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Passive bistatic radars (PBRs) use illuminators of opportunity to detect and localize targets. Exploiting signals of these sources which are not designed for radar applications results in essential challenges in target detection, and requires special signal processing techniques. In this paper, we propose a new approach for target detection in PBRs by formulating the problem as a linear regression. To solve this problem, we take advantage of the sparsity of received signals in the range-Doppler domain which enables us to employ statistical model selection algorithms, such as LASSO or LAR. In contrast to the most existing PBR algorithms, the proposed method does not require to specify a prior subspace for clutter and eliminate interferences before target detection. This advantage is achieved because our algorithm identifies targets, clutter, and direct-path simultaneously within a unified procedure. Our extensive simulation results illustrate that the proposed method performs very close to the optimal upper band performance (i.e., that of the matched-filter based detector) in the single-target scenario. Moreover, our results reveal that our algorithm has high detection performance in multitarget scenarios with the presence of interfering targets, strong clutter, and a very powerful direct-path. [ABSTRACT FROM AUTHOR]

We design an optimal non-uniform quantizer for collaborative spectrum sensing (CSS) in cognitive radio networks (CRNs). We assume non-ideal reporting channels are and consist of additive white Gaussian noise (AWGN). To design the optimum quantization at the transmitter, we minimize the mean square error (MMSE) at the receiver. The local data at each secondary user (SU) is quantized and transmitted via an AWGN reporting channel. Then, quantized symbols are received and decoded at the fusion center (FC) by a maximum a-posteriori probability (MAP) rule and, the final collaborative decision is made. By this method, a prediction is done for representation points of the quantizer and moreover, quantization and reporting channel noises are minimized, simultaneously. In addition, by taking into account the reporting channel noise in quantizer design, detection performance will be adaptive to change of signal to noise ratio (SNR) in the reporting channel. The simulation results demonstrate the superiority of the proposed quantizer compared to different quantizers in practical noisy reporting channels. [ABSTRACT FROM AUTHOR]

This letter presents a high resolution method which separates close components of a multi-component linear frequency modulated (LFM) signal and eliminates their Cross-Terms (CTs). We first investigate the energy distribution of the Auto-Terms (ATs) and CTs in ambiguity plane. This reveals that the energy of the CTs of parallel close components is significant around the origin. We propose to mask the samples in which the CTs may have interferences with the ATs. This mask is signal-dependent and its directions are determined using the relationship between the radial slices of ambiguity function (AF) and the fractional Fourier transform (FrFT). Exploiting sparsity in time-frequency (TF) domain and by solving an $\ell _1$ -norm minimization problem, the localized time-frequency distribution (TFD) is extracted from the acquired samples of the AF. Simulation results reveal significant improvements in the efficiency compared to previous works. [ABSTRACT FROM AUTHOR]

In this paper, we consider a cognitive radio system with some of secondary users (SUs) equipped with multiple antenna full-duplex transceivers. We design a hybrid system to sense and access the spectrum opportunistically such that the SU operates on both underlay and interweave hybrid methods while the primary user (PU) may be active or inactive, respectively, to perform the sensing as agile as possible. Moreover, we maximize the throughput by allocating the spectral resources assuming limited available energy during each time frame with constraints on the interference to the primary user. We show that this problem is convex and propose a simple solution algorithm for it. Our simulations confirm the accuracy of our analytical and theoretical results and illustrate that the total achieved throughput of full-duplex cognitive radio even by considering self-interference is maximized under the assumed conditions. [ABSTRACT FROM AUTHOR]

In this paper, we study the spectrum sensing problem when the sensing device is equipped with an uncalibrated multiantenna array. We consider an unknown Toeplitz structure for the covariance matrix of the signal of interest and unknown uncorrelated noise variances. We propose two new invariant constant false-alarm rate detectors with less computational cost than the generalized likelihood ratio test: 1) the Rao test and 2) a separating function estimation test (SFET). We calculate approximate expressions for the false-alarm and detection probabilities of the proposed Rao test-based detector. In addition, we derive the exact false-alarm probability of the proposed SFET for the case of two antennas. Moreover, we obtain an accurate approximation for the false-alarm probability of the proposed SFET for the general case using the method of moments. The accuracy of these expressions is validated by Monte-Carlo experiments. The simulation results show that the proposed detectors outperform state-of-the-art detectors. [ABSTRACT FROM AUTHOR]

We investigate a partially blind channel estimation and symbol detection in an amplify-and-forward two way relay scheme. We derive generalized likelihood ratio test detectors for reciprocal and non-reciprocal channels, whose computational complexity (CC) grow exponentially with the block length. Therefore, we propose a convergent iterative method for joint channel estimation and symbol detection with CC proportional to the block length. We also address the convergence proof. We further propose the simple blind and partially-blind approaches based on least squares (LS) and weighted LS estimation, respectively, for operation at low SNRs and analyze their mean square errors (MSE). Our simulations reveal the effectiveness of the proposed method compared with its counterparts. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach to approximate the $\ell _0$ -norm for linear sparse hyperspectral unmixing of images. We approximate the $\ell _0$-norm with the $\ell _p$-norm and iteratively reduce $p$ to enhance the results. The $p$-changing heuristic scheme that reduces the value of $p$ smoothly and iteratively, results in an enhanced sparse solution. We introduce an iteratively reweighted $\ell _2$ -norm to approximate the $\ell _p$ -norm. In this approach, a parameter $\epsilon$ is involved to deal with the fact that $\ell _p$ -norm problem is not Lipschitz continuous for the region of $p<1$. We propose two different methods to update the pair $(p,\epsilon)$. Finally, we evaluate our proposed heuristic $\ell _p$-norm method over the synthetic data as well as real hyperspectral dataset. Experimental results show that our algorithm outperforms several state-of-the-art algorithms in terms of the reconstruction errors and their probability of success. [ABSTRACT FROM PUBLISHER]

In this letter, we propose a sparsity promoting feedback acquisition and reconstruction scheme for sensing, encoding and subsequent reconstruction of spectrally sparse signals. In the proposed scheme, the spectral components are estimated utilizing a sparsity-promoting, sliding-window algorithm in a feedback loop. Utilizing the estimated spectral components, a level signal is predicted and sign measurements of the prediction error are acquired. The sparsity promoting algorithm can then estimate the spectral components iteratively from the sign measurements. Unlike many batch-based compressive sensing algorithms, our proposed algorithm gradually estimates and follows slow changes in the sparse components utilizing a sliding-window technique. We also consider the scenario in which possible flipping errors in the sign bits propagate along iterations (due to the feedback loop) during reconstruction. We propose an iterative error correction algorithm to cope with this error propagation phenomenon considering a binary-sparse occurrence model on the error sequence. Simulation results show effective performance of the proposed scheme in comparison with the literature. [ABSTRACT FROM AUTHOR]

In this letter, we propose a new accurate approach for target localization in multiple-input multiple-output (MIMO) radars, which exploits the sparse spatial distribution of targets to reduce the sampling rate. We express the received signal of a MIMO radar in terms of the deviations of target parameters from the grid points in the form of a block sparse signal using the expansion around all the neighbor points. Applying a block sparse recovery method, we can estimate both the grid-point locations of targets and these deviations. The proposed approach can yield more accurate localization with higher detection probability compared with its counterparts. Moreover, the proposed approach can reduce the computational complexity. [ABSTRACT FROM AUTHOR]

Objective: We introduce novel methods to identify the active intervals (AIs) of intracardiac electrograms (IEGMs) during complex arrhythmias, such as atrial fibrillation (AF). Methods: We formulate the AI extraction problem, which consists of estimating the beginning and duration of the AIs, as a sequence of hypothesis tests. In each test, we compare the variance of a small portion of the bipolar IEGM with its adjacent segments. We propose modified general-likelihood ratio (MGLR) and separating-function-estimation tests; we derive five test statistics (TSs), and show that the AIs can be obtained by threshold crossing the TSs. We apply the proposed methods to the IEGM segments collected from the left atrium of 16 patients (62.4 $\pm$ 8.2-years old, four females, four paroxysmal, and twelve persistent AF) prior to catheter ablation. The accuracy of our methods is evaluated by comparing them with previously developed methods and manual annotation (MA). Results: Our results show a high level of similarity between the AIs of the proposed methods and MA, e.g., the true and false positive rates of one of the MGLR-based methods were, respectively, 97.8% and 1.4%. The mean absolute error from estimation of the onset and end of AIs and also for the estimation of the mean cycle length for that approach was 8.7 $\pm$ 10.5, 13 $\pm$ 15.5, and 4.2 $\pm$ 9.4 ms, respectively. Conclusion: The proposed methods can accurately identify onset and duration of AI of the IEGM during AF. Significance: The proposed methods can be used for real-time automated analysis of AF, the most challenging complex arrhythmia. [ABSTRACT FROM AUTHOR]

In this paper, we propose a matrix factorization method for hyperspectral unmixing using the linear mixing model. In this method, we add the $\arctan$ functions of the endmembers to the $\ell _2$-norm of the error in order to exploit the sparse property of the fractional abundances. Most of the energy of spectral signatures of materials is concentrated around the first few subbands resulting in smooth spectral signatures. To exploit this smoothness, we also add a weighted norm of the spectral signatures of the materials and to limit their nonsmooth errors. We propose a multiplicative iterative algorithm to solve this minimization problem as a nonnegative matrix factorization (NMF) problem. We apply our proposed Arctan-NMF method on the synthetic data from real spectral library and compare the performance of Arctan-NMF method with several state-of-the-art unmixing methods. Moreover, we evaluate the efficiency of Arctan-NMF on two different types of real hyperspectral data. Our simulations show that the Arctan-NMF is more effective than the state-of-the-art methods in terms of spectral angle distance and abundance angle distance. [ABSTRACT FROM PUBLISHER]

The problem of multiple antenna spectrum sensing in cognitive radio (CR) networks is studied in this paper. We propose two new invariant constant false-alarm rate eigenvalue-based (EVB) detectors, using the higher order moments of the sample covariance matrix eigenvalues, by exploiting the separating function estimation test framework. We find closed-form expressions for the false-alarm and detection probabilities of the proposed detectors by providing moment-based approximations of their statistical distributions. The accuracy of the obtained closed-form expressions is validated by Monte Carlo simulations. In addition, we compare the performance of the proposed detectors with that of their two counterparts, i.e., John’s and the arithmetic to geometric mean (AGM) detectors, in terms of the asymptotic relative efficiency. This comparison enables us to demonstrate the superiority of our proposed detectors over those detectors within the typical range of signal-to-noise ratio in CR application. The comparative simulation results also illustrate the superiority of the proposed detectors over John’s and the AGM detectors as well as some other state-of-the-art EVB algorithms given in the literature. [ABSTRACT FROM PUBLISHER]

We investigate non-coherent detection of $M$ -ary orthogonal signaling in a denoise-and-forward two-way relay scheme in fast fading, as well as block fading channels. We first design the maximum likelihood, as well as maximum a-posteriori detectors, and then propose a simple suboptimal version for this digital network coding system in Rayleigh fast fading environment. We find exact expression of bit error rate for this suboptimal detector. Interestingly, this suboptimal detector performs only less than 0.25 dB away from optimum, i.e., this expression is a tight upper bound for the performance of the optimal one. Moreover, we derive the optimal detector for the case where the channel amplitudes are known at the relay. To make this detector practical, we propose efficient estimators for the channel amplitudes at the relay for block fading environments. Finally, our simulations reveal the effectiveness of the proposed methods compared with the state-of-the-art method. [ABSTRACT FROM PUBLISHER]

In this paper, we consider an exponentially windowed sample covariance matrix (EWSCM) and propose an improved estimator for its eigenvalues. We use new advances in random matrix theory, which describe the limiting spectral distribution of the large dimensional doubly correlated Wishart matrices to find the support and distribution of the eigenvalues of the EWSCM. We then employ the complex integration and residue theorem to design an estimator for the eigenvalues, which satisfies the cluster separability condition, assuming that the eigenvalue multiplicities are known. We show that the proposed estimator is consistent in the asymptotic regime and has good performance in finite sample size situations. Simulation results show that the proposed estimator outperforms the traditional estimator, significantly. [ABSTRACT FROM PUBLISHER]

In this paper, we deal with the problem of polarimetric diversity detection for point-like targets in the presence of Gaussian clutter with unknown covariance matrix. To this end, we jointly exploit the polarization diversity and the spillover of target energy to consecutive range samples to improve the performances of detection and range localization. For estimation purposes, we assume that a set of secondary data (free of signal components) is available with the same covariance matrix as the clutter in the cells under test. Because the uniformly most powerful test does not exist for this problem, we derive two adaptive detectors: the generalized likelihood ratio test and the Wald test. Interestingly, these new receivers ensure the constant false alarm rate property with respect to covariance matrix of the clutter. The performance assessments conducted on both simulated data and real recorded dataset reveal that the proposed detectors outperform, in both detection and localization, the traditional state-of-the-art counterparts that ignore either the polarimetry or the spillover. [ABSTRACT FROM PUBLISHER]

This paper investigates the asymptotic optimality of the Constant False Alarm Rate (CFAR) tests obtained using the Minimal Invariant Group (MIG) reduction. We show that the CFAR tests obtained after MIG reduction using the Wald test is a Separating Function Estimation Test (SFET) and that the Generalized Likelihood Ratio Test (GLRT) and the Rao test are asymptotically SFET using Maximum Likelihood Estimation (MLE) under some mild conditions. Thus, they are asymptotically optimal. In order to find an improved test and motivated by the invariance property of the MLE of induced maximal invariant, we maximize the asymptotic Probability of Detection of the SFET using the MLE after reduction. We propose a systematic method allowing to derive the asymptotically optimal Separating Function (AOSF). This AOSF is obtained as the Euclidean distance of the transformed parameters under two hypotheses such that the gradient of the transformed parameters is the Cholesky decomposition of the Fisher Information Matrix (FIM), i.e., the FIM is transformed into an identity matrix. Interestingly, the AOSF Estimation Test (AOSFET) using MLE simplifies to the Wald-CFAR wherever the FIM does not depend on the unknown parameters. The simulation results show that the proposed AOSFET usually outperforms the GLRT, Wald test and Rao test. [ABSTRACT FROM AUTHOR]