Study On Blind Source Separation Of Underwater Acoustic Signal Processing

In passive underwater acoustics signal processing, observed directly signals from thehydrophone (or hydrophone array) are a complex mixture signal which is a superposition ofmulti-sources via multipath propagation and interference noise or ocean backgroundnoise,because the actual acoustic environment is complex and changeable, interference andnoise are also difficult to be determined. Those affect seriously the performance and accuracyof target location, the detection and identification. Blind signal processing technology issuitable for application in passive underwater acoustic signal processing, because it can onlymake use of received signals and some statistical assumptions to gain expected information,without prior knowledge of source signals. The goal of blind separation as one of theimportant branches is to get the best estimation of multiple source signals, so blind separationtechnology has great application value in the many fields with the need to get the accurateunderwater source signals. The paper includes three main research contents.These areestimation method about the number of source under under-determined condition, blindmulti-source separation method from single beam signal or purification method in the light ofnoisy beam signal, and blind signal separation method suitable for single vector sensor.A prerequisite realizing blind signal separation is the accurate estimation of sourcenumbers, so the paper studies estimation problem of source number. In many estimationmethods, Gerschgorin Criterion method has important application value, because it can applyto both the Gauss white noise background and colored noise background. But the traditionalGDE criterion method has some disadvantages. Detection performance is not just as onewishes under low SNR condition or a small number of snapshots, and adjustment factor needto determine by people. According to this, a modified method of Gerschgorin radii wasintroduced based on the original Gerschgorin Criterion method. That modified method(MGDE) lessened independently Gerschgorin radii using center information of Gerschgorincircle in order to compression speed of noise Gerschgorin radii faster than that of signalGerschgorin radii, so the noise Gerschgorin disks could be kept as remote from the signalGerschgorin disks as possible. Therefore, the source number could be easily determined underlow SNR condition or a small number of snapshots. Meanwhile, in view of the existingproblems which adjustment factor need to determined by people, this paper proposes amethod, in which can determine automatically the adjustment factor by Gerschgorin disksinformation. And GDE criterion not only improved the detection performance but also avoiding the influence of artificial factors through use of proposed adjustment factor.Source number estimation problem under under-determined condition is one of thedifficulties in blind signal separation problem.According to this, we convert under-determinedcondition into overdetermined conditions by phase space reconstruction method to expand theobserved signal dimensions.For limiting of phase space reconstruction method can onlyreconstruct a signal, we will present a modified phase space reconstruction method which canreconstruct multiple times signals by modifying the original method.We can estimationcorrectly the number of sources more than2from single array signal combining with theappropriate source number estimation method.A lot of methods and theory of underwater acoustic signal processing are establishedbased on the Gauss noise model.In most cases, this assumption is reasonable and effective.But research show: acoustic environment noise and warship radiated noise often haveα-stable distribution characteristics. Probability density function has a relatively thick tail,that is In time waveform they have significant pulse characteristics.So algorithmsperformance degrade based on the Gauss noise models, even it can’t get the right result. Inview of this situation, the paper initially explores source number estimation and DOAestimation of random vector obeying to α-stable distribution. On the one hand sourcenumber is estimated using diagonal covariance matrix instead of the covariance matrix, andthe simulation results verify the source number estimation performance.On the other hand,wemake use of two spectral measure(empirical function method and the projection method) toestimate the number of sources and DOA. The former can only achieve single sound sourceDOA estimation. The latter can realize DOA estimation of multi-sources.This paper make use of blind signal separation means to analyze and process furtherbeam signal. The purposes are to solve the two aspects problems because rely solely onbeamforming cannot achieve these objectives. One is that more source signals can be separatefrom single beam signal, when they come from the same direction, or they are closer eachother. The other is the beam signal can be purified further containing noise. From the aspectof increasing signal dimensions in this paper, phase space reconstruction method is used toincrease signal dimensions. Then the determined blind separation technology further can beadopted to separate or purified single beam signal.Research show: it is reasonable andeffective that blind signal separation technology be used as post signal processing method ofbeamforming. It can not only separate more source signals from single beam signal when theycome from the same direction or they are closer each other, but also clear beam signalpolluted by noises. In addition, from the point of view of blind signal separation, it can reduce orders of blind separation algorithm, and reduce the complexity of the algorithms.It is a new hot research topic in recent years that parameter informations of multipletargets can be obtained accurately from signal of a single vector hydrophone using blindseparation technology. Therefore, two blind separation methods are propose based on thevector hydrophone signal characteristics. They are blind separation method based on hybridmatrix and blind separation method based on two order statistics analytic equation method.They are suitable for noncoherent source.And simulations analyzed performance and affectionfactors of separation algorithm.