Matched filtering using tapped bucket-brigade delay lines

The use of an m.o.s. tapped bucket-brigade shift register as a matched filter is discussed. Tapping is accomplished by sensing the node voltages at each delay stage with a source follower, and the weighted summation is accomplished by summing the resulting currents. Construction of a filter matched to a 13 bit Barker-coded p.n. sequence is described and results are presented.     

Optimum nonlinear filtering

This paper is composed of two parts. The first part surveys the literature regarding optimum nonlinear filtering from the (continuous-time) stochastic analysis point of view, and the other part explores the impact of recent applications of neural networks (in a discrete-time context) to nonlinear filtering. In particular, the results obtained by using a regularized form of radial basis function (RBF) networks are presented in fair detail     

Generation of fractional-nanosecond waveforms using nonlinear delay lines

The effect of diode-lead inductance is considered in nonlinear delay lines. Methods are described for producing subnanosecond-risetime pulses and a short-duration `spike? generator.     

Linear and nonlinear filtering

This paper provides a review of the theory of filtering for stochastic processes. In particular, the sequential theory of linear filtering is reviewed as well as the theory of nonlinear filtering.     

Elimination of interference terms of the discrete Wignerdistribution using nonlinear filtering

Methods for interference reduction in the Wigner distribution (WD) have traditionally relied on linear filtering. This paper introduces a new nonlinear filtering approach for the removal of cross terms in the discrete WD. Realizing that linear smoothing kernels are unable to completely cancel the cross-terms without compromising time-frequency concentration and resolution of the auto-terms, a nonlinear filtering algorithm is devised where the filter automatically adapts to the rapidly changing nature of the WD plane. Varying the filter behavior from an identity operation at one extreme to a lowpass linear filter at the other, a near-optimal removal of cross terms is achieved. Unlike traditional smoothing and optimal kernel design techniques, this algorithm does not reduce the time-frequency resolution and concentration of the auto-terms and performs equally well for a very large variety of signals     

Optimal nonlinear filtering with constraints

The impulse response of nonlinear systems with limitation of band has been considered. On the base of the minimum mean square error criterion the generalisation of the Wiener-Hopf's equation has been calculated.     

Unscented filtering and nonlinear estimation

The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overcome this limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. It is more accurate, easier to implement, and uses the same order of calculations as linearization. This paper reviews the motivation, development, use, and implications of the UT.     

Improvements of nuclear magnetic resonance image quality using iterations of adaptive nonlinear filtering

To improve nuclear magnetic resonance (NMR) image quality, a method using iterations of adaptive nonlinear filtering is presented. The method utilizes the properties of an NMR image's noises which are spike-like in the local region. An NMR image is iteratively filtered by a mode-change-type nonlinear filter which has two modes. The first mode reduces small amplitude noises in the NMR image, so that the S/N ratio of the image is improved, and then the residual noises become more spike-like. The second mode mainly reduces large amplitude spike-like noises of the image improved by the first mode, which cannot reduce spike-like noises. Using the proposed iterative filter, it is found by simulations that the S/N ratio of the NMR image is improved by as much as a factor of 2.7. A significant improvement in the image quality when this filter is used is demonstrated by applying it to an NMR image of a human head obtained from an actual NMR imaging apparatus.     

Remarks on linear and nonlinear filtering

This communication tries to give some insight into relationships existing between Viterbi and the forward-backward algorithm (used in the context of hidden Markov models) on the one hand and Kalman filtering and Rauch-Tung Striebel smoothing on the other. We give a unifying view which shows how those algorithms are related and give an example of a nonlinear hybrid system that can be filtered through a mixed algorithm     

On a nonlinear feedback filtering system

A nonlinear filtering scheme with noiseless feedback is presented, based on a consideration of the minimum-mean-squared error filtering of independent signal samples corrupted by additive noise. The explicit solution for the general case is very complex. However, if the signal-to-noise ratio is assumed to be large and the nonlinear estimating filter has zero memory, the problem may be simplified by reducing it to the zero-memory prefiltering problem combined with predictive feedback. The improvement over the linear case without feedback is shown to be the product of the improvements due to the zero-memory non-linearities and the feedback. An example is considered to illustrate the improvements in the error,     

非线性滤波算法性能对比

随着目标运动的多样性和复杂化,雷达非线性目标跟踪算法越来越受到重视。本文对目前非线性滤波的主要算法即扩展卡尔曼滤波、不敏卡尔曼滤波、粒子滤波的滤波模型、适用条件、性能进行了分析比较,通过一个非线性非高斯模型进行了仿真,验证了这些算法的性能,仿真结果表明非线性条件下粒子滤波算法要明显优于其它两种滤波算法。     

Active bandpass filtering with bucket-brigade delay lines

Bucket-brigade delay lines have been used to build a tunable active bandpass filter. Experimental results showing the dependence of center frequency and bandwidth on the delay-line clock frequency, and the dependence of the center frequency and the Q of the filter on internal gain parameters are presented.     

Inference of nonlinear state-space models for sandwich-type lateral flow immunoassay using extended Kalman filtering

In this paper, a mathematical model for sandwich-type lateral flow immunoassay is developed via short available time series. A nonlinear dynamic stochastic model is considered that consists of the biochemical reaction system equations and the observation equation. After specifying the model structure, we apply the extended Kalman filter (EKF) algorithm for identifying both the states and parameters of the nonlinear state-space model. It is shown that the EKF algorithm can accurately identify the parameters and also predict the system states in the nonlinear dynamic stochastic model through an iterative procedure by using a small number of observations. The identified mathematical model provides a powerful tool for testing the system hypotheses and also for inspecting the effects from various design parameters in both rapid and inexpensive way. Furthermore, by means of the established model, the dynamic changes in the concentration of antigens and antibodies can be predicted, thereby making it possible for us to analyze, optimize, and design the properties of lateral flow immunoassay devices.     

Implementation of nonlinear FM pulse compression filters using surface wave delay lines

The synthesis of a surface wave pulse compression filter with a non-linear FM dispersion relation to reduce time sidelobes is described. The significance of this synthesis is discussed and a comparison of the use of nonlinear FM and amplitude weighting of linear FM is made with regard to surface wave synthesis.     

非线性贝叶斯滤波算法综述

滤波的目的是从序贯量测中在线、实时地估计和预测出动态系统的状态和误差的统计量。从递归贝叶斯估计的框架出发,对非线性滤波算法作了统一描述,并根据对后验概率密度的近似方法的不同,把非线性滤波划归为3类:基于函数近似的滤波方法、基于确定性采样的滤波方法和基于随机采样的滤波方法。对这些非线性滤波的原理、方法及特点做了分析和评述,最后介绍了非线性滤波研究的新动态,并对其发展作了展望。     

Posterior Cramer-Rao bounds for discrete-time nonlinear filtering

A mean-square error lower bound for the discrete-time nonlinear filtering problem is derived based on the van Trees (1968) (posterior) version of the Cramer-Rao inequality. This lower bound is applicable to multidimensional nonlinear, possibly non-Gaussian, dynamical systems and is more general than the previous bounds in the literature. The case of singular conditional distribution of the one-step-ahead state vector given the present state is considered. The bound is evaluated for three important examples: the recursive estimation of slowly varying parameters of an autoregressive process, tracking a slowly varying frequency of a single cisoid in noise, and tracking parameters of a sinusoidal frequency with sinusoidal phase modulation     

Optimal nonlinear filtering with constraints—Part II

The impulse response of nonlinear systems with bounded memory and energy have been considered. On the base of the minimum mean square error criterion a generalization of the Wiener-Hopf equation is developed.     

Optimal nonlinear filtering for independent increment processes--Part I

A new formulation of the multidimensional optimal nonlinear filtering problem is presented in this two-part paper. This formulation permits generalization and unification of some well-known recent results on optimal nonlinear filtering theory. [1]-[7] Specifically, the problem investigated is that of determining the conditional probability density function ofx(t)given{y(tau); t_{0} leq tau leq t}, wherex(t)is then-dimensional state vector of a non-linear system perturbed by an independent increment noise process, andy(t)is anm-dimensional measurement vector which is a nonlinear function ofxand contains an additive independent increment noise process. The results are obtained through use of characteristic functions and the theory of independent increment processes. The foundation for the treatment of general independent increment noise processes is given in Part I, but the final results in Part I are restricted to Gaussian independent increment noise processes. The extension to general independent increment noise processes is considered in Part II. It is shown in Part I that the results for the linear-Gaussian case can be obtained in two different ways, one of which cannot be used for the general case. Some important properties of general independent increment processes and a special property of Gaussian independent increment processes are discussed.     

非线性滤波的遗传神经网络寻优方法

针对非线性滤波中存在的"实时"问题,提出了用神经网络实现非线性滤波的解决方案.为了提高神经网络学习速度,利用遗传算法全局搜索能力强的特点,对神经网络的结构和权阵进行优化,构成了遗传神经网络,实现了全局最佳逼近.仿真结果验证了遗传神经网络实现最优非线性滤波的有效性,并说明了该方法精度高,收敛性好.