Research On Jamming Modulation Recognition And Location Algorithm Of Navigation Signal

Nowadays,satellite navigation is gradually developing towards the complexity of electromagnetic environment and the diversification of application scenarios,which also leads to the prominent problem of satellite navigation signal interference,which seriously affects the development and application of navigation technology.In order to improve the service quality of navigation business,it is urgent to explore the methods of satellite navigation interference signal parameter detection and positioning in complex scenes,so as to fundamentally solve the problem of satellite navigation interference.Among them,the discrimination ability of jamming signal modulation mode and the positioning accuracy of jamming source,as two important links of navigation jamming monitoring,have important research value and application prospect.As the key technology of navigation jamming signal parameter detection,intelligent recognition of modulation mode of jamming signal faces the problems of less sample data,low quality of data set,low recognition accuracy and generalization ability of modulation mode.The research of jamming source location technology often considers the diversity of RF interference signals and the influence of multipath effect in environmental interference.It mostly depends on high-precision positioning equipment,resulting in high monitoring cost and easy to be affected by the results of a single equipment.These practical problems pose a serious threat to the safety of satellite navigation system.Therefore,this paper focuses on the research of jamming signal modulation recognition algorithm and jamming source location algorithm.The main innovations and work are summarized as follows:1.Research two jamming signal modulation recognition algorithms based on transfer learning and deep twin network.In view of the problem of small number of interference signal samples in the actual electromagnetic environment,the simulation experimental data show that the interference signal modulation recognition model based on transfer learning can complete learning reasoning,train the model to normal convergence under the small sample signal data set,so as to realize effective recognition on the test samples.Among them,when the signal-tonoise is high,the recognition accuracy can reach 98.5%.In addition,compared with the traditional recognition method and the recognition method based on transfer learning,when there is only one sample in each category,the algorithm based on twin network can still effectively infer and identify the modulation type of unknown interference signal,which proves the generalization of the algorithm.2.An innovative interference source location algorithm based on preprocessing and cluster analysis is proposed.Through the simulation of the electromagnetic environment in the target area,a large number of receiving terminal data with spatial distribution significance are obtained.Through dispersion standardization preprocessing and mean shift clustering,the estimation of the number and location of interference sources are realized.The importance of receiver distribution density and interference source distribution location to the average positioning error rate is analyzed and discussed,and the superiority of the algorithm applied to interference source positioning scene is demonstrated,the effectiveness of the algorithm is verified by simulation experimental data.This topic proposes a method to quickly and accurately identify and locate potential interference sources in complex electromagnetic environment,which is helpful to realize the monitoring of a wide range of target areas,so as to eliminate the influence of interference signals and ensure the communication quality of navigation signals.