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The instantaneous frequency (IF) is a very important feature of nonstationary signals in numerous applications. The first overview of the concept and application of the IF estimators is presented in seminal papers by Boashash. Since then, a significant knowledge has been gained about the performance of the IF estimators. This knowledge has been used not only for development of various IF estimators but also for introduction of novel time–frequency (TF) representations. The IF estimation in environments characterized by low signal-to-noise (SNR) has achieved significant benefits from these theoretical developments. In this paper, we review some of the most important developments in the last two decades related to the concept of the IF, performance analysis of IF estimators, and development of IF estimators for low SNR environments. [ABSTRACT FROM AUTHOR]

Abstract: The steady flow of new research results and developments in the field of adaptive equalization that was witnessed for at least the last four decades is clearly evidenced by the many footprints of success it left behind and shows no sign of ending. The thrust of research and implementation in this field is mainly powered by the use of the well-known mean-square cost function upon which relies the ubiquitous least-mean square (LMS) algorithm. However, such an algorithm is well-known to lead to sub-optimal solutions in the real world that is largely dominated by non-Gaussian interference signals. The use of a non-mean-square cost function would successfully tackle these types of interference signals but would invariably involve a higher computational cost. To address these important practical issues, this paper proposes a new adaptive equalization technique that combines both the least-mean-fourth (LMF) algorithm, which is governed by a non-mean-square cost function, with a power-of-two quantizer (PTQ) in the coefficient update process, which greatly reduces the computational cost involved and which therefore makes the proposed technique applicable to time-varying environments. This paper not only elaborates on the basic idea behind the proposed technique but also defines the necessary assumptions and provides a thorough statistical performance analysis (including a study of the convergence behavior) of the combined algorithm LMF-PTQ that is at the core of the proposed technique. An extensive simulation work was carried out and showed that the theoretical predictions are very well substantiated. [Copyright &y& Elsevier]

Abstract: This paper presents a method that deploys nonuniform sampling and appropriates to it a processing algorithm to monitor the activity of a number of non-overlapping spectral bands. The proposed approach facilitates the use of sampling rates well below the ones demanded by uniform-sampling-based DSP. Randomized sampling scheme, namely random sampling on grid, in conjunction with a periodogram-type spectral analysis tool is utilized to accomplish the task. The statistical characteristics of the endorsed analysis tool are examined for a finite set of nonuniformly distributed signal samples contaminated with noise. General guidelines are provided to ensure the reliability of the adopted sensing technique where it is affirmed that the sampling rates can be arbitrarily low. The additional requirements on such recommendations imposed by the presence of noise are given. It is demonstrated that in certain scenarios the proposed technique can considerably reduce the complexity of the spectrum sensing procedure. The presented analytical results are illustrated by numerical examples. This paper establishes a new framework for efficient spectrum sensing methods that exploit randomized sampling schemes. Unlike a number of similar approaches in the literature, it offers solutions that are well suited for practical implementation in hardware. [Copyright &y& Elsevier]

Abstract: This paper proposes an image enhancement method based on space-adaptive, 2-D lifting scheme. In the space-adaptive update-first lifting scheme, the prediction stage is adapted to the signal structure point-by-point which results in a better signal representation and enhancement result. In this paper, a novel edge-sensitive adaptive prediction method is introduced in the 2-D lifting framework. The method adaptively chooses the best predictor among a set of predictors minimizing the prediction error. The proposed prediction method is sensitive to both even and odd indexed edge pixels in the 2-D lifting context. The bivariate shrinkage which assumes the dependence of the subband wavelet coefficients is used for subband image enhancement. As an objective quality measure, the peak signal-to-noise ratio test is applied to the results of the proposed image enhancement algorithm. Results of the proposed algorithm are compared with those of the VisuShrink, BayesShrink, and NorShrink. Experimental and objective quality test results prove the superior performance of the proposed image enhancement method. [Copyright &y& Elsevier]

Direction-of-arrival (DOA) estimation for arbitrary array structures in the presence of mutual coupling (MC) is an important problem for antenna arrays. Previous methods in the literature are usually proposed for certain array geometries and show limited performance at low SNR or for small number of snapshots. In this paper, compressed sensing is used to exploit the joint-sparsity of the array model to estimate both DOA and MC coefficients with a single snapshot for an unstructured array where the antennas are placed arbitrarily in space. A joint-sparse recovery algorithm for a single snapshot (JSR-SS) is presented by embedding the source DOA angles and MC coefficients into a joint-sparse vector. A dictionary matrix is defined by considering the symmetricity of the MC matrix for the unstructured antenna array. The proposed method is extended to the multiple snapshots, and the joint-sparse recovery algorithm with multiple snapshots (JSR-MS) is developed. A new joint-sparsity structure, namely, joint-block-sparsity is introduced to take advantage of the structure in the composite matrix involving both DOA and MC coefficients. 1-D and 2-D DOA estimation performance of the proposed methods is provided in comparison to the conventional sparse recovery techniques, subspace methods, and the Cramer–Rao lower bound. It is shown that the proposed methods perform significantly better than the alternative methods. [ABSTRACT FROM AUTHOR]

Long-time coherent integration is conducive to improving the radar detection performance for weak moving targets, whereas range migration (RM) and Doppler frequency migration (DFM) might occur when the target has high speed and high maneuverability. Although a variety of methods have been proposed to correct RM and DFM, they are widely based on the target's radial motion model, which is expressed as a finite order polynomial with respect to slow time. However, when an offset exists between the target's moving direction and radar sight line, model mismatch will occur and deteriorate the integration performance. In this paper, the accurate echo model for the moving target with arbitrary moving direction is established, then the modified Radon Fourier transform (MRFT) algorithm is proposed to realize coherent integration via removing the complex RM and DFM. Moreover, the generalized form of MRFT, i.e., modified generalized RFT (MGRFT), is introduced to focus the target with high maneuverability. Both simulation and real data processed results have been provided to validate the effectiveness of the proposed method. According to the coherent integration results for the target with oblique maneuvering motion, the signal-to-noise ratio (SNR) gain of the proposed method is about 4.5 dB higher than that of the existing GRFT method with the same computational complexity. [ABSTRACT FROM AUTHOR]

This paper proposes a novel single-image super-resolution algorithm based on linear Bayesian maximum a posteriori (MAP) estimation and sparse representation. Starting from constructing several probability distribution priors in representation vector, we develop a linear Bayesian MAP estimator to acquire the most probable high-resolution (HR) image behind the low-resolution (LR) observation. Our new algorithm involves three main steps: (1) obtaining an initial estimate of the HR image via bi-cubic interpolation algorithm, (2) performing sparse coding on the initial estimate to get the representation vector and its support, (3) using the MAP estimator to restore the desired representation vector and then reconstructing the HR output. Simulated results show that the proposed method can achieve a more competitive performance both in subjective visual quality and in peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) measures, compared with other state-of-the-art super-resolution methods. [ABSTRACT FROM AUTHOR]

Most speech enhancement methods based on short-time spectral modification are generally expressed as a spectral gain depending on the estimate of the local signal-to-noise ratio (SNR) on each frequency bin. Several studies have analyzed the performance of a priori SNR estimation algorithms to improve speech quality and to reduce speech distortions. In this paper, we concentrate on the analysis of over- and under estimation of the a priori SNR in speech enhancement and noise reduction systems. We first show that conventional approaches such as the decision-directed approach proposed by Ephraïm and Malah lead to a biased estimator for the a priori SNR. To reduce this bias, our strategy relies on the introduction of a correction term in the a priori SNR estimate depending on the current state of both the available a posteriori SNR and the estimated a priori one. The proposed solution leads to a bias-compensated a priori SNR estimate, and allows to finely estimating the output speech signal to be very close to the original one on each frequency bin. Such refinement procedure in the a priori SNR estimate can be inserted in any type of spectral gain function to improve the output speech quality. Objective tests under various environments in terms of the Normalized Covariance Metric (NCM) criterion, the Coherence Speech Intelligibility Index (CSII) criterion, the segmental SNR criterion and the Perceptual Evaluation of Speech Quality (PESQ) measure are presented showing the superiority of the proposed method compared to competitive algorithms. [ABSTRACT FROM AUTHOR]

Principal Component Analysis (PCA) is a simple non-parametric method for extracting relevant information from high-dimensional data sets. In this paper, we analyze the data collected from the Indian MST (Mesosphere, Stratosphere, Troposphere) radar at Gadanki (13.5°N, 79.2°E) using PCA. We tested the PCA for various simulated signals like narrowband, wideband and exponential signals which may contain more than one frequency both in absence and presence of noise. For the simulated data, it is observed that PCA works for low SNR, i.e. it successfully detects the frequency in the highly noise-corrupted signal also. Finally, we applied PCA to the radar data for estimating the power spectrum and thus in turn estimating the Doppler frequency components. We estimate the zonal (U), meridional (V), wind speed (W) etc. from the Doppler frequencies. Compared with existing algorithms, PCA works well at higher altitudes and results have been validated using the GPS sonde data. [ABSTRACT FROM AUTHOR]

Sparse Recovery (SR) algorithms have been widely used for direction of arrival (DOA) estimation. At low values of signal to noise ratio (SNR) i.e. beyond -10 dB and with adequate number of sensors [1] , their estimates are incorrect. The magnitude spectrum-based Re-weighted sparse recovery (RWSR) algorithms improve the robustness by re-weighting the sparse estimates. But their efficiency degrades significantly with a fewer number of sensors. The significance of phase spectrum in the form of Group delays for robust DOA estimation using RWSR algorithms for spatially contiguous sources is explored in this paper. An optimal re-weighting methodology based on simultaneously minimizing average root mean square error (ARMSE) and maximizing the probability of separation is proposed. The simulations are carried for Gaussian and Laplacian noise to demonstrate the superior performance of the proposed method with a few sensors at low values of SNR. [ABSTRACT FROM AUTHOR]

Abstract: A hybrid method of block truncation coding (BTC) and differential pulse code modulation (DPCM) offers better visual quality than the standard BTC for small block sizes due to its inherent multitone representation. Recently, a two-level quantizer design method has been proposed to increase the coding performance of the DPCM-BTC framework. However, the design method is near optimal in the sense that its coding performance depends on the initial bit plane patterns. In this paper, we propose a bit plane modification (BPM) algorithm to achieve further performance improvement. The BPM algorithm, inspired by error diffusion, effectively distributes large quantization error at a certain pixel to its neighboring pixels having small quantization errors by changing partial bit patterns. Experimental results show that the proposed algorithm successfully achieves much higher coding performance than various conventional BTC methods. The average PSNR performance of the proposed method is 2.31 dB, 5.15 dB, and 5.15 dB higher than that of BTC, DPCM-BTC, and a recently developed BTC scheme using error diffusion and bilateral filtering, respectively. [Copyright &y& Elsevier]

Abstract: A robust template matching using occlusion-free correlation (OFC) coefficient is presented in this paper for the purpose of locating objects partially occluded under low PSNR (Peak Signal to Noise Ratio) environment. The OFC coefficient can effectively eliminate the negative effect of occlusion on matching score, resulting in providing better accuracy in recognizing and locating objects under bad environment. This algorithm provides a closed-loop scheme through a reliability assessment of occlusion detection which is able to check if the occlusion detection is well made. Extensive experimental results through occluded images with various noise levels demonstrate that the proposed algorithm is robust against objects partially occluded under low PSNR and superior in terms of the false alarm and miss rates in the identification problem. [Copyright &y& Elsevier]

Abstract: Accurate estimation of the attitude of unmanned aerial vehicles (UAVs) is crucial for their control and displacement. Errors in the attitude estimate may misuse the limited battery energy of UAVs or even cause an accident. For attitude estimation, proprioceptive sensors such as inertial measurement units (IMUs) are widely applied, but they are susceptible to inertial guidance error. With antenna arrays currently being installed in UAVs for communication with ground base stations, we can take advantage of the array structure in order to improve the estimates of IMUs via data fusion. In this paper, we therefore propose an attitude estimation system based on a hexagon-shaped 7-element electronically steerable parasitic antenna radiator (ESPAR) array. The ESPAR array is well-suited for installment in the UAVs with broad wings and short bodies. Our proposed solution returns an estimation for the pitch and roll based on the inter-element phase delay estimates of the line-of-sight path of the impinging signal over the antenna array. By exploiting the parallel and centrosymmetric structure in the hexagon-shaped ESPAR array, the 3-dimensional Unitary ESPRIT algorithm is applied for phase delay estimation to achieve high accuracy as well as computational efficiency. We devise an attitude estimation algorithm by exploiting the geometrical relationship between the UAV attitude and the estimated phase delays. An analytical closed-form expression of the attitude estimates is obtained by solving the established simultaneous nonlinear equations. Simulations results show the feasibility of our proposed solution for different signal-to-noise ratio levels as well as multipath scenarios. [Copyright &y& Elsevier]

Abstract: A reliable speech presence probability (SPP) estimator is important to many frequency domain speech enhancement algorithms. It is known that a good estimate of SPP can be obtained by having a smooth a-posteriori signal to noise ratio (SNR) function, which can be achieved by reducing the noise variance when estimating the speech power spectrum. Recently, the wavelet denoising with multitaper spectrum (MTS) estimation technique was suggested for such purpose. However, traditional approaches directly make use of the wavelet shrinkage denoiser which has not been fully optimized for denoising the MTS of noisy speech signals. In this paper, we firstly propose a two-stage wavelet denoising algorithm for estimating the speech power spectrum. First, we apply the wavelet transform to the periodogram of a noisy speech signal. Using the resulting wavelet coefficients, an oracle is developed to indicate the approximate locations of the noise floor in the periodogram. Second, we make use of the oracle developed in stage 1 to selectively remove the wavelet coefficients of the noise floor in the log MTS of the noisy speech. The wavelet coefficients that remained are then used to reconstruct a denoised MTS and in turn generate a smooth a-posteriori SNR function. To adapt to the enhanced a-posteriori SNR function, we further propose a new method to estimate the generalized likelihood ratio (GLR), which is an essential parameter for SPP estimation. Simulation results show that the new SPP estimator outperforms the traditional approaches and enables an improvement in both the quality and intelligibility of the enhanced speeches. [Copyright &y& Elsevier]

Abstract: This paper presents a new time domain noise reduction approach based on Singular Value Decomposition (SVD) technique. In the proposed approach, the noisy signal is initially represented in a Hankel Matrix. Then SVD is applied on the Hankel Matrix to divide the data into signal subspace and noise subspace. Since singular vectors are the span bases of the matrix, reducing the effect of noise from the singular vectors and using them in reproducing the matrix leads to considerable enhancement of information embedded in the matrix. The noise-reduced singular vectors from the signal subspace are utilized to reconstruct the data matrix. This matrix is finally used to obtain the time-series signal. The results of applying the proposed method to different synthetic noisy signals indicate a better efficiency in noise reduction compared to the other time series methods. [Copyright &y& Elsevier]

Abstract: A typical sensor data processing sequence uses a detection algorithm prior to tracking to extract point measurements from the observed sensor data. Track-before-detect (TkBD) is a paradigm which combines target detection and estimation by removing the detection algorithm and supplying the sensor data directly to the tracker. It is a non-linear non-Gaussian estimation problem, typically requiring numerical methods. While, in general, TkBD schemes are a more computationally expensive method of tracking, removal of the detection threshold allows for the detection and tracking of targets at much lower signal-to-noise ratios. In this paper we develop TkBD algorithms for multiple asynchronous dissimilar sensors. The problem of radar and IRST fusion is presented as an example and the performance of TkBD is compared with a Multi-Hypothesis Tracker. In the example, the TkBD algorithm is able to utilise IRST data to produce high accuracy in azimuth and elevation well beyond the conventional operating range of the sensor. [Copyright &y& Elsevier]

Abstract: This paper introduces a zerocrossing-based nonuniform sampling algorithm to deliver the low-frequency (LF) fine structure (FS) cue for a cochlear implant (CI). At LF channels, the zerocrossing times of the bandpassed speech signal are used to represent the FS cue and evoke the pulses. At high frequency channels, the interleaved pulses are generated when no zerocrossing appears at LF channels. The fact that only one pulse is elicited at any moment diminishes the interaction of pulses from adjacent channels. Three experiments were conducted to assess the performance of the proposed algorithm, acoustic simulation of Mandarin phrase identification, intelligibility measure of the synthesized speech, and estimating the envelope waveform at LF channel. Compared with traditional continuous-interleaved-sampling algorithm, the proposed algorithm improves the correct rate on identifying Mandarin phrase contaminated by speech-shaped noise at low signal-to-noise ratio (SNR). At high SNR conditions, the synthesized speech by the proposed algorithm leads to an improved speech intelligibility measure. The envelope waveform at LF channel is faithfully estimated when a low lowpass filtering cutoff frequency was applied to extract the envelope waveform. [Copyright &y& Elsevier]

Abstract: This paper proposes a new non-parametric method to estimate the power spectral density of atmospheric radar signals using uniform filter banks through polyphase approach. The performance of proposed method is investigated for a simulated broadband and narrowband signals. This method reduces the variance without affecting the resolution and improves the signal to noise ratio significantly when compared to the existing method (periodogram/modified periodogram). The variance reduction is observed to be more in case of narrowband signals. The method has the computational cost similar to modified periodogram approach. This method is tested for practical atmospheric data collected at NARL, Gadanki, India, through the mesosphere–stratosphere–troposphere (MST) radar, back scattered echoes, in particular from high altitudes, which has low signal to noise ratio. Results have been validated using simultaneous Global Positioning System (GPS) Sonde data. [Copyright &y& Elsevier]

In order to solve the Direction of Arrival (DOA) estimation of fast-moving targets in a strong noisy environment, an auto-paired two-dimensional (2D) DOA estimation algorithm architecture based on the sparse recovery of acoustic vector sensor arrays is proposed. The structure of the array signal autocorrelation operator is similar to the 2D compressed sampling model. Based on the structure of the autocorrelation operator, a 2D DOA estimation sparse recovery model for the Uniform Line Array (ULA) of the acoustic vector sensors is proposed. Meanwhile, the modified Compressive Sampling Matching Pursuit (CoSaMP) algorithm is used to realize the auto-paired 2D DOA estimation. The simulation results show that the method proposed in this paper can effectively suppress noise, and can be used to realize 2D DOA estimation of acoustic vector sensor ULA under a low Signal-to-Noise Ratio (SNR) and small snapshots. [ABSTRACT FROM AUTHOR]

The sparsity characteristic, which is the foundation of the compressive sensing (CS) theory, has been widely used in the inverse synthetic aperture radar (ISAR) imaging field. However, the performance of the traditional CS-based sparse aperture ISAR imaging method is limited in low signal to noise ratio (SNR) and/or sampling rate (SPR). Inspired by the low-rank property of the final ISAR image, a new sparse aperture ISAR imaging model by jointly using the sparse characteristic and low-rank property is proposed in this paper. Considering the effective solution of the proposed reconstruction model, the optimization problem is decomposed into several sub-problems under the framework of linearized alternating direction method with adaptive penalty (LADMAP) approach. Furthermore, in order to improve the computing efficiency of the proposed algorithm, a fast singular value decomposition (SVD) free algorithm is finally proposed. Both simulated and measured data experiments verify the effectiveness of the proposed algorithms, especially under the condition of low SNR and/or SPR. [ABSTRACT FROM AUTHOR]

This paper aims to develop an accurate stochastic model for the diffusion normalized least-mean-square (dNLMS) algorithm operating with both combine-then-adapt (CTA) and adapt-then-combine (ATC) strategies, aiming to provide a theoretical basis for supporting the study of this algorithm. In particular, considering uncorrelated and correlated Gaussian input data, model expressions are derived for predicting the mean and mean-square behavior of either an individual node or the whole adaptive network for both transient and steady-state phases. Based on these expressions, the impact of the diffusion strategy along with a combination rule on the algorithm performance is assessed and discussed. In addition, examples are presented to demonstrate how model expressions can help the designer in the adjustment of the algorithm parameters without the need of extensive trial-and-error procedures, making performance comparisons less laborious. The effectiveness of the proposed model is assessed through simulation results covering different operating conditions, network topologies, combination rules, step-size values, as well as for a wide range of eigenvalue spreads of the input data correlation matrix and signal-to-noise ratio (SNR) values. [ABSTRACT FROM AUTHOR]

Recently, many robust adaptive beamforming (RAB) methods based on covariance matrix reconstruction have been proposed. Motivated by the idea, in this paper, a novel and efficient signal power estimator is devised to reconstruct the interference-plus-noise covariance (INC) matrix, with the corresponding RAB algorithm proposed. Firstly, the steering vectors of the incoming sources are derived using the Capon spatial spectrum and known array geometry. Secondly, a set of linear equations is established based on the signal subspace projection, from which the powers of the incoming sources are estimated. Based on the presumed angular sector of the signal-of-interest (SOI), the steering vectors and powers of the SOI and interferences are distinguished, and the INC matrix is then reconstructed. Finally, the beamformer is determined by the estimated INC matrix and SOI steering vector. The proposed algorithm is computationally more efficient than other reconstruction-based methods because there are closed-form solutions for the signal powers. Simulation results indicate that our proposed algorithm performs better than the existing methods at high signal-to-noise ratios (SNRs), and achieves nearly optimal performance across a wide range of SNR. [ABSTRACT FROM AUTHOR]