A subband adaptive filter allowing maximally decimation

Conventional subband adaptive filters (ADF) using filter banks have shown degradation in performance because of the nonideal nature of filter banks. For this problem, the authors propose an alias free subband structure for adaptive filtering using polyphase frequency sampling filter (FSF) banks. As a preliminary, they make it clear that the conventional polyphase discrete Fourier transform (DFT) bank is equivalent to a class of the FSF bank. Then, they propose a new class of ADF using the FSF banks based on the frequency sampling theorem. As a result, the proposed technique enables subband adaptive filtering without the degradation effect of both the aliasing and cross terms, even if one chooses critical subsampling     

Design of M‐Channel IIR Uniform DFT Filter Banks Using Recursive Digital Filters

In this paper, we propose a method for designing a class of M‐channel, causal, stable, perfect reconstruction, infinite impulse response (IIR), and parallel uniform discrete Fourier transform (DFT) filter banks. It is based on a previously proposed structure by Martinez et al. [1] for IIR digital filter design for sampling rate reduction. The proposed filter bank has a modular structure and is therefore very well suited for VLSI implementation. Moreover, the current structure is more efficient in terms of computational complexity than the most general IIR DFT filter bank, and this results in a reduced computational complexity by more than 50% in both the critically sampled and oversampled cases. In the polyphase oversampled DFT filter bank case, we get flexible stop‐band attenuation, which is also taken care of in the proposed algorithm.     

Harmonic power detection in wideband cognitive radios

An analysis for simultaneous sensing of multiple primary user activity in cognitive radios has been presented here from a signal-processing perspective. First, the drawbacks of using simple harmonic power detection in wideband cognitive radios is demonstrated. Then, the impact of preprocessing the received signal to improve detection performance is highlighted along with its tradeoff involving complexity and corresponding delays in cognitive radio networks. It has been shown that a polyphase DFT filter bank with an optimal window as prototype filter can lead to reliable as well as efficient sensing and detection architecture in cognitive radios with minimal overhead. A detailed account of challenges, performance limits and computational complexity comparisons for various spectrum-sensing solutions is also provided. The study is supported through simulations with parameters corresponding to the IEEE 802.22 standard.     

A new approach to subband adaptive filtering

Subband adaptive filtering has attracted much attention lately. In this paper, we propose a new structure and a new formulation for adapting the filter coefficients. This structure is based on polyphase decomposition of the filter to be adapted and is independent of the type of filter banks used in the subband decomposition. The new formulation yields improved convergence rate when the LMS algorithm is used for coefficient adaptation. As we increase the number of bands in the filter, the convergence rate increases and approaches the rate that can be obtained with a flat input spectrum. The computational complexity of the proposed scheme is nearly the same as that of the fullband approach. Simulation results are included to demonstrate the efficacy of the new approach     

Discrete cosine harmonic wavelet transform and its application to signal compression and subband spectral estimation using modified group delay

This paper proposes a new harmonic wavelet transform (HWT) based on discrete cosine transform (DCTHWT) and its application for signal or image compression and subband spectral estimation using modified group delay (MGD). Further, the existing DFTHWT has also been explored for image compression. The DCTHWT provides better quality decomposed decimated signals, which enable improved compression and MGD processing. For signal/image compression, compared to the HWT based on DFT (DFTHWT), the DCTHWT reduces the reconstruction error. Compared to DFTHWT for the speech signal considered for a compression factor of 0.62, the DCTWHT provides a 30% reduction in reconstruction error. For an image, the DCTHWT algorithm due to its real nature, is computationally simple and more accurate than the DFTHWT. Further compared to Cohen–Daubechies–Feauveau 9/7 biorthogonal symmetric wavelet, the DCTHWT, with its computational advantage, gives a better or comparable performance. For an image with 6.25% coefficients, the reconstructed image by DFTHWT is significantly inferior in appearance to that by DCTHWT which is reflected in the error index as its values are 3.0 and 2.65%, respectively. For spectral estimation, DCTHWT reduces the bias both in frequency (frequency resolution) and spectral magnitude. The reduction in magnitude bias in turn improves the signal detectability. In DCTHWT, the improvement in frequency resolution and the signal detectability is not only due to good quality DCT subband signals but also due to their stretching (decimation) in the wavelet transform. The MGD reduces the variance while preserving the frequency resolution achieved by DCT and decimation. In view of these, the new spectral estimator facilitates a significant improvement both in magnitude and frequency bias, variance and signal detection ability; compared to those of MGD processing of both DFT and DCT fullband and DFT subband signals.     

Least squares approximation of perfect reconstruction filter banks

Designing good causal filters for perfect reconstruction (PR) filter banks is a challenging task due to the unusual nature of the design constraints. We present a new least squares (LS) design methodology for approximating PRFBs that avoids most of these difficult constraints. The designer first selects a set of subband analysis filters from an almost unrestricted class of rational filters. Then, given some desired reconstruction delay, this design procedure produces the causal and rational synthesis filters that result in the best least squares approximation to a PRFB. This technique is built on a multi-input multi-output (MIMO) system model for filter banks derived from the filter bank polyphase representation. Using this model, we frame the LS approximation problem for PRFBs as a causal LS equalization problem for MIMO systems. We derive the causal LS solution to this design problem and present an algorithm for computing this solution. The resulting algorithm includes a MIMO spectral factorization that accounts for most of the complexity and computational cost for this design technique. Finally, we consider some design examples and evaluate their performance     

A new delayless subband adaptive filter structure

Adaptive subband techniques have been developed to reduce complexity and slow convergence problems of the traditional fullband high-order adaptive filters. Some of the disadvantages often encountered in most of the proposed architectures are the effect of aliasing associated with the multirate structure, which is a source of error in the modeling of the unknown system, and the delay introduced in the signal path. We present a new delayless maximally decimated structure where the optimal subband filters are related to the wideband system in a closed form. They make use of a special DFT analysis filterbank where the polyphase components of the prototype filter represent fractional delays so that there is no need for adaptive cross-filters, and the unknown system can modeled perfectly in a closed-loop scheme. We interpret the proposed structure as a special case of a block adaptive filter with lower computational complexity than the conventional fullband LMS algorithm. Some computer simulations are presented in order to verify the good features of the proposed structure     

Polyphase analysis filter bank down-converts unequal channel bandwidths with arbitrary center frequencies

A cognitive radio (CR) receiver should be able to filter and simultaneously down convert multiple signals having arbitrary bandwidths and randomly located center frequencies. In this paper we present an efficient structure, based on polyphase filter banks, for CR receivers. Its core, an analysis channelizer, is a variant of the standard M-path polyphase down converter channelizer. It is able to perform M/2-to-1 down sampling of the input time series while it shifts, by aliasing, all the M channels to base-band. A perfect reconstruction (PR) filter is selected as low-pass prototype for avoiding energy losses during the signal processing. A post analysis block is designed for extracting, when necessary, from the base-band aliased channels, the spectra, or their fragments, belonging to different signals. A selector commutes the output ports of the post analysis block that contain spectral fragments of the same bandwidths and properly delivers them to the up converter synthesis channelizers that reassemble them. The synthesis channelizers are P_n-path polyphase up converter modified for performing 1-to-P_n/2 up sampling of the input time series. Complex frequency rotators, placed at the output of the synthesizers, compensate the frequency offsets, applied in the transmitter, that are responsible for the arbitrary center frequency positioning of the received signals. At the end of the receiver chain, arbitrary interpolators resample the base-band centered signals to obtain two samples per symbol needed for the further processing stages.     

A signal subspace-based subband approach to space-time adaptiveprocessing for mobile communications

In this paper, we present a novel space-time signal subspace-based subband approach to space-time adaptive processing (STAP) that has been shown to be an effective method to suppress both the intersymbol interference (ISI) and the cochannel interference (CCI) in mobile communications. We first study the performance of STAP and make clear the conditions of perfect processing (i.e., perfect equalization of the desired user signal and perfect suppression of CCI signals). Based on the polyphase representation and the subspace analysis of the signal channels, we propose a space-time signal subspace-based subband approach to STAP, namely the subband STAP, which highly improves the convergence rate without loss of the steady-state performance. Simulation results show its effectiveness under the procedure of signal subspace estimation and detection     

Multiplierless filter‐bank based multicarrier system by using canonical signed digit representation

The filter‐bank based multicarrier (FBMC) system is a candidate for designing the physical layer of a cognitive radio because of its spectral efficiency and the spectral containment. The main drawback of such a system compared with orthogonal frequency division multiplexing systems is its high computation complexity, because each subcarrier is shaped by a non‐rectangular prototype filter. Although poly‐phase decomposition is suggested to decrease the sampling rate of filtering, the number of filtering operations (multiplications) is dramatically increased by the growing number of subcarriers and the amount of the desired spectral containment. Hence, hardware implementation of the FBMC system faces challenges such as high electrical power consumption and large silicon area occupation. In order to reduce computational complexity, a multiplierless filter design based on the canonical signed digit (CSD) representation is proposed. In this technique, at first, a prototype filter is designed. Then a pre‐optimization step is employed to adapt the prototype filter coefficients to system objectives and produce an enriched initial seed for genetic algorithm (GA) optimization. Finally, a customized GA is employed to jointly optimize and synthesize filter coefficients into the finite precision CSD representation so that the system objectives such as intersymbol‐interference, interchannel‐interference, and stop‐band attenuation remain unchanged against the full‐precision representation of coefficients. Copyright © 2014 John Wiley & Sons, Ltd.     

A new approach to spectral estimation: a tunable high-resolutionspectral estimator

Traditional maximum entropy spectral estimation determines a power spectrum from covariance estimates. Here, we present a new approach to spectral estimation, which is based on the use of filter banks as a means of obtaining spectral interpolation data. Such data replaces standard covariance estimates. A computational procedure for obtaining suitable pole-zero (ARMA) models from such data is presented. The choice of the zeros (MA-part) of the model is completely arbitrary. By suitable choices of filter bank poles and spectral zeros, the estimator can be tuned to exhibit high resolution in targeted regions of the spectrum     

基于稀疏重构的色噪声背景下未知线谱信号估计

针对色噪声背景下的未知线谱信号估计问题,该文提出一种基于分子频带处理的稀疏重构类线谱估计方法。首先,利用多速率余弦调制滤波器组对观测信号进行子带分解,得到功率谱相对平坦的子带信号。之后,在每个子带信号上,利用基于迭代最小化的稀疏学习方法进行线谱估计,并将各子带上的线谱估计结果进行频域综合滤波以及门限判决等处理。最终得到色噪声背景下的线谱估计结果。理论推导及仿真实验表明所提方法在色噪声背景下具有较好的线谱估计性能。其能够有效地去除色噪声背景,同时保留稀疏重构类线谱估计方法所具有的高频率分辨力等优点。     

Correlation Matching Approach for Spectrum Sensing in Open Spectrum Communications

In the new paradigm of open spectrum access, the envisioned radio agility calls for fast and accurate spectrum sensing, this challenges traditional spectral estimation. In this study, we propose three new procedures that are able to sense the known spectrum of the candidate or primary user, fulfilling the requirements of open spectrum scenarios. These procedures are developed by following the framework of correlation matching, changing the traditional single frequency scan to a spectral scan with a particular shape and generalizing filter-bank designs. The proposed techniques are called Candidate methods, because their goal is to react only when the candidate's spectral shape is present. First, Candidate-F is proposed as a spectral detection method, where this is based on minimizing the Frobenius distance between correlation matrices, and can be viewed as an extended version of the weighted overlapped spectrum averaging estimate. Next, Candidate-G is presented, which is a new procedure that is based on a geodesic distance, and that presents the lowest complexity. Lastly, a third procedure is studied, Candidate-M, which provides the most compliant performance with the demanding open spectrum scenario by generalizing the Capon-spectral estimator. By means of the analytical results, simulations of receiver operating characteristics, and estimation variance, this study shows the advantages of Candidate-M over the existing filter bank or cyclostationary detector methods.      

Theory and design of dyadic subband tree structures with embeddedPDF-optimized quantizers

We develop a methodology for the analysis of signal quantization effects in critically sampled dyadic subband tree structures using a nonlinear gain-plus-additive-noise model for the probability density function (PDF)-optimized quantizer. We constrain the two-band nonquantized and uncompensated structure at each level to be perfect reconstruction (PR). We develop an equivalent uniform filter bank followed by its polyphase structure described by primitive submatrices and compute a rigorously correct mean squared error (MSE) in the frequency domain using cyclostationary concepts in terms of: (1) the allocated quantizer bits; (2) the filter coefficients; (3) an embedded compensation parameter vector. This MSE is then minimized over all three items above. Our optimization method is applied to the specific case of a four-channel dyadic tree with average bit rate constraint. This tree is represented by an eight-channel polyphase equivalent whose interchannel signals are correlated. We show how to represent rigorously the correlation of random noise between channels due to the embedded quantizers. Our design of paraunitary and biorthogonal structures with identical and nonidentical stages is performed, compared, and validated by computer simulation under the assumption of uncorrelated cross band noise. The nonidentical stage biorthogonal filter bank turned out to have the best performance in MSE sense, but the most robust structure is the nonidentical stage paraunitary filter bank     

基于加权平均一致算法的宽带压缩频谱感知

在认知无线电(CR)网络中进行频谱共享接入,首要的任务是进行频谱感知,并发现频谱空洞。基于认知无线网络中信号频域的固有稀疏性,本文结合了压缩感知(CS)技术与加权平均一致(weighted average consensus)算法,建立了分布式宽带压缩频谱感知模型。频谱感知分为两个阶段,在感知阶段,各个CR节点对接收到的主用户信号进行压缩采样以减少对宽带信号采样的开销和复杂度,并做出本地频谱估计;在信息融合阶段,各CR节点的本地频谱估计结果以分布式的方式进行信息融合,并得到最终的频谱估计结果,获得分集增益。仿真结果表明,结合压缩感知与加权平均一致算法增强了频谱感知的性能,比在相同的CR网络中使用平均一致算法时有了性能上的提升。      

基于代价参考粒子滤波器组的检测前跟踪算法

基于粒子滤波的检测前跟踪方法是检测和估计非线性调频信号的有效方法之一。但此类方法运算量大,难以并行执行。此外,由于粒子滤波算法收敛较慢,基于粒子滤波的检测前跟踪方法的检测和状态估计能力有待提高。针对上述问题,该文首先提出一种代价参考粒子滤波器组。该滤波器组收敛快速,具有完全的并行结构,可快速准确地估计非线性调频信号的瞬时频率。其次,提出基于代价参考滤波器组的检测前跟踪算法,可在给定虚警率下,在各个时刻检测目标和估计目标状态。两类非线性调频信号检测和估计的仿真结果表明,基于代价参考粒子滤波器组的检测前跟踪算法的检测性能、估计性能和运行速率均优于类似的方法,如基于粒子滤波的检测前跟踪方法,基于Rutten粒子滤波的检测前跟踪方法等。     

用于高精度∑-△数模转换的多级插值滤波器的设计技术

提出了一种用于20bit ∑-△数模转换器中的内插滤波器的有效实现方法,内插滤波器的过采样率为128.该方法使用多级结构以降低滤波器系数的复杂度和有限字长效应.同时提出了基于系数混合基分解的多相半带滤波器的无乘法器实现方法,它降低了控制逻辑的复杂程度,并大大节省了芯片面积.芯片采用0.13μm CMOS工艺实现,整个插值滤波器面积小于0.63mm2.整个电路系统仅用简单的硬件单元实现,且结构规整,这有利于大规模集成电路制造,并可应用于高精度数据转换电路中.     

一种迭代正弦频率估计硬件实现研究

 本文提出了一种基于迭代算法的八通道滤波器组单频正弦信号频率估计方法,该算法的基本思想是利用频域重叠的多通道滤波器来实现大的频率估计范围,同时提高单通道内的信噪比;还对滤波抽取后的数据进行迭代,这样即使采用运算量最小的不加权线性预测算法也能实现高精度测频,并采用FPGA实现了该算法,最后数值仿真结果证实所提算法的优异性能.     

On the Subband Orthogonality of Cosine-Modulated Filter Banks

In this brief, the second-order characteristics of cosine-modulated filter banks are formulated and analyzed. We show that, for any type of input spectrum, adjacent subband signals of a cosine-modulated filter bank are properly phase-aligned in such a way that they are nearly orthogonal at zero lag even though they are not mutually exclusive. The orthogonal properties are desirable in reducing the computational complexity of subband processing systems.     

基于子带矩阵CFAR的海面慢速小目标检测算法

对于K分布海杂波环境下的目标检测,基于信息几何理论的矩阵CFAR检测器是一种有效的目标检测方法。但矩阵CFAR方法计算复杂度高且当目标多普勒频率严重偏离杂波频谱中心时,其检测性能不如自适应归一化匹配滤波器(ANMF)方法,影响其实际应用。为此,该文以滤波器组对接收信号进行滤波处理,提出一种基于滤波器组子带分解最大特征值的矩阵CFAR检测方法(FD-MEMD),通过双杂波抑制来解决目标多普勒频率偏离杂波频谱中心时矩阵CFAR方法失效的难题。最后,仿真实验验证了所提FD-MEMD具有较好的检测性能。