Array processing

This work is devoted to the estimation of rectilinear and distorted contours in images by high-resolution methods. In the case of rectilinear contours, it has been shown that it is possible to transpose this image processing problem to an array processing problem. The existing straight line characterization method called subspace-based line detection (SLIDE) leads to models with orientations and offsets of straight lines as the desired parameters. Firstly, a high-resolution method of array processing leads to the orientation of the lines. Secondly, their offset can be estimated by either the well-known method of extension of the Hough transform or another method, namely, the variable speed propagation scheme, that belongs to the array processing applications field. We associate it with the method called “modified forward-backward linear prediction” (MFBLP). The signal generation process devoted to straight lines retrieval is retained for the case of distorted contours estimation. This issue is handled for the first time thanks to an inverse problem formulation and a phase model determination. The proposed method is initialized by means of the SLIDE algorithm.

We propose robust distant speech recognition by combining multiple microphone-array processing with position-dependent cep- stral mean normalization (CMN). In the recognition stage, the system estimates the speaker position and adopts compensation parameters estimated a priori corresponding to the estimated position. Then the system applies CMN to the speech (i.e., position- dependent CMN) and performs speech recognition for each channel. The features obtained from the multiple channels are inte- grated with the following two types of processings. The first method is to use the maximum vote or the maximum summation likelihood of recognition results from multiple channels to obtain the final result, which is called multiple-decoder processing. The second method is to calculate the output probability of each input at frame level, and a single decoder using these output prob- abilities is used to perform speech recognition. This is called single-decoder processing, resulting in lower computational cost. We combine the delay-and-sum beamforming with multiple-decoder processing or single-decoder processing, which is termed multiple microphone-array processing. We conducted the experiments of our proposed method using a limited vocabulary (100 words) distant isolated word recognition in a real environment. The proposed multiple microphone-array processing using multiple de- coders with position-dependent CMN achieved a 3.2% improvement (50% relative error reduction rate) over the delay-and-sum beamforming with conventional CMN (i.e., the conventional method). The multiple microphone-array processing using a single decoder needs about one-third the computational time of that using multiple decoders without degrading speech recognition per- formance.

Arrays of sensors are used in many fields to detect weak signals, to estimate the bearing and the strengths of sig- nals arriving from different directions. For example, in industrial environment an array of microphones is used to localize and to determine the strength of polluting noise sources. Conventional ways of noise source identi- fications include sound intensity measurement [1] and acoustic holography [2] but these techniques suffer from the drawbacks of being restricted in only small areas or short distances and cannot be applied in far fields or in a complex industrial environment. In this study, we pro- pose array processing algorithms which are useful in identifying acoustic sources in the far field of the array. Excellent text regarding the fundamental aspects of array signal processing techniques can be found in Stoica et al. [3]. Shan et al. proposed in [4] a spatial smoothing tech- nique to resolve the multipath problem in narrowband beamforming. Schmidt developed in [5] a multiple signal classification (MUSIC) algorithm that is essentially an eigenvalues-based approach to significantly improve the resolution of multiple sources. The extension of MUSIC in the presence of steering vector errors was developed in [6]. Yang et al. presented in [7] a spatial likelihood method to locate an acoustic source in real time by sum-

Sensor arrays are used in a wide range of applications such as radar, passive sensors, sonar, communications, seismology, radio astronomy, medical diagnosis and chemical analysis.Sensor array signal processing emerged as an active area of research and was centered on the ability to fuse data collected at several sensors to carry out space- time processing. The methods have proven useful for solving several real world problems; such as source localization in radar, sonar and wireless communication. The subject of an array processing is concerned with the extraction of information from the signals collected using an array of sensors. These signals propagate spatially through a medium, and the resulting wave front is sampled by the sensor array. The information of interest in the signal may be either the content of the signal itself or the location of the source or the reflection that produces the signal (radar and sonar). This paper deals with the problem of finding the Directions-Of-Arrival (DOAs) of electromagnetic waves impinging on a array of antenna elements. The attention is focused on radar applications, but the models and techniques treated in this paper are applicable to wide range of applications. In sensor array signal processing applications, especially in radar, there is a growing interest in the estimation of a target’s direction of arrival (DOA) in strong background noise/clutter or interference. Following the undesirable consequences of poor resolution of the classical beam forming techniques (e.g. Bartlett’s method, Min-Variance) in estimating the DOA, a number of efficient high-resolution algorithms have been developed to enhance the resolution of the estimated parameters.In this project the high resolution algorithms MUltiple SIgnal Classification (MUSIC), Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT)[5], are used since which give resolution far beyond the classical Fourier limit when the signal model is sufficiently accurate and require less computation compared to the optimal Maximum Likelihood method.These methods are compared with conventional beam former and capon’s beam former methods. High- resolution methods typically provide asymptotically unbiased estimation and have proved effective in a great variety of applications[6]. In this paper neural network is focused to obtain direction of arrival estimation, since neural network in an intelligent approach for solving problems efficiently and quickly. The direction of arrival estimation obtained from the neural network will be better in spite of corruption in the data due to atmospheric noise. In this paper Adaptive resonance theory(ART) is attempted to estimate direction of arrival.

analysis provide better wave characterizations and enhance the imaging resolution of geological features. In order to perform the characterization of each wave, separation of interfering wavefields is a crucial step. In the case of mul- ticomponent sensor arrays, methods of filtering, of source localization, and of polarization estimation have already been developed for acoustics and electromagnetic sources. In the last decade, many array processing techniques for source localization and polarization estimation using vector sensors have been developed, mainly in electromagnetics. Nehorai and Paldi [1] proposed the Cramer-Rao bound and the vector cross-product DOA. Li and Compton Jr [3] developed the ESPRIT algorithm for a vector-sensor array. MUSIC-based algorithms were also proposed by Wong and Zoltowski [6–8], who also developed vector-sensors versions of ESPRIT [9–13]. These approaches represent a highly popular subspace-based parameter estimation method and use matrix techniques directly derived from scalar-sensor array processing. Such a method is based on the long-vector approach, consisting in the concatenation of all components of the vector-sensor array in a long vector [9].

motivation for the search for new techniques of acquiring audio data directionally. Due to the wide availability of low-cost, high performance digital signal processors and the versatile nature of beamforming, array processing seems the best avenue to explore. Because the current beamforming methods available suffer from some of the same drawbacks as the conventional methods for highly- directional miking, new beamforming signal processing techniques are needed. The new beamforming techniques should allow for a small array to process a large bandwidth and provide a consistent narrow beamform over the bandwidth.

Circular features in digital images are sought very often in digital image processing. An image containing one or several contours is composed of black pixels with value “1” which represent the contours, over a white background with pixel value “0.” Circle fitting, in particular, is faced in several appli- cation fields such as quality inspection for food industry and mechanical parts, fitting particle trajectories [1, 2]. Circle fit- ting also has applications in microwave engineering, and ball detection in robotic vision systems [3]. Several methods have been proposed for solving this problem using, among others, the generalized Hough transform (GHT) [4, 5], array pro- cessing methods [6, 7], contour-based snakes methods [8, 9]. The formalism proposed by Aghajan [6] permits to detect circular or elliptic contours. The coordinates of the center of a circle are estimated by an array processing method [6] that works on virtual signals generated from the image. Each row or column of the image is associated with a sensor of a linear antenna.

Phase-to-height conversion is a very important step in the DEM generation procedure. Therefore, the relationship between phase and height should be calculated accurately. Successful phase-to-height conversion requires relative orbital parameters accuracy [14] and a fulfillment of the basic requirements for the InSAR system. These requirements consist of, first, stable atmospheric conditions and terrain backscatter and, second, the same InSAR geometry. In this paper, we propose a robust phase unwrapping method and an exact relationship of the unwrapped phase and the terrain height. The essence of the phase unwrapping method is based on the combination joint pixel approach, array processing technique and optimization algorithm, which is quite different from that of the interferogram filtering. The optimum scheme that jointly processes the signals from all sensors is based on the multibaseline joint block vector. Moreover, we present the general and exact formulation between the interferogram phase and the target height which is based on the interferometric SAR geometry. Based on this idea, the paper is arranged as follows: Section 2 presents signal model and unwrapped phase estimation. In Section 3, we formulate the relationship between the interferometric phase and the height profiles. The performance of the method is investigated with simulated data in Section 4. The conclusions are in Section 5.

From inspection of (3.26), we see that we may alter the wavenumber-frequency re­ sponse of an F&S system by varying the delays applied to the sensor outputs. Indeed, many array processing techniques are based upon a judicious choice of delays. For example, let us assume that Am— — Thi s, in effect, time-alligns our obser­ vations of the propagating wave in the output of each sensor. This time alignment is commonly referred to as “steering” . Following from our assumption, the wavenumber- frequency response is '^Wm- Thus, by steering we may apply some desired gain to a specified propagating wave. Traditionally, steering maximizes the system response for the specified wave, thus enhancing it relative to the remainder of the sampled wavefield. This is known as “beam-steering” or “beamforming” and is the classical array processing technique. Alternatively we may suppress a specified wave. This is known as “null-steering” and occurs when Yl = 0- Other steering applications include source localization, a simple implementation of which involves beamforming in multiple directions. The direction that maximizes the system output is then taken as corresponding to some active source. We shall review the applications which ex­ ploit steering more fully in chapters 4 and 5. For the moment, having highlighted its importance, we shall discuss those factors effecting our ability to steer with accuracy.

The number of antennas in the antenna array affects the spatial resolution of both DOA algorithms. More antennas result in a higher spatial resolution, and less antennas reduce the spatial resolution. In MUSIC the peaks in the MUSIC spectrum become narrower when more antennas are used, and therefore two adjacent sources may be located with a smaller mutual distance. Figure 4.4 shows the effect of different numbers of antennas on the MUSIC spectrum. In figure 4.4a the small peaks in the noise level located at the mirrored angle of a DOA of a signal exist due to the fact that the implementation of the Hilbert transform in Matlab suffers from the effect of a turn-on and turn- off transient. If more snapshots are used, the peaks become smaller and will disappear eventually. If two peaks merge into one peak, the MUSIC algorithm still has to find 5 peaks. Therefore, one of the small peaks mentioned above is chosen. This results in a wrong estimation of the DOA of source. In figure 4.5b several times the same wrong peak is chosen, which results in the peak at -63 degrees.

Abstract—We consider the problem of direction-of-arrival (DOA) estimation for distributed signals with electromagnetic vector sensors, of which each provides measurements of the complete electric and magnetic fields induced by electromagnetic (EM) signals. In this paper, we consider situations where the sources are distributed not only in space with a deterministic angular signal density, but also in polarization with partially polarized components. A distributed signals general model with electromagnetic vector-sensor array (EMVS-DIS) is established with some reasonable assumptions. Based on the EMVS- DIS model, the minimum-variance distortionless response (MVDR) estimators for distributed source DOA are derived. MVDR estimators do not require the knowledge of the effective dimension of the pseudosignal subspace. We compare our method with the distributed signal MUSIC-like estimator in electromagnetic vector-sensor arrays. The simulation studies show significant advantages in using the proposed EMVS-DIS model with electromagnetic vector sensors. Simulation results show that the new MVDR method outperforms the MUSIC-like algorithm by reducing the estimation RMSE and improving resolution performance for scenario with distributed sources. A robustness study of MVDR localizer was also conducted via simulations.

On the other hand, the synthesis stage does not assume a virtual loudspeaker setup nor makes a different treatment between diffuse and nondiffuse components. This makes the synthesis processing even more simple than in DirAC. In fact, in our method, diffuseness information is assumed to be inherently encoded by the DOA estimates since the variance found on the directional information over the time-frequency domain is already a representation of the di ff useness characteristics of the recorded sound. In this context, there is no need for assuming a specific loudspeaker reproduction setup since each time-frequency element is binaurally reproduced according to its estimated direction.

Research and development on smart antennas, which are recognized as a promising technique to improve the performance of mobile communications, have been extensive in the recent years. Smart antennas combine multiple antenna elements with a signal processing capability in both space and time to optimize its radiation and reception pattern automatically in response to the signal environment. This paper concentrates on the signal processing aspects of smart antenna systems. Smart antennas are often classified as either switched-beam or adaptive-array systems, for which a variety of algorithms have been developed to enhance the signal of interest and reject the interference. The antenna systems need to differentiate the desired signal from the interference, and normally requires either a priori knowledge or the signal direction to achieve its goal. There exists a variety of methods for direction of arrival (DOA) estimation with conflicting demands of accuracy and computation. Similarly, there are many algorithms to compute array weights to direct the maximum radiation of the array pattern toward the signal and place nulls toward the interference, each with its convergence property and computational complexity. This paper discusses some of the typical algorithms for DOA estimation and beamforming. The concept and details of each algorithm are provided. Smart antennas can significantly help in improving the performance of communication systems by increasing channel capacity and spectrum efficiency, extending range coverage, multiplexing channels with spatial division multiple access (SDMA), and compensating electronically for aperture distortion. They also reduce delay spread, multipath fading, co-channel interference, system complexity, bit error rates, and outage probability. In addition, smart antennas can locate mobile units or assist the location determination through DOA and range estimation. This capability can support and benefit many location-based services including emergency assistance, tracking services, safety services, billing services, and information services such as navigation, weather, traffic, and directory assistance.

The goal of this work is to explore alternative approaches to the cochlear implant array processing work recently published by Honert and Kelsall [Journal of the Acoustical Society of America, Vol. 121, No. 6, pp. 3703-3716, 2007]. They demonstrated the possibility of phased array excitation of cochlear implant electrodes in order to achieve focused intracochlear excitation. This memorandum outlines an extension of this work by means of more advanced matrix inversion techniques. These techniques allow one to solve for the electrode array impedance matrix; invert the matrix; and influence the inverse solution in desirable ways.

The celebrated Cramér-Rao bound (CRB), which has influenced our thinking for many decades of statistical signal processing, has found significant use in direction- of-arrival (DOA) problems, among others [37], [69], [109], [142], [188]. The DOA problem is of great importance in passive array processing [188], radar [52], [71], [157], digital communications [50], radio astronomy [58], and other applications [57], [77], [180]. The CRB offers a lower bound on the variances of unbiased es- timates of the parameters (e.g., DOA). Closed-form expressions for the CRB offer insights into the dependence of the array performance with respect to various pa- rameters such as the number of sensors N in the array, the array geometry, the num- ber of sources D , the number of snapshots, signal to noise ratio (SNR), and so forth. Two of the most influential papers in the DOA context are the papers by Stoica and Nehorai [166] and [167]. These papers distinguish between the deterministic CRB and the stochastic CRB (reviewed here in Section 6.3), and obtain closed-form ex- pressions for these. In both cases, the expressions for CRB come from the inversion of the Fisher information matrix (FIM), which contains information about all the unknown parameters. An appropriate principal submatrix of this inverse reveals the CRB of the DOAs, which we denote as CRB(¯ θ) . In this chapter, we will be espe- cially interested in the stochastic CRB because the model assumptions used therein are more appropriate in our context, namely sparse array processing using the dif- ference coarray (Section 6.3).

In this section, we consider the convergence performance of the subband arrays. The LMS algorithm is used. To take the advantages of subband array processing for improved con- vergence, we perform self-orthogonalization of the data sig- nals in each subband independently after the subband de- composition [6]. Because the number of the virtual chan- nels ( NJ ) is usually much smaller than that of the total STAP dimensions ( MNJ ), the additional computational cost of eigendecomposition at each subband is considerably lower than that of the whole-band subspace approach of subband array or STAP systems [21]. Note that, while power nor- malization is e ff ective in improving the convergence perfor- mance in single-antenna equalizers, the e ff ect of power nor- malization alone is not significant in subband arrays [6].

PCR reactions were performed in 96-well plates (Costar) using a Tetrad thermocycler (MJ Research). For each array element, two rounds of PCR reactions were performed. For the first PCR reaction, we used gene-specific primer pairs, with forward primers containing an additional uni- versal sequence (see above). As a template, we used genomic DNA prepared with a simple glass bead protocol [33]. To amplify array elements from genes containing only small exons (<250 bp), we used pools of cDNA libraries as a template ([37,38]; pREP3X: constructed by B. Edgar and C. Norbury; Clontech). PCR products from the first round were used as templates for the second round of PCR reactions, together with gene-specific reverse primers and a universal forward primer containing a 5'-amino modification (5'-GCTGAACAGCTATGACCATG-3'; Oswel). Details of the PCR reaction mixes and cycling parameters are available from our website [29]. All PCR products were checked for single strong bands of expected sizes on 2.5% agarose 1x TBE slab gels. Typically, the fail- ure rate was <3%. Failed PCR reactions were repeated, and new primer sequences were ordered in cases where PCR reactions failed repeatedly. At the time of writing, array elements for all predicted genes had been successfully amplified. The gene-specific primer pairs together with the two sequential and independent PCR reactions make it highly unlikely that array elements are assigned to wrong genes.

Suppose there are L independent signal sources impinging the antenna and we want to use a sensor array system to identify their directions of arrival (DOA). The input signal to each individual sensor is the combination of L independent signals. Every sensor in the array also receives random environmental ambient noise. This noise is modeled as Additive White Gaussian Noise (AWGN). The input waveform of the i th sensor element x

In shallow water environments, matched-field processing (MFP) and matched-mode processing (MMP) are proven techniques for doing source localization. In these environments, the acoustic field propagates at long range as depth-dependent modes. Given a knowledge of the modes, it is possible to estimate source depth. In MMP, the pressure field is typically sampled over depth with a vertical line array (VLA) in order to extract the mode amplitudes. In this paper, we focus on horizontal line arrays (HLA) as they are generally more practical for at sea applications. Considering an impulsive low-frequency source (1–100 Hz) in a shallow water environment (100–400 m), we propose an efficient method to estimate source depth by modal decomposition of the pressure field recorded on an HLA of sensors. Mode amplitudes are estimated using the frequency-wavenumber transform, which is the 2D Fourier transform of a time-distance section. We first study the robustness of the presented method against noise and against environmental mismatches on simulated data. Then, the method is applied both to at sea and laboratory data. We also show that the source depth estimation is drastically improved by incorporating the sign of the mode amplitudes.

This paper concentrates on the implementation of Empirical mode decomposition (EMD) using field-programmable gate array (FPGA) for denoising of ECG [5,6]. EMD is widely used for non-stationary and non-linear signal analysis procedures. The decomposition method that is used in the EMD algorithm is called shifting process. It has proved versatile in a wide range of applications for signal extraction from non- linear and non-stationary processes. It is an iterative algorithm which computes the maximum and minimum extreme. Main advantage of the EMD is that the basis functions are derived from the signal itself. Hence, the analysis is adaptive as compared to the traditional methods where the basis functions are fixed. The EMD is based on the sequential extraction of energy linked to various intrinsic time scales of the signal,