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Showing papers on "Kernel adaptive filter published in 2009"


Journal ArticleDOI
TL;DR: In this article, the authors proposed a new signal processing analysis of the bilateral filter, which complements the recent studies that analyzed it as a PDE or as a robust statistical estimator.
Abstract: The bilateral filter is a nonlinear filter that smoothes a signal while preserving strong edges. It has demonstrated great effectiveness for a variety of problems in computer vision and computer graphics, and fast versions have been proposed. Unfortunately, little is known about the accuracy of such accelerations. In this paper, we propose a new signal-processing analysis of the bilateral filter which complements the recent studies that analyzed it as a PDE or as a robust statistical estimator. The key to our analysis is to express the filter in a higher-dimensional space where the signal intensity is added to the original domain dimensions. Importantly, this signal-processing perspective allows us to develop a novel bilateral filtering acceleration using downsampling in space and intensity. This affords a principled expression of accuracy in terms of bandwidth and sampling. The bilateral filter can be expressed as linear convolutions in this augmented space followed by two simple nonlinearities. This allows us to derive criteria for downsampling the key operations and achieving important acceleration of the bilateral filter. We show that, for the same running time, our method is more accurate than previous acceleration techniques. Typically, we are able to process a 2 megapixel image using our acceleration technique in less than a second, and have the result be visually similar to the exact computation that takes several tens of minutes. The acceleration is most effective with large spatial kernels. Furthermore, this approach extends naturally to color images and cross bilateral filtering.

789 citations


Proceedings ArticleDOI
20 Jun 2009
TL;DR: A new bilateral filtering algorithm with computational complexity invariant to filter kernel size, so-called O(1) or constant time in the literature, that yields a new class of constant time bilateral filters that can have arbitrary spatial and arbitrary range kernels.
Abstract: We propose a new bilateral filtering algorithm with computational complexity invariant to filter kernel size, so-called O(1) or constant time in the literature. By showing that a bilateral filter can be decomposed into a number of constant time spatial filters, our method yields a new class of constant time bilateral filters that can have arbitrary spatial and arbitrary range kernels. In contrast, the current available constant time algorithm requires the use of specific spatial or specific range kernels. Also, our algorithm lends itself to a parallel implementation leading to the first real-time O(1) algorithm that we know of. Meanwhile, our algorithm yields higher quality results since we are effectively quantizing the range function instead of quantizing both the range function and the input image. Empirical experiments show that our algorithm not only gives higher PSNR, but is about 10× faster than the state-of-the-art. It also has a small memory footprint, needed only 2% of the memory required by the state-of-the-art for obtaining the same quality as exact using 8-bit images. We also show that our algorithm can be easily extended for O(1) median filtering. Our bilateral filtering algorithm was tested in a number of applications, including HD video conferencing, video abstraction, highlight removal, and multi-focus imaging.

325 citations


Journal ArticleDOI
TL;DR: A kernelized version of the extended recursive least squares (EX-KRLS) algorithm which implements for the first time a general linear state model in reproducing kernel Hilbert spaces (RKHS) which only requires inner product operations between input vectors, thus enabling the application of the kernel property.
Abstract: This paper presents a kernelized version of the extended recursive least squares (EX-KRLS) algorithm which implements for the first time a general linear state model in reproducing kernel Hilbert spaces (RKHS), or equivalently a general nonlinear state model in the input space. The center piece of this development is a reformulation of the well known extended recursive least squares (EX-RLS) algorithm in RKHS which only requires inner product operations between input vectors, thus enabling the application of the kernel property (commonly known as the kernel trick). The first part of the paper presents a set of theorems that shows the generality of the approach. The EX-KRLS is preferable to 1) a standard kernel recursive least squares (KRLS) in applications that require tracking the state-vector of general linear state-space models in the kernel space, or 2) an EX-RLS when the application requires a nonlinear observation and state models. The second part of the paper compares the EX-KRLS in nonlinear Rayleigh multipath channel tracking and in Lorenz system modeling problem. We show that the proposed algorithm is able to outperform the standard KRLS and EX-RLS in both simulations.

192 citations


Journal ArticleDOI
TL;DR: A two-stage algorithm, called switching-based adaptive weighted mean filter, is proposed to remove salt-and-pepper noise from the corrupted images by replacing each noisy pixel with the weighted mean of its noise-free neighbors in the filtering window.
Abstract: A two-stage algorithm, called switching-based adaptive weighted mean filter, is proposed to remove salt-and-pepper noise from the corrupted images. First, the directional difference based noise detector is used to identify the noisy pixels by comparing the minimum absolute value of four mean differences between the current pixel and its neighbors in four directional windows with a predefined threshold. Then, the adaptive weighted mean filter is adopted to remove the detected impulses by replacing each noisy pixel with the weighted mean of its noise-free neighbors in the filtering window. Numerous simulations demonstrate that the proposed filter outperforms many other existing algorithms in terms of effectiveness in noise detection, image restoration and computational efficiency.

183 citations


Journal ArticleDOI
TL;DR: A single-phase shunt active power filter for current harmonic compensation based on neural filtering is presented, which has been applied in numerical simulations and experimentally to a properly devised test setup, also in comparison with the classic sinusoidal current control based on the P-Q theory.
Abstract: This paper presents a single-phase shunt active power filter (APF) for current harmonic compensation based on neural filtering. The shunt active filter, realized by a current-controlled inverter, has been used to compensate a nonlinear current load by receiving its reference from a neural adaptive notch filter. This is a recursive notch filter for the fundamental grid frequency (50 Hz) and is based on the use of a linear adaptive neuron (ADALINE). The filter's parameters are made adaptive with respect to the grid frequency fluctuations. A phase-locked loop system is used to extract the fundamental component from the coupling point voltage and to estimate the actual grid frequency. The current control of the inverter has been performed by a multiresonant controller. The estimated grid frequency is fed to the neural adaptive filter and to the multiresonant controller. In this way, the inverter creates a current equal in amplitude and opposite in sign to the load harmonic current, thus producing an almost sinusoidal grid current. An automatic tuning of the multiresonant controller is implemented, which recognizes the largest three harmonics of the load current to be compensated by the APF. The stability analysis of the proposed control system is shown. The methodology has been applied in numerical simulations and experimentally to a properly devised test setup, also in comparison with the classic sinusoidal current control based on the P-Q theory.

176 citations


Journal ArticleDOI
TL;DR: In this article, an adaptive two-stage extended Kalman filter (ATEKF) using an adaptive fading EKF has been proposed to solve the problem of unknown bias.
Abstract: The well-known conventional Kalman filter requires an accurate system model and exact stochastic information. But in a number of situations, the system model has an unknown bias, which may degrade the performance of the Kalman filter or may cause the filter to diverge. The effect of the unknown bias may be more pronounced on the extended Kalman filter (EKF), which is a nonlinear filter. The two-stage extended Kalman filter (TEKF) with respect to this problem has been receiving considerable attention for a long time. Recently, the optimal two-stage Kalman filter (TKF) for linear stochastic systems with a constant bias or a random bias has been proposed by several researchers. A TEKF can also be similarly derived as the optimal TKF. In the case of a random bias, the TEKF assumes that the information of a random bias is known. But the information of a random bias is unknown or partially known in general. To solve this problem, this paper proposes an adaptive two-stage extended Kalman filter (ATEKF) using an adaptive fading EKF. To verify the performance of the proposed ATEKF, the ATEKF is applied to the INS-GPS (inertial navigation system-Global Positioning System) loosely coupled system with an unknown fault bias. The proposed ATEKF tracked/estimated the unknown bias effectively although the information about the random bias was unknown.

156 citations


Journal ArticleDOI
TL;DR: FanFan filter as discussed by the authors is a 2D double filter consisting of a low-pass along the degree n and a high-pass on the order m whose contour projection onto the (n, m) plane is fan-shaped.
Abstract: [1] Spatial low-pass filtering is necessary for processing the GRACE time-variable gravity (TVG) data which are otherwise plagued with short-wavelength noises. Here we devise a new non-isotropic filter, called the fan filter: In terms of the spherical harmonic spectrum, the fan filter is simply a 2-D double filter consisting of a low-pass along the degree n (the same as the conventional isotropic filter) simultaneously with a low-pass along the order m, whose contour projection onto the (n, m) plane is fan-shaped. It is deterministic and independent of a priori or external information, its implementation is straightforward, and the result is objective. Most importantly, we show that this simple filter performs well among its counterparts under similar conditions, in particular against the N-S striping noises prevalent in the GRACE TVG solutions. We demonstrate this with Gaussian weights at filter length and hence spatial resolution as fine as 300 km. We also deduce the fan filter's nominal amplitude-reduction factor as a function of the filter length for TVG signals that follow the Kaula rule.

118 citations


Journal ArticleDOI
TL;DR: This paper considers the impact of having a slowly time-varying domain over which the minimization takes place, and provides a general set of sufficient conditions for the convergence and correctness of the adaptive algorithm.
Abstract: The classical alternating minimization (or projection) algorithm has been successful in the context of solving optimization problems over two variables. The iterative nature and simplicity of the algorithm has led to its application in many areas such as signal processing, information theory, control, and finance. A general set of sufficient conditions for the convergence and correctness of the algorithm are known when the underlying problem parameters are fixed. In many practical situations, however, the underlying problem parameters are changing over time, and the use of an adaptive algorithm is more appropriate. In this paper, we study such an adaptive version of the alternating minimization algorithm. More precisely, we consider the impact of having a slowly time-varying domain over which the minimization takes place. As a main result of this paper, we provide a general set of sufficient conditions for the convergence and correctness of the adaptive algorithm. Perhaps somewhat surprisingly, these conditions seem to be the minimal ones one would expect in such an adaptive setting. We present applications of our results to adaptive decomposition of mixtures, adaptive log-optimal portfolio selection, and adaptive filter design.

113 citations


Patent
09 Jun 2009
TL;DR: In this article, an adaptive mode control apparatus and method for adaptive beamforming based on detection of a user direction sound are provided, which includes a signal intensity detector that searches for signal intensity of each designated direction to detect signal intensity having a maximum value when a voice signal of each direction is input through at least one microphone.
Abstract: An adaptive mode control apparatus and method for adaptive beamforming based on detection of a user direction sound are provided. The adaptive mode control apparatus includes a signal intensity detector that searches for signal intensity of each designated direction to detect signal intensity having a maximum value when a voice signal of each direction is input through at least one microphone; and an adaptive mode controller that compares the signal intensity having the maximum value detected through the signal intensity detector with a threshold value and determines whether to perform an adaptive mode of a Generalized Sidelobe Canceller (GSC) according to the comparison results. Therefore, a lack of control of adaptation of an adaptive filter of the conventional art is solved. That is, as one condition for guaranteeing performance of adaptive beamforming, adaptation of an adaptive filter is not performed when noise of a sound with a high autocorrelation is cancelled.

65 citations


Journal ArticleDOI
TL;DR: A novel adaptive version of the divided difference filter applicable to non-linear systems with a linear output equation is presented in this work, which demonstrates the superior performance of the proposed filter as compared to the standard DDF.

62 citations


Proceedings ArticleDOI
07 Nov 2009
TL;DR: The novel algorithm was well used in designing adaptive IIR digital filter about unknown system identification, and simulation results shown that the filter had more enhanced performance characteristics using the AIW-PSO algorithm and the complexity in calculation were improved greatly.
Abstract: An adaptive inertia weight particle swarm optimization (AIW-PSO) algorithm was presented for designing IIR digital filter. In this algorithm, modified Versoria function was employed in the new relation of adaptive inertia weight factor function instead of Sigmoid function for avoiding the exponential computation and ensuring the small final misadjustment. Furthermore, the novel algorithm was well used in designing adaptive IIR digital filter about unknown system identification, and simulation results shown that the filter had more enhanced performance characteristics using the AIW-PSO algorithm and the complexity in calculation were improved greatly.

Journal ArticleDOI
TL;DR: A novel scheme called the fuzzy strong tracking unscented Kalman filter (FSTUKF) is presented where the Fuzzy Logic Adaptive System (FLAS) is incorporated for determining the softening factor.
Abstract: A navigation integration processing scheme, called the strong tracking unscented Kalman filter (STUKF), is based on the combination of an unscented Kalman filter (UKF) and a strong tracking filter (STF). The UKF employs a set of sigma points by deterministic sampling, such that the linearization process is not necessary, and therefore the error caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. As a type of adaptive filter, the STF is essentially a nonlinear smoother algorithm that employs suboptimal multiple fading factors, in which the softening factors are involved. In order to resolve the shortcoming in traditional approach for selecting the softening factor through personal experience or computer simulation, a novel scheme called the fuzzy strong tracking unscented Kalman filter (FSTUKF) is presented where the Fuzzy Logic Adaptive System (FLAS) is incorporated for determining the softening factor. The proposed FSTUKF algorithm shows promising results in estimation accuracy when applied to the integrated navigation system design, as compared to the EKF, UKF and STUKF approaches.

Journal ArticleDOI
TL;DR: A new approach of filter design for nonlinear systems with time-varying delays is proposed using the linear matrix inequality (LMI) approach and may improve the existing ones due to estimate the upper bound of the derivative of Lyapunov functional without ignoring some useful terms.

Journal ArticleDOI
TL;DR: The paper investigates the possibility of decomposing vibration signals into deterministic and nondeterministic parts, based on the Wold theorem, and proposes a self-adaptive filter that shows a very good ability to decompose the signal.
Abstract: The paper investigates the possibility of decomposing vibration signals into deterministic and nondeterministic parts, based on the Wold theorem. A short description of the theory of adaptive filters is presented. When an adaptive filter uses the delayed version of the input signal as the reference signal, it is possible to divide the signal into a deterministic (gear and shaft related) part and a nondeterministic (noise and rolling bearings) part. The idea of the self-adaptive filter (in the literature referred to as SANC or ALE) is presented and its most important features are discussed. The flowchart of the Matlab-based SANC algorithm is also presented. In practice, bearing fault signals are in fact nondeterministic components, due to a little jitter in their fundamental period. This phenomenon is illustrated using a simple example. The paper proposes a simulation of a signal containing deterministic and nondeterministic components. The self-adaptive filter is then applied-first to the simulated data. Next, the filter is applied to a real vibration signal from a wind turbine with an outer race fault. The necessity of resampling the real signal is discussed. The signal from an actual source has a more complex structure and contains a significant noise component, which requires additional demodulation of the decomposed signal. For both types of signals the proposed SANC filter shows a very good ability to decompose the signal.

Journal ArticleDOI
TL;DR: In the proposed method, the prototype filter is optimized by using the windowing technique, with the novelty of exploiting spline functions in the transition band of the ideal filter, instead of using the conventional brick-wall filter.
Abstract: A very fast technique to design prototype filters for modulated filter banks without using time-consuming multivariable optimization is introduced. In the proposed method, the prototype filter is optimized by using the windowing technique, with the novelty of exploiting spline functions in the transition band of the ideal filter, instead of using the conventional brick-wall filter. A study of the optimization techniques and three different objective functions existing in the literature has been carried out, and more suitable redefinitions of these objective functions are employed to achieve as optimized prototype filters as possible. The resulting filter banks closely satisfy the perfect reconstruction property, as is illustrated by means of examples.

Journal ArticleDOI
TL;DR: In this paper, the adaptive fading extended Kalman filter (AFEKF) is analyzed and the stability of the filter is analyzed based on the analysis result of Reif and co-authors for the EKF.
Abstract: The well-known conventional Kalman filter gives the optimal solution but to do so, it requires an accurate system model and exact stochastic information. However, in a number of practical situations, the system model and the stochastic information are incomplete. The Kalman filter with incomplete information may be degraded or even diverged. To solve this problem, a new adaptive fading filter using a forgetting factor has recently been proposed by Kim and co-authors. This paper analyzes the stability of the adaptive fading extended Kalman filter (AFEKF), which is a nonlinear filter form of the adaptive fading filter. The stability analysis of the AFEKF is based on the analysis result of Reif and co-authors for the EKF. From the analysis results, this paper shows the upper bounded condition of the error covariance for the filter stability and the bounded value of the estimation error. Keywords: Adaptive Kalman filter, forgetting factor, nonlinear filter, stability analysis.

Proceedings Article
04 Jun 2009
TL;DR: In this paper, an efficient non-linear cascade filter for the removal of high density salt and pepper noise in image and video is proposed, which consists of two stages to enhance the filtering.
Abstract: In this paper, an efficient non-linear cascade filter for the removal of high density salt and pepper noise in image and video is proposed. The proposed method consists of two stages to enhance the filtering. The first stage is the Decision based Median Filter (DMF) which is used to identify pixels likely to be contaminated by salt and pepper noise and replaces them by the median value. The second stage is the Unsymmetric Trimmed Filter, either Mean Filter (UTMF) or Midpoint Filter (UTMP) which is used to trim the noisy pixels in an unsymmetric manner and processes with the remaining pixels The basic idea is that, though the level of denoising in the first stage is lesser at high noise densities, the second stage helps to increase the noise suppression. Hence, the proposed cascaded filter, as a whole is very suitable for low, medium as well as high noise densities even above 90%. The existing non-linear filters such as Standard Median Filter (SMF), Adaptive Median Filter (AMF), Weighted Median Filter (WMF), Recursive Weighted Median Filter (RWM) performs well only for low and medium noise densities. The recently proposed Decision Based Algorithm (DBA) shows better results upto 70% noise density and at high noise densities, the restored image quality is poor. The proposed algorithm shows better image and video quality in terms of visual appearance and quantitative measures.

Journal ArticleDOI
TL;DR: Compared with the conventional SOV filter, adaptive JPPSOV filter exhibits a better convergence performance with less computational burden in terms of convergence speed and steady-state error.
Abstract: Due to the computational complexity of the Volterra filter, there are limitations on the implementation in practice. In this paper, a novel adaptive joint process filter using pipelined feedforward second-order Volterra architecture (JPPSOV) to reduce the computational burdens of the Volterra filter is proposed. The proposed architecture consists of two subsections: nonlinear subsection performing a nonlinear mapping from the input space to an intermediate space by the feedforward second-order Volterra (SOV), and a linear combiner performing a linear mapping from the intermediate space to the output space. The corresponding adaptive algorithms are deduced for the nonlinear and linear combiner subsections, respectively. Moreover, the analysis of theory shows that these adaptive algorithms based on the pipelined architecture are stable and convergence under a certain condition. To evaluate the performance of the JPPSOV, a series of simulation experiments are presented including nonlinear system identification and predicting of speech signals. Compared with the conventional SOV filter, adaptive JPPSOV filter exhibits a litter better convergence performance with less computational burden in terms of convergence speed and steady-state error.

Patent
26 Jun 2009
TL;DR: In this paper, a method and a device are described for selecting between multiple available filters in an encoder to provide a frame having a low error and distortion rate for each full and sub-pixel position, determining whether to use an alternative filter over the default filter during interpolation.
Abstract: A method and a device are described for selecting between multiple available filters in an encoder to provide a frame having a low error and distortion rate. For each full and sub pixel position, determining whether to use an alternative filter over the default filter during interpolation by estimating the rate distortion gain of using each filter and signaling to the decoder the optimal filter(s) applied to each full and sub-pixel position. In one embodiment, identifying a reference frame and a current frame, interpolating the reference frame using a default filter to create a default interpolated frame, interpolating the reference frame using an alternative filter to create an alternative interpolated frame, determining for each sub-pixel position whether to use the default filter or the alternative filter based on a minimal cost to generate a final reference frame.

Journal ArticleDOI
TL;DR: The proposed Boosted Adaptive Particle Filter algorithm, based on the synthesis of an adaptive particle filtering algorithm and the AdaBoost face detection algorithm, is shown to yield more accurate estimates of the proposal distribution and the posterior distribution than the standard Particle filter thus enabling more accurate tracking in video sequences.

Proceedings ArticleDOI
03 Jun 2009
TL;DR: Sterling's polynomial interpolation method is employed to approximate nonlinear models and combined with the Kanlman filter framework, CDKF is proposed to solve the probabilistic state-space SLAM problem.
Abstract: This paper presents an central difference Kalman filter (CDKF) based Simultaneous Localization and Mapping (SLAM) algorithm, which is an alternative to the classical extended Kalman filter based SLAM solution (EKF-SLAM). EKF-SLAM suffers from two important problems, which are the calculation of Jacobians and the linear approximations to the nonlinear models. They can lead the filter to be inconsistent. To overcome the serious drawbacks of the previous frameworks, Sterling's polynomial interpolation method is employed to approximate nonlinear models. Combined with the Kanlman filter framework, CDKF is proposed to solve the probabilistic state-space SLAM problem. The proposed approach improves the filter consistency and state estimation accuracy. Both simulated experiments and bench mark data set are used to demonstrating the superiority of the proposed algorithm.

Journal ArticleDOI
TL;DR: A new efficient methodology for constraining the increase in length of a radial basis function (RBF) network resulting from the kernel LMS algorithm without significant sacrifice on performance is proposed.

Journal ArticleDOI
TL;DR: This paper presents efficient approaches for designing cosine-modulated filter banks with linear phase prototype filter by way of an efficient iterative algorithm in which the closed-form expression is given in each iteration.
Abstract: This paper presents efficient approaches for designing cosine-modulated filter banks with linear phase prototype filter. First, we show that the design problem of the prototype filter being a spectral factor of 2M th-band filter is a nonconvex optimization problem with low degree of nonconvexity. As a result, the nonconvex optimization problem can be cast into a semi-definite programming (SDP) problem by a convex relaxation technique. Then the reconstruction error is further minimized by an efficient iterative algorithm in which the closed-form expression is given in each iteration. Several examples are given to illustrate the effectiveness of the proposed method over the existing ones.

Journal ArticleDOI
TL;DR: This article presents a novel adaptive harmonic IIR notch filter with a single adaptive coefficient to efficiently perform frequency estimation and tracking in a harmonic frequency environment and devise a simple scheme to select the initial filter coefficient to insure algorithm convergence to its global minimum error.
Abstract: In many applications, a sinusoidal signal may be subjected to nonlinear effects in which possible harmonic frequency components are generated. In such an environment, we may want to estimate (track) the signal's fundamental frequency as well as any harmonic frequencies. Using a secondorder notch filter to estimate fundamental and harmonic frequencies is insufficient since it only accommodates one frequency component. On the other hand, applying a higher-order infinite impulse response (IIR) notch filter may not be efficient due to adopting multiple adaptive filter coefficients. In this article, we present a novel adaptive harmonic IIR notch filter with a single adaptive coefficient to efficiently perform frequency estimation and tracking in a harmonic frequency environment. Furthermore, we devise a simple scheme to select the initial filter coefficient to insure algorithm convergence to its global minimum error.

Patent
16 Jan 2009
TL;DR: In this article, an embodiment of an apparatus (200) for computing filter coefficients for an adaptive filter (210) for filtering a microphone signal so as to suppress an echo due to a loudspeaker signal is presented.
Abstract: An embodiment of an apparatus (200) for computing filter coefficients for an adaptive filter (210) for filtering a microphone signal so as to suppress an echo due to a loudspeaker signal includes extraction means (250) for extracting a stationary component signal or a non-stationary component signal from the loudspeaker signal or from a signal derived from the loudspeaker signal, and computing means (270) for computing the filter coefficients for the adaptive filter (210) on the basis of the extracted stationary component signal or the extracted non-stationary component signal.

Journal ArticleDOI
TL;DR: The overall technique is used in this paper to fit FIR filters to frequency domain specifications and is suitable to application in other problems of digital filter design, where the matter under study can be stated as finding the global minimum of a numerical function of filter parameters.
Abstract: An alternative approach to digital filter design is presented. The overall technique is as follows: Starting from frequency domain constraints and a parameterized expression of the filter family under adaptation, a corresponding training set is created, an error function is synthesized and a global minimization process is executed. At the end, the point that minimizes globally the particular cost function at hand determines the optimal filter. The adopted numerical optimization algorithm is based upon the well-known simulated annealing paradigm and its implementation is known as fuzzy adaptive simulated annealing. Although it is used in this paper to fit FIR filters to frequency domain specifications, the method is suitable to application in other problems of digital filter design, where the matter under study can be stated as finding the global minimum of a numerical function of filter parameters. Design examples are shown to verify the effectiveness of the proposed approach.

Proceedings ArticleDOI
TL;DR: In this paper, an adaptive step gradient descent method was used to iteratively optimize the three OT-MACH parameters, alpha, beta, and gamma, which corresponded to a substantial improvement in detection performance.
Abstract: An automatic Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter generator for use in a gray-scale optical correlator (GOC) has been developed for improved target detection at JPL. While the OT-MACH filter has been shown to be an optimal filter for target detection, actually solving for the optimum is too computationally intensive for multiple targets. Instead, an adaptive step gradient descent method was tested to iteratively optimize the three OT-MACH parameters, alpha, beta, and gamma. The feedback for the gradient descent method was a composite of the performance measures, correlation peak height and peak to side lobe ratio. The automated method generated and tested multiple filters in order to approach the optimal filter quicker and more reliably than the current manual method. Initial usage and testing has shown preliminary success at finding an approximation of the optimal filter, in terms of alpha, beta, gamma values. This corresponded to a substantial improvement in detection performance where the true positive rate increased for the same average false positives per image.

Journal ArticleDOI
TL;DR: In this paper, two adaptive algorithms, the optimum 2D median filter and the 2D adaptive Wiener filter, were designed by adopting adaptive algorithms to suppress speckle noise in 2D digital image data.

Journal ArticleDOI
TL;DR: A rigorous analysis of V-APSM regarding several invaluable properties including monotone approximation, which indicates stable tracking capability, and convergence to an asymptotically optimal point is provided.
Abstract: We present a unified analytic tool named variable-metric adaptive projected subgradient method (V-APSM) that encompasses the important family of adaptive variable-metric projection algorithms. The family includes the transform-domain adaptive filter, the Newton-method-based adaptive filters such as quasi-Newton, the proportionate adaptive filter, and the Krylov-proportionate adaptive filter. We provide a rigorous analysis of V-APSM regarding several invaluable properties including monotone approximation, which indicates stable tracking capability, and convergence to an asymptotically optimal point. Small metric-fluctuations are the key assumption for the analysis. Numerical examples show (i) the robustness of V-APSM against violation of the assumption and (ii) the remarkable advantages over its constant-metric counterpart for colored and nonstationary inputs under noisy situations.

Journal ArticleDOI
TL;DR: In this article, a beamformer based on particle fllter (PF) is proposed to improve the robustness by optimizing the diagonal loading factor in sample covariance matrix, which is regarded as a group of particles and optimized using PF.
Abstract: Adaptive beamforming, which uses a weight vector to maximize the signal-to-interference-plus-noise ratio (SINR), is often sensitive to estimation error and uncertainty in the parameters, such as direction of arrival (DOA), steering vector and covariance matrix. Robust beamforming attempts to mitigate this sensitivity and diagonal loading in sample covariance matrix can improve the robustness. In this paper, beamformer based on particle fllter (PF) is proposed to improve the robustness by optimizing the diagonal loading factor in sample covariance matrix. In the proposed approach, the level of diagonal loading is regarded as a group of particles and optimized using PF. In order to compute the post probability of particles beyond the knowledge of noise, a simplifled cost function is derived flrst. Then, a statistical approach is developed to decide the level of diagonal loading. Finally, simulations with several frequently encountered types of estimation error are conducted. Results show a better performance of the proposed beamformer than other typical beamformers using diagonal loading. In particular, the prominent advantage of the proposed approach is that it can perform well even noise and error in the steering vector are unknown.