Research On GNSS Receiver Capture Algorithm And Implementation On Its Verification Platform

Global Navigation Satellite System(GNSS)is a great invention in the history of human science and technology.It is not only used in the single military application but also used in many life and economy territory nowadays.Its influence is also increasing.With the continuous development of Chinese economy,the demand for high performance receivers is also growing.The traditional receiver's research takes a long time and consumes a lot of money.Based on the existing resources of the research group,this thesis has carried out relevant research on the receiver capture algorithm and algorithm verification platform.The main research contents of this thesis are as follows:1.Three common GNSS systems are introduced and their signals generation mechanisms are analyzed.The auto-correlation and cross-correlation properties of pseudo-random codes in satellite signals are analyzed.The digital baseband model of the receiver is established and the RF front-end and.2.The capture workflow is descried.Three classic capture algorithms are analyzed and are found that they are not suitable for practical receivers.Therefore,the partial matched filter-fast Fourier transformation(PMF-FFT)are introduced.Through the analysis of PMF-FFT,it can be found that the number of PMF-FFT segments and Fast Fourier Transformation(FFT)points have an impact on the capture performance and resource consumption.For solving this problem,a novel PMF-FFT distribution method is proposed.The minimum resource in theoretical is used to meet the required performance.3.To solve long dwell time of the traditional Tong detection algorithm in the noise unit,an improved detection algorithm based on the culling threshold is proposed.According to the characteristic that the difference between the noise base power and the signal power is large,the improved algorithm sets a culling threshold to reduce the number of detections of the noise unit.The simulation results show that the average number of Tong detection is 4.1955,when the signal strength is-133 dBm and the culling threshold coefficient is 3.But the average number of improved algorithms is only 3.3543.Moreover,with the weakening of signal intensity,the average number of detections of the improved algorithm hardly increases.When the signal does not exist,the average number of detections of the improved algorithm is significantly less than the Tong detection.Therefore,the improved algorithm is applicable to a wider range of applications while achieving rapid detection.4.It is found that the traditional M-of-N detection algorithm uses a fixed number of detection times,which makes the capture time longer,and when the signal strength is strong,it does not need to perform all the detection times.Therefore,an improved M-ofN algorithm is proposed.The idea of Tong detection is applied in this improved algorithm.As long as the signal is detected M times in N times,the signal detection is stop.The simulation results show that the average detection number of the M-of-N detection algorithm is 8 and the average detection number of the improved algorithm is 7.3694,when the signal strength is weaker.When signal strength is stronger and-131 dBm,the average detection number of the M-of-N detection algorithm is 8 and the average detection number of the improved algorithm is 5.2234.Therefore,the improved algorithm can improve the capture speed especially in some stronger signal environment.At the same time,the detection probability and the false alarm probability of the improved algorithm are consistent with those of the M-of-N detection algorithm.5.The implementation of the secure digital memory card(SD card)controller is analyzed and the workflow of the entire algorithm verification platform is described.The actual test verifies the correctness of the platform and the economic value.