is the highest; the accuracy rate of identifying ancient buildings using our proposed improved Inception V3 model can reach up to 98.64%. Compared with other classical models, the model accuracy rate has increased by 17.32%, and the average training time has accelerated by 2.29 times, reflecting the advantages of the model proposed in this paper. Finally, the improved Inception V3 model was loaded into the ancient building identification system to prove the practical application value of this research