Study On DOA Tracking Algorithm Of Array Signal In Impulse Noise

Array signal processing has important research in the field of signal processing,so its application has received widespread attention from scholars.The main principle is to process the signals or measurement information received by the array sensor,suppress noise and useless information,and enhance useful signal information.In classical DOA estimation,people usually assume that the target is stationary during the observation time.However,in practical applications,the DOA of the observation target may change with time,and the number of observation targets may also change,resulting in large direction-finding errors.Starting from array signal models,it is of great research significance to introduce Bayesian estimation and Monte Carlo methods into the field of time-varying DOA estimation.In this thesis,the theory of stochastic finite set is used in DOA estimation,and the following research results are obtained:1.Aiming at the problem of multiple-source direction of arrival(DOA)tracking in impulse noise,the impulse noise is simulated by the symmetric ? stable(S?S)distribution,and a DOA tracking algorithm based on the Unscented Transform Multi-target Multi-Bernoulli(UT-Me MBer)filter framework is proposed.In order to overcome the problem of particle decay in particle filtering,UT is adopted to select a group of sigma points with different weights to make them close to the posterior probability density of the state.Since the ? stable distribution does not have finite covariance,the Fractional Lower Order Moment(FLOM)matrix of the received array data is employed to replace the covariance matrix to formulate a MUSIC spatial spectra in the Me MBer filter.Further exponential weighting is used to enhance the weight of particles at high likelihood area and obtain a better resampling.Compared with the PASTD algorithm and the Me MBer DOA filter algorithm,the simulation results show that the proposed algorithm can more effectively solve the issue that the DOA and number of target are time-varying.In addition,we present the Sequential Monte Carlo(SMC)implementation of the UT-Me MBer algorithm.2.Aiming at the problem of Direction of Arrival(DOA)tracking for multiple target,a DOA tracking algorithm based on Propagator Method(PM)under Multi-Bernoulli filtering framework is proposed.The proposed algorithm uses particle filter to approximate the posterior distribution of target,where the calculation of likelihood function is the key of the update step.The eigendecomposition of the covariance matrix is needed when the likelihood function is replaced by MUSIC spatial spectrum function.In order to reduce thecomputational complexity of the matrix eigendecomposition,we use the spatial spectral function of PM to replace the pseudo-likelihood function of particle filter,and further exponential weighting is used to enhance the weight of particles at high likelihood area and make resampling more efficient.The simulation results show that the proposed algorithm can effectively track the DOA and estimate the number of multiple maneuvering target.3.Aiming at the tracking problem of multi-source Direction of arrival(DOA)in the pulse noise environment of Acoustic vector sensor(AVS),the DOA tracking algorithm based on ? stable distribution that can be better models the properties of impulse noise is proposed.Since the ? stable distribution does not have finite covariance,the covariance matrix is replaced by the fractional low order moment.The noise subspace is constructed by eigendecomposition of FLOM,and FLOM-MUSIC spatial spectrum function is generated as pseudo-likelihood function of Multi-Bernoulli filter.By exponential weighting,the problem of divergence and flatness of the traditional likelihood function is improved,and the resampling of the likelihood function in Gaussian region is more effective.The advantage of this algorithm is that it is not necessary to know the number of sound sources in advance,and the current sources can be directly tracked by using the prior information and the current measurement information.The simulation results show that the algorithm can effectively track the number and state of sources of a single AVS in the impulse noise environment.4.In order to solve the problem of time-varying direction of arrive(DOA)tracking in array signal processing,a DOA tracking algorithm based on generalized label Multi-Bernoulli(GLMB)filter is proposed.For array signal,the measured value has only one set of data,which will cause the GLMB filter update to be biased due to data association issues.Therefore,the estimated number of sources at the previous time is used to determine the number of measurements used at the current time.Subsequently,particle filtering is used to approximate the posterior distribution of DOA,in which the likelihood function can be calculated by MUSIC spatial spectrum function.Moreover,after the likelihood function is weighted exponentially,the number of particles in the high likelihood region increases,which makes GLMB filtering pruning and merging more effective.Simulation results show that the proposed method outperforms the PHD-DOA algorithm in tracking the target's track and estimating the number of targets.