A fast algorithm for designing stack filters

Stack filters are a class of nonlinear filters with excellent properties for signal restoration. Unfortunately, present algorithms for designing stack filters can only be used for small window sizes because of either their computational overhead or their serial nature. This paper presents a new adaptive algorithm for determining a stack filter that minimizes the mean absolute error criterion. The new algorithm retains the iterative nature of many current adaptive stack filtering algorithms, but significantly reduces the number of iterations required to converge to an optimal filter. This algorithm is faster than all currently available stack filter design algorithms, is simple to implement, and is shown in this paper to always converge to an optimal stack filter. Extensive comparisons between this new algorithm and all existing algorithms are provided. The comparisons are based both on the performance of the resulting filters and upon the time and space complexity of the algorithms. They demonstrate that the new algorithm has three advantages: it is faster than all other available algorithms; it can be used on standard workstations (SPARC 5 with 48 MB) to design filters with windows containing 20 or more points; and, its highly parallel structure allows very fast implementations on parallel machines. This new algorithm allows cascades of stack filters to be designed; stack filters with windows containing 72 points have been designed in a matter of minutes under this new approach.     

Adaptive algorithms for joint time delay estimation and IIRfiltering

A new class of gradient-based algorithms for joint adaptive time delay estimation and IIR filtering for the recursive identification of systems with long pure delays is introduced. The algorithms update an adaptive delay estimate and the coefficients of an IIR filter using familiar gradient-descent methods. In addition, interpolation of the input sequence is used to obtain delays that are not constrained to integer numbers of samples. Three algorithms using a direct form filter parametrization and one using a normalized lattice filter are derived, and computer simulations are used to demonstrate their convergence and tracking properties     

Resampling algorithms and architectures for distributed particle filters

In this paper, we propose novel resampling algorithms with architectures for efficient distributed implementation of particle filters. The proposed algorithms improve the scalability of the filter architectures affected by the resampling process. Problems in the particle filter implementation due to resampling are described, and appropriate modifications of the resampling algorithms are proposed so that distributed implementations are developed and studied. Distributed resampling algorithms with proportional allocation (RPA) and nonproportional allocation (RNA) of particles are considered. The components of the filter architectures are the processing elements (PEs), a central unit (CU), and an interconnection network. One of the main advantages of the new resampling algorithms is that communication through the interconnection network is reduced and made deterministic, which results in simpler network structure and increased sampling frequency. Particle filter performances are estimated for the bearings-only tracking applications. In the architectural part of the analysis, the area and speed of the particle filter implementation are estimated for a different number of particles and a different level of parallelism with field programmable gate array (FPGA) implementation. In this paper, only sampling importance resampling (SIR) particle filters are considered, but the analysis can be extended to any particle filters with resampling.     

Robust adaptive estimator for filtering noise in images

Provides three new methods for storing images corrupted by additive noise. One is the adaptive mean median filter for preserving the details of images when restored from additive Gaussian noise. Another is the minimum-maximum method for moving outlier noise. The third method, the robust adaptive mean p-median filter, is based on a combination of the previous two methods. In the past, proposed restoration methods have generally proven to be inadequate for both detail preservation and noise suppression, but the new adaptive mean p-median filter is shown to be good at both of these tasks, while the robust adaptive mean p-median filter can give good performance even in the presence of outliers. Degraded images are processed by the proposed algorithms, with the results compared with a selection of other median-based algorithms that have been proposed in the literature.     

The genetic search approach. A new learning algorithm for adaptiveIIR filtering

An “evolutionary” approach called the genetic algorithm (GA) was introduced for multimodal optimization in adaptive IIR filtering. However, the disadvantages of using such an algorithm are slow convergence and high computational complexity. Initiated by the merits and shortcomings of the gradient-based algorithms and the evolutionary algorithms, we developed a new hybrid search methodology in which the genetic-type search is embedded into gradient-descent algorithms (such as the LMS algorithm). The new algorithm has the characteristics of faster convergence, global search capability, less sensitivity to the choice of parameters, and simple implementation. The basic idea of the new algorithm is that the filter coefficients are evolved in a random manner once the filter is found to be stuck at a local minimum or to have a slow convergence rate. Only the fittest coefficient set survives and is adapted according to the gradient-descent algorithm until the next evolution. As the random perturbation will be subject to the stability constraint, the filter can always minimum in a stable manner and achieve a smaller error performance with a fast rate. The article reviews adaptive IIR filtering and discusses common learning algorithms for adaptive filtering. It then presents a new learning algorithm based on the genetic search approach and shows how it can help overcome the problems associated with gradient-based and GA algorithms     

Bayesian techniques for known channel deconvolution

This paper introduces a new family of deconvolution filters for digital communications subject to severe intersymbol interference. These fixed lag smoothing filters for known channel demodulation are called Bayesian filters. Bayesian filters are derived using a new approach to suboptimal recursive minimum mean square error estimation for non-Gaussian processes. The family of Bayesian filters interpolates between the optimum fixed lag linear filter (i.e., the Kalman filter) and the optimum fixed lag symbol-by-symbol demodulator in both performance and complexity. The complexity of the Bayesian filter is exponential in a parameter, typically chosen smaller than the channel length and the filter lag. Hence, the Bayesian filter decouples the channel length and the filter lag from the exponential complexity in these parameters found in many other high performance demodulation algorithms. Simulations characterize the performance and compare the Bayesian filter to both optimal and reduced complexity demodulation algorithms     

Partial update and sparse adaptive filters

There has been increasing research interest in developing adaptive filters with partial update (PU) and adaptive filters for sparse impulse responses. On the basis of maximum a posteriori (MAP) estimation, new adaptive filters are developed by determining the update when a new set of training data is received. The MAP estimation formulation permits the study of a number of different prior distributions which naturally incorporate the sparse property of the filter coefficients. First, the Gaussian prior is studied, and a new adaptive filter with PU is proposed. A theoretical basis for an existing PU adaptive filter is also studied. Then new adaptive filters that directly exploit the sparsity of the filter are developed by using the scale mixture Gaussian distribution as the prior. Two new algorithms based on the Student's-t and power-exponential distributions are presented. The minorisation-maximisation algorithm is employed as an optimisation tool. Simulation results show that the learning performance of the proposed algorithms is better than or similar to that of some recently published algorithms     

A class of modified adaptive recursive filter algorithms

A new class or adaptive infinite impulse response ( IIR) or recursive filter algorithms is obtained by incorporating a simple modification in the correction vectors of some existing algorithms. The resulting class of algorithms explores the inherent parallelism present in their coefficient update equations, and hence the possibility of faster initial convergence. Two IIR filter algorithms are considered for discussion and simulation.     

Time-varying performance surfaces for adaptive IIR filters:geometric properties and implications for filter stability

We present a new framework for understanding the performance of adaptive IIR filters which enhances the understanding of filter stability during on-line operation. This new understanding arises from examining geometric properties of time-varying performance surfaces which are defined by the data, rather than the standard steady-state error surfaces as defined by statistics of the data. Data-dependent descent directions used in adaptive algorithms to update filter coefficients typically are viewed as functions of gradients defined on fixed performance surfaces, and data is used to approximate these gradients at each iteration. In contrast, we view data-dependent descent directions at each iteration as functions of exact gradients on time-varying performance surfaces. By examining the shape of these time-varying performance surfaces near filter stability boundaries, we are able to identify the origin of on-line stability problems associated with existing adaptive IIR filtering formulations, and suggest corrective measures. Specifically, by using exact z-domain methods, we define time-varying performance surfaces which geometrically enforce filter stability, and maintain the geometric and physical properties of the “true” error surface at each iteration. Development of adaptive algorithms based on this measure is expected to result in adaptive filters having improved stability performance during on-line operation     

An LMS style variable tap-length algorithm for structure adaptation

Searching for the optimum tap-length that best balances the complexity and steady-state performance of an adaptive filter has attracted attention recently. Among existing algorithms that can be found in the literature, two of which, namely the segmented filter (SF) and gradient descent (GD) algorithms, are of particular interest as they can search for the optimum tap-length quickly. In this paper, at first, we carefully compare the SF and GD algorithms and show that the two algorithms are equivalent in performance under some constraints, but each has advantages/disadvantages relative to the other. Then, we propose an improved variable tap-length algorithm using the concept of the pseudo fractional tap-length (FT). Updating the tap-length with instantaneous errors in a style similar to that used in the stochastic gradient [or least mean squares (LMS)] algorithm, the proposed FT algorithm not only retains the advantages from both the SF and the GD algorithms but also has significantly less complexity than existing algorithms. Both performance analysis and numerical simulations are given to verify the new proposed algorithm.     

Construction of point process adaptive filter algorithms for neural systems using sequential Monte Carlo methods

The stochastic state point process filter (SSPPF) and steepest descent point process filter (SDPPF) are adaptive filter algorithms for state estimation from point process observations that have been used to track neural receptive field plasticity and to decode the representations of biological signals in ensemble neural spiking activity. The SSPPF and SDPPF are constructed using, respectively, Gaussian and steepest descent approximations to the standard Bayes and Chapman-Kolmogorov (BCK) system of filter equations. To extend these approaches for constructing point process adaptive filters, we develop sequential Monte Carlo (SMC) approximations to the BCK equations in which the SSPPF and SDPPF serve as the proposal densities. We term the two new SMC point process filters SMC-PPFs and SMC-PPFD, respectively. We illustrate the new filter algorithms by decoding the wind stimulus magnitude from simulated neural spiking activity in the cricket cercal system. The SMC-PPFs and SMC-PPFD provide more accurate state estimates at low number of particles than a conventional bootstrap SMC filter algorithm in which the state transition probability density is the proposal density. We also use the SMC-PPFs algorithm to track the temporal evolution of a spatial receptive field of a rat hippocampal neuron recorded while the animal foraged in an open environment. Our results suggest an approach for constructing point process adaptive filters using SMC methods.     

Demosaicing algorithms for area- and line-scan cameras in print inspection

Most color image sensors use color filter arrays (CFA). With this sensor design the captured information at each sensor pixel position is restricted to a specific spectral portion (typically red, green and blue bands). To obtain the missing color responses at each pixel position, so-called CFA demosaicing algorithms are commonly used. We propose two new CFA demosaicing algorithms, which are well suited for industrial print inspection with respect to the requirements in accuracy and speed. As a main contribution, we introduce novel demosaicing algorithms for specific high-speed color digital time delay and integration (DTDI) CFA line-scan cameras. We compare the suggested CFA demosaicing algorithms to state-of-the art algorithms for area and line-scan camera operation modes. We show that the two new algorithms perform superior to conventional algorithms as indicated by reconstruction error.     

A novel method for designing FIR digital filters with nonuniform frequency samples

A new method of designing FIR digital filters using nonuniform frequency samples is presented. There is no restriction on the phase to be linear. The method is based on a Newton-type polynomial interpolating on the unit circle of the complex plane. Attractive features of the proposed method are the applicability to unequally spaced samples, the recursive and semipermanent computation of filter parameters, the capability of obtaining short transition bands or sharp cut-off frequency responses, and the design of efficient algorithms for real-time applications. In the serial case, when the next sample appears, the design parameters are evaluated only by updating the old ones with correction terms that could be used as indicators for convergence, approximation, or filter reduction. The method can be extended to m-D filter design, DFT calculation, design of parallel algorithms, etc.<>     

卫星红外图像点目标检测预处理

对卫星红外图像点目标检测预处理方法做了研究,采用线性滤波器和非线性滤波器并行对图像进行预处理,仿真实验表明该方法可以在保证很高的检测率的同时大大降低虚警率,为后续处理提供有力支持。算法简单可行,利于硬件实现。     

Bayesian methods for multiaspect target tracking in image sequences

In this paper, we introduce new algorithms for automatic tracking of multiaspect targets in cluttered image sequences. We depart from the conventional correlation filter/Kalman filter association approach to target tracking and propose instead a nonlinear Bayesian methodology that enables direct tracking from the image sequence incorporating the statistical models for the background clutter, target motion, and target aspect change. Proposed algorithms include 1) a batch hidden Markov model (HMM) smoother and a sequential HMM filter for joint multiframe target detection and tracking and 2) two mixed-state sequential importance sampling trackers based on the sampling/importance resampling (SIR) and the auxiliary particle filtering (APF) techniques. Performance studies show that the proposed algorithms outperform the association of a bank of template correlators and a Kalman filter in adverse scenarios of low target-to-clutter ratio and uncertainty in the true target aspect.     

广义镜像对称频率响应线性相位准正交滤波器组

本文研究了镜像对称频率响应特性的线性相位准正交滤波器组,新算法利于设计的快速收敛.信道数为偶数时,提出了一个广义线性乘积因子, 新的格型结构带有附加的自由变量参数,可用来提高滤波器的特性.信道数是奇数时,研究了新的结构因子,设计了奇数信道数的滤波器组.     

纯方位被动目标运动分析的修正增益卡尔曼滤波算法研究

通过对滤波状态协方差估计的修改,将水下纯方位被动目标运动分析中的扩展卡尔曼滤波(EKF)算法改进为修正增益扩展卡尔曼滤波(MGEKF)算法,并指出了两者的联系与区别。对比仿真分析表明,MGEKF较之EKF滤波效果有所改善,增强了稳定性,提高了精度,为水下纯方位被动目标运动分析的实现提供新的途径。     

MPEG-2音频实时压缩编解码的一种快速算法

该文介绍采用一片TI公司的数字信号处理芯片TMS320C31实现了MPEG-2音频Layer-1,2实时压缩编解码器。为了达到实时的目的,对MPEG建议的子带分析和子带合成方案分别提出了一种新的快速算法,采用该算法的运算量分别是MPEG标准建议算法运算量的1/5和1/10。所有算法都经过了软件模拟和硬件实时仿真,通过仿真器装载到一片TMS320C31上实现了实时编解码运算。     

基于Lyapunov稳定理论的有源噪声控制算法研究

在有源噪声控制中引入基于Lyapunov稳定理论的目标函数,给出了应用于有源噪声控制的约束条件,推导出应用于有源噪声控制的单通道和多通道的Lyapunov稳定算法。通过计算机仿真证实新算法的性能要优于FXLMS算法。     

A class of adaptive step-size control algorithms for adaptivefilters

A class of new adaptive step-size control algorithms, which is applicable to most of the LMS-derived tap weight adaptation algorithms, is proposed. Analysis yields a set of difference equations for theoretically calculating the transient behavior of the filter convergence and derives an explicit formula for the steady-state excess mean-square error (EMSE). Experiments for some examples prove that the proposed algorithm is highly effective in improving the convergence rate in both transient and tracking phases. The theoretically calculated convergence is shown to be in good agreement with that obtained through simulations. Alternative formulae of the step-size adaptation for specific tap weight adaptation algorithms are also proposed