This paper provides a novel normalized sign least-mean square (NSLMS) algorithm which updates only a part of the filter coefficients and simultaneously performs sparse updates with the goal of reducing computational complexity. A combination of the partial-update scheme and the set-membership framework is incorporated into the context of L∞-norm adaptive filtering, thus yielding computational efficiency. For the stabilized convergence, we formulate a robust update recursion by imposing an upper bound of a step size. Furthermore, we analyzed a mean-square stability of the proposed algorithm for white input signals. Experimental results show that the proposed low-complexity NSLMS algorithm has similar convergence performance with greatly reduced computational complexity compared to the partial-update NSLMS, and is comparable to the set-membership partial-update NLMS.

We present a low-complexity complementary pair affine projection (CP-AP) adaptive filter which employs the intermittent update of the filter coefficients. To achieve both a fast convergence rate and a small residual error, we use a scheme combining fast and slow AP filters, while significantly reducing the computational complexity. By employing an evolutionary method which automatically determines the update intervals, the update frequencies of the two constituent filters are significantly decreased. Experimental results show that the proposed CP-AP adaptive filter has an advantage over conventional adaptive filters with a parallel structure in that it has a similar convergence performance with a substantial reduction in the total number of updates.

In this paper, we propose a new matting algorithm using local and nonlocal neighbors. We assume that K nearest neighbors satisfy the color line model that RGB distribution of the neighbors is roughly linear and combine this assumption with the local color line model that RGB distribution of local neighbors is roughly linear. Our assumptions are appropriate for various regions such as those that are smooth, contain holes or have complex color. Experimental results show that the proposed method outperforms previous propagation-based matting methods. Further, it is competitive with sampling-based matting methods that require complex sampling or learning methods.

In this paper we present a new post enhancement method for single look complex (SLC) SAR imagery, which is based on phase-extension inverse filtering. To obtain a high-quality SAR image, the proposed method improves the mainlobe resolution as well as efficiently suppresses the sidelobes with low computational complexity. The proposed method extends the effective signal band up to the maximum-bandwidth allowed by a SAR system. The band-extension is achieved by adjusting the magnitude level of matched filtered SAR spectrum. Because the proposed method preserves the phase components of the spectrum unlike other super-resolution techniques and deconvolution techniques, it enhances a SAR image without causing any displacement. To verify the efficacy of the proposed method we apply it to a simulated SAR image and a real ERS-1 SAR image. The result images show that the proposed method improves the mainlobe resolution with low sidelobe levels.

This paper presents a new algorithm that can solve the problem of selecting appropriate update step size in the LMS algorithm. The proposed algorithm, called a Complementary Pair LMS (CP-LMS) algorithm, consists of two adaptive filters with different update step sizes operating in parallel, one filter re-initializing the other with the better coefficient estimates whenever possible. This new algorithm provides the faster convergence speed and the smaller steady-state error than those of a single filter with a fixed or variable step size.

When time difference of arrival (TDOA)-based bearing measurements are used in passive triangulation, the accuracy of localization depends on the geometric relationship between the emitter and the sensors. In particular, the localization accuracy varies with the geometric conditions in TDOA-based direction finding (DF) for bearing measurement and lines of bearing (LOBs) crossing for triangulation. To obtain an accurate estimate in passive triangulation using TDOA-based bearing measurements, we shall use these bearings selectively by considering geometric dilution of precision (GDOP) between the emitter and the sensors. To achieve this goal, we first define two GDOPs related to TDOA-based DF and LOBs crossing geometries, and then propose a new hybrid GDOP by combining these GDOPs for a better selection of bearings. Subsequently, two bearings with the lowest hybrid GDOP condition are chosen as the inputs to a triangulation localization algorithm. In simulations, the proposed method shows its enhancement to the localization accuracy.

Spectrum sensing is one of the main functions in cognitive radio networks. To improve the sensing performance and increase spectrum efficiency, a number of cooperative spectrum sensing methods have been proposed. However, most of these methods focused on a single-channel environment. In this letter, we present a novel cooperative spectrum sensing method based on cooperator selection in a multi-channel cognitive radio network. Using reinforcement learning, a cognitive radio user can select reliable and robust cooperators, without any a priori knowledge. Using the proposed method, a cognitive radio user can achieve better sensing capability and overcome performance degradation problems due to malicious users or erratic user behavior. Numerical results show that the proposed method can achieve excellent performance.

We propose a novel bias-free adaptive beamformer employing an affine projection algorithm with the optimal regularization parameter. The generalized sidelobe canceller affine projection algorithm suffers from a bias of a weight vectors under the condition of no reference signals for output of an array in the beamforming application. First, we analyze the bias in the algorithm and prove that the bias can be eliminated through a large regularization parameter. However, this causes slow convergence at the initial state, so the regularization parameter should be controlled. Through the optimization of the regularization parameter, the proposed method achieves fast convergence without the bias at the steady-state. Experimental results show that the proposed beamformer not only removes the bias but also achieves both fast convergence and high steady-state output signal-to-interference-plus-noise ratio.

We present a new structure for parallel affine projection (AP) filters with different step-sizes. By observing their error signals, the proposed alternating AP (A-AP) filter selects one of the two AP filters and updates the weights of the selected filter for each iteration. As a result, the total computations required for the proposed structure is almost the same as that for a single AP filter. Experimental results show that the proposed alternating selection scheme extracts the best properties of each component filter, namely fast convergence and small steady-state error.

We present a new family of algorithms that solve the bias problem in the equation-error based adaptive infinite impulse response (IIR) filtering. A novel constraint, called the constant-norm constraint, unifies the quadratic constraint and the monic one. By imposing the monic constraint on the mean square error (MSE) optimization, the merits of both constraints are inherited and the shortcomings are overcome. A new cost function based on the constant-norm constraint and Lagrange multiplier is defined. Minimizing the cost function gives birth to a new family of bias-free adaptive IIR filtering algorithms. For example, two efficient algorithms belonging to the family are proposed. The analysis of the stationary points is presented to show that the proposed methods can indeed produce bias-free parameter estimates in the presence of white noise. The simulation results demonstrate that the proposed methods indeed produce unbiased parameter estimation, while being simple both in computation and implementation.

We present a new framework of the data-reusing (DR) adaptive algorithms by incorporating a constraint on noise, referred to as a noise constraint. The motivation behind this work is that the use of the statistical knowledge of the channel noise can contribute toward improving the convergence performance of an adaptive filter in identifying a noisy linear finite impulse response (FIR) channel. By incorporating the noise constraint into the cost function of the DR adaptive algorithms, the noise constrained DR (NC-DR) adaptive algorithms are derived. Experimental results clearly indicate their superior performance over the conventional DR ones.

In this paper, a new hierarchical block matching algorithm using mean and difference pyramids is presented. The detection of motion vectors at each level of the pyramid is accomplished by selectively eliminating the candidate motion vectors that cannot provide the best match at the next lower level. The remaining motion vectors at each level are propagated and used as the initial motion vectors at the next lower level. Therefore, the possibility of falling into local minima can be significantly reduced. The simulation results show that the proposed method has excellent performance with reduced computational complexity.

In this letter we propose a simple adaptive algorithm which solves the unit-norm constrained optimization problem. Instead of conventional parameter norm based normalization, the proposed algorithm incorporates single parameter normalization which is computationally much simpler. The simulation results illustrate that the proposed algorithm performs as good as conventional ones while being computationally simpler.