Author: ScienceDirect Publication: Signal Processing

This paper presents a solution to the problem of reconstructing sparsely sampled signals using time-frequency (TF) filtering. The proposed method employs a modified Viterbi algorithm and adaptive directional TF distributions (ADTFD) for the accurate estimation of the instantaneous frequency (IF) estimation of sparsely sampled multi-component signals from a given signal. Using the IF information, TF filtering is performed to separate the signal components. This TF filtering operation also fills the gaps caused by missing samples. The separated components are then added up, and known values are re-inserted to obtain a reconstructed signal. The steps above involving IF estimation, TF filtering, and re-insertion of known values are again applied with the reconstructed signal as an input signal. This algorithm is iterated until the difference between the signal energy in two successive iterations falls below a certain threshold. Experimental results indicate the superiority of the proposed method. The code for reproducing the results can be accessed from https://github.com/mokhtarmohammadi/Sparse-Reconstruction.

Three-dimensional (3-D) aircraft tracking using two-dimensional (2-D) radar measurements is a common task in air traffic control (ATC) systems. In this paper, a new algorithm using a 2-D ATC radar, called HPEKF with probabilistic data association (HP-PDA-EKF), is proposed for aircraft tracking with false alarms and missed detections. An improved filter initialization technique is also proposed to yield better estimates. To mitigate the degradation in altitude estimation accuracy with increasing aircraft distance, ATC waypoints are used as extra measurement information, and a new height-parametrized cascaded PDA filter (HP-CPDAF) algorithm is proposed to make use of waypoints. The posterior Cramér–Rao lower bounds (PCRLBs) quantifying the achievable accuracies of these two filters are derived. Simulations are carried out to analyze the relationship between aircraft distance, waypoint accuracy and state estimation accuracy. The viability of using a single 2-D radar for 3-D ATC and the usefulness of the proposed algorithms under different operating conditions are analyzed. Simulation results demonstrate the validity of the proposed algorithms.

This paper proposes two tensor-based receivers for multiple-input multiple-output (MIMO) multi-relaying systems capable of jointly estimating the channels and symbols in a semi-blind fashion. Assuming space-time coding at the source and relay stations, we propose an orthogonal design based on a parallel factor (PARAFAC) analysis of the coding structure. Exploiting the proposed tensor codes and the multi-linear structure of the resulting received signals, we show that the data model for every relay-assisted link after space-time combining/decoding has a Kronecker structure, which can be recast as a rank-one tensor corrupted by noise. The proposed receivers combine the tensor signals for the multiple cooperative links for joint channel and symbol estimation by coupling multiple rank-one tensor approximation problems. The first one is a coupled-SVD based receiver that estimates all the involved communication channels and transmitted symbols in closed form. The second one is an iterative solution based on alternating least squares. The performances of both receivers are evaluated by means of computer simulations in a variety of system configurations. Our results show the effectiveness of the proposed receivers and its good performance-complexity trade-off in comparison with competing receivers.

This paper considers the problem of recovering sparse signals with cluster structure of unknown sizes and locations. A hybrid prior is proposed by introducing a local continuity indicator, which adaptively imposes cluster information on the sparse coefficients according to the inherent data structure. The local continuity indicator flexibly switches the prior for a sparse coefficient between a fully pattern-coupled one and an independent one, so that the estimation of the sparse coefficient can selectively use the statistical information of its neighbors. Variational Bayesian inference is used to estimate the hidden variables based on the constructed probabilistic modeling. Numerical results of comprehensive simulations and real data experiments demonstrate that the proposed algorithm can effectively avoid the problem of structural mismatch and outperform other recently reported clustered sparse signal recovery algorithms in noisy environments.

Images captured in unfavorable weather usually exhibit poor visibility, which results from scattering and absorption that the propagated light suffers in the atmosphere. To improve the quality of degraded images, multitudes of algorithms have been exploited based on traditional atmospheric scattering model. However, in the traditional model, a phenomenon is neglected that the radiance projected on scenes is uneven, which leads to low brightness in processed image. Targeted the inherent limitation of the traditional model, we propose a robust atmospheric scattering model by decomposing the real scene into incident light and reflectance component and attaching a noise term in the traditional model. Then an objective function which includes novel regularization terms for the incident light and reflectance is formulated based on the proposed model, and an alternating direction method of multipliers is adopted to jointly estimate the incident light and reflectance. Moreover, a compensation term with regard to transmission map is introduced to ameliorate over-enhancement in thick haze regions. Ultimately, comprehensive tests are implemented to compare our method with other exceptional haze removal methods. Experiments on images with different characteristics manifest excellent performance of the proposed method in terms of haze removal and brightness enhancement.

This paper is concerned with mixed far-field and near-field source localization in the presence of gain-phase perturbations. Under some mild assumptions, we propose a new mixed source localization algorithm with the partly calibrated symmetric uniform linear array. By constructing a special second-order statistical vector, the DOAs and powers of all sources are first estimated by the modified sparse total least square (M-STLS) algorithm after compensating gain errors. Based on the estimated DOAs, the phase errors are successively obtained by the least squares criterion and a discriminant function formed by using the MUSIC null spectrum property. Finally, the mixed sources are classified and the range of near-field sources are achieved via one-dimensional spectral search. Meanwhile, the stochastic Cramér-Rao bound (CRB) for the considered problem is also given. The proposed algorithm can lead to a good mixed source classification and localization result. Numerical simulations validate the effectiveness of the proposed algorithm.

This paper studies the design of efficient detector for radar targets in compound-Gaussian clutter with inverse Gaussian texture (CG-IG clutter). Due to inclusion of the modified Bessel function, the optimum coherent detector in CG-IG clutter cannot be easily implemented in radar systems. Through deriving the mathematical relationship between the shape parameters of the K and CG-IG distributions, the efficient α matched filter (α-MF) detector in K-distributed clutter, is improved to realize the near-optimum detection with real-time and fast implementation in CG-IG clutter. The control parameter α in CG-IG clutter is calculated by a new formula. The constant false alarm rate (CFAR) property of α-MF and adaptive α-MF (α-AMF) is proved in CG-IG clutter. Numerical results indicate that the α-AMF CFAR detector has almost the same detection performance as the adaptive optimum detector and outperforms the adaptive MF (AMF) and adaptive normalized MF (ANMF) in CG-IG clutter.

System identification problems are very difficult in the scenario of long length impulse responses, raising challenges in terms of convergence, complexity, and accuracy of the solution. However, we can take advantage of the characteristics of the impulse response, in order to improve the overall performance. In this context, a recently introduced approach exploits a Kronecker product decomposition of the impulse response in tandem with low-rank approximations. Also, a recursive least-squares (RLS) algorithm was developed based on this idea, showing appealing results for the identification of low-rank systems, like typical echo paths. In this short communication, we propose a Kalman filter tailored for the identification of such low-rank systems. Simulations performed in the context of echo cancellation indicate that the proposed algorithm outperforms the regular Kalman filter, but also its RLS-based counterpart.

The seam-carving algorithm is a classic context-aware image retargeting method and has been extensively studied for years; however, distortions exist as the discrete iteration of least- energy computation. We propose a continuous seam carving model through the just noticeable distortion (JND) detection at every iteration and accumulative energy weight. The JND of every pixel is calculated by the minimal just-noticeable distortion energy of adjacent-pixel conflicting displacements between seam carving iterations. The mean field approximation is used to efficiently solve the problem. In this way, the proposed energy weight can accumulatively calculate the JND information of recent k iterations and passed down to the next iteration for distortion avoidance. The superior performance of the proposed continuous seam carving model, as compared with state-of-art image retargeting approaches in RetargetMe database, was demonstrated experimentally.

Time-modulated arrays (TMAs) are able to improve the side-lobe level of the radiation pattern at the fundamental mode but cannot steer the beam at such a mode towards a given direction. Beam-steering is possible in a TMA, but only at the harmonic patterns and at the expense of a severe TMA efficiency reduction. In this work we propose a TMA approach that simultaneously performs both features over the same beam by using two sets of switches: (1) single-pole four-throw switches to generate periodic stair-step pulses suitable for efficiently synthesizing a uniform steerable beam over the first positive harmonic, and (2) single-pole single-throw switches to reconfigure the side-lobe level of the previous beam. Performance, small size, cost-effectiveness, and performance invariability with the carrier frequency are features that make this TMA approach a competitive solution for analog beamforming. Accordingly, the structure is an attractive proposal for the design of multibeam transceivers.