Radar Signal Sorting Algorithm On Intensive Signal Condition

The electronic warfare plays a more and more important role in modern warfare because the performance of detection signal processing can affect win or lose of warfare, and its role has become increasingly important. Due to diversity of the Radar system in modern warfare, battlefield signals environment has become increasingly complex, so requirements for real-time sorting and reliability to the overlapped dense signal flow are also increasing. Radar signal sorting is a important component of Investigation Guidance System. Currently several algorithms for radar signal sorting have been well studied, more about traditional methods including heuristics, statistical assessment technique, nearest neighbor classifier, parametric range matching method, the correlation function repetition frequency identification method and so on. These algorithms have mature theory basis and widely applied. However, when used to dense signal environment for separation, the algorithm's shortcomings are prominent, that is, poor real-time capability, low reliability, and sometimes even can not complete separation. Aiming to the current dense electromagnetic environment, there have been many improved method based on traditional algorithms, among of which some method are used more often, for instance, the histogram (including the cumulative difference histogram CDIF and sequence difference histogram SDIF), sorting method based on the sub-plane transform, cluster analysis, PRI transform, wavelet transform method and so on.The initial pulse flow separation for dilution of the pulse stream by use of clustering methods has been widely used. This paper mainly addresses the K-means clustering algorithm application in the radar signal sorting. Using K-means clustering algorithm to pre-pulse flow separation, which in addition to using the conventional carrier frequency (CF), pulse width (PW), angle of arrival (DOA) of these three parameters, also join the polarization characteristics of radar parameter to make use of the four possible polarization states, namely, horizontal polarization, vertical polarization, left-hand circularly polarized and right-hand circular polarization of the signal separation. Because the only four states exist in the polarization characteristics, so we can sort them first. After diluting the pulse flow, the polarization can be seen as another reliable parameter than sorting parameters. Using K-means algorithm for sorting, one of the disadvantage is that, the initial cluster centers determined through repeated iterations may lead to local optimum because of the radar signal sorting separation using the way in such a sequence, first pole parameter, then the pulse width and arrival angle separation, the final sorting of the carrier frequency. Thus, no matter how poor the initial cluster centers is, after sorting DOA and polarization parameters, the results will not fall into local optimum, that is not affected by the impact of noise. The simulation results have further verified the effectiveness of the method. The analysis to Signal flow in the intensive statistical of sorting demonstrates that the algorithm possesses high reliability and good sorting effect. K-means clustering algorithm has a drawback that needs a longer iteration computing time, which will be the next focus of the study.For the pre-sorted signal flow, the primary sorting procedure will be performed later. This paper mainly studies the correlation function method of repetition frequency identification, the histogram method and the PRI transform, among of which Correlation function method of repetition frequency identification based on PRI transform is a typical primary sorting algorithm. The harmonic compression method is often taken due to the poor suppression effect of traditional correlation function method to harmonic components. PRI transform algorithm based on the autocorrelation function method after transformation after joining phase factor expressing by the vector, we find its summation becomes zero, which means that harmonics are suppressed, on the basis of this, to overcome the disadvantage of the algorithm we made two improvements. The improved algorithm can eliminate the noise effect on separation results. Additionally, the paper also addresses the improved Method for the cumulative difference histogram (CDIF) and the sequence difference histogram (SDIF). The two methods have both advantages and disadvantages. CDIF algorithm possesses high accuracy and reliability as well as the weakness of the large computation amount and not optimal threshold. If a large number of pulses are lost CDIF algorithm can detect the harmonic. SDIF, improved on the basis of the CDIF, which is faster than the CDIF algorithm, has the best detection threshold. But it does not apply for sorting the random PRI signal.Lastly, the paper gives a whole sorting process. In the process, First step is to search the Pre-sorted sequence, and then to recognize the residual, finally to process the false signal. Simulation experiments show that the algorithm has good robustness and sorting performance.