Linear periodic systems and multirate filter design

We present a systematic procedure for the design of filters intended for multirate systems. This procedure Is motivated by viewing the equiripple design of filters in linear time-invariant systems as a process of obtaining optimum minimax filters for a class of bounded energy input signals. The philosophy of designing optimum minimax filters for classes of input signals is extended to multirate systems, which are not time-invariant. We develop a generalized Fourier analysis appropriate for linear periodic systems and use it to derive new error criteria for multirate filter design. Using such criteria yields optimum minimax multirate filters for the input signal class. The utility of our method is demonstrated by using it to analyze several multirate systems. We give numerical results on the design of a multirate implementation of a narrowband filter and compare our work to previous work on multirate filter design. Our numerical analysis is based upon a new formulation of the design as a semi-infinite linear programming problem     

Optimal signal reconstruction in noisy filter bank systems:multirate Kalman synthesis filtering approach

A multirate Kalman synthesis filter is proposed in this paper to replace the conventional synthesis filters in a noisy filter bank system to achieve optimal reconstruction of the input signal. Based on an equivalent block representation of subband signals, a state-space model is introduced for an M-band filter bank system with subband noises. The composite effect of the input signal, analysis filter bank, decimators, and interpolators is represented by a multirate state-space model. The input signal is embedded in the state vector, and the corrupting noises in subband paths are generally considered as additive noises. Hence, the signal reconstruction problem in the M-band filter bank systems with subband noises becomes a state estimation procedure in the resultant multirate state-space model. The multirate Kalman filtering algorithm is then derived according to the multirate state-space model to achieve optimal signal reconstruction in noisy filter bank systems. Based on the optimal state estimation theory, the proposed multirate Kalman synthesis filter provides the minimum-variance reconstruction of the input signal. Two numerical examples are also included. The simulation results indicate that the performance improvement of signal reconstruction in noisy filter bank systems is remarkable     

Mixed H2/H∞ filtering design inmultirate transmultiplexer systems: LMI approach

A mixed H₂/H_∞ filter design is proposed for multirate transmultiplexer systems with dispersive channel and additive noise. First, a multirate state-space representation is introduced for the transmultiplexer with the consideration of channel dispersion. Then, the problem of signal reconstruction can be regarded as a state estimation problem. In order to design an efficient separating filterbank for a transmultiplexer system with uncertain input signal and additive noise, the H_∞ filter is employed for robust signal reconstruction. The H₂ filter design is considered to be a suboptimal approach to achieve the optimal signal reconstruction in transmultiplexer system under unitary noise power. Finally, a mixed H₂/H_∞ filter is proposed to achieve a better signal reconstruction performance in transmultiplexer systems. These design problems can be transformed to solving the eigenvalue problems (EVP) under some linear matrix inequality (LMI) constraint. The LMI Matlab toolbox can be applied to efficiently solve the EVP by convex optimization technique     

A direct approach to H2 optimal deconvolution ofperiodic digital channels

This paper studies the H₂ optimal deconvolution problem for periodic finite impulse response (FIR) and infinite impulse response (IIR) channels. It shows that the H₂ norm of a periodic filter can be directly quantified in terms of periodic system matrices and linear matrix inequalities (LMIs) without resorting to the commonly used lifting technique. The optimal signal reconstruction problem is then formulated as an optimization problem subject to a set of matrix inequality constraints. Under this framework, the optimization of both the FIR and IIR periodic deconvolution filters can be made convex, solved using the interior point method, and computed by using the Matlab LMI Toolbox. The robust deconvolution problem for periodic FIR and IIR channels with polytopic uncertainties are further formulated and solved, also by convex optimization and the LMIs. Compared with the lifting approach to the design of periodic filters, the proposed approach is simpler yet more powerful in dealing with multiobjective deconvolution problems and channel uncertainties, especially for IIR deconvolution filter design. The obtained solutions are applied to the design of an optimal filterbank yielding satisfactory performance     

Filter design for MIMO sampling and reconstruction

We address the problem of finite impulse response (FIR) filter design for uniform multiple-input multiple-output (MIMO) sampling. This scheme encompasses Papoulis' generalized sampling and several nonuniform sampling schemes as special cases. The input signals are modeled as either continuous-time or discrete-time multiband input signals, with different band structures. We present conditions on the channel and the sampling rate that allow perfect inversion of the channel. Additionally, we provide a stronger set of conditions under which the reconstruction filters can be chosen to have frequency responses that are continuous. We also provide conditions for the existence of FIR perfect reconstruction filters, and when such do not exist, we address the optimal approximation of the ideal filters using FIR filters and a minmax l/sub 2/ end-to-end distortion criterion. The design problem is then reduced to a standard semi-infinite linear program. An example design of FIR reconstruction filters is given.     

基于自适应Kalman滤波的二维有噪子带信号恢复

基于子带信号的多通道表示（multichannel representation)和输入信号的动态特征,本文尝试推出了一种多分辨率状态空间模型,它与带相加子带噪声的滤波器组（Filter Bank)系统是等价的,于是使有噪子带信号的恢复可表述为相应多分辨率态空间模型的最优状态估计问题。进一步又利用信号的向量动态模型,发展了适于二维Kalman滤波的二维多分辨率状态空间模型,根据信号行为的分布,目标平面（object plane)可分割为不同的区域并用不同的向量动态模型来表征信号的非平衡分布,计算机数字仿真结果进一步证实了本文所提出了二维多分辨率Kalman滤波器性能的优越性。     

Minimax robust deconvolution filters under stochastic parametricand noise uncertainties

The author consider the design of robust deconvolution filters for linear discrete time systems with stochastic parameter and noise uncertainties. It is assumed that some large but bounded uncertainties exist in the driving and measurement noise covariances as well as the second-order statistics of stochastic parameters and initial conditions. Three kinds of minimax sensitivity criteria are used to develop the techniques to the synthesis of minimax deconvolution filters under uncertain linear stochastic systems. Their approach is based on saddle-point theory and the sensitivity analysis of Kalman filters. The design algorithms give the recursive realization of the minimax deconvolution filters for the time-varying uncertain systems under fairly general conditions. For the time-invariant uncertain case the existence and solutions of steady-state deconvolution filters are further developed. Finally, the utility of the minimax design approaches and the properties of the resulting minimax deconvolution filters are illustrated by a numerical example     

Boundary filter optimization for segmentation-based subband coding

This paper presents boundary optimization techniques for the nonexpansive decomposition of arbitrary-length signals with multirate filterbanks. Both biorthogonal and paraunitary filterbanks are considered. The paper shows how matching moments and orthonormality can be imposed as additional conditions during the boundary filter optimization process. It provides direct solutions to the problem of finding good boundary filters for the following cases: (a) biorthogonal boundary filters with exactly matching moments and (b) orthonormal boundary filters with almost matching moments. With the proposed methods, numerical optimization is only needed if orthonormality and exactly matching moments are demanded. The proposed direct solutions are applicable to systems with a large number of subbands and/or very long filter impulse responses. Design examples show that the methods allow the design of boundary filters with good frequency selectivity     

Deconvolution filter design via l1 optimizationtechnique

A new l₁ optimal deconvolution filter design approach for systems with uncertain (or unknown)-but-bounded inputs and external noises is proposed. The purpose of this deconvolution filter is to minimize the peak gain from the input signal and noise to the error by the viewpoint of the time domain. The solution consists of two steps. In the first step, the l₁ norm minimization problem is transferred to an equivalent A-norm minimization problem, and the minimum value of the peak gain is calculated. In the second step, based on the minimum peak gain, the l₁ optimal deconvolution filter is constructed by solving a set of constrained linear equations. Some techniques of inner-outer factorization, polynominal spectral factorization, linear programming, and some optimization theorems found in a book by Luenberger are applied to treat the l₁ optimal deconvolution filter design problem. Although the analysis of the algorithm seems complicated, the calculation of the proposed design algorithm for actual systems is simple. Finally, one numerical example is given to illustrate the proposed design approach. Several simulation results have confirmed that the proposed l₁ optimal deconvolution filter has more robustness than the l₂ optimal deconvolution filter under uncertain driving signals and noises     

Multirate filter banks with block sampling

Multirate filter banks with block sampling were recently studied by Khansari and Leon-Garcia (1993). In this paper, we want to systematically study multirate filter banks with block sampling by studying general vector filter banks where the input signals and transfer functions in conventional multirate filter banks are replaced by vector signals and transfer matrices, respectively. We show that multirate filter banks with block sampling studied by Khansari and Leon-Garcia are special vector filter banks where the transfer matrices are pseudocirculant. We present some fundamental properties for the basic building blocks, such as Noble identities, interchangeability of down/up sampling, polyphase representations of M-channel vector filter banks, and multirate filter banks with block sampling. We then present necessary and sufficient conditions for the alias-free property, finite impulse response (FIR) systems with FIR inverses, paraunitariness, and lattice structures for paraunitary vector filter banks. We also present a necessary and sufficient condition for paraunitary multirate filter banks with block sampling. As an application of this theory, we present all possible perfect reconstruction delay chain systems with block sampling. We also show some examples that are not paraunitary for conventional multirate filter banks but are paraunitary for multirate filter banks with proper block sampling. In this paper, we also present a connection between vector filter banks and vector transforms studied by Li. Vector filter banks also play important roles in multiwavelet transforms and vector subband coding     

Rate-Distortion Optimized Video Transmission Over DS-CDMA Channels with Auxiliary Vector Filter Single-User Multirate Detection

In this paper, we consider the rate-distortion optimized resource allocation for video transmission over multi-rate wireless direct-sequence code-division-multiple-access (DS-CDMA) channels. We consider the performance of transmitting scalable video over a multipath Rayleigh fading channel via a combination of multi-code multirate CDMA and variable sequence length multirate CDMA channel system. At the receiver, despreading is done using adaptive space-time auxiliary-vector (AV) filters. We propose a new interference cancelling design that uses just a single AV filter for single-user mutirate despreading. Our experimental results show that the proposed interference cancelling design has excellent performance in scalable video transmission over DS-CDMA systems that use a combination of multicode multirate and variable processing gain multirate CDMA. The proposed design takes advantage of the fact that single user's video data is transmitted using two spreading codes, one for the base layer and one for the enhancement layers, and of the fact that these spreading codes can have different processing gains. The proposed interference cancelling design is compared with two conventional single-user multirate CDMA receiver configurations, however now we use an AV filter rather than a simple matched filter. We also propose a resource allocation algorithm for the optimal determination of source coding rate, channel coding rate and processing gain for each scalable layer, in order to minimize the expected distortion at the receiver.     

Robust filtering for discrete-time systems with saturation and its application to transmultiplexers

This paper considers the problem of robust filtering for discrete-time linear systems subject to saturation. A generalized dynamic filter architecture is proposed, and a filter design method is developed. Our approach incorporates the conventional linear H/sub 2/ and H/sub /spl infin// filtering as well as a regional l/sub 2/ gain filtering feature developed specially for the saturation nonlinearity and is applicable to the digital transmultiplexer systems for the purpose of separating filterbank design. It turns out that our filter design can be carried out by solving a constrained optimization problem with linear matrix inequality (LMI) constraints. Simulations show that the resultant separating filters possess satisfactory reconstruction performance while working in the linear range and less degraded reconstruction performance in the presence of saturation.     

On Bounds of Shift Variance in Two-Channel Multirate Filter Banks

Critically sampled multirate FIR filter banks exhibit periodically shift variant behavior caused by nonideal antialiasing filtering in the decimation stage. We assess their shift variance quantitatively by analysing changes in the output signal when the filter bank operator and shift operator are interchanged. We express these changes by a so-called commutator. We then derive a sharp upper bound for shift variance via the operator norm of the commutator, which is independent of the input signal. Its core is an eigensystem analysis carried out within a frequency domain formulation of the commutator, leading to a matrix norm which depends on frequency. This bound can be regarded as a worst case instance holding for all input signals. For two channel FIR filter banks with perfect reconstruction (PR), we show that the bound is predominantly determined by the structure of the filter bank rather than by the type of filters used. Moreover, the framework allows to identify the signals for which the upper bound is almost reached as so-called near maximizers of the frequency-dependent matrix norm. For unitary PR filter banks, these near maximizers are shown to be narrow-band signals. To complement this worst-case bound, we derive an additional bound on shift variance for input signals with given amplitude spectra, where we use wide-band model spectra instead of narrow-band signals. Like the operator norm, this additional bound is based on the above frequency-dependent matrix norm. We provide results for various critically sampled two-channel filter banks, such as quadrature mirror filters, PR conjugated quadrature filters, wavelets, and biorthogonal filters banks.     

Synthesis filter bank optimization in two-dimensional separablesubband coding systems

Subband coding is a popular and well established technique used in visual communications, such as image and video transmission. In the absence of quantization and transmission errors, the analysis and synthesis filters in a subband coding scheme can be designed to obtain perfect reconstruction of the input signal, but this is no longer the optimal solution in the presence of quantization of the subband coefficients. We presuppose the use of a two-dimensional (2-D) separable subband scheme and we address the problem of designing, for a given analysis filter bank and assuming uniform quantization of the subband coefficients, the set of row and column synthesis filters that minimize the mean squared reconstruction error at the output of the subband system. Since the corresponding optimization problem is inherently nonlinear, we propose a suboptimal solution that extends a one-dimensional (l-D) optimal filter design procedure, already presented in the literature, to a 2-D separable synthesis filter bank. The separable 2-D extension is not trivial, since the processing in one direction, e.g., the rows, alters the statistics of the signals for the design of the filters in the other direction, e.g., the columns. To further simplify the filter design, we propose to model the input image as a 2-D separable Markov process plus an additive white component. Several design examples using both synthetic signals and real world images are presented, showing that the filters designed using the proposed technique can give a significant gain with respect to the perfect reconstruction solution, especially when the dither technique is used for quantization. The simulation results also show that the proposed image model can be conveniently used in the synthesis filter design procedure.     

Minimax design of two-dimensional parallelogram filter banks

The authors present a technique for the minimax design of two-dimensional (2-D) parallelogram filter bank (PFB) systems with linear-phase analysis/synthesis filters. To achieve perfect reconstruction, the required analysis filters must have parallelogram-shaped frequency responses. In general, the original design problem is found to be an optimisation problem with nonlinear constraints. The authors present a linearisation approach to reformulate the design problem. As a result, updating the filter coefficient vector at each iteration for the original design problem can be accomplished by searching the gradient of the linearised optimisation problem. They further present an efficient method based on a modified Karmarkar's algorithm for computing the required gradient vector and finding the required step size analytically. Therefore the filter coefficients can easily be computed by solving only linear equations at each iteration during the design process. The effectiveness of the proposed technique is shown by computer simulations     

Multirate modeling of AR/MA stochastic signals and its applicationto the combined estimation-interpolation problem

The use of the Kalman filter is investigated in this work for interpolating and estimating values of an AR or MA stochastic signal when only a noisy, down-sampled version of the signal can be measured. A multirate modeling theory of the AR/MA stochastic signals is first derived from a block state-space viewpoint. The missing samples are embedded in the state vector so that missing signal reconstruction problem becomes a state estimation scheme. Next, Kalman state estimation theory is introduced to treat the combined estimation-interpolation problem. Some extensions are also discussed for variations of the original basic problem. The proposed Kalman reconstruction filter can be also applied toward recovering missing speech packets in a packet switching network with packet interleaving configuration. By analysis of state estimation theory, the proposed Kalman reconstruction filters produce minimum-variance estimates of the original signals. Simulation results indicate that the multirate Kalman reconstruction filters possess better estimation/interpolation performances than a Wiener reconstruction filter under adequate numerical complexity     

H<Subscript>∞</Subscript> Deconvolution Filters for Stochastic Systems with Interval Uncertainties

This paper is concerned with the robust H_∞ deconvolution filtering problem for continuous- and discrete-time stochastic systems with interval uncertainties. The matrices of the system describing the signal transmissions are assumed to be uncertain within given intervals, and the stochastic perturbation is in the form of multiplicative Gaussian white noise with constant variance. The purpose of the addressed problem is to design a robust H_∞ deconvolution filter such that the input signal distorted by the transmission channel could recover to a specified extent γ. By using stochastic analysis techniques and the Lyapunov stability theory, sufficient conditions are first derived for ensuring the asymptotical stability of the filtering error system. Then the filter parameters are characterized in terms of the solution to linear matrix inequalities, which can be easily solved by using available software packages. Two simulation examples are exploited to demonstrate the effectiveness of the proposed design procedures, respectively, for continuous- and discrete-time systems.     

近似完全重构IIR余弦调制滤波器组的优化设计

提出一种新的近似完全重构因果稳定的IIR余弦调制滤波器组的设计方法。基于预先给定的极点值,IIR原型滤波器的设计问题可以简化成一个凸极大值极小化的优化问题,从而采用二阶锥规划法求解。所得余弦调制滤波器组具有良好的频率特性和合理的完全重构误差。所设计的原型滤波器是因果稳定的,并且其多相因子分母相同,简化了完全重构条件,可以用来进一步优化得到的完全重构系统。     

Recursive Robust Filtering with Finite-Step Correlated Process Noises and Missing Measurements

In this paper, the robust filter design problem is studied for a class of uncertain dynamical systems with finite-step correlated process noises and missing measurements. The dynamical system under consideration is subject to both deterministic norm-bounded uncertainties in the measurement output and stochastic uncertainties on the system states. The process noises are assumed to be finite-step correlated. The missing measurement phenomenon is modeled as a binary switching sequence. Based on the min-max game theory, a recursive robust filter is designed that is suitable for online application. A particular feature is that, as the proposed robust filters work in a recursive fashion, there is no need to investigate the existence issue of the filters. A simulation example is presented to illustrate the usefulness of the proposed filter.     

Time-domain design of FIR multirate filter banks

A new time-domain methodology for designing FIR multirate filter banks is proposed. The conditions for perfect reconstruction systems can only be met by a limited number of systems, and consequently one of the major problems is to design analysis and synthesis filters which reduce the reconstruction error to a minimum. A recursive technique is proposed which uses the synthesis filters from one iteration to update the analysis filters for the next. The Letter shows that this is computationally simpler than previously proposed time-domain methods and produces filter banks in which the reconstruction error is reduced to practically acceptable levels.<>