Research On Electromagnetic Spectrum Intrusive Signal Detection Technology

In the actual electromagnetic spectrum intrusive signals detection scenario,it is difficult to obtain a large number of intrusive signal samples and prior knowledge of the intrusive signal.However,the traditional electromagnetic spectrum intrusion detection technology has a good priori basis for intrusive signals,and then uses multi-classification algorithm to identify the types of interference signals in intrusive behavior.Therefore,in view of the above problems,the main research direction of this paper is to study the electromagnetic spectrum intrusive signal detection technology and methods under the condition of less prior knowledge of intrusive signals.The main contribution of this paper is that on the basis of defining the electromagnetic spectrum intrusive signal,three intrusive signal detection algorithms using different electromagnetic spectrum information are proposed,which can realize the detection of electromagnetic spectrum intrusive signal more effectively.The main contents of this paper include the following four aspects:1.The electromagnetic spectrum intrusive behavior is defined,and the problem of lack of data sets is solved.Through the literature review and comparative analysis,the electromagnetic spectrum intrusive behavior is defined,and the difference and connection between the intrusive signal and the interference signal are clarified.An automated electromagnetic signal data set acquisition process was constructed,and electromagnetic spectrum data was collected using more advanced equipment.The signal spectrum data is separated and extracted based on noise cancellation and signal detection techniques,it also provide data support for subsequent research.2.Artificial immune algorithm based on variable discontinuous r-bit matching rules.Aiming at the false positive and false negative caused by the fluctuation of electromagnetic signal,a variable r-bit discontinuous matching rule is proposed,and its advantages are simulated and theoretically analyzed.An improved artificial immune algorithm is designed,which adds a refined training process of setting adaptive parameter interval matrix and detection threshold for each detector to reduce detection vulnerabilities.Features selection is carried out,and the performance of the improved algorithm is evaluated and analyzed through experiments.3.Electromagnetic spectrum intrusive signal detection based on signal's fluctuation characteristics is proposed.Aiming at the problem that the parameters of intrusive signal with deceptive purpose is similar to the legitimate signal and it is difficult to use the typical signal features to realize intrusive signal detection,this paper presents a new feature:signal fluctuation characteristic,describes the characteristic of signal fluctuation with probability mass function as a new signal feature,and uses KL divergence to measure the difference between different signals.An algorithm is designed to detect intrusive signal based on the difference of signal fluctuation characteristics.The feasibility of intrusive signal detection using signal fluctuation characteristics is analyzed through the real intrusion scenario:radio equipments' signals invade broadcasting band,and the effectiveness of the algorithm is verified.4.Electromagnetic spectrum intrusive signal detection based on power entropy.For the problem that the low-power intrusive signal hidden in the noise is difficult to detect,the analysis and simulation show that the power entropy of each frequency point changes due to the existence of the intrusive signal,and the power entropy curve of the monitoring frequency band is used as a feature to train OcSVM model.Thus to determine whether there is an intrusive signal.Three kinds of experimental scenarios in which the spread spectrum signal invades the frequency bands of FM,QPSK and 16QAM signals are designed,and the effectiveness of the algorithm is verified.