This paper considers the uplink of a single-cell multi-user massive multiple-input multiple-output (MIMO) system. Each receiver antenna of the base station is assumed to be equipped with a pair of analog-to-digital converters (ADCs) to quantize the real and imaginary part of the received signal. We propose a novel Bussgang-based weighted zero-forcing (B-WZF) receiver, which distinguishes the clipping and granular distortion. Numerical results demonstrate that for sufficiently high SNR and users that do not experience deep large-scale fading, the B-WZF brings significant performance gain over existing linear receivers in the literature, when the training sequence length is higher than the number of users.

This paper considers the uplink of a single-cell multiuser massive multiple-input multiple-output system. Each receiver antenna of the base station (BS) is assumed to be equipped with a pair of analog-to-digital converters to quantize the real and imaginary part of the received signal. We propose a novel clipping-aware receiver (CA-MMSE), which performs minimum mean square error (MMSE) reconstruction only on the clipped received samples, while the granular samples are left unchanged after the quantization. On this basis, we present an iterative algorithm to implement the CA-MMSE receiver and derive a sufficient condition for its geometrical convergence to a fixed point. We show that as long as the number of BS antennas or the quantization resolution is sufficiently high, then, the performance of the CA-MMSE is as good as the optimal MMSE receiver which reconstructs all quantized received symbols. Additionally, we propose a novel Bussgang-based weighted zero-forcing (B-WZF) receiver which distinguishes the clipping and granular distortion and it is shown that as long as the received training symbols per antenna are correlated, the CA-MMSE brings significant improvements compared to conventional receivers in the literature while for users that do not experience deep large-scale fading the simpler B-WZF is near to the CA-MMSE for sufficiently high signal-to-noise ratio and quantization resolution.

In this paper, the Cramér-Rao Bound for the Direction-ofArrival parameter under the partial relaxation framework is derived. We introduce a non-redundant parameterization of the signal model corresponding to the partial relaxation framework, in which the array structure in part of the steering matrix is neglected while the rank of the relaxed steering matrix is maintained. We prove that the stochastic Cramér-Rao Bound for the Direction-of-Arrival parameter under the partial relaxation signal model is lower-bounded by that of the conventional stochastic Cramér-Rao Bound. Furthermore, we prove that the partial relaxation estimator for the Weighted Subspace Fitting criterion asymptotically achieves the conventional Cramér-Rao Bound in the case of uncorrelated source signals.

In the partial relaxation approach, at each desired direction, the manifold structure of the remaining interfering signals impinging on the sensor array is relaxed, which results in closed form estimates for the interference parameters. By adopting this approach, in this paper, a new estimator based on the unconstrained covariance fitting problem is proposed. To obtain the null-spectra efficiently, an iterative rooting scheme based on the rational function approximation is applied. Simulation results show that the performance of the proposed estimator is superior to the classical and other partial relaxation methods, especially in the case of low number of snapshots, irrespectively of any specific structure of the sensor array while maintaining a reasonable computational cost.

Remote estimation with an energy harvesting sensor with a limited data and energy buffer is considered. The sensor node observes an unknown temporally correlated field and communicates its observations to a remote fusion center using the energy it harvested. The fusion center employs linear minimum mean-square error estimation to reconstruct the unknown field. We provide performance guarantees for the estimation error under a block transmission scheme, where at each transmission block, data and energy buffers are completely emptied. Our bounds provide insights into how statistical properties of the energy harvesting process and buffer sizes may affect the estimation error. In particular, these bounds suggest insensitivity of the performance to buffer sizes for signals with low degree of freedom and suggest performance improvements with increasing buffer sizes for signals with relatively higher degree of freedom. Depending only on the mean, variance, and finite support of the energy arrival process, these results provide insights for the energy and data buffer sizes for deployment in future energy harvesting wireless sensing systems.

Hybrid beamforming (HBF) is a potential solution to reduce the baseband hardware cost in massive multiple-input multiple-output (MIMO) systems that has drawn considerable attention recently. In this paper, we consider the uplink of a multiuser massive MIMO system in the presence of blockers. Such blockers, which arise from, for example, users served by other non-cooperative base stations (BSs), can limit the system performance if not handled properly. We propose a HBF scheme that can remove the impact of blockers, while preserving signals from intended users. Specifically, in our two-step receive beamforming scheme, the analog beamformer (ABF) is designed based on channel covariance matrices to minimize the powers of blockers, while the digital beamformer (DBF) deals with inter-user interference. We propose an iterative algorithm that efficiently gives a good sub-optimal solution to the NP-hard problem in the ABF design. Moreover, we consider a more complete BS architecture by incorporating automatic gain control (AGC) and analog-to-digital converter (ADC). As we show in the simulations, for a system with full-precision ADCs, our scheme approaches the sum rate of an ideal fully-digital system. Interestingly, for a system with low-resolution ADCs, our HBF scheme can even outperform a fully-digital system.

This paper studies the impact of in-phase and quadrature imbalance (IQI) on the downlink of a single-cell multiuser multiple-input multiple-output (MU-MIMO) system with large antenna arrays. Moreover, we consider a time-division duplex (TDD) system where we assume uplink/downlink channel reciprocity in the downlink precoding design. We study the impact of uplink/downlink channel mismatch on the downlink rate caused by different IQI at the base station (BS) and users equipment (UEs). A tractable analytical expression to predict the achievable downlink rates is derived for the regularized zero-forcing (RZF) and based on this expression, we prove that, as the number of BS antennas grow large, the achievable downlink rate of each UE is limited either by the RX IQI at the UEs or by the simultaneous presence of the transmit/receive IQI at the BS and TX IQI at the UEs, when there is uplink/downlink channel mismatch.

In this paper, we study the impact of in-phase and quadrature imbalance (IQI) on single-cell multiuser multiple-input multiple-output systems with large antenna arrays. Moreover, we consider a time-division duplex system, where we assume uplink/downlink channel reciprocity in the downlink precoding design. First, we investigate the effect of transceiver IQI on the uplink channel estimation by deriving the linear minimum-mean-square-error estimator for the IQ-impaired model and prove that only the receiver IQI at the base station (BS) limits the estimation accuracy. Then, we study the impact of uplink/downlink channel mismatch on the downlink rate caused by different IQI at the BS and user equipments (UEs). We prove that the achievable downlink rate of each UE is limited either by the receiver IQI at the UEs or jointly by the transmit and receive IQI at the BS, when there is mismatch between the uplink and downlink channels.

We consider the remote estimation of a time-correlated signal using an energy harvesting (EH) sensor. The sensor observes the unknown signal and communicates its observations to a remote fusion center using an amplify-and-forward strategy. We consider the design of optimal power allocation strategies in order to minimize the mean-square error at the fusion center. Contrary to the traditional approaches, the degree of correlation between the signal values constitutes an important aspect of our formulation. We provide the optimal power allocation strategies for a number of illustrative scenarios. We show that the most majorized power allocation strategy, i.e., the power allocation as balanced as possible, is optimal for the cases of circularly wide-sense stationary (c.w.s.s.) signals with a static correlation coefficient and sampled low-pass c.w.s.s. signals for a static channel. We show that the optimal strategy can be characterized as a water-filling type solution for sampled low-pass c.w.s.s. signals for a fading channel. Motivated by the high-complexity of the numerical solution of the optimization problem, we propose low-complexity policies for the general scenario. Numerical evaluations illustrate the close performance of these low-complexity policies to that of the optimal policies and demonstrate the effect of the EH constraints and the degree of freedom of the signal.

Remote estimation with an energy harvesting sensor with a limited data buffer is considered. The sensor node observes an unknown correlated circularly wide-sense stationary (c.w.s.s.) Gaussian field and communicates its observations to a remote fusion center using the energy it harvested. The fusion center employs minimum mean-square error (MMSE) estimation to reconstruct the unknown field. The distortion minimization problem under the online scheme, where the sensor has only access to the statistical information for the future energy packets is considered. We provide performance bounds on the achievable distortion under a slotted block transmission scheme, where at each transmission time slot, the data and the energy buffer is completely emptied. Our bounds provide insight to the trade-off between the buffer sizes and the achievable distortion. These trade-offs illustrate the insensitivity of the performance to the buffer size for signals with low degree of freedom and suggest performance improvements with increasing buffer size for signals with relatively higher degree of freedom.

Simultaneous wireless information and power transfer (SWIPT) offers an attractive alternative to the traditional battery limited or grid dependent communication system design. In this paper, we investigate robust precoder designs for systems with SWIPT capabilities under a stochastic Rician fading framework. Under a multiple-input multiple-output (MIMO) channel model, we formulate the problem of minimizing the average mean-square error at the information receiver (IR) while keeping the average energy harvested at the energy receiver (ER) above given levels. We consider two different strategies that can be adopted by the IR: i) simple estimation filters based solely on the channel mean, ii) robust estimation filters aiming to minimize the average mean-square error. Both of these scenarios lead to non-convex formulations. For the first scenario, we propose a convex relaxation that is tight. For the second scenario, we propose an alternating minimization method that provides precoder designs even in the scenarios where the number of transmit antennas is larger than the length of data vector. Our numerical results show that the proposed designs provide significant performance gains especially when the scattering component of the channel is strong.

Future radar systems are expected to use waveforms of a high bandwidth, with an advantage of an improved range resolution. Herein, a technique to design robust wideband waveforms is developed. The context is detection of a single object with partially unknown parameters. The technique achieves an optimal detection speed for a desired resolution, maintaining a high detection performance. Many radar systems also require fast adaptation to a variable environment. Hence, the technique is devoted to rapidly design waveforms. In terms of probabilities of detection and false alarm, numerical evaluation shows the efficiency of the method when compared with a chirp signal and a Gaussian pulse.

Atomic norm denoising has been recently introduced as a generalization of the Least Absolute Shrinkage and Selection Operator (LASSO) to overcome the problem of off-grid parameters. The method has been found to possess many interesting theoretical properties. However, its implementation has been only discussed in a special case of spectral line estimation by uniform sampling. In this paper, we propose a general numerical method to solve the atomic norm denoising problem. The complexity of the proposed algorithm is proportional to the complexity of a single-parameter search in the parameter space and thus in many interesting cases, including frequency estimation it enjoys fast realization.

The effect of off-grid atoms has become the prominent problem in application of the Compressed Sensing (CS) techniques to the cases where there is an underlying continuous parametrization. In this work, we develop a generalizing CS framework which shows that sampling to a finite grid is not necessary toward compressive estimation. We propose an alternative procedure over infinite dictionaries, which we show to be theoretically consistent in many cases of interest and then propose a robust implementation. We illustrate the general properties of our technique in some difficult practical instances of frequency estimation.

This paper discusses resolution enhancement of a set of images with varying exposure durations, having a high combined dynamic range. So far, little has been said in relation to the Human Visual System when it comes to Super-Resolution and High Dynamic Range fusion, unlike the case for traditional Super-Resolution where errors are measured with respect to human perception in the pixel domain. We propose a Super-Resolution method in the L*a*b* domain to bridge that gap and present some image reconstruction results.

In this paper, we consider the problem of channel estimation in multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying systems operating over time varying channels. Only data at the receiving end are assumed available for the estimation. By employing a first-order autoregressive (AR) model for characterizing the time-varying nature of the channels to be estimated, we derive an expectation-maximization (EM) Kalman filter (KF) that utilizes the received signal at the destination to track the individual channel links. The extended KF algorithm is also derived and compared to the proposed EM-based KF. Our simulation results show that the proposed EM-based KF offers better estimation performance with less complexity when compared to the EKF algorithm.

We study the downlink of a multicell MIMO system where clusters of multi-antenna base stations jointly serve multiple single-antenna users, commonly referred to as a network MIMO system. Most of the previous studies on network MIMO have only considered the azimuth pattern of the antenna, while ignoring the elevation pattern. In this paper, we consider both the azimuth and the elevation patterns and investigate the impact of the elevation angle tuning parameter, denoted as the antenna tilt, on the performance of such systems. Using system simulations, it is shown that the promised performance gains of network MIMO systems over conventional non-coordinated systems, crucially depend on the choice of the right tilt setting including the tilt type, i.e., mechanical or electrical, and the tilt angle. In particular, for tilt angles smaller than the optimum, network MIMO with intra-site coordination performs almost as well as the conventional system; while for tilt angles larger than the optimum, the performance of network MIMO with intra-site is similar to that of network MIMO with inter-site coordination.

In this paper, we investigate the problem of channel estimation in amplify-and-forward multiple-input multiple-output relaying systems operating over random wireless channels. Using the Bayesian framework, novel linear minimum mean square error and expectation-maximization based maximum a posteriori channel estimation algorithms are developed, that provide the destination with full knowledge of all channel parameters involved in the transmission. Moreover, new, explicit expressions for the Bayesian Cramer-Rao bound are deduced for predicting and evaluating the channel estimation accuracy. Our simulation results demonstrate that the incorporation of prior knowledge into the channel estimation algorithm offers significantly improved performance, especially in the low signal-to-noise ratio regime.

The LASSO sparse regression method has recently received attention in a variety of applications from image compression techniques to parameter estimation problems. This paper addresses the problem of regularization parameter selection in this method in a general case of complex-valued regressors and bases. Generally, this parameter controls the degree of sparsity or equivalently, the estimated model order. However, with the same sparsity/model order, the smallest regularization parameter is desired. We relate such points to the nonsmooth points in the path of LASSO solutions and give an analytical expression for them. Then, we introduce a numerically fast method of approximating the desired points by a recursive algorithm. The procedure decreases the necessary number of solutions of the LASSO problem dramatically, which is an important issue due to the polynomial computational cost of the convex optimization techniques. We illustrate our method in the context of DOA estimation.

In this paper, we present a channel estimation scheme for Amplify-and-Forward (AF) relaying systems, using measurements at the destination only. A Least-Squares (LS) based channel estimation algorithm is developed, that provides the destination with full knowledge of all channel responses involved in the transmission. To investigate the algorithm performance, the Cramer-Rao lower bound (CRB) is analytically computed and compared with the asymptotic covariance of the proposed estimator. Since the existing estimator does not reach the CRB, we also propose and analyze an improved algorithm by taking into account the noise characteristics via weighted LS (WLS). The improved algorithm is asymptotically efficient, since it attains the CRB as the SNR tends to infinity.

In this paper, we describe a minimal mean square error (MMSE) optimal interpolation filter for discrete random signals. We explicitly derive the interpolation filter for a first-order autoregressive process (AR(1)), and show that the filter depends only on the two adjacent points. The result is extended by developing an algorithm called local AR approximation (LARA), where a random signal is locally estimated as an AR(1) process. Experimental evaluation illustrates that LARA interpolation yields a lower mean square error than other common interpolation techniques, including linear, spline and local polynomial approximation (LPA).

The ℓ1norm regularized least square technique has been proposed as an efficient method to calculate sparse solutions. However, the choice of the regularization parameter is still an unsolved problem, especially when the number of nonzero elements is unknown. In this paper we first design different ML estimators by interpreting the ℓ1norm regularization as a MAP estimator with a Laplacian model for data. We also utilize the MDL criterion to decide on the regularization parameter. The performance of these new methods are evaluated in the context of estimating the Directions Of Arrival (DOA) for the simulated data and compared. The simulations show that the performance of the different forms of the MAP estimator are approximately equal in the one snapshot case, where MDL may not work. But for the multiple snapshot case both methods can be used.

We propose a novel, coordinated user scheduling (CUS) algorithm for inter-cell interference (ICI) mitigation in the downlink of a multi-cell multi-user MIMO system. In the proposed algorithm, ICI mitigation is performed through the exchange of necessary channel state information (CSI) among the base stations, and the revision of the scheduling decisions and beamformer designs at each base station. Furthermore, ICI mitigation is performed only for the cell-edge users so that the amount of inter-base station signaling overhead is minimized. Our simulation results demonstrate that the proposed coordination scheduling algorithm significantly improves the cell-edge users' throughput compared to conventional systems with only a negligible amount of CSI sharing among the base stations and a relatively small throughput loss for the cell-interior users.

In this paper, we consider the fundamental problem of channel estimation in multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying systems operating over random channels. Using the Bayesian framework, linear minimum mean square error (LMMSE) and expectation-maximization (EM) based maximum a posteriori (MAP) channel estimation algorithms are developed, that provide the destination with full knowledge of all channel parameters involved in the transmission. The performance of the proposed algorithms is evaluated in terms of the mean square error (MSE) as a function of the signal-tonoise ratio (SNR) during the training interval. Our simulation results show that the incorporation of prior knowledge into the channel estimation algorithm offers improved performance, especially in the low SNR regime.

We address the problem of human detection with mobile platforms such as robots. Instead of using an optical system, we propose to employ an acoustic 2D array to reliably obtain an image of a human in a 3D spatial power spectrum which is independent of lighting conditions and uses cheap acoustic sensors. We show that humans have a distinct acoustic signature and propose to model the echoes from reflecting parts of objects in the scene by a Gaussian-Mixture-Model. When it is fitted to the acoustic image, we can extract geometric relations between the present echoes and represent the acoustic signatures in a low-dimensional parameter space. We present results based on real data measurements that demonstrate that different objects can be reconstructed from the data and discriminated. The obtained parameter space forms the basis for subsequent detection and classification of humans.

In this paper the downlink of a multi-user MIMO (MUMIMO) system with multi-mode transmission is considered. We propose a low-complexity algorithm for selecting users and the corresponding number of data streams to each user, denoted as user transmission mode (UTM). The selection is only based on the average received signal-to-noise ratio (SNR) from the base station (BS) for each user. This reduces the overall amount of feedback for scheduling, as opposed to techniques that assume perfect instantaneous channel state information (CSI) from all users. Analytical average throughput approximations are derived for each user at different UTMs. Simulation results demonstrate that the proposed algorithm provides performance close to dirty paper coding (DPC) with considerably reduced feedback.

The number of signals plays a crucial role in array processing. The performance of most direction finding algorithms relies strongly on a correctly specified number of signals. When this information is not available, conventional approaches apply information theoretic criteria or multiple hypothesis tests to simultaneously estimate model order and parameter. These methods are usually computationally intensive, since ML estimates are required for a hierarchy of nested models. In the previous work, we proposed a computationally efficient solution to avoid this full search procedure and demonstrated its feasibility by extensive simulations. Here we extend to broadband data, and address issues unique to the broadband case. Our max-search approach computes ML estimates only for the maximally hypothesized number of signals, and selects relevant components through hypothesis testing. Another novelty of this work is the reduction of indistinguishable components caused by overparameterization. Our approach is based on the rank of the estimated steering matrix. Numerical experiments show that despite an unknown number of signals, the proposed method achieves comparable estimation and detection accuracy as standard methods, but at much lower computational expense.

The problem of direction-of-arrival (DOA) estimation using an array of sensors has received much attention for more than 3 decades. This is due to a rich interest from application areas such as radar, sonar and wireless communication channel characterization. However, high resolution DOA estimation requires an accurate model of the array response. This is usually achieved by measuring the response using sources at known positions (calibration). This paper considers interpolation of the calibration measurements using knowledge of a nominal response model. Standard linear interpolation is compared to an approach based on local polynomial approximation (LPA). We also derive a weighted MUSIC estimator, which is applied using error estimates from the interpolation. Both LPA interpolation and weighted MUSIC are found to improve the performance, but not uniformly in all scenarios.

Motivated by recently published physics based scattering SISO and MIMO channel models in mobile communications [1, 2], a new adaptive channel prediction based on non-stationary polynomial phase signals is proposed. To mitigate the influence of the time-varying amplitudes and to reduce the computation complexity, an iterative estimation of the polynomial phase parameters using the Non-linear instantaneous LS criterion is proposed. Given the polynomial phase parameters, the time-varying amplitudes are estimated using the Kalman filter. The performance of the new predictor is evaluated by Monte Carlo simulations in SISO scenarios with multiple scattering clusters. The new predictor outperforms the classical Linear Prediction and previous prediction methods based on sinusoidal modeling.

Scattering of radio waves on rough surfaces is investigated using ray tracing techniques, which results in a sinusoidal model with time varying amplitudes. An AR(d) model with nonzero mean is proposed to characterize and predict the time variation of the amplitudes. A covariance sequence based method is proposed to estimate the autoregressive coefficients from the channel observations. An adaptive channel predictor using a Kalman filter is proposed to predict the complex amplitudes of the scattering signal. The proposed method outperforms other sinusoidal modeling based channel predictors and linear predictors with single scattering scenarios in simulations.

For arrays with position and channel errors the calibration becomes very crucial. In the traditional calibration methods one can choose between an optimal SNR and a beam pattern with low side lobes. In this paper we formulate a beam pattern synthesis method which optimize the trade-off between the two criteria. A classical problem with position errors in an array, is that it is difficult to get low side lobes over the whole side lobe region, since the position errors give rise to direction dependent errors. In this paper this problem is solved by using local (direction dependent) correction matrices in the beam pattern optimization. The new way of using local correction matrices leads to the lowest possible uniform side lobe level, for the chosen SNR, beamwidth and beam pointing direction.

In arrays with scan dependent errors, such as large position errors, a dense calibration grid can become necessary. Calibration time is, however, very expensive and keeping the measured calibration grid as sparse as possible is important. In this paper it is shown how interpolation using local models can be used to make the calibration grid more dense without increasing the number of measurements. Furthermore, it is shown how the performance of the DOA estimation with ESPRIT using arrays with large position errors can be improved by a second step including weighted calibration.

A new 3D face representation and recognition approach is presented in this paper. Two sets of facial curves are extracted from a face range image, and a novel facial feature representation, the affine integral invariant, is introduced to mitigate the effect of pose on the facial curves. A human face is shown to be representable by a small subset of those affine integral invariant curves. A recognition procedure based on the discriminant analysis and Jensen-Shannon divergence analysis is proposed. Substantiating examples are provided with an achieved classification accuracy of 92.57%

This paper proposes a novel region-based scheme for dynamically modeling time-evolving statistics of video background, leading to an effective segmentation of foreground moving objects for a video surveillance system. In (L. Li et al., 2004) statistical-based video surveillance systems employ a Bayes decision rule for classifying foreground and background changes in individual pixels. Although principal feature representations significantly reduce the size of tables of statistics, pixel-wise maintenance remains a challenge due to the computations and memory requirement. The proposed region-based scheme, which is an extension of the above method, replaces pixel-based statistics by region-based statistics through introducing dynamic background region (or pixel) merging and splitting. Simulations have been performed to several outdoor and indoor image sequences, and results have shown a significant reduction of memory requirements for tables of statistics while maintaining relatively good quality in foreground segmented video objects.

Many popular direction-of-arrival (DOA) estimators rely on the fact that the array response vector of the array is Vandermonde, for example, that of a uniform linear array (ULA). Array interpolation is a preprocessing technique to transform the array response vector of a planar array of arbitrary geometry to that of a ULA over an angular sector. While good approximation within the target sector is attained with the various existing array interpolation approaches, the response of the interpolated array in the out-of-sector region is at best partially controlled. Accordingly, out-of-sector signals, especially those highly correlated with the in-sector signals, can degrade significantly the performance of DOA estimators that rely on the Vandermonde form to work correctly. Recently, we proposed an improved array interpolation approach that takes into account the array response over the full azimuth. In this paper, we develop the idea further and present a simple data-adaptive array interpolation scheme that can provide significantly better accuracy in the DOA estimates. We present examples to demonstrate the effectiveness of our proposal.

Multipath is a major impairment in a wireless communications environment, and channel estimation algorithms are of interest. We propose a superimposed periodic pilot scheme for finite-impulse response (FIR) channel estimation. A simple first-order statistic is used, and any FIR channel can be estimated. There is no loss of information rate but a controllable increase in transmission power. We derive the variance expression of our linear channel estimate and compare with the Cramer-Rao bound. Numerical examples illustrate the effectiveness of the proposed method.

This paper focuses on beamspace DOA estimation in colored noise. We present a beamspace transformation that preserves the Cramer Rao bounds for the parameters of interest, and at the same time interpolates the true array manifold in such a way that a virtual uniform linear array is obtained in beamspace. Besides the computational savings offered by the dimensionality reduction, the virtual array enables the use of computationally simple algorithms such as root-MUSIC or ESPRIT.

A novel approach for signal parameter estimation, named the non-linear instantaneous least squares (NILS) estimator, is proposed and a high SNR statistical analysis of the estimates is presented. The algorithm is generally applicable to deterministic signal in noise models. However, it is of particular interest in applications where the "conventional" non-linear least squares criterion suffers from numerous local minima. The key idea here is to apply a sliding window to estimate the instantaneous amplitude, which is then used in a separable least squares criterion-function. Hereby the radius of attraction of the global minimum is under the control of the user, which makes the NILS approach advantageous to use in practical applications. Numerical results using polynomial-phase signals validate the theoretical results.

A number of methods for blindly separating superimposed digitally modulated signals arriving at an antenna array have been proposed. These techniques are efficient at the up-link (mobile to base) in a mobile communication system. However, for solving the base-to-mobile beamforming problem it may be necessary to also estimate the directions-of-arrival (DOAs) of the various signal paths. We present an optimal decoupled DOA estimation procedure based on information from the blind separation algorithm. Its performance is evaluated in the presence of spatially correlated noise and array modeling errors. The proposed technique has computational advantages as compared to traditional DOA estimation, because the different signal waveforms are treated in a separated fashion. Yet, the decoupled approach is shown to be substantially less sensitive to modeling errors and interference.

The problem of estimating the parameters of several wavefronts from the measurements of multiple sensors is often referred to as array signal processing. The maximum likelihood (ML) estimator in array signal processing for the case of non-coherent signals has been studied extensively. The focus here is on the ML estimator for the case of stochastic coherent signals which arises due to, for example, specular multipath propagation. We show the very surprising fact that the ML estimates of the signal parameters obtained by ignoring the information that the sources are coherent, coincide in large samples with the ML estimates obtained by exploiting the coherent source information. Thus, the ML signal parameter estimator derived for the non-coherent case (or its large-sample realizations such as MODE os WSF) asymptotically achieves the lowest possible estimation error variance (corresponding to the coherent Cramer-Rao bound).

The paper describes and analyses, in a unifying manner, the spatial and temporal IV-SSF (instrumental variable-signal subspace fitting) approaches recently proposed for array signal processing in colored noise fields. We derive a general, optimally-weighted, IV-SSF direction estimator and show that this estimator encompasses the UNCLE estimator of Wong and Wu (see IEEE Trans.SP, vol.42, Sept. 1994), which is a spatial IV-SSF method; and the temporal IV-SSF estimator of Viberg, Stoica and Ottersten (see IEEE Trans.SP, May 1995). The latter two estimators have seemingly different forms, so their asymptotic equivalence shown in this paper comes as a surprising unifying result.

Most methods for sensor array signal processing require the covariance matrix of the background noise to be known. Various techniques for overcoming this limitation have recently been proposed. While most of these are based on assumptions on the noise, we present herein an alternative approach based on partial knowledge of the signals. Methods yielding minimum variance estimates for the model in question are presented and analyzed.>

With few exceptions, high-resolution source localization algorithms require an exact characterization of the array, including knowledge of the sensor positions, sensor gain/phase response, mutual coupling, and receiver equipment effects. In practice, all such information is inevitably subject to errors. Recently, several different methods have been proposed for alleviating the inherent sensitivity of parametric methods to such modeling errors. The technique proposed herein is related to the class of so-called auto-calibration procedures, but it is assumed that certain prior knowledge of the array response errors is available. The optimal maximum a posteriori (MAP) estimator for the problem at hand is formulated, and a more computationally attractive large-sample approximation is derived. In addition, the performance advantage of the algorithm is illustrated by an example involving a linear array mounted on a flexible structure.>

Proposes a novel approach for separating and estimating multiple co-channel digital signals using an antenna array. The spatial response of the array is unknown. The authors exploit the temporal structure of the digital signals to simultaneously determine the array response and the bit sequence for each signal. Uniqueness of the estimates is established for signals with BPSK modulation format. This new approach is applicable to an unknown array geometry and propagation environment, which is particularly useful in digital mobile communications. Simulation results demonstrate its promising performance.>

Many practical applications of signal processing require accurate determination of signal parameters from sensor array measurements. Most estimation techniques are sensitive to errors in the array response model. Thus, reliable array calibration schemes are of great importance. A paradigm for generating an array model from noise corrupted calibration vectors is developed. The key idea is to use a local parametric model of the sensor responses. The potential improvement using the suggested scheme is demonstrated on real data collected from a full-scale hydroacoustic array.>

Signal parameter estimation from sensor array data is of great interest in a variety of applications, including radar, sonar, and radio communication. A large number of high-resolution (i.e., model-based) techniques have been suggested in the literature. The vast majority of these require knowledge of the spatial noise correlation matrix, which constitutes a significant drawback. A novel instrumental variable (IV) approach to the sensor array problem is proposed. By exploiting temporal correlatedness of the source signals, knowledge of the spatial noise covariance is not required. The asymptotic properties of the IV estimator are examined, and an optimal IV method is derived. Simulations are presented examining the properties of the IV estimators in data segments of realistic lengths.>

The application of adaptive antenna techniques to increase the channel capacity in mobile radio communication is discussed. Directional sensitivity is obtained by using an antenna array at the base station, possibly both in receiving and transmitting mode. A scheme for separating several signals at the same frequency is proposed. The method is based on high-resolution direction finding following by optimal combination of the antenna outputs. Comparisons to a method based on reference signals are made. Computer simulations are carried out to test the applicability of the technique to scattering scenarios that typically arise in urban areas. The proposed scheme is found to have great potential in rejecting cochannel interference, albeit at the expense of high computational requirements.>

Signal parameter estimators which are less sensitive to perturbations in the array manifold are presented. A parametrized stochastic model for the array uncertainties is introduced. The unknown array parameters can include the individual gain and phase responses of the sensors as well as their positions. Based on this model, a maximum a posteriori (MAP) estimator is formulated. This results in a fairly complex optimization problem which is computationally expensive. The MAP estimator is simplified by exploiting properties of the weighted subspace fitting method. An approximate method that further reduces the complexity is also presented, assuming small array perturbations. A compact expression for the MAP Cramer-Rao bound (CRB) on the signal and array parameter estimates is derived. A simulation study indicates that the proposed robust estimation procedures achieve the MAP-CRB even for moderate sample sizes.>

Methods for estimating the parameters of narrowband signals arriving at an array of sensors are analyzed. Asymptotic results for several estimators have recently appeared in the literature. With few exceptions, the previous analysis requires the incident signal waveforms to be Gaussian random variables. These results are shown to be valid under much more general conditions, i.e. the actual distribution of the signal waveforms does not affect the asymptotic properties of the parameter.>

The problem of estimating signal parameters from sensor array data is addressed. If the array is composed of two identical subarrays, (i.e. one invariance) the ESPRIT algorithm is known to yield parameter estimates in a very cost efficient manner. Recently, the total least squares (TLS) version of ESPRIT has been formulated in a subspace fitting framework. In this formulation, the ESPRIT concept is easily generalized to arrays exhibiting more than one invariance. The asymptotic properties for this class of algorithms are derived. The estimates are shown to be statistically efficient under certain assumptions. The case of a uniform linear array is studied in more detail, and a generalization of the ESPRIT algorithm is proposed by introducing row weighting of the subspace estimate.>

The problem of estimating signal parameters from sensor array measurements is addressed. A general multidimensional signal subspace method, called the weighted subspace fitting (WSF) method, is proposed. The relationship of WSF to other signal subspace methods as well as the relation to the deterministic maximum-likelihood (ML) method is discussed. The asymptotic properties of WSF are presented for a general weighting. This result includes the properties of ML as a special case. The weighting that minimizes the estimation error covariance is given, resulting in a method that always outperforms ML. A numerical example is presented, demonstrating that the optimally weighted WSF method can give notably lower variance for highly correlated signals. Simulations are included to substantiate the analysis.>

The author investigates the optimal output SNR (signal-to-noise ratio) for adaptive arrays. His main interest is what happens in the case of coherent multipath propagation. He casts the multipath into a statistical framework and derives an approximation of the average optimal SNR. He concludes that the average optimal SNR increases linearly with the number of reflections of the desired signal. The reason for this is that it is possible for the receiver to take advantage of the extra desired signal energy that comes from the reflections.>