Adaptive Weight Vector Research Articles

RETRACTED ARTICLE: An improved FCM algorithm with adaptive weights based on SA-PSO

Fuzzy c-means clustering algorithm (FCM) often used in pattern recognition is an important method that has been successfully used in large amounts of practical applications. The FCM algorithm assumes that the significance of each data point is equal, which is obviously inappropriate from the viewpoint of adaptively adjusting the importance of each data point. In this paper, considering the different importance of each data point, a new clustering algorithm based on FCM is proposed, in which an adaptive weight vector W and an adaptive exponent p are introduced and the optimal values of the fuzziness parameter m and adaptive exponent p are determined by SA-PSO when the objective function reaches its minimum value. In this method, the particle swarm optimization (PSO) is integrated with simulated annealing (SA), which can improve the global search ability of PSO. Experimental results have demonstrated that the proposed algorithm can avoid local optima and significantly improve the clustering performance.

Robust mainlobe interference suppression for coherent interference environment

A mainlobe interference suppression method is proposed in this paper, which can still work when the signal of interest (SOI) is present in the training data. In this method, the iterative adaptive approach (IAA) is applied to spatial spectrum estimation at first. Then, IAA spatial spectrum is used to reconstruct the interference-plus-noise covariance matrix (INCM). Next, the eigenvector associated with mainlobe interference in INCM is determined, and the eigen-projection matrix can be calculated to suppress the mainlobe interference. Meanwhile, the sidelobe-interference-plus-noise covariance matrix (SINCM) can be reconstructed. Finally, the adaptive weight vector is obtained. One main advantage is that the proposed method can deal with coherent mainlobe interference and sidelobe interferences simultaneously. Simulation results demonstrate the effectiveness and robustness of the proposed method.

Fast STAP Method Based on PAST with Sparse Constraint for Airborne Phased Array Radar

Conventional space-time adaptive processing (STAP) is difficult to effectively suppress nonhomogeneous clutter in practical airborne phased array radar systems owing to the requirements of large independent and identically distributed training samples and the high complexity involved in the inversion of a high dimensional matrix. In order to improve the clutter suppression performance with a small training sample supports, a fast STAP method based on projection approximation subspace tracking (PAST) with sparse constraint is proposed in this paper. In the proposed method, based on the low-rank property of the clutter covariance matrix, a sparse constraint is imposed in the cost function of PAST, and the adaptive weight vector is then derived iteratively. Because of the sparse constraint in PAST, the proposed method provides a more robust and stable estimation of the clutter subspace, especially when only a small set of training samples is available. Moreover, the proposed method not only achieves lower computational complexity compared with existing sparsity constrained STAP methods, but also better performance and faster convergence compared with conventional STAP methods without sparse constraint. The effectiveness of the proposed method is verified based on simulated and actual airborne phased array radar data.

Multiple mainlobe interferences suppression based on subspace matrix filtering and covariance matrix reconstruction

In order to suppress multiple mainlobe interferences and sidelobe interferences simultaneously, a mainlobe interference suppression algorithm is proposed. In this algorithm, the number of mainlobe interferences is estimated through a matrix filter at first. Then, the eigenvectors associated with mainlobe interference are determined and the eigen-projection matrix can be calculated. Next, the sidelobe-interference-plus-noise covariance matrix is reconstructed through eigenvalue replacement procedure. Finally, we can get the adaptive weight vector. Simulation results demonstrate the effectiveness of the proposed method when multiple mainlobe interferences exist.

A robust generalized sidelobe canceller via steering vector estimation

In the presence of the direction of arrival (DOA) mismatch, the performance of generalized sidelobe canceller (GSC) may suffer severe degradation due to the gain loss of the desired signal in the main array and cancellation. In this paper, one effective GSC algorithm is proposed to improve the robustness against the DOA mismatch of the desired signal. Firstly, two subspaces, which contain the desired signal’s actual steering vectors of the main and auxiliary arrays, can be obtained by using the range information of the angle which the desired signal may come from. By rotating these two subspaces, the desired signal’s actual steering vectors of the main and auxiliary arrays can be estimated based on the maximum output power criterion. Then, with the estimates of the steering vectors in the former step, the gain loss of the desired signal in the main array can be alleviated. Moreover, one adaptive weight vector with the ability to block the desired signal in the auxiliary array can be obtained simultaneously, which effectively avoids the signal of interest cancellation consequently. Cycle iterative approach is also applied to guarantee the estimation accuracy of a wide range of angle deviation. Numerical simulations demonstrate the effectiveness and applicability of the proposed method.

Mainlobe interference suppression with eigenprojection algorithm and similarity constraints

Here a novel mainlobe interference suppression technique based on eigenprojection algorithm and beam pattern similarity constraints is proposed. This method can effectively suppress the multiple mainlobe interferences and sidelobe interferences. The output of adaptive beamforming is calculated via adaptive weight vector obtained by the convex objective function and processed echo data based on the eigenprojection matrix. The simulations have been presented to evaluate the theoretical analysis.

A Novel STAP Algorithm for Airborne MIMO Radar Based on Temporally Correlated Multiple Sparse Bayesian Learning

In a heterogeneous environment, to efficiently suppress clutter with only one snapshot, a novel STAP algorithm for multiple-input multiple-output (MIMO) radar based on sparse representation, referred to as MIMOSR-STAP in this paper, is presented. By exploiting the waveform diversity of MIMO radar, each snapshot at the tested range cell can be transformed into multisnapshots for the phased array radar, which can estimate the high-resolution space-time spectrum by using multiple measurement vectors (MMV) technique. The proposed approach is effective in estimating the spectrum by utilizing Temporally Correlated Multiple Sparse Bayesian Learning (TMSBL). In the sequel, the clutter covariance matrix (CCM) and the corresponding adaptive weight vector can be efficiently obtained. MIMOSR-STAP enjoys high accuracy and robustness so that it can achieve better performance of output signal-to-clutter-plus-noise ratio (SCNR) and minimum detectable velocity (MDV) than the single measurement vector sparse representation methods in the literature. Thus, MIMOSR-STAP can deal with badly inhomogeneous clutter scenario more effectively, especially suitable for insufficient independent and identically distributed (IID) samples environment.

Sidelobe Reduction in Non-Contiguous OFDM-Based Cognitive Radio Systems Using a Generalized Sidelobe Canceller

In orthogonal frequency division multiplexing (OFDM), sidelobes of the modulated subcarriers cause high out-of-band (OOB) radiation, resulting in interference to licensed and un-licensed users in a cognitive radio system environment. In this work, we present a novel technique based on a generalized sidelobe canceller (GSC) for the reduction of sidelobes. The upper branch of the GSC consists of a weight vector designed by multiple constraints to preserve the desired portion of the input signal. The lower branch has a blocking matrix that blocks the desired portion and preserves the undesired portion (the sidelobes) of the input signal, followed by an adaptive weight vector. The adaptive weight vector adjusts the amplitudes of the undesired portion (the sidelobes) so that when the signal from the lower branch is subtracted from the signal from the upper branch, it results in cancellation of the sidelobes of the input signal. The effectiveness and strength of the proposed technique are verified through extensive simulations. The proposed technique produces competitive results in terms of sidelobe reduction as compared to existing techniques.

Semi-supervised local ridge regression for local matching based face recognition

In this paper, a novel algorithm named Semi-supervised Local Ridge Regression (SSLRR) is proposed for local matching based face recognition. Compared with other algorithms, the proposed algorithm possesses two advantages. Firstly, SSLRR utilizes a multiple graph based semi-supervised technique to propagate the class labels of labeled samples to the unlabeled ones. Thus, the information of both labeled and unlabeled data can be employed in our algorithm to improve its performance. Secondly, unlike most local matching based face recognition algorithms which assume different sub-images from the same face are independent, an adaptive non-negative weight vector is introduced into our SSLRR to combine the Laplacian matrices obtained by different sub-images. Therefore, the latent complementary information of multiple sub-patterns from the same face image can be taken into account. Moreover, a simple yet efficient iterative update scheme is also proposed to solve our SSLRR model. Extensive experiments are performed on five standard face databases (Yale, Extended YaleB, AR, CMU PIE and LFW) to demonstrate the efficiency of the proposed algorithm. Experimental results show that SSLRR obtains better recognition performance than some other state-of-the-art approaches.

Fast reduced-rank STAP algorithm based on Gram–Schmidt orthogonalisation for airborne radar

In the reduced-rank space-time adaptive processing (STAP) methods, especially the principal component (PC) analysis STAP method, a set of dominant eigenvectors must be obtained by singular value decomposition of the space-time covariance matrix. Therefore, it is very difficult to be applied in practical system due to the intense computational complexity. In order to reduce the computational burden, a fast reduced-rank STAP algorithm based on Gram–Schmidt (GS) orthogonalisation is proposed in this article. In the proposed GS-PC STAP method, the clutter subspace is reconstructed by the GS orthogonalisation of training samples. Then, the STAP adaptive weight vector is calculated by orthogonally projecting the quiescent weight vector into clutter subspace, which can hold fast convergence measure of effectiveness (MOE) and require less computational complexity by compared with the conventional PC method. Based on the simulated data and multichannel airborne radar measurements data, the corresponding convergence MOE and the clutter suppression performances are verified in the article.

Improved orthogonal projection approach utilising interference covariance matrix reconstruction for adaptive beamforming

When the training snapshots are contaminated by the desired signal, the performance of the orthogonal projection (OP) approach degrades significantly. Therefore, an improved OP adaptive beamforming is proposed by reconstructing interference covariance matrix to eliminate the desired signal cancellation effect. In the proposed method, by integrating the Capon spatial spectrum over a region separated from the desired signal direction, the interference-plus-noise covariance matrix is reconstructed first to remove the desired signal component from the sample covariance matrix. Subsequently, the interference subspace is estimated by eigenvalue decomposition, and then the adaptive weight vector is calculated using the OP algorithm to eliminate the noise perturbation. Simulation results show that the performance of the proposed algorithm is almost the same as the optimum beamforming.

A New Variable Step Size LMS Adaptive Filtering Algorithm and its Simulations

. Aiming at the disadvantage of the variable step size LMS adaptive filtering algorithms' convergence speed contradicting its steady-state error, a novel non-liner functional relationship between μ (n) and error signal e (n) was established. On the basis of the functional relationship, a new algorithm of variable step size LMS adaptive filtering was presented. The step size factor of the new algorithm is adjusted by the absolute value of the product of the current and former errors. It also uses the absolute estimation error compensation terms disturbance to speed up the convergence of adaptive filter tap weight vector. At the same time, the algorithm considers the relationship between step length of the last iteration and the former M error signal. As a result the algorithm has higher convergence characteristic and small steady state error. The theoretical analysis and simulation results show that the new algorithm has faster convergence speed, lower steady state error and better performance of noise suppression, also show the overall performance of this algorithm exceeds some others condition.

MOEA/D with Adaptive Weight Adjustment

Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved great success in the field of evolutionary multi-objective optimization and has attracted a lot of attention. It decomposes a multi-objective optimization problem (MOP) into a set of scalar subproblems using uniformly distributed aggregation weight vectors and provides an excellent general algorithmic framework of evolutionary multi-objective optimization. Generally, the uniformity of weight vectors in MOEA/D can ensure the diversity of the Pareto optimal solutions, however, it cannot work as well when the target MOP has a complex Pareto front (PF; i.e., discontinuous PF or PF with sharp peak or low tail). To remedy this, we propose an improved MOEA/D with adaptive weight vector adjustment (MOEA/D-AWA). According to the analysis of the geometric relationship between the weight vectors and the optimal solutions under the Chebyshev decomposition scheme, a new weight vector initialization method and an adaptive weight vector adjustment strategy are introduced in MOEA/D-AWA. The weights are adjusted periodically so that the weights of subproblems can be redistributed adaptively to obtain better uniformity of solutions. Meanwhile, computing efforts devoted to subproblems with duplicate optimal solution can be saved. Moreover, an external elite population is introduced to help adding new subproblems into real sparse regions rather than pseudo sparse regions of the complex PF, that is, discontinuous regions of the PF. MOEA/D-AWA has been compared with four state of the art MOEAs, namely the original MOEA/D, Adaptive-MOEA/D, [Formula: see text]-MOEA/D, and NSGA-II on 10 widely used test problems, two newly constructed complex problems, and two many-objective problems. Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.

Mainlobe Interference Suppression Based on Eigen-Projection Processing and Covariance Matrix Reconstruction

A mainlobe interference suppression method is proposed based on eigen-projection processing and covariance matrix reconstruction. In the proposed method, the eigen-projection matrix is calculated based on the eigenvector of mainlobe interference to suppress the mainlobe interference in received echo data. The interference covariance matrix is reconstructed after the eigenvalue of mainlobe interference is replaced by the average value of noise eigenvalues to eliminate the effect of mainlobe interference to adaptive weight vector. Finally, the output of adaptive beamforming is calculated by processed echo data and adaptive weight vector obtained based on reconstructed covariance matrix. Simulation results show that the proposed method can suppress the mainlobe interference effectively and achieve excellent performance .

A Denoising Method Based on the Variable Step NLMP for the Pulsar Signal

In order to denoise the pulsar signal, a variable step NLMP algorithm was introduced under the-stable distribution. The algorithm introduced a step update factor. By adjusting parameters and error information, the algorithm can adjust the incremental direction of the adaptive filter weight vector accurately, and improve the convergence performance. Simulation results show that the variable step-size NLMP algorithm is better than the NLMP algorithm in the denoising effect in-stable distribution noise environments.

A Denoising Method Based on the Variable Step NLMP for the Pulsar Signal

In order to denoise the pulsar signal, a variable step NLMP algorithm was introduced under the-stable distribution. The algorithm introduced a step update factor. By adjusting parameters and error information, the algorithm can adjust the incremental direction of the adaptive filter weight vector accurately, and improve the convergence performance. Simulation results show that the variable step-size NLMP algorithm is better than the NLMP algorithm in the denoising effect in-stable distribution noise environments.

FFT implementation of Doppler dependent pre-Doppler STAP

The Doppler dependent pre-Doppler STAP method can obtain better performance than the Doppler independent pre-Doppler STAP method at the cost of increased computational complexity. A computationally efficient way of carrying out the Doppler dependent pre-Doppler STAP method is developed without performance degradation. It evaluates all Doppler filter outputs by two groups of FFT, which avoids the need to evaluate the adaptive weight vector and to carry out Doppler processing independently for each adaptive space-time filter.

Design of generalised 2D adaptive sidelobe blanking detectors using the detection margin

We consider the radar detection problem with adaptive arrays in practical situations where the training data differ from the test data due to surprise interference (non-homogeneous scenario). We develop a general 2-step detection and sidelobe blanking procedure which can be applied to any kind of adaptive weight vector. For sidelobe blanking a generalised adaptive guard channel is developed which is based on the outputs of all array channels. For setting the thresholds the known theoretical results for classical GLRT, AMF and ACE tests are used as initial values. For fine tuning we introduce the detection margin as a direction sensitive criterion against inhomogeneous interference. Simulations with various adaptive algorithms and adaptive guard channels verify the usefulness of this strategy to design the tests.

A new variable step-size NLMS algorithm designed for applications with exponential decay impulse responses

A new variable step-size normalized least mean square algorithm (VSSNLMS) is proposed which is designed for applications where the unknown filter has an exponential decay impulse response. In the proposed algorithm a variable vector step size is used on the basis of maximizing the decrease of the mean square deviation (MSD) between the optimal and adaptive filter weight vectors at each iteration. Faster convergence rate of the resulting algorithm as compared with existing VSSNLMS algorithms is supported by simulations.

A New Variable Tap-Length LMS Algorithm to Model an Exponential Decay Impulse Response

This letter proposes a new variable tap-length least-mean-square (LMS) algorithm for applications in which the unknown filter impulse response sequence has an exponential decay envelope. The algorithm is designed to minimize the mean-square deviation (MSD) between the optimal and adaptive filter weight vectors at each iteration. Simulation results show the proposed algorithm has a faster convergence rate as compared with the fixed tap-length LMS algorithm and is robust to the initial tap-length choice