Research On Blind Frequency Hopping Technology Of Tactical Ad Hoc Network

For the analysis and processing of frequency hopping signals(Frequency Hopping,FH),the main challenge is that a large amount of data cannot be received and processed in real-time.The performance of the receiving device is huge for a large amount of signal data storage,and the calculation process of the signal analysis process leads to the analysis process.Poor real-time performance.Therefore,how to reduce the sampling rate of received data,and thus decrease the computing and storage pressure of the device is a key research point.The main work of this thesis as follows:First,this paper outlines the characteristics of frequency hopping communication and the main flow of blind hopping signals.Combined with the current popular theory of compressed sensing,the paper summarizes and analyzes the current research progress in the analysis and processing of frequency hopping signals.Secondly,for the problem of high computational complexity and low computational efficiency of the sparse decomposition process of frequency hopping signals based on compressed sensing theory,this paper studies the non-coherent decomposition of atomic dictionary by decomposing redundant dictionary into several sub-dictionaries.Combined with the improved matching pursuit algorithm,the sub-dictionaries are searched for the optimal atom respectively.To further reduce the computational complexity,this paper analyzes the theoretical characteristics of the algorithm,and searches for the high probability of the high atomic probability for the adjacent generation algorithm to satisfy the non-coherence.The successive iterations are avoided,further reducing the number of calculation.Finally,through performance analysis,the algorithm compares to the traditional single-atom search method.When the signal-to-noise ratio is higher than-6dB,the error of this algorithm can be less than10-5.From the perspective of decomposition speed,when the signal-to-noise ratio higher than 20 dB,the operation of algorithms is 22 times that of the traditional single-atom matching algorithm,which further demonstrates the effectiveness of the algorithm.Thirdly,in the process of reconstructing the measurement signal under the compressed sensing framework,the traditional greedy tracking algorithm is based on the low reconstruction precision and the convex relaxation based algorithm has highreconstruction precision but prohibitive computational complexity and high complexity.From the perspective of reconstruction accuracy,the sparse projection reconstruction algorithm is improved.For the problem of low computational efficiency of the projection process,the penalty weight coefficient and step size parameters of the algorithm are improved,and the weight coefficients is adaptively adapted to different components select different penalty terms,which reduce the complexity of the algorithm.In order to speed up the convergence of the objective function and improve the efficiency of the algorithm,the step size design of the algorithm is improved by combining the current iterative point approximation model and the delay strategy.Finally,the simulation results demonstrate that the objective function of the algorithm is slower than the classical projection reconstruction algorithm,and the performance in terms of computation time cost and reconstruction accuracy is obvious.