Few-shot segmentation (FSS) tackles data scarcity using multiple priors, but its simplicity limits handling base and novel classes with limited data access. Generalized few-shot semantic segmentation (GFSS) enhances model performance for base classes with abundant data, while novel classes have limited data access, improving generalization with scarce data. Building on the design of query-based segmentation models, which decouple the mask and classification tasks for individual optimization, we here present the Dynamic Category Queries Transformer (DCQ-Former) which forms a novel approach to the GFSS. The proposed DCQ-Former first uses category suggested dynamic queries to perform mask segmentation and category classification tasks on a large amount of base class data. Considering the case when the novel classes only have access to a limited amount of training data, the queries for the novel classes are instead dynamically composed from the base classes in order to prevent the category suggested module from providing limited suggestion queries given the representativeness of the fewshot samples. Extensive experiments on COCO-20iand Pascal-5idatasets show that DCQ-Former achieves superior accuracy and generalization than current state-of-the-art methods. Our code are available at https://github.com/fallpavilion/DCQ-Former.

Many forms of small-size radars suffer from minor clock oscillations due to crystalline impurities in their internal clocks, causing time-varying clock offsets between the transmitter and the receivers. This offset causes a bias and an increased variance in the positioning of close-range targets, limiting the achievable accuracy well beyond the expected estimation limitations. This performance is further limited by the commonly used approximative model ignoring the curvature of the impinging wavefront. This is done to allow for computationally efficient estimators, but notably reduces the performance for close-range reflectors. In this paper, we introduce a computationally efficient and robust direction of arrival estimator that partly allows for these model mismatches, significantly improving the estimation performance for close-range reflectors as compared to traditional estimators.

The estimation of impulse responses (IRs) is fundamental to various audio applications, including active noise control, telecommunication, and sound zone control. Despite its long history, estimating impulse responses remains challenging when dealing with short signals and with signals having poor spectral excitation. However, in many applications the source is moving such that one has access to several input-output signal pairs corresponding to closely spaced source positions. Intuitively, exploiting this spatial proximity when jointly estimating the full set of IRs should allow for improved estimation performance. In this work, we propose to leverage the information shared between the closely spaced source positions by means of an optimal transport regularizer when estimating IRs from noisy input-output relations. In particular, the proposed transport formulation allows for modeling shifts in time-delays, corresponding to the IR filter taps, caused by the spatial displacement. The method is validated through numerical experiments using a real voice recording as input signal, demonstrating its superior performance in the challenging scenario.

This paper develops an adaptive grid two-dimensional (2-D) direction-of-arrival (DOA) estimation technique employing an integrated dictionary framework. In contrast to using a traditional grid over distinct angles, combined angle information atoms are adopted to construct the dictionary, using a SPICE-based noise power estimate to select the active regions. To compensate for off-grid bias effects, a 2-D dictionary learning procedure is proposed, which is able to yield accurate estimates even in single snapshot scenarios without assuming information about the number of sources. Numerical simulations illustrate the preferable performance of the proposed method as compared to recent 2-D DOA estimation algorithms.

Current research on lithium-ion battery state of health (SOH) estimation predominantly focuses on a single battery, not on an entire battery pack, which makes these methods inadequate for describing the SOH of energy systems that work in real-life situations. Furthermore, the current SOH estimation methods using graph neural networks (GNNs) inherently adopt suboptimal graph construction approaches, which makes them fail to accurately extract the most pertinent spatial dependencies, resulting in a reduction in prediction accuracy. In this work, we propose a pioneering GNN-based model to predict battery pack SOH. Specifically, an optimal graph structure, learned by the introduced optimal graph extractor, is used to capture the feature dependencies that are most appropriate for the downstream SOH prediction task. Additionally, a Graph Attention network (GAT) and a Gated Recurrent Unit (GRU) are employed to learn the spatial and temporal features, respectively. Lastly, we introduce an effective and simple spatio-temporal feature fusion module to ensure that the extracted features are fully utilized, and the fused features are then used for prediction. Experimental results on the NASA and CALCE datasets validate the superiority of the proposed approach over existing state-of-the-art methods for battery SOH estimation.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affected people. Besides pulmonary function failure, another harmful problem of COPD is the systemic effects, e.g., heart failure or voice distortion. However, the systemic effects of COPD might provide valuable information for early detection. In other words, symptoms caused by systemic effects could be helpful to detect the condition in its early stages. OBJECTIVE: The proposed study aims to explore whether the voice features extracted from the vowel "a" utterance carry any information that can be predictive of COPD by employing Machine Learning (ML) on a newly collected voice dataset. METHODS: Forty-eight participants were recruited from the pool of research clinic visitors at Blekinge Institute of Technology (BTH) in Sweden between January 2022 and May 2023. A dataset consisting of 1246 recordings from 48 participants was gathered. The collection of voice recordings containing the vowel "a" utterance commenced following an information and consent meeting with each participant using the VoiceDiagnostic application. The collected voice data was subjected to silence segment removal, feature extraction of baseline acoustic features, and Mel Frequency Cepstrum Coefficients (MFCC). Sociodemographic data was also collected from the participants. Three ML models were investigated for the binary classification of COPD and healthy controls: Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB). A nested k-fold cross-validation approach was employed. Additionally, the hyperparameters were optimized using grid-search on each ML model. For best performance assessment, accuracy, F1-score, precision, and recall metrics were computed. Afterward, we further examined the best classifier by utilizing the Area Under the Curve (AUC), Average Precision (AP), and SHapley Additive exPlanations (SHAP) feature-importance measures. RESULTS: The classifiers RF, SVM, and CB achieved a maximum accuracy of 77 %, 69 %, and 78 % on the test set and 93 %, 78 % and 97 % on the validation set, respectively. The CB classifier outperformed RF and SVM. After further investigation of the best-performing classifier, CB demonstrated the highest performance, producing an AUC of 82 % and AP of 76 %. In addition to age and gender, the mean values of baseline acoustic and MFCC features demonstrate high importance and deterministic characteristics for classification performance in both test and validation sets, though in varied order. CONCLUSION: This study concludes that the utterance of vowel "a" recordings contain information that can be captured by the CatBoost classifier with high accuracy for the classification of COPD. Additionally, baseline acoustic and MFCC features, in conjunction with age and gender information, can be employed for classification purposes and benefit healthcare for decision support in COPD diagnosis. CLINICAL TRIAL REGISTRATION NUMBER: NCT05897944.

This work investigates the design of beampatterns for frequency diverse arrays-multiple-input multiple-output (FDA-MIMO) in the range-angle plane, in order to improve the approximation of a desired beampattern. Recognizing that the energy radiated by the array cannot be locked at a fixed range and angle, the beampattern is designed for the equivalent beampattern at the receiving end, differing from the traditional emphasis on the transmit beampattern. The proposed scheme formulates the beampattern design as a minimization problem, where the cost function is defined as the squared error between the designed and the given beampatterns. The developed scheme is then implemented through an optimal selection of both the FDA frequency offsets and the receiver steering weights. To this end, an iterative algorithm with monotonic convergence is introduced to solve the resulting non-convex optimization problem involving a fourth-order polynomial objective function as well as multiple non-convex constraints. Numerical simulations verify the performance of the algorithm and show that the proposed scheme can efficiently concentrate the energy of the echo signal on the desired region in the range-angle plane.

The separation of multiple transmit waveforms with time and frequency synchronization constitutes a considerable challenge for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) systems. It is well-known that aliased signal returns may be separable by digital beamforming (DBF) on receive in elevation. However, the current orthogonal-waveform beamforming schemes significantly increase the hardware complexity. Moreover, the direction of arrival (DOA) mismatch issue caused by topographical variations significantly increases the complexity of the DBF process. To alleviate these issues, we here introduce a multiple-subpulse separation and weighting synthesis (MSS-WS) echo separation framework, which is formed using segmented phase coding (SPC) waveforms. The proposed MSS-WS scheme can halve the number of interferences from far arrival angles, allowing for a reduction of the system complexity. In addition, constrained/generalized least absolute shrinkage and selection operator (LASSO) regression is exploited to form the beamformer with relatively high robustness in terms of dealing with the presence of topographical variations. The so-called LASSO-based dynamic beam response (LASSO-DBR) technique introduced here contains two parts: the source localization and the beamforming based on the designed constraint matrices. In this respect, the proposed LASSO-DBR beamformer can produce a distortionless response to the desired signal and still yield wide nulls for the unwanted interferences. Using numerical simulations, we illustrate the feasibility and performance of the proposed MSS-WS framework using the LASSO-DBR beamforming technique.

In this work, we introduce a novel approach for designing the transmit frequency offset scheme based on Cramér-Rao lower bound (CRLB) minimization for a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. The problem originates in non-uniform FDA radar where each frequency offset scheme derives from a specific mathematical model, but where no optimization is conducted with respect to the frequency offset design. We propose two frequency selection schemes for FDA-MIMO radar based on A-optimal minimization, both incorporating a priori knowlegdge. The worst case optimal FDA-MIMO (OFDA-MIMOW) radar forms the frequency selection by minimizing the CRLB over a grid of the unknown parameters, whereas the Bayesian optimal FDA-MIMO (OFDA-MIMO-B) radar instead minimizes the Bayesian CRLB (BCRLB). The performances of the methods are evaluated using simulated data and compared to other common frequency selection schemes.

In this paper, we consider real-time modelling of an underwater acoustic channel impulse response (CIR), exploiting the inherent structure and sparsity of such channels. Building on the recent development to model acoustic channels using a Kronecker structure, we propose a sparse block updating conjugate gradient algorithm. The method initially compensate for the drift common in underwater measurements, to allow the CIR to be modelled as sparse, and then forms a first sparse structured update of the CIR. Should the signal-to-noise ratio of the measurement be high enough to allow for it, the estimate is then refined by relaxing first the assumed Kronecker structure, and then, if still improving the estimate, the sparsity assumption. The proposed method is evaluated using both simulated and measured underwater signals, clearly illustrating the preferable performance of the proposed method.

This work is concerned with determining optimal sensor placements that allow for an accurate location estimate of structured signal sources, taking into account the expected location areas and the typical range of the parameters detailing the signals. In the presentation, we illustrate the technique for tonal sound signals, exploiting the expected harmonic structure of such signals. To determine preferable sensor placements, we propose a computationally efficient scheme that minimizes theoretical lower bounds on the variance of the location estimate over the possible sensor placements, while taking into account the expected variability in the impinging signals, and introducing various forms of constraints on the optimization. Numerical examples and real measurements illustrate the performance of the proposed scheme.

The Polar Format Algorithm (PFA) is a popular choice for general bistatic synthetic aperture radar (BiSAR) imaging due to its computational efficiency and adaptability to situations with complicated geometries or arbitrary flight trajectories. However, efficient and accurate compensation of two-dimensional (2-D) residual phase errors induced by the wavefront curvature remains challenging when obtaining high quality BiSAR PFA images. In this paper, an analytical expression for the phase errors in the wavenumber domain is derived. With it, the inherent structural characteristics of the phase errors are formulated, where the 2-D phase errors can be reduced to one-dimensional (1-D) phase errors for an optimal coordinate system. Moreover, the mapping relationship between distorted point targets in the optimal imaging coordinates and their actual point targets in the original imaging coordinates is investigated. By exploiting the structural characteristics and the mapping relationship, a highly efficient BiSAR PFA wavefront curvature compensation method is proposed. This allows a reduced-dimensional space-variant filter to be constructed to mitigate the 2-D defocusing effect without compromising the compensation accuracy, with the distortion correction being performed using interpolation. The proposed method significantly reduces the complexity required for residual 2-D phase error compensation while simplifying the distortion correction, resulting in a notable robustness. The effectiveness of the method is demonstrated using point target and scene target simulations.

The problem of echo separation using digital beamforming (DBF) on receive for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is of notable importance to allow for practical systems. Regrettably, current DBF-MIMO-SAR schemes, such as the short-term shift-orthogonal (STSO) scheme, are computationally cumbersome, increasing the required hardware complexity. To alleviate this problem, we here propose an improved echo separation solution for realizing a low-cost MIMO-SAR system. We detail a generic waveform design scheme as well as optimized monostatic radar waveforms (e.g., nonlinear frequency modulation (NLFM) signal) showing how these can be directly adopted in the proposed scheme to improve the imaging performance. The proposed scheme enables the number of the interference segments generated by unmatched waveforms to be halved by the use of the fast time spectrum preprocessing and segment synthesis, dramatically simplifying the array configuration and reduces the system complexity. By exploiting inter-pulse phase coding techniques, the proposed method can provide a reconfigurable waveform transmitting scheme, allowing the system resources in range frequency, elevation space, and Doppler domains to be jointly exploited for the separation of aliased signal returns. The proposed scheme is evaluated using extensive numerical and measured data sets, demonstrating the feasibility and potential of the proposed method for resource-limited spaceborne/airborne MIMO-SAR systems.

This paper addresses the detection and estimation problems of a distributed multiple-input and multiple-output (MIMO) radar system with synchronization errors. In such cases, the outputs of all waveform-specific matched filters contain errors in the resulting time delay estimates, which will cause biases in the corresponding estimation of the active range cells. To overcome the impact resulting from the presence of such errors, a joint robust detection and estimation framework is introduced. We first propose an extended generalized likelihood ratio test (GLRT) detector exhibiting the constant false alarm rate property to robustly detect multi-channel misaligned data by extending the matching range cells among multiple channels, on which a Radon-Fourier transform is employed to coherently accumulate the response of potentially moving targets. Then, a clustering algorithm is employed to obtain the unique time delays and Doppler shifts for each channel from the redundant results generated by the detector. Finally, we estimate the target locations and their velocities using the estimated multi-channel time delays and Doppler shifts using a weighted least squares formulation, which is reformulated as a convex optimization problem in order to allow for an efficient solution. Both Numerical and experimental results demonstrate the performance and the robustness of the proposed framework as compared to other recent approaches.

The sparse iterative covariance estimation (SPICE) algorithm is promising for hyperparameter-free sparse source location for time-division-multiplexing multiple-input multiple-output (TDM-MIMO) radar systems, with well-documented merits in resolution enhancement and sidelobe suppression. Regrettably, the method typically requires a large number of iterations to converge, each requiring high-dimensional matrix operations, rendering the existing batch SPICE method impractical and expensive to implement in hardware when dealing with massive TDM-MIMO observations. In order to enable real-time processing, this paper presents a sub-aperture-recursive (SAR) SPICE method, allowing for recursively refining the location parameters for each received (RX) block observation that becomes available sequentially in time. The proposed method not only offers the same benefits as the batch SPICE method, but also allows for a computationally efficient online processing, without the need for high-dimensional matrix operations, notably reducing the required hardware resources as well as processing time. We further present a high-throughput architecture for the resulting method on a XCZU15EG-FFVB1156 field-programmable gate array (FPGA). In combination with simulation results, we demonstrate the effectiveness through experimental data measured by a cascaded MIMO radar system with 12 transmit (Tx) and 16 Rx antennas, demonstrating that the computational time of resolving closely spaced sources on 256 predefined grid points can be processed in merely 12 ms.

Variational nonlinear chirp mode decomposition (VNCMD) is a recently introduced method for nonlinear chirp signal decomposition that has aroused notable attention in various fields. One limiting aspect of the method is that its performance relies heavily on the setting of the bandwidth parameter. To overcome this problem, we here propose a Bayesian implementation of the VNCMD, which can adaptively estimate the instantaneous amplitudes and frequencies of the nonlinear chirp signals, and then learn the active dictionary in a data-driven manner, thereby enabling a high-resolution time-frequency representation. Numerical example of both simulated and measured data illustrate the resulting improvement performance of the proposed method.

In this work, we introduce a novel approach for determining a joint sparse spectrum from several non-uniformly sampled data sets, where each data set is assumed to have its own, and only partially known, sampling times. The problem originates in paleoclimatology, where each data point derives from a separate ice core measurement, resulting in that even though all measurements reflect the same periodicities, the sampling times and phases differ among the data sets, with the sampling times being only approximately known. The proposed estimator exploits all available data using a sparse reconstruction framework allowing for a reliable and robust estimation of the underlying periodicities. The performance of the method is illustrated using both simulated and measured ice core data sets.

In this paper, we present a system for extracting tabular information from loosely structured handwritten documents. The system consists of three parts, (i) a u-net like CNN-based method for text detection and segmentation, (ii) a new attention-based method for simultaneous text recognition and classification of word-parts, and (iii) a method for matching the word parts into a tabular structure for each entry. A key contribution is the observation that the new attention-based recognition and classification module makes it possible for improved spatial analysis of the tabular information. The method is evaluated on a unique historical document: The Swedish Wealth Tax of 1571, consisting of 11,453 pages of hand-written tax records. The evaluation shows that the system provides a significant improvement to the state-of-the-art to the problem of tabular extraction from loosely structured historical documents.

The phase gratitude algorithm (PGA) is a robust autofocusing approach that can efficiently refocus defocused SAR imagery produced by frequency-domain algorithms. However, from a conventional viewpoint, PGA cannot be extended to refocus SAR imagery produced by time-domain algorithms, such as the Filtered Back Projection (FBP), as the spectrum of the FBP imagery is range ambiguous and azimuth space-variant. In this letter, a novel interpretation of FBP is presented, in which the spectrum structure of the FBP imagery is analyzed in detail. By incorporating the derived spectral information, an efficient spectrum preprocessing is proposed for spectrum restructuring. After this preprocessing, PGA is shown to be able to refocus defocused FBP imagery. The validity and feasibility of the proposed autofocusing approach are demonstrated using both simulated and experimental data.

The generalized sparse iterative covariance-based estimation ($q$-SPICE) algorithm was recently introduced for scanning radar applications, resulting in substantial improvements in the angular resolution and quality of the processed images. Regrettably, the computational complexity and storage cost are high and quickly increase with growing data size, limiting the applicability of the estimator. In this letter, we strive to alleviate this problem, deriving a beam-updating$q$-SPICE algorithm, allowing for efficiently updating of the sparse reconstruction result for each online radar measurement along the scanned beam. The resulting method is a regularized extension of the current online$q$-SPICE implementation, which not only offers constant computational and storage cost, independent of the data size, but also provides enhanced robustness over the current online$q$-SPICE. Our experimental assessment, conducted using both simulated and real data, demonstrates the advantage of the beam-updating$q$-SPICE method in the task of sparse reconstruction for scanning radar.

The filtered backprojection (FBP) algorithm is viewed as a preferred candidate for general bistatic synthetic aperture radar (BiSAR) imaging since it does not pose any restrictions on SAR configurations or flight paths. However, high-efficient autofocus methods such as phase gradient autofocus (PGA) or Mapdrift (MD) cannot be effectively integrated with the FBP algorithm due to the unknown properties of the BiSAR FBP imagery spectrum. In this article, a novel Fourier-based interpretation of the BiSAR FBP algorithm is presented. Based on the new viewpoint, spectral characteristics of the BiSAR FBP imagery in the wavenumber domain, including range spectral ambiguity, space-variant spectral support, and the structural 2-D phase error, are derived in detail. Using these characteristics, a computationally efficient 2-D autofocus approach is proposed. First, a preprocessing is performed to eliminate the range spectral ambiguity and to align the skewed spectrum support, which facilitates the following phase error estimation and correction. Then, an estimation of the 1-D azimuth phase error (APE) is applied by combining multiple estimation results from different subband data. Finally, the 2-D phase error is computed directly from the estimated APE by exploiting the derived analytical structure of the 2-D phase error, which is then applied to restore the BiSAR FBP image. The simulation results are presented to show the effectiveness of the proposed approach.

In this paper, we propose a novel solution to detect multiple targets using a distributed multiple-input multiple-output (MIMO) radar under the so-called “defocused transmit-defocused receive” operating mode. The proposed method employs a grid-based data matching algorithm, aiming to associate the target responses to potential target locations, solving the resulting data puzzle that evaluates the various cells under test (CUTs) in the surveillance area resulting from the intertwined range cells across in all transmit-receive channels. Sketchily, the approach divides the surveillance area into identically interlocking and analytically expressible grid cells, and then selects the grid cells with the best fitting multi-channel data to be equivalently regarded as the CUTs. Next, the generalized likelihood ratio test (GLRT) detector is derived to test for target presence in each of the selected grid cells. A separate procedure is introduced to eliminate the spurious “shadow targets”, false alarms occurring in the grid cells without target while sharing range cells with the targets. The essence of this procedure is to find the source of the observed contributions to the grid cells whose test statistics exceed their thresholds, and simultaneously to obtain the positions of the targets. The proposed method is evaluated using both numerical simulations and experimental data recorded by five small radars, demonstrating the effectiveness of the proposed technique.

In this work, we present the misspecified Gaussian Cramér-Rao lower bound for the parameters of a harmonic signal, or pitch, when signal measurements are collected from an almost, but not quite, harmonic model. For the asymptotic case of large sample sizes, we present a closed-form expression for the bound corresponding to the pseduo-true fundamental frequency. Using simulation studies, it is shown that the bound is sharp and is attained by maximum likelihood estimators derived under the misspecified harmonic assumption. It is shown that misspecified harmonic models achieve a lower mean squared error than correctly specified unstructured models for moderately inharmonic signals. Examining voices from a speech database, we conclude that human speech belongs to this class of signals, verifying that the use of a harmonic model for voiced speech is preferable.

Most parametric fundamental frequency estimators make the implicit assumption that any corrupting noise is additive, white Gaus-sian. Under this assumption, the maximum likelihood (ML) and the least squares estimators are the same, and statistically efficient. However, in the coloured noise case, the estimators differ, and the spectral shape of the corrupting noise should be taken into account. To allow for this, we here propose two schemes that refine the noise statistics and parameter estimates in an iterative manner, one of them based on an approximate ML solution and the other one based on removing the periodic signal obtained from a linearly constrained minimum variance (LCMV) filter. Evaluations on real speech data indicate that the iteration steps improve the estimation accuracy, therefore offering improvement over traditional non-parametric fundamental frequency methods in most of the evaluated scenarios.

This work presents a method for information fusion in source localization applications. The method utilizes the concept of optimal mass transport in order to construct estimates of the spatial spectrum using a convex barycenter formulation. We introduce an entropy regularization term to the convex objective, which allows for low-complexity iterations of the solution algorithm and thus makes the proposed method applicable also to higher-dimensional problems. We illustrate the proposed method's inherent robustness to misalignment and miscalibration of the sensor arrays using numerical examples of localization in two dimensions.

Stochastic radiation radar (SRR) systems allow for the forming of radar images by transmitting stochastic signals to form the stochastic radiation field and thereby increase the target observation information to achieve high resolution imaging. In this paper, we examine the use of the online SParse Iterative Covariance-based Estimation (SPICE) algorithm to suppress the noise and improve the operational efficiency. The SPICE algorithm is based on a weighted covariance fitting criterion, and has recently been generalized to allow for an improved reconstruction performance. The used online extension can take advantage of echoes non-correlation along time, allowing for updating the imaging result through successive echo sequences. The simulation results verify the superior performance of the resulting estimator as compared to other recent SRR imaging methods.

In this work, we introduce an antenna placement algorithm for indoor radio networks. The algorithm aims to minimize the number of antennas required to provide sufficient coverage in an area of interest, minimizing the cost of equipment and installation work. The optimization algorithm exploits a semi-deterministic model for the most dominant radio paths. Each path is in turn determined with the A* path finding algorithm. Both the proposed antenna placement algorithm and the used indoor radio propagation model are evaluated using real measurements, confirming the efficiency of the method.

In this work, we propose a novel method for interpolation and extrapolation of Toeplitz structured covariance matrices. By considering a spectral representation of Toeplitz matrices, we use an optimal mass transport problem in the spectral domain in order to define a notion of distance between such matrices. The obtained optimal transport plan naturally induces a way of interpolating, as well as extrapolating, Toeplitz matrices. The constructed covariance matrix interpolants and ex-trapolants preserve the Toeplitz structure, as well as the positive semi-definiteness and the zeroth covariance of the original matrices. We demonstrate the proposed method's ability to model locally linear shifts of spectral power for slowly varying stochastic processes, illustrating the achievable performance using a simple tracking problem.

In this paper, we propose a gridless method for estimating an unknown number of fundamental frequencies. Starting with a conventional dictionary matrix, containing sets of candidate fundamental frequencies and their corresponding harmonics, a nonconvex log-sum cost function is formed such that it imposes the harmonic structure and treats every fundamental frequency in the dictionary as a parameter. The cost function is iteratively decreased by minimizing a surrogate function, and, in each iteration, the fundamental frequencies are refined, whereas redundant parameters are omitted from the dictionary. The proposed method is tested on both real and simulated data, showing its preferred performance as compared to other state-of-the-art multipitch estimators.

Recently, the generalized sparse iterative covariance-based estimation algorithm was extended to allow for varying norm constraints in scanning radar applications. In this paper, further to this development, we introduce a wideband dictionary framework which can provide a computationally efficient estimation of sparse signals. The technique is formed by initially introducing a coarse grid dictionary constructed from integrating elements, spanning bands of the considered parameter space. After forming estimates of the initially activated bands, these are retained and refined, whereas nonactivated bands are discarded from the further optimization, resulting in a smaller and zoomed dictionary with a finer grid. Implementing this scheme allows for reliable sparse signal reconstruction, at a much lower computational cost as compared to directly forming a larger dictionary spanning the whole parameter space. Simulation and real data processing results demonstrate that the proposed wideband estimator offers significant computational savings, without noticeable loss of performance.

In this paper, we present a technique for reducing the size of the dictionary in sparse signal reconstruction by formulating an initial dictionary containing elements that spans bands of the considered parameter space. We allow for the use of this banded dictionary in a first-stage estimation procedure, in which large parts of the parameter space is discarded for further analysis, thereby reducing the overall computationally complexity required to allow for a reliable signal reconstruction. We illustrate the presented principle on the problem of estimating sinusoidal components corrupted by white noise.

In this work, we propose a novel multi-pitch estimation technique that is robust with respect to the inharmonicity commonly occurring in many applications. The method does not require any a priori knowledge of the number of signal sources, the number of harmonics of each source, nor the structure or scope of any possibly occurring inharmonicity. Formulated as a minimum transport distance problem, the proposed method finds an estimate of the present pitches by mapping any found spectral line to the closest harmonic structure. The resulting optimization is a convex and highly tractable linear programming problem. The preferable performance of the proposed method is illustrated using both simulated and real audio signals.

Many state-of-the-art multichannel speech enhancement methods rely on second-order statistics of the desired speech signal, the noise signal, or both. Estimation of those are difficult in practice, resulting in a practical performance that is typically much lower than their potential theoretical performance. We propose two multichannel enhancement techniques that instead rely on a model for voiced speech. That is, the proposed methods are driven by the signals' fundamental frequencies, which may be accurately estimated even in noisy scenarios. The first method is designed independently of the microphone array geometry and source position, whereas these are utilized in the second approach. Thereby, we can investigate when to exploit such information in the case of localization errors and violations of the spatial assumptions. Numerical results show that the proposed method is able to outperform competing methods in terms of both output SNRs and PESQ scores.

Given its high specificity, the use of nuclear quadrupole resonance (NQR) spectroscopy allows for a reliable identification and quantification of substances containing quadrupolar nuclei, such as the14N nucleus prevalent in many explosives, medicines, and narcotics. Regrettably, the measured signals are typically weak and suffers from interference signals often being several orders of magnitude stronger than the signal of interest. In this work, we propose a two-channel setup allowing for interference cancellation in applications such as demining. The proposed techniques forms an estimate of the interference using the secondary channel, and then removes it from the primary channel. The improved performance of the resulting detector is illustrated using real measurements of NaNO2.

In this work, we extend the popular sparse iterative covariance-based estimator (SPICE) by generalizing the formulation to allow for different norm constraint on the signal and noise parameters in the covariance model. For any choice of norms, the resulting generalized SPICE method enjoys the same benefits as the regular SPICE method, including being hyperparameter free, although the choice of norm is shown to govern the sparsity in the resulting solution. Furthermore, we show that there is a connection between the generalized SPICE and a penalized regression problem, both for the case were one allows the noise parameters to differ for each sample, and when treating each noise parameter as being equal. We examine the performance of the method for different choices of norms, and compare the results to the original SPICE method, showing the benefits of using the generalized version. We also provide a way of solving the generalized SPICE using a gridless method, which solves a semi-definite programming problem.

Recently, the iterative adaptive approach (IAA) was adopted to allow for the estimation of high-resolution scanning radar images. In this letter, we further develop this approach by introducing a range-recursive IAA (IAA-RR) formulation allowing for a computationally efficient updating of the resulting estimates along range. Besides exploiting the rich matrix structure to mitigate the computational complexity for each iteration, the correlation between adjacent range cells is exploited to accelerate the convergence of the IAA iterations. When an additional range measurement becomes available, further acceleration is available by exploiting the estimates already formed for the adjacent range cells. Compared with the existing fast IAA implementation, the proposed IAA-RR is shown to offer significant computational savings, without noticeable loss in performance. Numerical results illustrate the superior performance of the proposed IAA-RR algorithm.

In this paper, we propose a time-recursive multipitch estimation algorithm using a sparse reconstruction framework, assuming that only a few pitches from a large set of candidates are active at each time instant. The proposed algorithm does not require any training data, and instead utilizes a sparse recursive least-squares formulation augmented by an adaptive penalty term specifically designed to enforce a pitch structure on the solution. The amplitudes of the active pitches are also recursively updated, allowing for a smooth and more accurate representation. When evaluated on a set of ten music pieces, the proposed method is shown to outperform other general purpose multipitch estimators in either accuracy or computational speed, although not being able to yield performance as good as the state-of-the art methods, which are being optimally tuned and specifically trained on the present instruments. However, the method is able to outperform such a technique when used without optimal tuning, or when applied to instruments not included in the training data.

In this work, we propose a computationally efficient algorithm for estimating multi-dimensional spectral lines. The method treats the data tensor's dimensions separately, yielding the corresponding frequency estimates for each dimension. Then, in a second step, the estimates are ordered over dimensions, thus forming the resulting multidimensional parameter estimates. For high dimensional data, the proposed method offers statistically efficient estimates for moderate to high signal to noise ratios, at a computational cost substantially lower than typical non-parametric Fourier-transform based periodogram solutions, as well as to state-of-the-art parametric estimators.

In this paper, we propose a novel method for estimating the locations of near- and/or far-field harmonic audio sources impinging on an arbitrary, but calibrated, sensor array. Using a joint pitch and location estimation formed in two steps, we first estimate the fundamental frequencies and complex amplitudes under a sinusoidal model assumption, whereafter the location of each source is found by utilizing both the difference in phase and the relative attenuation of the magnitude estimates. As audio recordings often consist of multi-pitch signals exhibiting some degree of reverberation, where both the number of pitches and the source locations are unknown, we propose to use sparse heuristics to avoid the necessity of detailed a priori assumptions on the spectral and spatial model orders. The method’s performance is evaluated using both simulated and measured audio data, with the former showing that the proposed method achieves near-optimal performance, whereas the latter confirms the method’s feasibility when used with real recordings.

This work treats the estimation of the chromagram for harmonic audio signals using a block sparse reconstruction framework. Chroma has been used for decades as a key tool in audio analysis, and is typically formed using a Fourier-based framework that maps the fundamental frequency of a musical tone to its corresponding chroma. Such an approach often leads to problems with tone ambiguity, which we avoid by taking into account the harmonic structure and perceptional attributes in music. The performance of the proposed method is evaluated using real audio files, clearly showing preferable performance as compared to other commonly used methods.

In this paper, we introduce a time-frequency spectral estimator for smooth spectra, allowing for irregularly sampled measurements. A non-parametric representation of the time dependent (TD) covariance matrix is formed by assuming that the spectrum is piecewise linear. Using this representation, the time-frequency spectrum is then estimated by solving a convex covariance fitting problem, which also, as a byproduct, provides an enhanced estimation of the TD covariance matrix. Numerical examples using simulated non-stationary processes show the preferable performance of the proposed method as compared to the classical Wigner-Ville distribution and a smoothed spectrogram.

In this work, we examine a computationally efficient block-updating scheme for estimating the spectral content of signals with missing samples. The work is an extension of our recent single-sample data interpolation updating of the Iterative Adaptive Approach (IAA), being reformulated to incorporate blocks of samples. The proposed implementation offers a substantial complexity reduction as compared to earlier presented updating schemes, without sacrificing the quality of the resulting spectral estimates more than marginally (if at all).

In this paper, we propose a novel method to estimate the fundamental frequencies and directions-of-arrival (DOA) of multi-pitch signals impinging on a sensor array. Formulating the estimation as a group sparse convex optimization problem, we use the alternating direction of multipliers method (ADMM) to estimate both temporal and spatial correlation of the array signal. By first jointly estimating both fundamental frequencies and time-of-arrivals (TOAs) for each sensor and sound source, we then form a non-linear least squares estimate to obtain the DOAs. Numerical simulations indicate the preferable performance of the proposed estimator as compared to current state-of-the-art methods.

The examination of blood flow inside the body may yield important information about vascular anomalies, such as possible indications of, for example, stenosis. Current medical ultrasound systems suffer from only allowing for measuring the blood flow velocity along the direction of irradiation, posing natural difficulties due to the complex behaviour of blood flow, and due to the natural orientation of most blood vessels. Recently, a transversal modulation scheme was introduced to induce also an oscillation along the transversal direction, thereby allowing for the measurement of also the transversal blood flow. In this paper, we propose a novel data-adaptive blood flow estimator exploiting this modulation scheme. Using realistic Field II simulations, the proposed estimator is shown to achieve a notable performance improvement as compared to current state-of-the-art techniques.

We consider the problem of sparse modeling of a signal consisting of an unknown number of exponentially decaying sinusoids. Since such signals are not sparse in an oversampled Fourier matrix, earlier approaches typically exploit large dictionary matrices that include not only a finely spaced frequency grid but also a grid over the considered damping factors. The resulting dictionary is often very large, resulting in a computationally cumbersome optimization problem. Here, we instead introduce a novel dictionary learning approach that iteratively refines the estimate of the candidate damping factor for each sinusoid, thus allowing for both a quite small dictionary and for arbitrary damping factors, not being restricted to a grid. The performance of the proposed method is illustrated using simulated data, clearly showing the improved performance as compared to previous techniques.

We study the problem of estimating the fundamental frequencies of a signal containing multiple harmonically related sinusoidal signals using a novel block sparsity representation of the signal model. An efficient algorithm for solving the resulting optimization is devised exploiting an alternating directions method of multipliers (ADMM) formulation of the problem. The superiority of the proposed method, as compared to earlier methods, is demonstrated using both simulated and measured audio signals.

We develop a general robust fundamental frequency estimator that allows for non-parametric inharmonicities in the observed signal. To this end, we incorporate the recently developed multi-dimensional covariance fitting approach by allowing the Fourier vector corresponding to each perturbed harmonic to lie within a small uncertainty hypersphere centered around its strictly harmonic counterpart. Within these hyperspheres, we find the best perturbed vectors fitting the covariance of the observed data. The proposed approach provides the estimate of the fundamental frequency in two steps, and, unlike other recentmethods, involves only a single 1-D search over a range of candidate fundamental frequencies. The proposed algorithm is numerically shown to outperform the current competitors under a variety of practical conditions, including various degrees of inharmonicity and different levels of noise.

In this work, we propose a computationally efficient time-updating algorithm for estimating the spectral content of a signal with missing samples. The algorithm extends earlier work on the topic by formulating a data-interpolation scheme reducing the required complexity to a fraction of the earlier efficient implementation, without resulting in any noticeable loss of performance for even a quite large degree of missing samples.

This paper proposes a novel non-parametric estimator for spectroscopic echo-train signals, termed ETCAPA, to be used as a robust and reliable first-approach-technique for new, unknown, or partly disturbed substances. Exploiting the complete echo structure for the signal of interest, the method reliably estimates all parameters of interest, enabling initial estimates for the identification procedure to follow. Extending the recent dCapon and dAPES algorithms, ETCAPA exploits a data-dependent filter-bank formulation together with a non-linear minimization to give a hitherto unobtained non-parametric estimate of the echo train decay. The proposed estimator is evaluated on both simulated and measured NQR signals, clearly showing the excellent performance of the method, even in the case of strong interferences.

In this work, we propose a novel subspace-based estimator of periodicities in symbolic sequences. The estimator exploits the harmonic structure naturally occurring in symbolic sequences and iteratively forms the estimate of the periodicities using a MUSIC-like formulation. The estimator allows for alphabets of different sizes, but is here illustrated using both simulated and real DNA measurements, showing a notable performance gain as compared to other common estimators.

The emerging concept of cognitive radios offers a way to use the limited radio-spectrum more efficiently by allowing networks and nodes to adaptively vary their parameters. An important element in the successful implementation of cognitive radios is the ability to estimate the varying state of spectrum usage in a wide-band channel quickly and at minimum cost. In this work, we utilize a powerful non-convex optimization approach to provide sparse and unbiased estimates of the spectrum from limited non-uniformly sampled data. Simulation results for a wide-band communication scenario show that the noise floor is significantly reduced compared to other commonly used approaches. This should help in reducing the miss-identification of occupied and vacant sub-bands of the spectrum, being a key requirement in spectrum sensing cognitive radios.

The robust Capon beamformer has been shown to alleviate the problem of signal cancellation resulting from steering vector errors, caused, for example, by calibration and/or angle-of-arrival (AOA) errors, which would, otherwise, seriously degrade the performance of an adaptive beamformer. Here, we examine robust Capon beamforming of multidimensional arrays, where robustness to AOA errors is needed in both azimuth and elevation. It is shown that the commonly used spherical uncertainty sets are unable to control robustness in each of these directions independently. Here, we instead propose the use of flat ellipsoidal sets to control the AOA uncertainty. To also allow for other errors, such as calibration errors, we combine these flat ellipsoids with a higher dimension error ellipsoid. Computationally efficient automatic techniques for estimating the necessary uncertainty sets are derived, and the proposed methods are evaluated using both simulated data and experimental underwater acoustic measurements, clearly showing the benefits of the technique.

This paper presents computationally efficient implementations for Iterative Adaptive Approach (IAA) spectral estimation techniques for uniformly sampled data sets. By exploiting the methods inherent low displacement rank, together with the development of suitable Gohberg-Semencul representations, and the use of data dependent trigonometric polynomials, the proposed implementations are shown to offer a reduction of the necessary computational complexity with at least one order of magnitude. Numerical simulations together with theoretical complexity measures illustrate the achieved performance gain.

Raman spectroscopy is a laser-based vibrational technique that can provide spectral signatures unique to a multitude of compounds. The technique is gaining widespread interest as a method for detecting hidden explosives due to its sensitivity and ease of use. In this letter, we present a computationally efficient classification scheme for accurate standoff identification of several common explosives using visible-range Raman spectroscopy. Using real measurements, we evaluate and modify a recent correlation-based approach to classify Raman spectra from various harmful and commonplace substances. The results show that the proposed approach can, at a distance of 30 m, or more, successfully classify measured Raman spectra from several explosive substances, including nitromethane, trinitrotoluene, dinitrotoluene, hydrogen peroxide, triacetone triperoxide, and ammonium nitrate.

This letter presents computationally efficient time-updating algorithms of the recent Iterative Adaptive Approach (IAA) spectral estimation technique. By exploiting the inherently low displacement rank, together with the development of suitable Gohberg-Semencul (GS) representations, and the use of data dependent trigonometric polynomials, the proposed time-recursive IAA algorithm offers a reduction of the necessary computational complexity with at least one order of magnitude. The resulting complexity can also be reduced further by allowing for approximate solutions. Numerical simulations together with theoretical complexity measures illustrate the achieved performance gain.

In this letter, we consider adaptive detection of a partly known signal corrupted by additive noise and strong interference with support that is only partly known. Assuming a homogeneous environment where the covariance matrix of the additive noise is the same for the primary and secondary data sets, although with the secondary data set also being affected by the interference, we allow for conic uncertainty models for both the signal and interference subspaces, developing a generalized likelihood ratio detector for the signal of interest. Numerical examples indicate that the proposed method offers a notable performance gain as compared to other recent related methods.

Magnitude squared coherence (MSC) is a useful bivariate spectral measure that finds application in a wide variety of fields. In this paper, we develop a nonparametric Capon-based MSC estimator that utilizes a segmented reformulation of the recently introduced iterative adaptive approach (IAA) to provide high resolution MSC spectrum estimates. The proposed estimator, termed segmented-IAA-MSC (or SIAA-MSC, for short), allows for unevenly sampled data as well as for sequences with arbitrarily missing samples. The estimator first uses segmented-IAA to find accurate estimates of the auto- and cross-covariance matrices of the given sequences. These estimates are then used in a Capon-based MSC estimator reformulated to allow for nonuniformly sampled sequences. To achieve higher statistical accuracy, the estimation problem is formulated so as to allow for overlapped segmentation of the available data. The proposed SIAA-MSC estimator is found to yield improved estimates as compared to the more commonly used least squares Fourier transform (LSFT) based MSC estimator.

In this work, we present a novel method for non-invasive identification of liquids, for instance to allow for the detection of liquid explosives at airports or border controls. The approach is based on a nuclear magnetic resonance technique with an inhomogeneous magnetic field, forming estimates of the liquid's spin-spin relaxation time, T2, and diffusion constant, D, thereby allowing for a unique classification of the liquid. The proposed detectors are evaluated using both simulated and measured data sets.

Nuclear quadrupole resonance (NQR) is a solid-state radio frequency (RF) spectroscopic technique that allows for the detection of many narcotics and highly explosive substances. Unfortunately, the practical use of NQR is often restricted by the presence of strong RF interference (RFI). In this letter, extending our recent work on stochastic NQR (sNQR), we propose acquiring signal-of-interest free samples, containing only corrupting signals, and exploiting them to reduce the effects of RFI on conventional NQR (cNQR) measurements. Similar to the sNQR case, the presented detectors are able to substantially outperform previous cNQR detectors when RFI is present.

The problem of fundamental frequency estimation is considered in the context of signals where the frequencies of the harmonics are not exact integer multiples of a fundamental frequency. This frequently occurs in audio signals produced by, for example, stiff-stringed musical instruments, and is sometimes referred to as inharmonicity. We derive a novel robust method based on the subspace orthogonality property of MUSIC and show how it may be used for analyzing audio signals. The proposed method is both more general and less complex than a straight-forward implementation of a parametric model of the inharmonicity derived from a physical instrument model. Additionally, it leads to more accurate estimates of the individual frequencies than the method based on the parametric inharmonicity model and a reduced bias of the fundamental frequency compared to the perfectly harmonic model.

The problem of estimating a spectral representation of damped sinusoidal signals from a gapped data set is of considerable interest in several applications. In this paper, we propose a filterbank approach to provide such an estimate, by first reconstructing the missing data samples assuming that the spectral content of the missing data is similar to that of the available samples, and then forming a spectral representation of the reconstructed data set as a function of frequency and damping. Numerical examples illustrate the benefits of the proposed estimator as compared to currently available methods.

Nuclear quadrupole resonance (NQR) is a radio frequency (RF) spectroscopic technique, allowing the detection of many high explosives and narcotics. In practice, NQR is restricted by the low signal-to-noise ratio of the observed signals, a problem further exacerbated by the presence of strong RF interference (RFI). The current literature focuses on the use of conventional, multiple-pulsed NQR (cNQR) to obtain signals. Here, we investigate an alternative method called stochastic NQR (sNQR), having many advantages over cNQR, one of which is the availability of signal-of-interest free samples. We exploit these samples forming a matched subspace-type detector, able to efficiently reduce the influence of RFI. Further, many of the ideas already developed for cNQR, including providing robustness to uncertainties in the assumed complex amplitudes and exploiting the temperature dependencies of the NQR spectral components, are recast for sNQR. The presented detector is evaluated on both simulated and measured trinitrotoluene (TNT) data.

We address the problem of transmit beamforming under channel uncertainties for a multiuser MIMO system, where both the transmitter and the receiver are equipped with multiple antennas. In transmit beamforming multi-user multiplexing is performed using spatial diversity techniques so that a base station could serve multiple users in the same frequency band enabling a substantial saving in bandwidth utilization. However, such techniques require nearly perfect knowledge of the channel state information at the transmitter, which is generally not available in practise. In this paper, we propose robust spatial multiplexing schemes based on a worst case performance optimization by incorporating imperfect channel state information. In the simulation, we have examined two scenarios. In the first the channel state information is assumed to have Gaussian distribution errors. In the second scenario, we analyze the performance for errors introduced due to a partial channel state information feedback scheme. In both scenarios, the proposed robust scheme outperforms the conventional scheme.

Recently, we proposed using Capon's minimum variance principle to find the fundamental frequency of a periodic waveform. The resulting estimator is formed such that it maximizes the output power of a bank of filters. We present an alternative optimal single filter design and then proceed to quantify the similarities and differences between the estimators using asymptotic analysis and Monte Carlo simulations. Our analysis shows that the single filter can be expressed in terms of the optimal filterbank and that the methods are asymptotically equivalent but generally different for finite length signals.

In this paper, we formulate the multi-pitch estimation problem and propose a number of methods to estimate the set of fundamental frequencies. The methods, which are based on nonlinear least-squares, multiple signal classification (MUSIC) and the Capon principles, have in common the fact that the multiple fundamental frequencies are estimated by means of a one-dimensional search. The statistical properties of the methods are evaluated via Monte Carlo simulations.

This paper investigates the use of data-adaptive spectral estimation techniques for blood velocity estimation in medical ultrasound. Current commercial systems are based on the averaged periodogram, which requires a large observation window to give sufficient spectral resolution. Herein, we propose a novel data-adaptive method to form the blood velocity spectral estimate. The method is evaluated using realistic field II simulations for both steady and unsteady flow. The latter representing the femoral artery with strong tissue interference. The method is compared to the averaged periodogram and a Capon-based estimator. The simulations indicate that the proposed method offer a significant performance gain, suggesting that the frame-rate may be increased dramatically by using adaptive spectral estimation techniques.

In this paper, we extend the one-dimensional Capon-based magnitude square coherence (MSC) spectral estimator, to form two-dimensional Capon- and APES-based MSC spectral estimators. The resulting estimators are found to yield significantly improved estimates as compared to the typical Welch-based estimator. Furthermore, we introduce a computationally efficient time-updating of the presented MSC estimators, exploiting their inherent time-varying displacement structure. The presented updating is found to dramatically lower the computational requirement of reevaluating the MSC spectral estimates.

Nuclear quadrupole resonance (NQR) is a spectroscopic technique that can be used to detect many high explosives and narcotics. Unfortunately, the measured signals are weak, thereby inhibiting the widespread use of the technique. Current state-of-the-art detectors, which exploit realistic NQR data models, assume that the complex amplitudes of the NQR signal components are known, to within a multiplicative constant. However, these amplitudes are typically prone to some level of uncertainty, thus leading to performance loss in these algorithms. Herein, we develop a frequency selective algorithm, robust to uncertainties in the assumed amplitudes, that offers a significant performance gain over current state-of-the art techniques.

In this paper, we present a novel method for joint estimation of the fundamental frequency and order of a set of harmonically related sinusoids based on the multiple signal classification (MUSIC) estimation criterion. The presented method, termed HMUSIC, is shown to have an efficient implementation using fast Fourier transforms (FFTs). Furthermore, refined estimates can be obtained using a gradient-based method. Illustrative examples of the application of the algorithm to real-life speech and audio signals are given, and the statistical performance of the estimator is evaluated using synthetic signals, demonstrating its good statistical properties.

Nuclear quadrupole resonance (NQR) is a radio-frequency technique that can be used to detect the presence of quadrupolar nuclei, such as the$^{14}{\rm N}$nucleus prevalent in many explosives and narcotics. In a typical application, one observes trains of decaying NQR echoes, in which the decay is governed by the spin echo decay time(s) of the resonant line(s). In most detection algorithms, these echoes are simply summed to produce a single echo with a higher signal-to-noise ratio, ignoring the decaying echo structure of the signal. In this paper, after reviewing current NQR signal models, we propose a novel NQR data model of the full echo train and detail why and how these echo trains are produced. Furthermore, we refine two recently proposed approximative maximum-likelihood detectors that enable the algorithms to optimally exploit the proposed echo train model. Extensive numerical evaluations based on both simulated and measured NQR data indicate that the proposed detectors offer a significant improvement as compared to current state-of-the-art detectors.

By exploiting the relative motion between the target and the radar, high-resolution images of moving targets can be produced using inverse synthetic aperture radar (ISAR). Recent studies have shown that accurate ISAR images can be obtained using the non-parametric high-resolution Capon and APES spectral estimators. In this paper, we propose a computationally efficient time-updating of the two-dimensional (2-D) Capon and APES spectral estimators using their inherent time-varying displacement structures. Numerical simulations indicate that the proposed implementation offers a significant reduction in computational complexity as compared to other recent implementations

In this letter, we present a decomposition for sinusoidal coding of audio, based on an amplitude modulation of sinusoids via a linear combination of arbitrary basis vectors. The proposed method, which incorporates a perceptual distortion measure, is based on a relaxation of a nonlinear least-squares minimization. Rate-distortion curves and listening tests show that, compared to a constant-amplitude sinusoidal coder, the proposed decomposition offers perceptually significant improvements in critical transient signals

A well-performing double-talk detection (DTD) algorithm is a vital part of an acoustic echo canceller. However, recent algorithms are typically evaluated using a static time-invariant room acoustic impulse response, omitting a proper treatment of the case when the acoustic echo path is changing. In this work, we introduce a common framework to objectively evaluate how path changes affect the DTD performance. Via extensive numerical simulations, we conclude that the main factor in acoustic path changes affecting the DTD performance, for some of the more common DTD algorithms, is variations in the damping of the echo path.

We present a novel decomposition for sinusoidal audio coding using amplitude modulation of sinusoids via a linear combination of arbitrary basis vectors. The proposed method, which incorporates a perceptual distortion measure, is based on a relaxation of a non-linear least squares minimization. It offers benefits in the modeling of transients in audio signals. We compare the decomposition to constant-amplitude sinusoidal coding using rate-distortion curves and listening tests. Both indicate that, at the same bit-rate, perceptually significant improvements can be achieved using the proposed decomposition.

Nuclear quadrupole resonance (NQR) offers an unequivocal method of detecting and identifying land mines. Unfortunately, the practical use of NQR is restricted by the low signal to noise ratio (SNR), and means to improve the SNR are vital to enable a rapid, reliable and convenient system. In this paper, we develop a non-linear least squares detector exploiting the temperature dependency of the NQR frequencies as a way to enhance the SNR. Numerical simulations on both synthetic and real measured data indicate an excellent performance of the method.

The topic of low computational complexity frequency estimation of a single complex sinusoid corrupted by additive white Gaussian noise has received significant attention over the last decades due to the wide applicability of such estimators in a variety of fields. In this letter, we propose a computationally fast and statistically improved hybrid phase-based estimator that outperforms other recently proposed approaches, lowering the signal-to-noise ratio at which the Crame/spl acute/r-Rao lower bound is closely followed.

We consider estimation of frequency offset (FO) and equalization of a wireless communication channel, within a general framework which allows for different frequency offsets for various multipaths. Such a scenario may arise due to different Doppler shifts associated with various multipaths, or in situations where multiple basestations are used to transmit identical information. For this general framework, we propose an approximative maximum-likelihood estimator exploiting the correlation property of the transmitted pilot signal. We further show that the conventional minimum mean-square error equalizer is computationally cumbersome, as the effective channel-convolution matrix changes deterministically between symbols, due to the multiple FOs. Exploiting the structural property of these variations, we propose a computationally efficient recursive algorithm for the equalizer design. Simulation results show that the proposed estimator is statistically efficient, as the mean-square estimation error attains the Crame/spl acute/r-Rao lower bound. Further, we show via extensive simulations that our proposed scheme significantly outperforms equalizers not employing FO estimation.

Nuclear quadrupole resonance (NQR) offers an unequivocal method of detecting and identifying both hidden explosives, such as land mines, and a variety of narcotics. Unfortunately, the practical use of NQR is restricted by a low signal-to-noise ratio (SNR), and means to improve the SNR are vital to enable a rapid, reliable, and convenient system. In this paper, we introduce a frequency-selective approximate maximum-likelihood (FSAML) detector, operating on a subset of the available frequencies, making it robust to the typically present narrow-band interference. The method exploits the inherent temperature dependency of the NQR frequencies as a way to enhance the SNR. Numerical evaluations, using both simulated and real NQR data, indicate a significant gain in probability of accurate detection as compared to a current state-of-the-art approach.

We present a method for the estimation of possible cell-cyclic elements in mis-sampled microarray data. Accurate assessment of the frequency content of microarray data gives insight into genes which could be cell-cycle regulated. Cell-cycle regulation is one component of the complex network of genetic regulatory processes and is especially relevant to the study of cancer. As cDNA microarray experiments involve human sampling of cell populations, slight variations in the sampling times invariably occur. We propose estimating the frequency content of microarray data using the recent robust Capon estimator, and formulate a suitable uncertainty region over which to minimize. The estimator is shown to yield robust estimates with real microarray data and to identify cell-cyclic genes that elude both the traditional periodogram and the Capon spectral estimator.

In this paper, we examine how one can exploit baseline diversity of a multichannel interferometric SAR system to overcome the layover problem. The problem arises when different height contributions collapse in the same range-azimuth resolution cell, due to the presence of strong terrain slopes or discontinuities in the sensed scene. We propose a multilook approach to counteract the presence of the time-and-space varying amplitude distortion which is due to the extended nature of natural targets; to this purpose we extend a recent relaxation based approach by estimating the interferometric phases using a nonlinear least squares estimation technique that is based on a deterministic modelling of the amplitude distortion.

We present an efficient implementation of the 2-D amplitude spectrum capon (ASC) estimator, denoted the 2-D Burg-based ASC (BASC) estimator. The algorithm, which will depend only on the (forward) linear prediction matrices and the (forward) prediction error covariance matrices, can be implemented using the 2-D fast Fourier transform. To compute the needed prediction matrices, we make use of a recently proposed 2-D lattice algorithm, which computes the linear prediction matrices directly from the multichannel data without first computing the autocorrelation sequence.

This paper considers the problem of estimating the time delays and Doppler shifts of a known waveform received via several distinct paths by an array of antennas. The general maximum likelihood estimator is presented, and is shown to require a 2d-dimensional non-linear minimization, where d is the number of received signal reflections. Two alternative solutions based on signal and noise subspace fitting are proposed, requiring only a d-dimensional minimization. In particular, we show how to decouple the required search into a two-step procedure, where the delays are estimated and the Dopplers solved for explicitly. Initial conditions for the time delay search can be obtained by applying generalizations of the MUSIC and ESPRIT algorithms.