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As a blind equalization algorithm, the fractional lower-order statistics based multimodulus algorithm (FLOS-MMA) is suitable for equalizing multilevel quadrature amplitude modulation (QAM) signals in impulsive noise. However, the equalization performance of FLOS-MMA is severely degraded in strong impulsive noise because the first-order gradient of the cost function constrains the ability of FLOS to suppress large outliers in the equalizer output signal and is sensitive to large outliers in the equalizer input signal. To enhance the robustness of multimodulus blind equalization algorithm against impulsive noise, a FLOS based momentum fractional-order multimodulus algorithm (FLOS-MFOMMA) is proposed in this paper. The proposed algorithm mitigates the adverse effects of impulsive noise on the updating of equalizer coefficients by utilizing the fractional-order gradient and accelerates the convergence speed by incorporating a momentum term. Then, the steady-state excess mean square error and the computational complexity of FLOS-MFOMMA are analyzed theoretically. Finally, numerical simulations, for equalizing 16-QAM signals under impulsive noise environments, show that the proposed algorithm is superior to FLOS-MMA in terms of robustness and convergence speed. [ABSTRACT FROM AUTHOR]

Nonlinear adaptive filters, such as Volterra and functional link adaptive filter (FLAF), face increased computational complexity with rising input dimensionality or expansion order. To enhance computational efficiency, this paper introduces a nonlinear adaptive algorithm based on Rectified Linear Unit (ReLU). Applying ReLU to a tonal signal generates components corresponding to the original frequency and even harmonics. Additionally, applying ReLU to the sum of two tonal signals yields a spectrum encompassing intermodulation distortion frequencies. These traits enable the algorithm to mitigate harmonic and intermodulation distortions while maintaining judicious computational complexity. The numerical simulation results for the identification of nonlinear systems validate the algorithm's ability to fit harmonic and intermodulation nonlinear distortions. In simulations of nonlinear echo cancellation with diverse test signals and various distortion models, our proposed algorithm consistently demonstrates competitive performance, notably excelling in music and speech scenarios. [ABSTRACT FROM AUTHOR]

Highlights • In this paper, we focus on the recursive least-squares algorithm for bilinear forms, i.e., RLS-BF. • The bilinear term is defined with respect to the impulse responses of a spatiotemporal model. • Low-complexity versions of the RLS-BF algorithm are proposed, based on the DCD method. • A robust variable-regularized RLS-BF (VR-RLS-BF) algorithm is developed, using an estimate of the SNR. • Low-complexity versions of the VR-RLS-BF algorithm are derived, involving the DCD method. Abstract Due to its fast convergence rate, the recursive least-squares (RLS) algorithm is very popular in many applications of adaptive filtering, including system identification scenarios. However, the computational complexity of this algorithm represents a major limitation in applications that involve long filters. Moreover, when the parameter space becomes large, the system identification problem is more challenging and the adaptive filters should be able to cope with this aspect. In this paper, we focus on the identification of bilinear forms, where the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. From this perspective, the solution requires a multidimensional adaptive filtering technique. Recently, the RLS algorithm tailored for bilinear forms (namely RLS-BF) was developed for this purpose. In this framework, the contribution of this paper is mainly twofold. First, in order to reduce the computational complexity of the RLS-BF algorithm, two versions based on the dichotomous coordinate descent (DCD) method are proposed; due to its arithmetic features, the DCD algorithm represents one of the most attractive alternatives to solve the normal equations. However, in the bilinear context, we need to consider the particular structure of the input data and the additional related challenges. Second, in order to improve the robustness of the RLS-BF algorithm in noisy environments, a regularized version is developed, together with a method to find the regularization parameters, which are related to the signal-to-noise ratio (SNR). Furthermore, using a proper estimation of the SNR, a variable-regularized RLS-BF algorithm is designed and two DCD-based low-complexity versions are proposed. Due to their nature, these variable-regularized algorithms have good robustness features against additive noise, which make them behave well in different noisy condition scenarios. Simulation results indicate the good performance of the proposed low-complexity RLS-BF algorithms, with appealing features for practical implementations. [ABSTRACT FROM AUTHOR]

Aiming the extract micro-motion features of rotor targets when the flashes are in the echo, this paper proposes a method based on the GS-IRadon algorithm. At first, we analyze the time-frequency characteristics of rotor targets through a scattering point signal model. Then, we analyze and study the usage conditions of the algorithm and use the Moving Target Indication (MTI) algorithm to remove the direct-current (DC) frequency component in signal's time-frequency results. Finally, we utilize the Inverse Radon transform algorithm based on the golden section method (GS-IRadon) to extract the target micro-motion features. The estimation of speed, length, and number of blades in the presence of flashes phenomenon in rotor targets was achieved. Simulation results showed that the algorithm can significantly reduce computational complexity while maintaining high estimation accuracy and has a more stable performance than regular algorithms. [ABSTRACT FROM AUTHOR]

Abstract The affine projection (AP) algorithm is one of the most celebrated adaptive filtering algorithms, and it achieves a good tradeoff between the convergence rate and computational complexity. However, the complexity of the AP algorithm increases with the projection order. A wealth of fast AP algorithms have been proposed to reduce the complexity in the last two decades. However, those low-complexity methods have not been well analyzed and compared. To fill this gap, this paper reviews the fast AP algorithms, including both fast filtering approaches and efficient solutions of the linear system of equations. The advantages and disadvantages of each fast implementation version are clarified based on an extensive performance evaluation, which could be useful to engineers when selecting a suitable algorithm for their specific applications and could also be a starting point for experts in this field to develop better solutions. [ABSTRACT FROM AUTHOR]

For wideband array signals, existing self-calibration methods use a first order approximation to simplify the estimation procedure under the assumption of small sensor position errors. However, the performance will degrade severely as the position errors increase. In this paper, a novel self-calibration method based on the time-frequency distributions (TFDs) is proposed to relieve the small errors constraint. We firstly develop an approach for wideband signals to select the TFDs of each source. Then the TFDs are utilized to obtain the propagation delays, which have a simple linear relationship with the sensor positions. The direction of arrival (DOA) and positions are finally estimated by a two-step iteration without any approximation. Simulation results demonstrate that the proposed method can achieve accurate DOA and position estimation over a broad range of position errors with lower computational complexity, and outperforms existing self-calibration algorithms. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the implementation of two hardware architectures, i.e., A2 Lattice Vector Quantization (LVQ) and Multistage A2LVQ (MA2LVQ), using a Field-Programmable Gate Array (FPGA). First, the renowned LVQ quantizer by Conway and Sloane is implemented followed by a low-complexity A2LVQ based on a new A2LVQ algorithm. It is revealed that the implementation requires high number of multiplier circuits. Then the implementation of a low-complexity A2LVQ is presented. This implementation uses only the first quadrant of the A2 lattice Voronoi region formed by W and T regions. This paper also presents the implementation of a multistage A2LVQ (MA2LVQ) with an architecture built from successive A2 quantizer blocks. Synthesis results show that the execution time of the low-complexity A2LVQ reaches up to 35.97 ns. The MA2LVQ is implemented using both low-complexity A2LVQ and ordinary A2 architectures. The system with the former architecture utilizes less logic and register elements by 47%. [Copyright &y& Elsevier]

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]

In digital communication systems, typical methodologies in determining loop parameters of the digital phase-locked loop (DPLL) are based on the mapping transformation from the analog domain to the digital domain. However, such transform based algorithms are relatively complicated and not straightforward, and they also cause the problem that loop parameters are affected by the pre-detection integration time greatly. To solve these issues, an effective direct method of determining loop parameters of the second-order DPLL in the z -domain is proposed in this paper. Through ascertaining specific positions of the closed-loop system function's poles inside the right-hand side of the z -plane's unit circle, unknown parameters are calculated directly and flexibly in this method, which enables the DPLL to acquire good low-pass filtering characteristic and system stability. This novel method not only reduces the complexity of solving the parameters, but also eliminates the effect of the pre-detection integration time on loop parameters. Simulation results are provided to confirm the feasibility of the proposed method and to show that the DPLL obtained by this method achieves the similar tracking performance to the discretized PLL. [ABSTRACT FROM AUTHOR]

Massive (or large-scale) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is widely acknowledged as a key technology for future communication. One main challenge to implement this system in practice is the high dimensional channel estimation, where the large number of channel matrix entries requires prohibitively high computational complexity. To solve this problem efficiently, a channel estimation approach using few number of pilots is necessary. In this paper, we propose a weighted Homotopy based channel estimation approach which utilizes the sparse nature in MIMO channels to achieve a decent channel estimation performance with much less pilot overhead. Moreover, inspired by the fact that MIMO channels are observed to have approximately common support in a neighborhood, an information exchange strategy based on the proposed approach is developed to further improve the estimation accuracy and reduce the required number of pilots through joint channel estimation. Compared with the traditional sparse channel estimation methods, the proposed approach can achieve more than 2 dB gain in terms of mean square error (MSE) with the same number of pilots, or achieve the same performance with much less pilots. [ABSTRACT FROM AUTHOR]

The Normalized Least Mean Square (NLMS) algorithm with a filtered-x structure (FxNLMS) is a widely used adaptive algorithm for Active Noise Control (ANC) due to its simplicity and ease of implementation. One of the major drawbacks is its slow convergence. A modified filtered-x structure (MFxNLMS) can be used to moderately improve the speed of convergence, but it does not offer a huge improvement. A greater increase in the speed of convergence can be obtained by using the MFxNLMS algorithm with orthogonal correction factors (M-OCF), but the usage of orthogonal correction factors also increases the computational complexity and limits the usage of the M-OCF in massive real-time applications. However, Graphics Processing Units (GPUs) are well known for their potential for highly parallel data processing. Therefore, GPUs seem to be a suitable platform to ameliorate this computational drawback. In this paper, we propose to derive the M-OCF algorithm to a partitioned block-based version in the frequency domain (FPM-OCF) for multichannel ANC systems in order to better exploit the parallel capabilities of the GPUs. The results show improvements in the convergence rate of the FPM-OCF algorithm in comparison to other NLMS-type algorithms and the usefulness of GPU devices for developing versatile, scalable, and low-cost multichannel ANC systems. [ABSTRACT FROM AUTHOR]

• This paper presents a family of normalized DSAs (NDSAs). • Low-order norm constraints are used to improve the performance for sparse system identification. • A variable step-size is derived for the family of DSAs to reduce steady-state misalignment. • Simulation results are provided to verify the effectiveness of the proposed algorithms. The classical sign algorithm (SA) has attracted much attention in many applications because of its low computational complexity and robustness against impulsive noise. However, its steady-state mean-square derivation (MSD) is large when a large step-size is used to guarantee a relatively fast convergence rate. To address this problem, the dual sign algorithm (DSA) was developed by using a piecewise cost function in the literature. In this paper a family of normalized DSAs (NDSAs) is proposed to further improve the performance of the DSA in terms of MSD. Specifically, two sparse NDSAs are firstly developed, by using the ℓ 1 -norm and ℓ 0 -norm constraints, respectively; on this basis, some variable step-size algorithms are then proposed based on mean-square a posteriori error minimization. Finally, simulation results are provided to show the superior performance of our proposed algorithms. [ABSTRACT FROM AUTHOR]

When using convex probability sets (or, equivalently, lower previsions) as uncertainty models, identifying extreme points can help simplifying various computations or the use of some algorithms. In general, sets induced by specific models such as possibility distributions, linear vacuous mixtures or 2-monotone measures may have extreme points easier to compute than generic convex sets. In this paper, we study extreme points of another specific model: comparative probability orderings between the singletons of a finite space. We characterize these extreme points by means of a graphical representation of the comparative model, and use them to study the properties of the lower probability induced by this set. By doing so, we show that 2-monotone capacities are not informative enough to handle this type of comparisons without a loss of information. In addition, we connect comparative probabilities with other uncertainty models, such as imprecise probability masses. [ABSTRACT FROM AUTHOR]

This paper discusses beamspace target height estimation for bistatic multiple-input multiple-output (MIMO) radars. Beamspace super-resolution algorithms for target height estimation have attracted significant attention due to low data transmission, storage, and computation. However, current literature has not yet considered beamspace target height estimation for a bistatic MIMO radar. Although target height estimation algorithms can be directly applied to a bistatic MIMO radar after minor modifications, such a direct application does not afford optimum performance. Therefore, we develop a three-dimensional beamspace maximum likelihood data fusion (3D-BMLF) algorithm appropriate for the target height estimation algorithm of a bistatic MIMO radar. The 3D-BMLF algorithm first converts target signals from the element space to the 3D beamspace, separates the transmitter and the receiver signals, and obtains two closed-form solutions of target height using the beamspace ML algorithm. Finally, the proposed method fuses the two closed-form solutions by minimizing the mean square error (MSE) of estimation. As a result, the 3D-BMLF algorithm has a very low computational burden and good estimation accuracy. Our computational complexity analysis and simulation results demonstrate the suitability of the 3D-BMLF algorithm for engineering applications. [ABSTRACT FROM AUTHOR]

Abstract: This paper shows a new algorithm to improve the performance of IQ demodulators and frequency converters exhibiting gain and phase imbalances between their branches. This algorithm does not require any input calibration signal and is independent of the input signal level. It exploits the spectral coherence (SC) concept using a monobit kernel to achieve optimization targets with minimum time to convergence, low computational load, and a wide range of input levels. Its effectiveness is shown through a low-IF receiver that improves its image rejection ratio (IRR) from 30 dB to 60 dB. [Copyright &y& Elsevier]

Abstract: This paper presents new algorithms that exploit uncorrelation between adjacent data samples produced by IFFT in ADSL systems. These algorithms update time-domain equalizer (TEQ) coefficients by adjacent lag autocorrelation minimization (ALAM) to shorten the channel. ALAM is an addition to the family of several existing autocorrelation-based algorithms that can achieve similar or better performance at lower complexity compared to other algorithms. The complexity reduction is achieved by designing a cost function without the sum-square, and further utilizing symmetrical TEQ property to reduce the complexity of the adaptation of TEQ to half of other existing algorithms. Furthermore, to avoid the limitations of lower bitrate after reaching the maximum bitrate and high complexity, an adaptive TEQ using equal-taps constraint (ETC) is introduced to maximize the bitrate along with achieving the lowest complexity. In addition, a simplified approach is used to calculate the expectation to reduce the computational complexity for ALAM and the other existing autocorrelation-based algorithms. Simulation results demonstrate that the proposed algorithms and modified existing algorithms achieve similar or better performance figures with reduced complexities. [Copyright &y& Elsevier]

Abstract: Traditional minimum spanning tree-based clustering algorithms only make use of information about edges contained in the tree to partition a data set. As a result, with limited information about the structure underlying a data set, these algorithms are vulnerable to outliers. To address this issue, this paper presents a simple while efficient MST-inspired clustering algorithm. It works by finding a local density factor for each data point during the construction of an MST and discarding outliers, i.e., those whose local density factor is larger than a threshold, to increase the separation between clusters. This algorithm is easy to implement, requiring an implementation of iDistance as the only k-nearest neighbor search structure. Experiments performed on both small low-dimensional data sets and large high-dimensional data sets demonstrate the efficacy of our method. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce a novel image adaptive technique for high capacity watermarking scheme using accurate and fast radial harmonic Fourier moments (RHFMs). The high embedding capacity is achieved by improving the hiding ratio after reducing inaccuracies in the computation of RHFMs. The binary watermark is embedded by performing the conditional quantization of selected RHFMs magnitudes to minimize the spatial distortion added to the host image. In addition, fast algorithms based on 8-way symmetry/anti-symmetry properties and recursive relations for the computation of sinusoidal kernel functions are adopted to enhance the speed of RHFMs-based watermarking process. Experimental studies show that the proposed watermarking scheme provides higher embedding capacity, good visual imperceptibility, better robustness to geometric distortions and common signal processing transformations, and lower computational complexity compared to the existing Zernike and pseudo-Zernike moments (ZMs/PZMs)-based watermarking schemes. [Copyright &y& Elsevier]

Abstract: In this paper, a local weighted interpolation method for intra-field deinterlacing is proposed as an improved version of the DCS (deinterlacing with awareness of closeness and similarity) algorithm. The original DCS method is derived from bilateral filter which takes the local spatial closeness and pixel similarity into account when calculating the weight of interpolation. The proposed algorithm achieves three improvements: 1) instead of the line average, a more accurate interpolation filter is used to estimate the center missing pixel; 2) the center-independent interpolation method is proposed to replace the center-dependent interpolation strategy; 3) the adaptive weighted interpolation method is used to improve the accuracy of interpolation. Experimental results show that the proposed algorithm provides superior performance in terms of both objective and subjective image qualities when compared with other conventional benchmarks, including DCS algorithms with low complexity. [Copyright &y& Elsevier]

Abstract: Multi-element synthetic aperture focusing (n-SAF) methods have been proposed as a suitable way to reduce cost and size for complex ultrasonic imaging systems. In this method, the larger the sub-aperture, the better the image contrast. However, when the number n of elements in the sub-aperture increases, some problems arise due to the great number of signals involved in the beamforming process, demanding a more complex electronic architecture, a higher bandwidth to manage the signals, and a greater computational power to compose the images in real time. This paper presents the n-SAF method in combination with a data reduction algorithm that reduces the number of signals intervening in the image data processing. The proposed method, called nR-SAF, simplifies the beamforming process; the hardware adds all signals in phase from identical coarray elements (similar spatial frequencies) thus a reduction of approximately 2/n of the data intervening in the dynamical focusing process is attained. The phase errors due to the simplified algorithms are also analyzed. We concluded that there are some limitations for sub-aperture size when image points are very near the transducer. Finally, an electronic architecture is presented, which is able to implement high velocity images from nR-SAF methods. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a source localization algorithm based on a sparse Fast Fourier Transform (FFT)-based feature extraction method and spatial sparsity. We represent the sound source positions as a sparse vector by discretely segmenting the space with a circular grid. The location vector is related to microphone measurements through a linear equation, which can be estimated at each microphone. For this linear dimensionality reduction, we have utilized a Compressive Sensing (CS) and two-level FFT-based feature extraction method which combines two sets of audio signal features and covers both short-time and long-time properties of the signal. The proposed feature extraction method leads to a sparse representation of audio signals. As a result, a significant reduction in the dimensionality of the signals is achieved. In comparison to the state-of-the-art methods, the proposed method improves the accuracy while the complexity is reduced in some cases. [Copyright &y& Elsevier]

Abstract: Locating an unknown-position source using received signal strength (RSS) measurements in an accurate and low-complexity manner is addressed in this paper. Given that the source transmit power is unknown, we employ the differential RSS information to devise two computationally attractive localization methods based on the weighted least squares (WLS) approach. The main ingredients in the first algorithm development are to obtain the unbiased estimates of the squared ranges and introduce an extra variable. The second method improves the first version by implicitly exploiting the relationship between the extra variable and source location through a second WLS step. The performance of the two estimators is analyzed in the presence of zero-mean white Gaussian disturbances. Numerical examples are also included to evaluate their localization accuracy by comparing with the maximum likelihood approach and Cramér–Rao lower bound. [Copyright &y& Elsevier]

Abstract: Adaptive arrays suffer from performance degradation in the presence of steering vector errors. The doubly constrained robust Capon beamformer (DCRCB) can deal with the problem, utilizing all the eigenvalues and eigenvectors of the covariance matrix, which leads to high computational complexity. This paper presents a robust beamforming method which is computationally efficient, exploiting principal eigenpairs only. The eigenpairs can be estimated based on the projection approximation subspace tracking with deflation (PASTd). The original PASTd algorithm, which does not provide orthonormal eigenvectors in general, is modified so that the orthonormalization of eigenvectors can be efficiently made using the structure of the modified algorithm. The proposed beamforming method significantly reduces the computational load, particularly when the number of the directional signals is much less than that of sensor elements, and substantially has the same performance as the conventional one utilizing all the eigenpairs. [Copyright &y& Elsevier]

Abstract: The Vertical Bell Laboratories Layer Space–Time (V-BLAST) system using ordered successive interference cancellation (OSIC) detection is a popular system architecture featuring high data rate and moderate computational complexity. The performance of the V-BLAST system can be further improved by exploiting the diversity and closed-loop gain provided by the technique of combined antenna selection (CAS). In this paper, we investigate the design of CAS algorithms with a goal to minimize the systemʼs error rate. As the error rate of a system varies dramatically with the transceiver architecture, the proposed CAS algorithms explicitly take the VBLAST detection structure into account. Despite the optimal CAS can be achieved by exhaustively searching all the transmit and receive antenna subsets, the applicability of this brute-force approach is excessively limited due to its huge computational complexity. To this end, we propose two computationally efficient CAS algorithms by implementing a greedy strategy incrementally or decrementally to fully exploit the recursive structure of mean-square-error matrices between consecutive V-BLAST detection stages. Near optimal error rate performance can therefore be obtained with significantly lower complexity as verified by the extensive computer simulations. [Copyright &y& Elsevier]

Abstract: Selective partial update of the adaptive filter coefficients has been a popular method for reducing the computational complexity of least mean-square (LMS)-type adaptive algorithms. These algorithms use a fixed step-size that forces a performance compromise between fast convergence speed and small steady state misadjustment. This paper proposes a variable step-size (VSS) selective partial update LMS algorithm, where the VSS is an approximation of an optimal derived one. The VSS equations are controlled by only one parameter, and do not require any a priori information about the statistics of the system environment. Mean-square performance analysis will be provided for independent and identically distributed (i.i.d.) input signals, and an expression for the algorithm steady state excess mean-square error (MSE) will be presented. Simulation experiments are conducted to compare the proposed algorithm with existing full-update VSS LMS algorithms, which indicate that the proposed algorithm performs as well as these algorithms while requiring less computational complexity. [Copyright &y& Elsevier]

Abstract: Square Contour Algorithm (SCA) is a relatively new blind equalization algorithm which reduces the dispersion between the output of the equalizer and a square contour. In this paper we develop Frequency Domain Square Contour Algorithm (FD-SCA) which is based on frequency domain implementation of block based SCA. Simulation results reveal that FD-SCA exhibits the same performance as that of existing time domain SCA with significantly low computational complexity. The performance of the FD-SCA in terms of convergence rate is further enhanced using normalization in frequency bins. The complete hardware architecture for the proposed FD-SCA based on ASM design flow along with implementation results is also presented. [Copyright &y& Elsevier]

Abstract: In the context of sample rate conversion (SRC) filter design; this paper makes two key contributions. Firstly, a practical factorization algorithm has been formulated that iteratively assigns rate change factors to filtering stages of a proposed multi-standard SRC architecture. This multi-stage architecture is composed of multiplexed CIC filters, Farrow interpolators and half-band filters. Secondly, a novel frequency domain technique for joint CIC compensation filter and Farrow interpolator design has been proposed. Based on these contributions; a programmable and computationally efficient SRC filter design scheme with utilization in the digital IF stage of a software radio has been derived. The proposed ideas are explained using design examples and simulation results with parameters corresponding to various commercial wireless communications standards including GSM, IS-95, UMTS and the emerging WiMax IEEE 802.16 standard. [Copyright &y& Elsevier]

Abstract: This paper provides a comprehensive review of user parameter-free robust adaptive beamforming algorithms. We present the ridge regression Capon beamformers (RRCBs), the mid-way (MW) algorithm, and the convex combination (CC) as well as the general linear combination (GLC) approaches. The purpose of these methods is to mitigate the effect of small sample size and steering vector errors on the standard Capon beamformer (SCB). We also present sparsity based iterative beamforming algorithms, namely the iterative adaptive approach (IAA), maximum likelihood based IAA (referred to as IAA-ML) and M-SBL (multi-snapshot sparse Bayesian learning), which exploit sparsity to estimate the signal parameters. We provide a thorough evaluation of these beamforming methods in terms of power and spatial spectrum estimation accuracies, output signal-to-interference-plus-noise ratio (SINR) and resolution under various scenarios including coherent, non-coherent and distributed sources, steering vector mismatches, snapshot limitations and low signal-to-noise ratio (SNR) levels. Furthermore, we discuss the computational complexities of the algorithms and provide insights into which algorithm is the best choice under which circumstances. [Copyright &y& Elsevier]

Abstract: Near-field source localization using passive sensor arrays plays an important role in array signal processing areas. Although many algorithms have been developed to deal with this issue, most of them suffer from either parameter match or heavy loss of the aperture or high computational complexity problems. To overcome these problems, a new algorithm is proposed in this paper to jointly estimate the ranges, directions-of-arrival (DOAs), and frequencies of multiple near-field narrow-band sources. Simulation results verify that the proposed algorithm can resolve these problems and give much better performance. [Copyright &y& Elsevier]

In this paper, two low computational complexity accurate array response control algorithms for large-scale antenna array are presented. The first proposed algorithm is an Improved Weight vector ORthogonal Decomposition (I-WORD) approach. We extend the WORD method by selecting the non-negative coefficient as the ultimate solution, and thus the ultimate weight vector will be in an exact form. Therefore, the proposed I-WORD algorithm has no longer a selection procedure and has low computational complexity compared with the WORD algorithm. Moreover, to achieve multi-point accurate array response control, we further develop a Multi-point control based on I-WORD (MI-WORD) algorithm. The MI-WORD algorithm is able to control multi-point response simultaneously by finding the weight vector from the intersection of weight vector sets with a new matrix constructing manner, which is different from the multi-point accurate array response control (MA2RC) method. Both the proposed algorithms can be applied to adjust the response accurately to synthesize the beampattern. Furthermore, the proposed MI-WORD method has the advantage of low computational complexity, especially when the number of array antennas is large. Simulation results show the effectiveness of the two algorithms and the property of low complexity of the MI-WORD algorithm for beampattern synthesis. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a computationally efficient method for extracting individual radio channels from the output of the wideband analog to digital converter (ADC) is presented. In a software radio, the extraction of individual channels from the output of the wideband ADC is by far the most computationally demanding task; hence it is very important to devise computationally efficient algorithms for this task. We proposed a new algorithm by assuming the symmetric signal with periods of the length-P (number of coefficients in low pass filter prototype) as an input signal to the subsampled filter bank. Also we divide the complex input into real and imaginary parts, then we perform operations in each part using two parallel filter banks. Finally, we add the outputs in two parts. By employing this algorithm to the subsampled filter bank channelizer, the complexity of the proposed algorithm was reduced by considerable amount of 81%. [Copyright &y& Elsevier]

Abstract: Subset choice denotes the task of choosing a subset of items from among a set of available items. Because the number of possible choice options in subset choice grows exponentially with the size of the choice set, subset choice tasks can be computationally challenging. This paper discusses how the computational complexity of subset choice under different models can be utilized in the quest for descriptive models of subset choice. We consider several models of subset choice (including the additive model, the binary-interaction model and the h-ary interaction model) and show how the theory of computational complexity (including the theory of NP-completeness and fixed-parameter tractability) can be used to evaluate the psychological plausibility of such models under different assumptions of processing speed, parallelism and size of problem parameters. [Copyright &y& Elsevier]

Transform-based block digital filtering (BDF) is a powerful tool for reducing computational complexity and increasing the parallelism of digital filtering systems. Most commonly used transforms, such as the discrete Fourier transform (DFT) and the discrete cosine transform (DCT), are not suitable for fixed-point implementation since they lead to a large quantization error. Otherwise, Fermat number transform (FNT) ensures an error-free computation in addition to a lower computational cost. In this paper, we propose an efficient algorithm for the optimal FNT-BDF design based on a quadratic criterion. A significant reduction in the computational cost of the algorithm is achieved through the use of the properties of circulant matrices and the Babai estimate method, used to solve integer least squares problem. Compared to DFT-BDF design, simulation results verify that our optimal FNT-BDF is more efficient and more accurate. [ABSTRACT FROM AUTHOR]

The frequency diverse array multiple-input multiple-output (FDA-MIMO) radar can detect target range by exploiting a small frequency offset across the transmit sensors, which can be utilized to jointly estimate angle and range. Nevertheless, the detection performance is basically restricted by the array aperture and signal bandwidth. In this paper, we propose a joint optimization design scheme for FDA-MIMO radar, i.e., the virtual coprime planar array with 'unfolded' coprime frequency offsets (VCPA-UCFO) framework, for 3D localization without ambiguity. The VCPA-UCFO framework can significantly save sensors and physical space while remarkably extend array aperture and signal bandwidth, which brings about remarkably enhanced economic benefits and estimation performance. Additionally, we construct the 3D localization problem as 3D-MUSIC spatial spectrum function and transform the 3D total spectrum search of the conventional 3D-MUSIC algorithm into 1D local spectrum search by cooperating ESPRIT algorithm and twice reduce dimension MUSIC (TRD-MUSIC) algorithm. The TRD-MUSIC algorithm can significantly relieve computational burden but with no performance degradation. The CRBs are given as performance benchmark. The analysis and simulations have verified the effectiveness and advantages of VCPA-UCFO framework and TRD-MUSIC algorithm in system cost, localization accuracy, resolution and computational complexity. [ABSTRACT FROM AUTHOR]

Two-dimensional (2-D) direction of arrival (DOA) estimation exploiting interlaced uniform planar array (IUPA) motion is discussed in this paper, and a Discrete Fourier Transform cascading Taylor Expansion (DFT-TE) algorithm is proposed. Specifically, the proposed IUPA structure possesses larger inter-element spacing than traditional uniform planar array (UPA), and it can mitigate the mutual coupling effectively. Simultaneously, the proposed IUPA structure is suitable for array motion. The synthetic IUPA generated by IUPA motion can offer more available array sensors and better DOA estimation performance than synthetic UPA and synthetic sparse uniform planar array (SUPA). Furthermore, considering the high computational complexity of 2-D DOA estimation, we propose the DFT-TE method. The DFT-TE method requires neither eigenvalue decomposition nor search operation. It gets the initial angles by employing the direct DFT method, and obtains the offset compensation for refined estimates by utilizing the Taylor Expansion method and total least squares (TLS) criterion. The proposed method has lower complexity and better estimation performance than the traditional 2-D DFT cascading search (DFT-S) method. Simulation results verify the effectiveness and advantages of the IUPA structure and DFT-TE method. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive algorithm is derived by considering that the beamforming vector can be decomposed as a Kronecker product of two smaller vectors. Such a decomposition leads to a joint optimization problem, which is then solved by using an alternating optimization strategy along with the steepest-descent method. The resulting algorithm, termed here Kronecker product constrained least-mean-square (KCLMS) algorithm, exhibits (in comparison to the well-known CLMS) improved convergence speed and reduced computational complexity; especially, for arrays with a large number of antennas. Simulation results are shown aiming to confirm the robustness of the proposed algorithm under different operating conditions. [ABSTRACT FROM AUTHOR]

In this paper, a one-step method is proposed for the classification and estimation of the parameters in the scenario of mixed near-field and far-field stationary sources. By constructing several temporal-spatial cumulant matrices, utilizing the stationary property of sources, and introducing a mechanism for controlling the rank, a parameters estimation matrix is formed. This matrix contains electric angles and signals information. By introducing a method based on the rotational invariant technique and using the estimation signal parameter, the direction-of-arrival of all sources' signals and the range of the near-field sources (NFSs) are estimated. Also, a reasonable classification of the signals types is provided. Unlike conventional methods, the proposed method does not require any spectral search stage, so it reduces the computational complexity to a great extent. The proposed algorithm prevents the array aperture loss, has no need to know the number of NFSs or field-field sources, and does not require any extra stage to pair the parameters. Mathematical analysis and simulation results validate the good performance of parameters estimation and sources classification in the proposed method. [ABSTRACT FROM AUTHOR]

Compared with the traditional subspace methods, the application of compressed sensing (CS) to the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar can achieve robustness to coherent targets and high localization accuracy with a limited number of snapshots. However, the performance of grid-based CS reconstruction methods degrades since there is an unavoidable basis mismatch between the actual DOD/DOA and the assumed basis. As a gridless CS method, the atomic norm minimization (ANM) has attracted much attention. Specifically, the use of multiple snapshots in ANM has improved the parameter estimation accuracy in contrast with a single snapshot. In this paper, we address the problem of gridless DOD and DOA estimation in bistatic MIMO radar, and develop a multiple-snapshot 2D-ANM algorithm and its two low complexity versions. We start with a rigorous derivation on how to convert the multiple-snapshot 2D-ANM into a semi-definite programming problem, and then explore its dual problem. To overcome the heavy computational burden of the 2D-ANM when the numbers of snapshots and array elements increase, we further propose a 2D-ANM algorithm with snapshot reduction (2D-ANM-SR), as well as an improved 2D-ANM-SR algorithm based on alternating direction method of multipliers (2D-ANM-SR-ADMM). Numerical examples show that with similar accuracy in comparison to 2D-ANM, much lower computational complexity can be achieved via 2D-ANM-SR and 2D-ANM-SR-ADMM. [ABSTRACT FROM AUTHOR]

This paper focuses on the weak maneuvering target detection problem in the alpha-stable noise (ASN) environment. A novel coherent integration framework is proposed, where the range migration (RM) is first removed via approximate linear methods to make the noise distribution character remain unchanged, and then the ASN suppression, Doppler frequency migration (DFM) compensation, and coherent integration are simultaneously achieved in the proper transform domain. Based on this framework, a robust detection method is developed. First, the second-order keystone transform (SoKT) is employed to correct the range curve (RC) induced by the target's acceleration. Thereafter, the centroid axis rotation (CAR) is presented to remove the residual range walk (RW) by rotating the slow time axis parallel to the RW line in the fast-time and slow-time domain. Finally, the phase fractional lower-order Lv's distribution (PFLOLVD) is proposed to compensate the DFM and realize the energy accumulation of the target in the ASN environment. Furthermore, the performance of proposed algorithm in aspects of coherent integration time, computational complexity, and multiple targets detection are analyzed in detail. Compared with the existing coherent integration detection methods, the proposed method is both robust for ASN environments and superior in the detection performance. [ABSTRACT FROM AUTHOR]

Direction-of-arrival (DOA) estimation is a fundamental problem in many applications of array signal processing. In practice, it is more important to track the DOAs of moving target signals. In this paper, we study the problem of tracking the DOAs of multiple signals in the frequency diversity array (FDA)-MIMO radar system. Aiming at FDA signals, we propose an efficient algorithm for DOA tracking, which combines subspace updating with the DOA estimation of the FDA signal. The proposed algorithm effectively solves the problem of angle-range coupling in the FDA signal. Moreover, it achieves excellent tracking performance and requires low computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To locate mixed far-field (FF) and near-field (NF) sources, most existing methods require two or three steps to first find the direction-of-arrival (DOA) and then retrieve the range parameters based on the DOA estimates. Hence, the range estimation performance highly relies on the DOA estimation accuracy in these methods. On the other hand, the MUSIC-like or on-grid sparse methods require discretization of the angle and range spaces which may bring in high computations and grid mismatch problem. In this paper, we propose a new method named Gridless OnE-Step (GOES) to jointly retrieve the DOA and range without requiring the pairing and discretization of the angle and range. In particular, we first construct a virtual single-snapshot signal model by the fourth-order cumulants, and then formulate a semidefinite programming (SDP) based on the atomic norm technique to simultaneously estimate the angle and range information. To reduce the computational complexity, we also propose a projected Wirtinger gradient descent algorithm Wirtinger-GOES (W-GOES) for the implementation. Because of the one-step procedure, our method not only has higher resolution than many existing methods in distinguishing both the angles and the ranges of different sources, but also can locate multiple NF or mixed FF and NF sources closely-located in angle domain or even with the same DOA. Extensive simulations are carried out to verify the superiorities of our method. [ABSTRACT FROM AUTHOR]

In this paper, a novel method called fourth-order spatiotemporal algorithm (FOSA) is presented for classification, 2D DOA estimation and 3D localization of mixed near-field and far-field sources using a uniform circular array with a center sensor. By constructing two spatiotemporal cumulant matrices, without range information, and regarding the fourth-order stationary property, the 2D DOA estimation matrix is extracted. By applying the eigenvalue decomposition, the virtual steering matrix is estimated. The sensor in the center of the array is used to extract the valid virtual steering vectors. The 2D DOAs are estimated by a least-squares method and without the use of spectral search. Ranges of near-field sources are estimated by constructing a spatial cumulant matrix and using the 1D MUSIC technique. FOSA provides a reasonable classification of signals types. It can detect near and far field sources possessing the same azimuth or elevation DOAs. Furthermore, it has a much low computational complexity. FOSA prevents array aperture loss without any pairing parameters. The simulation results validate its high performance in terms of estimation accuracy, resolution and the probability of correct classification of the signals types. [ABSTRACT FROM AUTHOR]

In this paper, we study anomaly detection in a distributed network of nodes and introduce a novel algorithm based on Support Vector Machines (SVMs). We first reformulate the conventional SVM optimization problem for a distributed network of nodes. We then directly train the parameters of this SVM architecture in its primal form using a gradient based algorithm in a fully distributed manner, i.e., each node in our network is allowed to communicate only with its neighboring nodes in order to train the parameters. Therefore, we not only obtain a high performing anomaly detection algorithm thanks to strong modeling capabilities of SVMs, but also achieve significantly reduced communication load and computational complexity due to our fully distributed and efficient gradient based training. Here, we provide a training algorithm in a supervised framework, however, we also provide the extensions of our implementation to an unsupervised framework. We illustrate the performance gains achieved by our algorithm via several benchmark real life and synthetic experiments. [ABSTRACT FROM AUTHOR]