Research On Parameter Estimation Method For Distributed Sources

In the field of array signal processing, as a result of dispersion, reflection, diffraction andrefraction under complicated circumstance, impinging sources will bring about angular spreadand therefore have more complex spatial distribution characteristics than point sources. So aparameterized distributed sources model is needed. Compared with point source model,distributed source model has a higher dimension of parameters, a larger computation complexityand demands an accurate acknowledgement of distribution function of distributed sources.Consequently, it is necessary to study low complexity estimation algorithms which are notsensitive to distribution function. Take above problems into consideration, this paper makes adeep study on parameters estimation algorithms for coherent， incoherent and compositedistributed sources. The main research content is as follows:Parameter estimation algorithms for one-dimensional coherent distributed sources arestudied. Subspace-based methods are often used to settle the estimation problem of coherentdistributed sources. So, a low complexity estimation method based on Root-MUSIC is proposedfirstly. Then, on the basis of space sparsity that exists in both central DOAs and angular spread, adecoupled estimation algorithm is given. This algorithm displays a nice performance under lowsignal-to-noise ratio (SNR) as well as under small snapshot and could distinguish closed-placeddistributed sources. Furthermore, it does not need transcendent information of distributionfunction and is suitable for distributed sources with different distribution functions.The parameters estimation algorithms for two-dimensional coherent distributed sources areresearched. Because two-dimensional coherent distributed source is described usingfour-dimensional parameters, it has a high computation burden. Based on space frequencyapproximation model, two low complexity estimation algorithms for two-dimensional coherentdistributed sources are proposed. Firstly, cumulants is used in the estimation for two-dimensionalcoherent distributed sources. It asks for no peak search, has low complexity and has no bearingupon array structure. Then the application of interpolation array in the distributed source modelis also validated. The algorithm possesses a low-complexity through converting two-dimensionalsearch into one-dimensional search. The above two low complexity algorithms do not need theacknowledgement of accuracy angle weighting function of coherent distributed sources, so theyare suitable for multi sources with different distribution functions.The parameters estimation algorithms for one-dimensional incoherent distributed sourcesare researched. Because the rank of noise-free covariance matrix is greater than the number ofdistributed sources, subspace based algorithms are no more applicable. Firstly, on the basis of the characteristic that the phase information of covariance matrix of distributed sources only hasrelation to the nominal DOAs of distributed sources, a kind of decoupled estimation algorithm,mis proposed. This algorithm could estimates effectively nominal DOAs and angle spread ofincoherent distributed sources. Moreover, it shows very nice estimation performance as well asresolving power under low SNR and small snapshot. Then considering the orthogonalityproperty between noise-free covariance matrix and pseudonoise subspace, a kind of SOCP basedestimation algorithm for incoherent distributed sources is presented. This algorithm convertstwo-dimensional search to one-dimensional search. it has low-complexity and robust todistribution function error. Both of the two methods mentioned above can achieve the estimationfor nominal DOAs of distributed sources without the acknowledgement of angular power densityfunction of incoherent distributed sources, and they are both suitable for multi incoherentdistributed sources with different distribution type.The parameters estimation algorithms for two-dimensional incoherent distributed sourcesare researched. Two-dimensional incoherent distributed sources are similarly described usingfour-dimensional parameters, which results in a high computation burden due to high dimensionnonlinear optimization. In order to reduce the complexity, two low-complexity estimationalgorithms for two-dimensional incoherent distributed sources are proposed. Firstly, covariancematching estimator used in the estimation for one-dimensional incoherent distributed sources isextended to two-dimensional incoherent distributed sources. The algorithm only needs severaliterations to obtain the nominal DOAs of distributed sources without peak search, so it has a lowcomplexity. Then, a decoupled estimation algorithm for nominal DOAs of two-dimensionalincoherent distributed source is proposed based on the orthogonality property between noise-freecovariance matrix and pseudonoise subspace. In the proposed algorithm, four-dimensional searchis transformed into twice two-dimensional search, which effectively reduces the complexity. Thetwo low complexity algorithms mentioned above are both independent of array structure, andbecause it dose not need the acknowledgement of acknowledgement of angular power densityfunction of incoherent distributed sources, they are also suitable for multi distributed sources ofdifferent types.The parameters estimation algorithm for composite distributed source is studied. Firstly,composite signal models of incoherent distributed sources and coherent distributed sources areunified, and a parameters estimation algorithm for composite distributed source is introduced.This algorithm realizes the estimation for both incoherent and coherent distributed sources at thesame time, and the acknowledgement of angular signal density function and angular powerdensity function is not necessary while during the estimation for nominal DOAs of distributedsources.