Dynamic contrast-enhanced ultrasound (DCEUS) is an imaging modality for assessing micro- vascular perfusion and dispersion kinetics. However, the presence of speckle noise may hamper the quantitative analysis of the contrast kinetics. Common speckle denoising techniques based on low-rank approximations typically model the speckle noise as white Gaussian noise (WGN) after the log transformation and apply matrix-based algorithms. We address the high dimensionality of the 4D DCEUS data and apply low-rank tensor decomposition techniques to denoise speckles. Although there are many tensor decompositions that can describe low rankness, we limit our research to multilinear rank and tubal rank. We introduce a gradient-based extension of the multilinear singular value decomposition to model low multilinear rankness, assuming that the log-transformed speckle noise follows a Fisher-tippet distribution. In addition, we apply an algorithm based on tensor singular value decomposition to model low tubal rankness, assuming that the log-transformed speckle noise is WGN with sparse outliers. The effectiveness of the methods is evaluated through simulations and phantom studies. Additionally, the tensor-based algorithms' real-world performance is assessed using DCEUS prostate recordings. Comparative analyses with existing DCEUS denoising literature are conducted, and the algorithms' capabilities are showcased in the context of prostate cancer classification. The addition of Fisher-tippet distribution did not improve the results of tr-MLSVD in the in vivo case. However, most cancer markers are better distinguishable when using a tensor denoising technique than state-of-the-art approaches.

In wireless networks, an essential step for precise range-based localization is the high-resolution estimation of multipath channel delays. The resolution of traditional delay estimation algorithms is inversely proportional to the bandwidth of the training signals used for channel probing. Considering that typical training signals have limited bandwidth, delay estimation using these algorithms often leads to poor localization performance. To mitigate these constraints, we exploit the multiband and carrier frequency switching capabilities of wireless transceivers and propose to acquire channel state information (CSI) in multiple bands spread over a large frequency aperture. The data model of the acquired measurements has a multiple shift-invariance structure, and we use this property to develop a high-resolution delay estimation algorithm. We derive the Cramér-Rao Bound (CRB) for the data model and perform numerical simulations of the algorithm using system parameters of the emerging IEEE 802.11be standard. Simulations show that the algorithm is asymptotically efficient and converges to the CRB. To validate modeling assumptions, we test the algorithm using channel measurements acquired in real indoor scenarios. From these results, it is seen that delays (ranges) estimated from multiband CSI with a total bandwidth of 320 MHz show an average RMSE of less than 0.3 ns (10 cm) in 90% of the cases.

In this paper, we focus on the problem of blind joint calibration of multiband transceivers and time-delay (TD) estimation of multipath channels. We show that this problem can be formulated as a particular case of covariance matching. Although this problem is severely ill-posed, prior information about radio-frequency chain distortions and multipath channel sparsity is used for regularization. This approach leads to a biconvex optimization problem, which is formulated as a rank-constrained linear system and solved by a simple group Lasso algorithm. Numerical experiments show that the proposed algorithm provides better calibration and higher resolution for TD estimation than current state-of-the-art methods.

Achieving high resolution time-of-arrival (TOA) estimation in multipath propagation scenarios from bandlimited observations of communication signals is challenging because the multipath channel impulse response (CIR) is not bandlimited. Modeling the CIR as a sparse sequence of Diracs, TOA estimation becomes a problem of parametric spectral inference from observed bandlimited signals. To increase resolution without arriving at unrealistic sampling rates, we consider multiband sampling approach, and propose a practical multibranch receiver for the acquisition. The resulting data model exhibits multiple shift invariance structures, and we propose a corresponding multiresolution TOA estimation algorithm based on the ESPRIT algorithm. The performance of the algorithm is compared against the derived Cramér Rao Lower Bound, using simulations with standardized ultra-wideband (UWB) channel models. We show that the proposed approach provides high resolution estimates while reducing spectral occupancy and sampling costs compared to traditional UWB approaches.

A fast iterative method based on projection onto Krylov subspaces has been proposed for Radio Astronomical (RA) image reconstruction from telescope array measurements. The image formation problem is formulated as a linear least squares (LS) estimation problem by discretizing the Field of View (FoV) of the telescope array into a number of pixels. The ill-posed imaging problem is regularized by the Krylov iterations and the system matrix is prior conditioned by the weights attained from the matched filter beamformed data. The performance of the proposed method is shown based on simulated data from a single station of the the Low Frequency Array Radio Telescope (LOFAR) antenna configuration on a test radio astronomical image. It has been shown that the prior conditioning of the system matrix results in a more accurate image estimate by reducing the artifacts introduced in the empty parts of the image. Furthermore, it was shown that Krylov-based methods fit very well in the context of large scale RA image reconstruction due to their speed and computational benefits.

Image formation using the data from an array of sensors is a familiar problem in many fields such as radio astronomy, biomedical and geodetic imaging. The problem can be formulated as a least squares (LS) estimation problem and becomes ill-posed at high resolutions, i.e. large number of image pixels. In this paper we propose two regularization methods, one based on weighted truncation of the eigenvalue decomposition of the image deconvolution matrix and the other based on the prior knowledge of the "dirty image" using the available data. The methods are evaluated by simulations as well as actual data from a phased-array radio telescope in the Netherlands, the Low Frequency Array Radio Telescope (LOFAR).

A clock synchronization scheme is crucial for obtaining accuracy in time-based positioning systems. Existing clock synchronization schemes are mostly based on a simplified linear clock model, which unfortunately have a poor long-term synchronization accuracy. Assuming a two-way time transfer protocol, we propose a novel clock synchronization algorithm based on a precise physical clock model to realize joint clock synchronization and propagation delay estimation. The Cramer-Rao lower bound (CRLB) is derived for this clock model and shows that the estimation is asymptotically efficient and converges to the lower bound. For long time spans, this model performs better than a linear clock model.

Linear image deconvolution for radio-astronomy is an ill-posed problem. For this reason, a-priori knowledge is crucial for improving the performance of the deconvolution. In this paper we show that combining non-negativity constraints with an upper bound on the magnitude of each pixel in the image can significantly improve the image formation algorithm. We also show that the minimum variance distortionless response (MVDR) dirty image provides the tightest upper bound out of all beamformers. We then show how the LS-MVI image formation algorithm can be reformulated as a preconditioned weighted least squares algorithm. The resulting algorithm can be efficiently solved using the active-set method. The performance of the algorithm is demonstrated in simulation and compared with constrained least squares based on the classical dirty image.

This paper presents the channel measurements performed within a closed metal cabinet at 60 GHz covering the frequency range 57-62 GHz. Two different volumes of an empty metal cupboard are considered to emulate the environment of interest (an industrial machine). Furthermore, we have considered a number of scenarios such as line of sight, non line of sight, and placing absorbers. A statistical channel model is provided to aid short-range wireless link design within such a reflective and confined environment. Based on the measurements, the large- and small-scale parameters are extracted and fitted using the standard log-normal and Saleh-Valenzuela models, respectively. The obtained results are characterized by a very small path loss exponent, a single cluster phenomenon, and a significantly large root-mean-square (RMS) delay spread. The results show that covering a wall with absorber material dramatically reduces the RMS delay spread. Finally, the proposed channel model is validated by comparing the measured channel with a simulated channel, where the simulated channel is generated from the extracted parameters.

Current and future radio telescopes, in particular the Square Kilometre Array (SKA), are envisaged to produce large images (> 108pixels) with over 60 dB dynamic range. This poses a number of image reconstruction and technological challenges, which will require novel approaches to image reconstruction and design of data processing systems. In this paper, we sketch the limitations of current algorithms by extrapolating their computational requirements to future radio telescopes as well as by discussing their imaging limitations. We discuss a number of potential research directions to cope with these challenges.

The automatic identification system (AIS) is a wireless synchronous narrowband communication system for exchanging navigational data through short messages between ships and base stations in the maritime VHF frequency band. The service coverage of each communication cell is limited within the horizon by the line of sight propagation of VHF signals. AIS receivers are put on high buildings besides harbors, lighthouses along coastlines and even on LEO satellites in space to detect signals from more cells. The toughest problem that these receivers are facing is the partial overlapping of messages from multiple cells. Antenna arrays are used in AIS receivers to suppress the interference. A good beamforming algorithm is needed for computing beamformers. Previously, we proposed blind beamforming techniques based on subspace intersection to solve this problem. In this paper, we propose more simple and efficient blind beamforming techniques using the generalized singular value decomposition (GSVD) and its tracking version for the same problem. The proposed algorithms are simulated in an exact dynamic AIS software model, and are tested on real-world AIS signals collected from our experimental hardware. Simulation results show that the proposed algorithms achieve satisfactory performance. Experimental results confirm the effectiveness of the proposed algorithms.

Rigid body localization refers to a problem of estimating the position of a rigid body along with its orientation using anchors. We consider a setup in which a few sensors are mounted on a rigid body. The absolute position of the rigid body is not known, but, the relative position of the sensors or the topology of the sensors on the rigid body is known. We express the absolute position of the sensors as an affine function of the Stiefel manifold and propose a simple least-squares (LS) estimator as well as a constrained total least-squares (CTLS) estimator to jointly estimate the orientation and the position of the rigid body. To account for the perturbations of the sensors, we also propose a constrained total least-squares (CTLS) estimator. Analytical closed-form solutions for the proposed estimators are provided. Simulations are used to corroborate and analyze the performance of the proposed estimators.

In this paper, we jointly solve the problem of transmit antenna selection and zero-forcing (ZF) precoding in a multiple input multiple output (MIMO) system. A new problem formulation is proposed which enables efficient semi-definite programming (SDP) to solve the originally non-convex problem of antenna selection. This has been accomplished by imposing the Group Lasso sparsity promoting term in the precoding design criterium as a convex relaxation of the ℓ0-norm operation. For the selected set of antennas, we then minimize the overall transmit power, subject to a constraint on the maximum achievable throughput. Simulation results reveal the power saving advantage of the proposed algorithm compared to a randomly selected subset of antennas.

A fully asynchronous network with one sensor andManchors (nodes with known locations) is considered in this letter. We propose a novel asymmetrical time-stamping and passive listening (ATPL) protocol for joint clock synchronization and ranging. The ATPL protocol exploits broadcast to not only reduce the number of active transmissions between the nodes, but also to obtain more information. This is used in a simple estimator based on least-squares (LS) to jointly estimate all the unknown clock-skews, clock-offsets, and pairwise distances of the sensor to each anchor. The Cramér-Rao lower bound (CRLB) is derived for the considered problem. The proposed estimator is shown to be asymptotically efficient, meets the CRLB, and also performs better than the available clock synchronization algorithms.

A new peak-to-average power ratio (PAPR) reduction approach for MIMO-OFDM/A is developed based on the well-known constant modulus algorithm (CMA). This combines two ideas: 1) time domain signals from “resource blocks” (consisting of several subcarriers) may be linearly combined using precoding weights, transparent to the receiver; 2) the precoding weights can be designed to minimize the modulus variations of the resulting signal, leading generally to a reduction in PAPR. This technique is compatible with various beamforming modes in single antenna and MIMO systems. Simulation results show a noticeable improvement relative to the Partial Transmit Sequences (PTS) technique with significantly less complexity.

The paper develops a new technique to reduce the peak to average power ratio (PAPR) in OFDM modulation for a MIMO system. The proposed method exploits the eigen-beamforming mode (EM) in MIMO systems which is a common feature in 4th generation standards: WiMAX and LTE. These systems use the same beamforming weights for dedicated pilots and data so the weights are interpreted as a channel effect from the receiver perspective. There is no need to invert the weights at the receiver side since it is compensated for in channel equalization. Beamforming performance depends on the relative phase difference between antennas but is unaffected by a phase shift common to all antennas. In contrast, PAPR changes with the common phase shift. An effective optimization technique based on Sequential Quadratic Programming is proposed to compute the common phase shifts that minimize the PAPR.

Localization and synchronization are critical challenges for a wireless network, which are conventionally solved independently. Recently, various estimators have been proposed to jointly synchronize and localize a node in a static network based on two way communication. In this paper, we present a novel and generic model based on two way communication between nodes, which are in relative motion with respect to each other. Furthermore, for the entire network we propose a closed form Extended Global Least Squares (EGLS) solution to solve for all the unknown clock skews, clock offsets, initial pairwise distances and relative radial velocities using a single clock reference within the network. A new Cramer Rao Bound (CRB) is derived and the proposed fusion center based Extended Global Least Squares (EGLS) solution is shown to be asymptotically optimal.

The coverage of the terrestrial automatic identification system (AIS) is limited to close areas off the coast. Low earth orbit (LEO) satellites can expand the service of AIS to a global range but it brings large variance in Doppler shift, path loss and propagation delay. The communication between ships and LEO satellites becomes asynchronous. The collision of AIS messages from thousands of ground cells results in loss of all collided messages. Previous papers discussed the use of a single user receiver to detect AIS messages under heavy co-channel interference but the problem was never well solved. In this paper, we present a multi-user receiver equipped with an antenna array on LEO satellites, which explores the spatial multiplexing in space detection of AIS messages and significantly improves the detection performance. The proposed receiver performs rank tracking and subspace intersection based on the signed URV decomposition ahead of blind source separation to provide robust separation of user data for single user receivers. The proposed receiver is tested in an exact dynamic AIS model.

For this year's 36th edition of ICASSP, we received 2946 submissions, which is probably an all-time high: it represents an increase of 5% over last year and 12% over two years ago. The overall acceptance rate was 49%. Distributed over the various technical areas, as covered by the Signal Processing Society Technical Committees (TCs), the submission statistics are as follows:

Image reconstruction problems in radio astronomy and other fields like biomedical imaging are often ill-posed and some form of regularization is required. This imposes user specified constraints to the reconstruction process that may produce an undesirable bias to the solution. We propose a data driven model based least squares reconstruction method based on the Karhunen-Loève transform. We show that this constraint stems from intrinsic physical properties of the measurement process and demonstrate the improvement of the method over unregularized least squares reconstruction using actual data from the Low Frequency Array (LOFAR), a phased array radio telescope in the Netherlands.

The class of Schur subspace estimators provides a parametrization of all minimal-rank matrix approximants that lie within a specified distance of a given matrix, and in particular gives expressions for the column spans of these approximants. Unlike previous numerically unstable algorithms, this paper presents a signed URV decomposition (SURV) that efficiently and stably computes the Schur subspace estimator. Given a threshold on the singular values of the data matrix, SURV tracks the orthonormal basis of the principal/minor subspace and the rank of the subspace at the same time exactly with respect to the threshold at a computational complexity of O(m2) per vector update or downdate. SURV is not an iterative method.

This paper introduces an analog preprocessing network (APN) operating in radio frequency (RF), to cancel signals from interferers in an antenna array system. Interference cancellation facilitates the use of low resolution ADCs. For a given ADC resolution, we will propose the optimal beamformer to minimize the overall mean squared error (MSE) between the desired user and its received estimate, while maximizing the desired signal to quantization noise ratio (SQNR). Subsequently, propose a matching pursuit design technique to represent the analytical APN transform as a linear combination of implementable phase shifters.

This paper designs a feedback beamformer (FBB) operating in combination with multiple low resolution ADCs of a MIMO communication system, to cancel the interfering users. Interference cancellation before the ADC operation saves the power spent in digitizing the interferers. We specify the necessary conditions required to cancel the interferers and propose a FBB design technique using only the training information from the desired user. We show using simulation results, that the FBB improves the dynamic range by a factor of three.

Self localization in sensor networks with measurements that include outliers is an important problem. E.g., distance measurements based on non-line-of-sight observations can be quite wrong. If not handled properly, such outliers can greatly influence the positioning accuracy. To achieve robustness we consider positioning with Huber estimators. The Huber cost function interpolates between the lscr1and the lscr2norms. The minimization of the Huber cost function can be efficiently obtained via iterative majorization techniques, with the advantageous property of guaranteed convergence to a local minimum.

In this paper, we derive the Cramér-Rao bound (CRB) for range estimation, which does not only exploit the range information in the time delay, but also in the amplitude of the received signal. This new bound is lower than the conventional CRB that only makes use of the range information in the time delay. We investigate the new bound in an additive white Gaussian noise (AWGN) channel with attenuation by employing both narrowband (NB) signals and ultra-wideband (UWB) signals. For NB signals, the new bound can be 3dB lower than the conventional CRB under certain conditions. However, there is not much difference between the new bound and the conventional CRB for UWB signals. Further, shadowing effects are added into the data model. Several CRB-like bounds for range estimation are derived to take these shadowing effects into account.

Radio frequency identification (RFID) is a technology to wirelessly transmit the identity of tagged objects. For long-range (UHF) systems with multiple tags, the tag replies may overlap. Current solutions are based on collision avoidance using MAC protocols (e.g. slotted ALOHA and binary tree algorithms). This can be a time-consuming process. In this paper, it is shown how an antenna array in combination with blind source separation techniques can be used to separate multiple overlapping tag signals. The source signals are modeled as Zero Constant Modulus (ZCM) signals, and the corresponding ZCM algorithms are tested on synthetic and measured data sets.

Transmit-reference (TR) schemes are commonly used only in low data rate ultra-wideband (UWB) systems because of many restrictions on the pulse spacing, frame and symbol periods (should be longer than the channel length). This paper extends our previous research that tries to remove these restrictions to enable a higher data rate application in a multiuser context. Based on the fact that most UWB channels are highly uncorrelated, we can formulate a CMDA-like signal processing model for an asynchronous multiuser system. Blind and iterative algorithms are derived, of which the performances are compared and verified in simulations.

In multi-user orthogonal frequency division multiplexing (OFDM) systems, an asynchronous interferer is defined as a signal whose cyclic prefix (CP) does not align with the desired user CP. Such ad-hoc multi-user OFDM systems are considered and an algorithm to perform blind space-time source separation is presented. In this paper we consider a scheme where a periodic sequence is added to the transmitted OFDM signal to be able to identify it. The periodic sequence has a cyclostationary property and can be used to estimate the channel and to separate the interfering users from the desired user signals.

Transmit-reference ultra-wideband (TR-UWB) systems are attractive due to their relatively low complexity at both the transmitter and the receiver. Partly, this is achieved by making restrictive assumptions such as a frame length which should be much larger than the channel length. This limits their use to low data rate applications. In this paper, we lift this restriction and allow inter-frame interference FI) to occur. We propose a suitable signal processing data model and corresponding receiver algorithms which take the IFI into account. The performance of the algorithms are verified using simulations.

The LOFAR astronomical array currently under development in the Netherlands produce synthesis images of the most distant (and thus oldest) space objects by observing at unusually low frequencies (30-250 MHz) over a large aperture (100 km) using many antennas (on the order of 10,000). This presents some significant challenges for sensor calibration because at these frequencies Earth's ionosphere acts as a random refractive sheet which over the large aperture induces source direction dependent gain and phase errors that must be estimated and calibrated out. Existing array self calibration algorithms used at higher frequencies by other radio astronomy instruments such as the very large array (VLA) do not address direction dependence and does not work in the LOFAR environment. A new algorithm called "demixed peeling" is presented and assessed as a solution to the direction dependent calibration problem for large distributed sensor arrays

In the design of a packet-oriented impulse-radio UWB communication system, the main challenge at the receiver is to have a fast synchronization to the coded pulses, along with a detection of the message. We consider schemes that are straightforward to implement in practical systems and propose two methods to realize the synchronization algorithms: a serial and a parallel method. The algorithms for synchronization and demodulation are implemented in a receiver prototype based on an FPGA.

A communication system based on transmit-reference (TR) ultra-wideband (UWB) is studied and further developed. Introduced by Hoctor and Tomlinson, the aim of the TR-UWB transceiver is to provide a straightforward impulse radio system, feasible to implement with current technology, and to achieve either high data rate transmissions at short distances or low data rate transmissions in typical office or industrial environments. The main contribution in this paper is the derivation of a signal processing model that takes into account the effects of the radio propagation channel, in particular, for the case where the two pulses in a doublet are closely spaced. Several receivers based on the code-division multiple-access-like properties of the proposed model are derived, and the performance of the algorithms is tested in a simulation.

The performance of extensions of digital subscriber line to higher data rates (VDSL) is limited by the effects of crosstalk among the wires in a bundle. For the downstream, a precoder can compensate for this. Without knowledge of the crosstalk channel matrix, the problem is to compute a suitable precoder based on limited feedback from the receivers. In this letter, an adaptive precoder design algorithm is derived. The algorithm works on a per-tone basis and assumes that each receiver sends back the sign of the symbol error to the transmitter. The algorithm computes the precoder iteratively using this information, without the need to estimate the crosstalk channel as an intermediate step. The asymptotic and acquisition performance is verified by simulations

The problem of estimating the direction-independent gain and phase characteristics of sensor arrays requires a boundary condition to solve the phase ambiguity of the solution. It has become common practice to use the constraint that the phase of the first element of the array is zero. By Cramer-Rao lower bound (CRLB) analysis, we show analytically for calibration on a single point source of an array of identical elements that the variance of the phase estimates decreases by a factor 2p/(p-1) under the boundary condition that the sum of all phases equals zero, where p is the number of elements in the array. We also show that this constraint is the one that among all possible constraints minimizes the total variance on all estimated parameters. Our analysis suggests that this conclusion also holds for arbitrary source models and arrays of nonidentical elements. This statement is confirmed by repeating the CRLB analysis in simulation, showing that the CRLB under this constraint coincides within the numerical accuracy with the minimal CRLB

The delay-hopped transmit-reference ultra wideband communication system introduced by R. Hoctor and H. Tomlinson (see IEEE Conf. Ultra Wideband Systems and Technologies, p.93-8, p.265-70, 2002) results in a receiver based on a bank of correlators and a sliding window integrator. We derive a complete signal processing model for this receiver structure. In particular, we take the effects of the radio propagation channel on the correlations into account, as well as the effects of the additive noise. Blind and semi-blind receiver algorithms are proposed to estimate the effective channel coefficients and the data; the performance of the algorithms is tested in simulations.

With increasing bandwidths and decreasing loop lengths, crosstalk becomes the main impairment in VDSL systems. For downstream communication, crosstalk precompensation techniques have been designed to cope with this issue by using the collocation of the transmitters. These techniques naturally need an accurate estimation of the crosstalk channel impulse responses. We investigate the issue of tracking these channels. Due to the lack of coordination between the receivers, and because the amplitude levels of the remaining crosstalk channels after precompensation are very low, blind estimation schemes are inefficient in this case. So some part of the upstream or downstream bit rate needs to be used to help the estimation. In this paper, we design a new algorithm to try to limit the bandwidth used for the estimation purpose by exploiting the collocation at the transmitter side. The principle is to use feedback from the receiver to the transmitter instead of using pilots in the downstream signal. It is shown by simulations that the proposed estimation is more efficient, in terms of needed overhead, than a classical scheme using pilot symbols.

Radio telescopes based on large phased arrays form an interesting application area for array signal processing. LOFAR is a large low frequency (10-240 MHz) array consisting of 13,000 antennas grouped into 50 stations, currently under construction in the Netherlands. Data from a 60-element test station for LOFAR is available to evaluate the performance of calibration and imaging algorithms. We apply the robust Capon beamformer (RCB) to make images of the sky from measured data, and compare them to classical Fourier-based images. The RCB takes uncertainty in the calibration into account. Instead of the usual spherical uncertainty sets, we have also derived a more constrained uncertainty set specifically for imaging with the RCB. The results are images with a higher dynamic range than classical or Capon beamforming. Additional simulations confirm that the images are more accurate.

The algebraic constant modulus algorithm (ACMA) is a very effective block-algorithm for blind source separation. A particular feature is that it finds beamformers for all sources simultaneously. However, in cases with continuously transmitting sources and varying environments, data-adaptive algorithms may be more appropriate. In this paper, we derive an adaptive version of the ACMA, with a reasonable computational complexity. Simulations indicate that it is reliably tracking beamformers to all sources even in a highly time-varying scenario.

In mobile asynchronous ad hoc networks, multiple users may transmit packets at the same time. If a collision occurs, then in current systems both packets are lost and need to be retransmitted, reducing the overall throughput. To mitigate this, we consider to extend the receiver with a small antenna array, so that it can suppress interfering signals. To characterize the signal of interest, we propose to modulate it at the symbol rate by a known amplitude variation. This allows the corresponding multichannel receiver to estimate the beamformer weights that will suppress the interfering sources. We introduce "known modulus algorithms" to achieve this. We also derive synchronization algorithms to estimate the offset of the desired packet in an observation window, among interfering data packets. The algorithms are illustrated via simulations.

We investigate the separation of a linear mixture of secondary surveillance radar (SSR) replies impinging on an M-element antenna array. At base-band, a received SSR signal consists of a binary sequence with alphabet {0, 1}, modulated by a complex exponential due to the residual carrier frequency. We present two algebraic algorithms to compute the separating beamformers by taking into account the particular modulation format of the received signal.

Radio astronomical observations are increasingly contaminated by RF interference. Assuming an array of telescopes, we have previously considered spatial filtering techniques based on projecting out the interferer array signature vector. In this paper, we consider extending the astronomical array with a reference antenna (or array), and develop spatial filtering algorithms for this situation. The information from the reference antenna improves the quality of the interferer signature vector estimation, hence more of the interference can be projected out. The conditioning of the problem improves as well. The algorithms are tested both on simulated and experimental data.

The complex receiver gains and sensor noise powers of radio telescope arrays are initially unknown and need to be calibrated. This can be accomplished by observing a relatively strong astronomical point source. Here we consider calibration algorithms for the case of dual polarized arrays and formulate new least squares algorithms based on factor analysis, parallel factor analysis, and eigenvalue decompositions. We show that at least three sources with different polarization states are needed to obtain a unique solution for the gains. We also propose a closed form solution which has a performance comparable to the iterative parallel factor method.

Constant modulus algorithms try to separate linear mixtures of sources with modulus 1. We study the identifiability of this problem: the number of samples needed to ensure that in the noiseless case we have a unique solution. For finite alphabet (L-PSK) sources, finite sample identifiability can hold only with a probability close to but not equal to 1. In a previous paper (Leshem, A. et al., Proc. IEEE Workshop on Sensor Array and Multichannel Signal Processing, 2002), we provided a subexponentially decaying upper bound on the probability of non-identifiability. Here, we provide an improved exponentially decaying upper bound, based on Chernoff bounds. We show that, under practical assumptions, this upper bound is much tighter than previously known bounds.

Long-code wideband CDMA receivers are for computational reasons usually based on simple matched-filter techniques, and hence suffer from multiaccess interference. To mitigate this problem, we propose computationally efficient decorrelating RAKE and MMSE receivers for the uplink of long-code CDMA systems. The decorrelating matched filter eliminates multiaccess interference by code matrix inversion. Channel parameters and data symbols are then estimated jointly via rank-one decompositions. The feasibility of the proposed schemes hinges upon their efficient implementation in terms of time-varying state space realizations, with a complexity comparable to that of the conventional RAKE receiver.

We prove that mixtures of continuous alphabet constant modulus sources can be identified with probability 1 with a finite number of samples (under noise-free conditions). This strengthens earlier results which only considered an infinite number of samples. The proof is based on the linearization technique of the analytical constant modulus algorithm (ACMA), together with a simple inductive argument. We then study the finite-alphabet case. In this case, we provide a subexponentially decaying upper bound on the probability of nonidentifiability for a finite number of samples. We show that under practical assumptions, this upper bound is tighter than the currently known bound. We then provide an improved exponentially decaying upper bound for the case of L-PSK signals (L is even).

We investigate spatial filtering techniques for interference removal in multichannel radio astronomical observations. The techniques are based on the estimation of the spatial signature vector of the interferer from short-term spatial covariance matrices followed by a subspace projection to remove that dimension from the covariance matrix, and by further averaging. The projections will also modify the astronomical data, and hence a correction has to be applied to the long-term average to compensate for this. As shown by experimental results, the proposed technique leads to significantly improved estimates of the interference-free covariance matrix.

We consider an asynchronous ad-hoc network with multiple users transmitting packets at the same time. The signal of interest is modulated by a known amplitude variation. This allows the corresponding multichannel receiver to estimate the beam former weights that will suppress the interfering sources. We introduce “known modulus algorithms” (KMAs) to achieve this, and illustrate the, throughput improvements that can be expected.

We investigate spatial filtering techniques for interference removal in multichannel radio astronomical observations. The techniques are based on the estimation of the spatial signature vector of the interferer from short-term spatial covariance matrices followed by a subspace projection to remove that dimension from the covariance matrix, and by further averaging. The projections will also modify the astronomical data, and hence a correction has to be applied to the long-term average to compensate for this. As shown by experimental results, the proposed technique leads to significantly improved estimates of the interference-free covariance matrix.

The implementation of a RAKE receiver for a quasi-synchronous DS-CDMA (direct sequence code division multiple access) system requires the estimation of the dominant path delays for each user. We present a blind method to estimate these parameters. The algorithm takes advantage of various signal space invariances in the frequency domain to isolate the subspace of interest for each user, then uses ESPRIT on these subspaces to estimate the delays. The method processes each user independently of the others, is near-far resistant, and allows several delays per user. Simulations indicate a fair accuracy.

Joint diagonalization problems of Hermitian or non-Hermitian matrices occur as the final parameter estimation step in several blind source separation problems such as ACMA, JADE, PARAFAC, and SOBI. Previous approaches have been Jacobi iteration schemes and alternating projections. Here we show how the joint diagonalization problem can be formulated as a (weighted) subspace fitting problem so that it can be solved using the efficient Gauss-Newton optimization algorithm proposed for that problem. Since a good initial point is usually available, the algorithm converges very fast.

Radio-astronomical observations are increasingly contaminated by interference, and suppression techniques become essential. A powerful candidate for interference mitigation is adaptive spatial filtering. We study the effect of spatial filtering techniques on radio astronomical imaging. Current deconvolution procedures such as CLEAN are shown to be unsuitable for spatially filtered data, and the necessary corrections are derived. To that end, we reformulate the imaging (deconvolution/calibration) process as a sequential estimation of the locations of astronomical sources.

We consider the detection of unknown Gaussian signals received by an array of uncalibrated nonidentical sensors, which is a problem that appears in radio astronomy. The problem is formulated as a test on the covariance structure. The generalized likelihood ratio test (GLRT) for this problem is stated and related to a simpler ad-hoc detector. We compare the method to the conventional multichannel subspace detector and show its robustness to nonidentical channels on data collected with the Westerbork radio telescope.

The fast growth of the wireless communication industry poses severe limitations on radio-astronomical observations. This is largely due to the fact that in radio astronomy, in contrast to communication systems, the signals of interest are many orders of magnitude below the receiver noise power levels. The structure of some communication signals opens the possibility to reduce the effect on radio-astronomical observations using advanced array processing techniques. One such structure is time slots, used in TDMA communication systems such as the Iridium system and the GSM system. We present the results of blanking of time-slotted interfering signals measured at the Westerbork Synthesis Radio Telescope.

High-resolution parameter estimation techniques have been applied to jointly estimate multiple signal parameters. We consider the problem of determining the directions and center frequencies of a number of narrow-band sources in a certain frequency band of interest. We present a joint angle-frequency estimation method, based on the multidimensional ESPRIT algorithm. A perturbation error analysis gives bounds on the parameter estimates, and provides optimal values for the temporal and spatial smoothing parameters. The analysis is shown to be consistent with simulation results.

We study the location of local minima of the finite sample approximation to the constant modulus cost function. This paper concentrates on source separation. The main result is a connection between the number of samples and the probability of obtaining a local minimum of the finite approximation within a given sphere around the local minimum of the CM cost function. The motivations for our study are two problems: equalization of communication signals, and blind separation of a desired signal in multiuser environment. In order to maintain simplicity we focus on the case of blind beamforming which is somewhat simpler to analyze.

The algebraic constant modulus algorithm (ACMA) is a noniterative blind source separation algorithm. It computes jointly beamforming vectors for all constant modulus sources as the solution of a joint diagonalization problem. In this paper we analyze its asymptotic properties and show that (unlike the iterative CMA) it converges to the Wiener solution in samples or SNR. We also sketch its connection to the related JADE algorithm.

Radio-astronomical observations are increasingly contaminated by interference, and suppression techniques become essential. A powerful candidate for interference mitigation is adaptive spatial filtering. We study the effect of spatial filtering techniques on radio-astronomical imaging. Current deconvolution procedures, such as CLEAN, are shown to be unsuitable for spatially filtered data, and the necessary corrections are derived. To that end, we reformulate the imaging (deconvolution/calibration) process as a sequential estimation of the locations of astronomical sources. This not only leads to an extended CLEAN algorithm, but also the formulation allows the insertion of other array signal processing techniques for direction finding and gives estimates of the expected image quality and the amount of interference suppression that can be achieved. Finally, a maximum-likelihood (ML) procedure for the imaging is derived, and an approximate ML image formation technique is proposed to overcome the computational burden involved. Some of the effects of the new algorithms are shown in simulated images.

The analytical constant modulus algorithm (ACMA) is a deterministic array processing algorithm to separate sources based on their constant modulus. It has been derived without detailed regard to noise processing. In particular, the estimates of the beamformer are known to be asymptotically biased. In the present paper, we investigate this bias, and obtain a straightforward weighting scheme that will whiten the noise and remove the bias. This leads to improved performance for larger data sets.

Multi-resolution ESPRIT is an extension of the ESPRIT direction finding algorithm to antenna arrays with multiple baselines. A short (half wavelength) baseline is necessary to avoid aliasing, a long baseline is preferred for accuracy. The MR-ESPRIT algorithm allows to combine both estimates. The same algorithm can be used for multi-resolution frequency estimation, by combining two different sampling frequencies. We show how this can be used to construct a joint angle-frequency estimator.

The class of Schur subspace estimators provides a parametrization of all minimal-rank matrix approximants that lie within a specified distance of a given matrix, and in particular gives expressions for the column spans of these approximants. In this paper, we derive an updating algorithm for an interesting member of the class, making use of a constrained hyperbolic URV-like decomposition.

Deterministic blind beamforming algorithms try to separate superpositions of source signals impinging on a phased antenna array by using the deterministic properties of the signals or the channels such as their constant modulus or directions-of-arrival. Progress in this area has been abundant over the past ten years and has resulted in several powerful algorithms. Unlike optimal or adaptive methods, the algebraic methods discussed in this review act on a fixed block of data and give closed-form expressions for beamformers by focusing on algebraic structures. This typically leads to subspace estimation and generalized eigenvalue problems. After introducing a simple and widely used multipath channel model, the paper provides an anthology of properties that are available, as well as generic algorithms that exploit them.

Zero/constant modulus (ZCM) signals are complex signals for which every sample is either zero or has modulus 1. Such signals arise after imprecise carrier-to-baseband conversion of binary {0,1}-modulated signals, or with intermittent phase-modulated signals. We consider the separation of linear superpositions of such signals using analytic techniques. An application is the separation of multiple partly coinciding aircraft transponder signals (secondary surveillance radar reply signals).

Assuming a multipath propagation scenario, we derive a closed-form subspace-based method for the simultaneous estimation of arrival angles and path delays from measured channel impulse responses, using knowledge of the transmitted pulse shape function and assuming a uniform linear array and uniform sampling. The algorithm uses a two-dimensional (2-D) ESPRIT-like shift-invariance technique to separate and estimate the phase shifts due to delay and direction-of-incidence, with automatic pairing of the two parameter sets. A straightforward extension to the multiuser case allows to connect rays to users as well.

Blind space-time equalization and separation of multi-user digital communication signals presumes that the number of antennas M and the oversampling rate P is sufficiently large to be able to detect the number of sources and all channel lengths, and that the channel matrix is sufficiently well conditioned to allow inversion. A singular value analysis of the channel matrix for bandwidth limited signals provides necessary conditions for sufficient resolution, and guidelines for the selection of suitable M, P and equalizer lengths in relation to the bandwidth.

Singular value analysis of a GMSK-modulated signal such as employed by GSM reveals that it admits a reasonably accurate linear model, enabling the use of linear space-time equalizers to retrieve the data symbols. The analysis also shows that one antenna does not provide sufficient resolution to allow estimation of the channel length, so that the performance of the equalizers is limited. An algorithm is proposed for the blind space-time equalization and separation of multiple co-channel GMSK signals, based on their fixed symbol rate, finite alphabet and constant modulus properties.

The finite alphabet property of digital communication signals, along with oversampling techniques, enables the blind identification and equalization of an unknown FIR channel carrying a superposition of such signals, provided they have the same (known) period. Applied to multi-user wireless communications, the same framework allows the blind separation of multiple finite alphabet signals received at all arbitrary antenna arrays through an unknown multipath propagation environment with finite delay spread. An algorithm is proposed and tested on simulated data.

Using oversampling and the finite-alphabet property of digital communication signals, it is possible to blindly identify an FIR channel carrying a superposition of such signals, provided they have the same (known) period, and certain rank conditions on the data and channel matrices are satisfied. In particular, this technique allows separation of finite alphabet signals, removal of intersymbol interference, and synchronization of the signals. An algorithm is proposed and tested on simulated data.>

A unified approach is presented to the related problems of recovering signal parameters from noisy observations and identifying linear system model parameters from observed input/output signals, both using singular value decomposition (SVD) techniques. Both known and new SVD-based identification methods are classified in a subspace-oriented scheme. The SVD of a matrix constructed from the observed signal data provides the key step in a robust discrimination between desired signals and disturbing signals in terms of signal and noise subspaces. The methods that are presented are distinguished by the way in which the subspaces are determined and how the signal or system model parameters are extracted from these subspaces. Typical examples, such as the direction-of-arrival problem and system identification from input/output measurements, are elaborated upon, and some extensions to time-varying systems are given.>

A number of high-resolution methods used to determine the two-dimensional ( alpha /sub i/, beta /sub i/) directions of arrival (DOAs) of a number of plane waves, impinging on a sensor array are discussed. The array possesses an arbitrary geometry, but consists of triplets or quadruplets of sensors that are identical. The data from these sensors is first processed for each independent dimension using an ESPRIT-like subspace-based method. The correct ( alpha /sub i/, beta /sub i/) pairs are then determined by considering subspace properties that the proposed data would possess in the noise-free case. An algorithm is devised that potentially improves the parameter estimates and yields the correct parameter pairs while avoiding an extensive search over the two separate one-dimensional parameter sets. Resolution is improved by exploiting the spatial dependence of the measurements when sensor quadruplets are used.>

A number of high-speed multiprocessor applications of a fast custom VLSI floating-point pipeline CORDIC processor are presented. The applications are concerned with speech processing, matrix arithmetic, antenna array processing, and computer graphics. The applications are designed using an interactive high-level functional design environment for multiprocessor systems and are implemented on a test bed for high-speed parallel processor arrays.>