Research On The Estimation Of The Number Of Sources Based On Multiple Features Fusion

With the development of array signal processing technology,it is more and more widely used in information fields such as field communication,medical detection,seismic exploration,and image processing.In these fields the direction of the signal(DOA)estimation is also become a hotspot.Although many super-resolution high-precision algorithms have been used in DOA estimation,the premise of ensuring the performance of these super-resolution algorithms is to know the correct number of sources.When the number of sources is not konw,the estimation performance of these algorithms will be affected.Therefore,it is very necessary to estimate the exact number of sources.So estimating the number of sources in array signal processing has become one of the basic research tasks.At present,although many scholars have proposed their own algorithms for estimating the number of sources.But after careful study,we can find that in order to estimate as many sources as possible,these algorithms use more array antennas to build a signal model.However,in actual applications,the more antennas used the number of interfering signals is larger and the more difficult to build a signal model.Therefore,this article proposes a method for estimating the number of sources based on multi-feature fusion,which can estimate as many sources as possible with as few antennas as possible.After understanding the relevant source number estimation algorithm.This article takes Weighted Gerschgorin Disk Estimation(WGDE)as the theoretical basis and combines Support Vector Machine(SVM)to design the algorithm and mainly discusses the following aspects:1.The first is to understand the background knowledge of array signal processing and the research status of source number estimation algorithms.2.Introduce the array signal model used in this article and several typical source estimation algorithms.Including: hypothesis testing method,source estimation algorithms based on information theory criterion,source estimation algorithm after diagonal loading and Gerschgorin Disk Estimation algorithm,and also introduced a class of source estimation algorithm based on pattern classification.The theoreticaldata experimental simulation of the classic source estimation algorithm is carried out under white noise and colored noise respectively.3.Because this article takes Weighted Gerschgorin Disk Estimation and support vector machine asthe theoretical basis for modeling,so the two algorithms are specifically introduced.Then a software package for training multi-classifier mathematical models based on support vector machine is introduced.4.Introduced the multi-features used in this article,the training process of the multi-classifier mathematical model and the entire design process of the source estimation algorithm proposed in this article.First,the Uniform Circular Array(UCA)is used to receive independent and incident far-field Gaussian signal source and then use the WGDE criterion to transform the receive signal.Obtain the augmented Gerschgorin center value,augmented Gerschgorin radius value and augmented weighted Gerschgorin radius value that can be used to describe the number of sources at the same time.Use these three values as the characteristic parameters of the estimated source,and the features are merged into high-dimensional feature vectors,labeled and substituted these vectors into Lib SVM to train the mathematical model of multi-classifier.Finally,theoretical data simulation and radio frequency silencing laboratory data simulation verify the source estimate performance of the classifier mathematical model.The experimental results show: when the incident angle,signal-to-noise ratio,and number of snapshots of the array signal change,the mathematical model still has a good source estimation performance,and when the number of sources is one less than the number of array antennas,it can also effectively estimate the number of sources.