Investigation On High Resolution Array Signal Processing Methods

Array signal processing technique has played a fundamental role in many appli-cations involving radar, sonar and communications. However, with the development of array signal processing approaches in practical applications, the investigations on robust and high accurate array signal processing methods have received great interest.Following the recent advances of array signal processing, this dissertation inves-tigates robust and high accurate array signal processing methods using scalar sensors and vector sensors. This dissertation mainly consists of the following parts.Part I:High accurate array signal processing methods in SaS impulsive noiseRecently, various experiment measurements have shown that atmospheric noise, underwater acoustic noise and electromagnetic disturbance noise have "impulsive" characteristics, which are inappropriatlly modeled as Gaussian noise in applications. As the SaS processes do not possess second-order and high order moments, conven-tional second-order-based array signal processing method can not apply to SaS noise environments directly. In order to deal with this problem, several array signal process-ing methods in impulsive noise environments are proposed in this chapter.Firstly the fractional lower order moment based weighted subspace fitting (FLOM-WSF) algorithm and the Screened Ratio based weighted subspace fitting (SR-WSF) algorithm are proposed for direction finding under SaS noise. The FLOM-WSF and SR-WSF respectively form the array FLOM matrix and array covariation matrix, and then obtain DOA estimation using subspace fitting techniques. Comparing with the traditional DOA techniques, these two algorithms can improve estimation performance in impulsive noise. Incidentally, the SR-WSF algorithm requires no estimation of the noise parameter, hence showing high robustness. The infinity-norm normalization al-gorithm adaptively normalizes each sensor-array snapshot's spatial data vector by its infinity-norm, constructs a pseudo-correlation function, and then, ESPRIT algorithm is adapted to achieve DOA estimation. Simulations show that the IN-ESPRIT algo-rithm has superior estimation-accuracy over the FLOM-ESPRIT algorithm. Then a new minimum mean squared "normalized-error" (MMSNE) beamforming technique is investigated. This new beamformer aims to minimize the "normalized error " between the desired signal and the the beamformer's output. This normalized error is defined in terms of the instantaneously adaptive infinity-norm snapshot-normalized data, as an alter-native to the customary "fractional-order-error" for impulsive noise environ-ments. Sample matrix inversion (SMI) algorithm is used for updating the beamformer weights. Simulation results show the algorithm produces higher estimation-accuracy and offers better interference-rejection for several impulsive noise environments com-paring with the earlier FLOM-based beamformer.Part II:Extended aperture high resolution parameter estimationExtending the inter-sensor spacing beyond a half-wavelength can offer enhanced array resolution and direction-finding precision, but will lead to a set of cyclically am-biguous angle estimates. This chapter proposes two extended aperture two-directional DOA estimation algorithms using two parallel-shape-array:extended aperture ES-PRIT (EA-ESPRIT) algorithm and extended aperture DOA matrix (EA-DOAM) algo-rithm. The two algorithms use 4L+1 and 4L array elements to form two-parallel-shape array geometry, high-variance unambiguous direction cosine estimates and low-variance cyclically ambiguous direction cosine estimates are obtained from the different sensors space. The key idea underlying these two algorithms is to use the high-variance un-ambiguous direction cosine estimates to resolve the low-variance cyclically ambiguous direction cosine estimates to extract azimuth-elevation angle estimates. Two remedial methods to solve the identical eigenvalues problem have also been presented.Part III:2D-DOA estimation using an array of acoustic and electro-magnetic vector sensors.Firstly the data models of acoustic and electromagnetic vector sensor array are introduced. Then several DOA estimation methods using vector sensor array are developed.A new azimuth, elevation angle (Polarization) estimation algorithm for multiple broadband chirp signals using a single acoustic or electromagnetic vector sensor is proposed in this chapter. Two distinct signal scenarios are considered. For multiple signals with same power, the proposed algorithm applies the fractional Fourier trans-form (FrFT) to estimate the steering vectors of vector sensors; For multiple signals with different powers, the proposed algorithm uses fractional Fourier domain filter-ing to estimate and eliminate steering vectors of vector sensors one by one, and then to get elevation-azimuth angle (Polarization) estimates of different signals separately. The proposed algorithm requires no iterative searching and pair matching procedures, works well for multiple chirps having the same or distinct chirp rates. Then a propagator-based algorithm for two-dimensional direction finding of co-herent sources under spatially correlated noise is developed in this paper. The planar-plus-an-isolated array geometry based on acoustic or electromagnetic vector sensors is adopted, and a full rank cross-covariance matrix is defined. Then the propagator method is used to estimate the steering vectors of acoustic or electromagnetic vector sensor. The Characteristic of steering vector is developed to obtain the closed-form azimuth-elevation angle (Polarization) estimates.A propagator-based algorithm for underwater acoustic 2-D direction-of-arrival (DOA) estimation with an array of acoustic vector sensors is proposed. The prop-agator method is exploited to extract extended aperture parameter estimation.The proposed algorithm requires no eigen-decomposition or singular value decomposition into the signal and noise subspaces. Comparing with its ESPRIT counterpart, the pro-posed propagator algorithm has its computational complexity reduced and the similar accuracy.Finally a computationally simple azimuth-elevation direction finding algorithm in spatially correlated noise fields is developed. The algorithm uses two-far-separated subarray geometry based on electromagnetic vector sensors and scalar sensors, and all sensors are arbitrarily placed at unknown locations.A cross matrix to eliminate the effect of the spatially correlated noise is defined. The proposed algorithm has no restrict to the subarray geometry and sensors locations, does not need 2D iterative searching and shows low computational complexity.