Bias correction and forward-backward averaging in frequency/DOA tracking problems

In this paper, we discuss some issues relevant to frequency and direction of arrival (DOA) tracking problems. First, we develop a linear Frequency Modulated (FM) signal model for accurately describing windowed, slowly time varying narrowband signals that typically occur in tracking problems. We then derive first order bias expressions for the peak locations of a Discrete Time Fourier Transform (DTFT) spectrum of a windowed, slowly time varying linear FM signal. We also show that Forward-Backward (FB) averaging is generally inappropriate for nonstationary data, but that it is appropriate when applied to tracking the frequencies of windowed, slowly time varying narrowband signals. A major motivation for using FB averaging is to increase the efficiency of subspace based frequency/DOA estimation in tracking problems. Finally, simulations confirm our first order bias expressions, and show that FB averaging does not significantly alter (or degrade) the time varying MUSIC based frequency estimation performance over that of Forward only averaging.This research was supported in part by the National Science Foundation Grant MIP-9203296 and Texas Advanced Research Program Grant 009741-022 and 009741-065.     

Efficient direction-finding methods employing forward/backwardaveraging

In this paper, we develop a general approach for reducing the computational complexity of any direction finding method implemented with forward/backward (FB) averaging. We develop simplified FB data matrices in a manner paralleling previous work related to centrohermitian (correlation) matrices. Based on these simplified data matrices, efficient construction and updating of the FB correlation matrix is developed. In addition, efficient FB FFT, FB beamspace, FB EVD updating, FB SVD, and FB SVD updating methods are derived. In most cases, FB-based direction-finding methods can be simplified so that the computational complexity is reduced below that of an analogous forward only implementation. Thus, effectively twice the amount of data is processed with less total computation     

DOA estimation for monostatic MIMO radar based on unitary root-MUSIC

Direction of arrival (DOA) estimation is an important issue for monostatic MIMO radar. A DOA estimation method for monostatic MIMO radar based on unitary root-MUSIC is presented in this article. In the presented method, a reduced-dimension matrix is first utilised to transform the high dimension of received signal data into low dimension one. Then, a low-dimension real-value covariance matrix is obtained by forward–backward (FB) averaging and unitary transformation. The DOA of targets can be achieved by unitary root-MUSIC. Due to the FB averaging of received signal data and the eigendecomposition of the real-valued matrix covariance, the proposed method owns better angle estimation performance and lower computational complexity. The simulation results of the proposed method are presented and the performances are investigated and discussed.     

A beamformer for the acquisition of evoked potentials

Evoked potentials (EP) contain information about various physiological parameters and the estimation and detection of these signals can aid in the diagnosis of many pathological conditions. However, the signal-to-noise ratio (SNR) for EP measurement is often very low, and thus signal processing techniques must be employed to enhance the SNR. A delay and sum beamformer acquisition system has the potential for significant SNR improvement in EP measurements. In this communication it is shown that an electrode array acquisition system implements a uniform coherent delay and sum beamformer. The performance of the beamformer is characterized in terms of the number of electrodes, and cross-channel correlation. When compared to conventional ensemble averaging, the beamformer reduces the number of response repetitions required to achieve a given SNR by a factor which approaches the number of channels in the acquisition system.     

A subspace method for signature waveform estimation in synchronousCDMA systems

Synchronous code-division multiple-access (CDMA) techniques possess intrinsic protection against co-channel interference due to orthogonal codes employed and thus, offers higher capacity than existing frequency-division multiple-access (FDMA) or time-division multiple-access (TDMA) systems. In the presence of multipath, however, each signal is subject to frequency-selective fading and the orthogonality condition does not necessarily hold leading to increased cross correlation. In these scenarios, multiuser detection need to be performed to suppress interference and recover the message symbols. To implement such a technique, explicit knowledge of the (nonorthogonal) signature waveforms of all users is required. We propose a blind estimation scheme that provides closed-form estimates of the signature waveforms by exploiting the structure information of the data output. In particular, we show that the subspace of the data matrix contains sufficient information for unique determination of the signature waveforms. Based on this observation, a multiple signal classification (MUSIC)-like algorithm is derived. Performance analysis of the new approach is also presented     

一种CFAR检测雷达的可控干扰新方法

针对目前常见恒虚警（CFAR）检测雷达干扰方法所产生干扰效果单一的问题,提出一种基于多相位分段调制的干扰方法。对该方法的基本原理进行说明,推导干扰信号的脉压输出结果,基于单元平均恒虚警检测方法,分析了该方法对CFAR检测雷达的干扰效果。最后进行仿真与分析,说明该干扰不仅可以形成压制干扰,还能够形成假目标欺骗干扰,并且通过对分路数、调制相位数以及调制相位值三个参数的变化,可以实现对假目标数与假目标幅值的控制,是一种灵活可控的干扰方法。     

共轭梯度自适应波束形成算法

本文讨论了共轭梯度算法在自适应波束形成中的应用,指出在时域和空域同时处理,与仅在时域或空域处理的方法相比,不仅在性能上得到了改进,而且放宽了对阵元数目的要求。同时提出了一种前后向平均和多层前后向处理的结构和方法,使算法在性能上得到了进一步的改善。     

Simple and Efficient Nonparametric Method for Estimating the Number of Signals Without Eigendecomposition

Inspired by the computational simplicity and numerical stability of QR decomposition, a nonparametric method for estimating the number of signals without eigendecomposition (MENSE) is proposed for the coherent narrowband signals impinging on a uniform linear array (ULA). By exploiting the array geometry and its shift invariance property to decorrelate the coherency of signals through subarray averaging, the number of signals is revealed in the rank of the QR upper-trapezoidal factor of the autoproduct of a combined Hankel matrix formed from the cross correlations between some sensor data. Since the infection of additive noise is defused, signal detection capability is improved. A new detection criterion is then formulated in terms of the row elements of the QR upper-triangular factor when finite array data are available, and the number of signals is determined as a value of the running index for which this ratio criterion is maximized, where the QR decomposition with column pivoting is also used to improve detection performance. The statistical analysis clarifies that the MENSE detection criterion is asymptotically consistent. Furthermore, the proposed MENSE algorithm is robust against the array uncertainties including sensor gain and phase errors and mutual coupling and against the deviations from the spatial homogeneity of noise model. The effectiveness of the MENSE is verified through numerical examples, and the simulation results show that the MENSE is superior in detecting closely spaced signals with a small number of snapshots and/or at relatively low signal-to-noise ratio (SNR)     

Design and Factorization of Two-Channel Perfect Reconstruction Filter Banks with Causal-Stable IIR Filters

In this paper, new design and factorization methods of two-channel perfect reconstruction (PR) filter banks (FBs) with casual-stable IIR filters are introduced. The polyphase components of the analysis filters are assumed to have an identical denominator in order to simplify the PR condition. A modified model reduction is employed to derive a nearly PR causal-stable IIR FB as the initial guess to obtain a PR IIR FB from a PR FIR FB. To obtain high quality PR FIR FBs for carrying out model reduction, cosine-rolloff FIR filters are used as the initial guess to a nonlinear optimization software for solving to the PR solution. A factorization based on the lifting scheme is proposed to convert the IIR FB so obtained to a structurally PR system. The arithmetic complexity of this FB, after factorization, can be reduced asymptotically by a factor of two. Multiplier-less IIR FB can be obtained by replacing the lifting coefficients with the canonical signal digitals (CSD) or sum of powers of two (SOPOT) coefficients.     

基于压缩感知的循环平稳特征检测方法

在认知无线电中,传统的循环平稳特征检测技术为了达到理想的感知效果,需要大量的数据采样点,导致其感知过程复杂度大,感知时间长.针对此问题,提出了一种基于压缩感知的改进循环平稳特征检测方法,该算法利用信号循环自相关函数(Cyclic Autocorrelation Function,CAF)的稀疏特性,基于分段平均的时变自相关函数估计值,通过压缩感知技术重构二维CAF矩阵,再根据重构结果实现循环平稳特征检测.该方法不仅可有效降低计算复杂度和检测时间,而且提高了二维CAF的估计精度.仿真结果表明该方法的检测性能优于基于经典CAF估计的循环平稳特征检测技术.     

Analysis of Multi-Component Non-Stationary Signals Using Fourier-Bessel Series and Wigner-Hough Transform

We presented a novel Fourier-Bessel (FB) series and Wigner-Hough transform (WHT) method for the analysis of multi-component non-stationary signals. The FB series decomposed multi-component non-stationary signals into mono-component signals. The Wigner-Ville distribution (WVD) was applied to each mono-component signal to analyze its time-frequency distribution (TFD). Summing up the WVDs of the individual components resulted in TFDs of the multi-component signals, where the cross terms and noise were significantly reduced. The Hough transform (HT) was applied on the TFD of the multi-component signal (obtained from FB-WVD). The HT provides an important tool for mapping the signals onto a parameter space where the detection and estimation problems are made easier. This mapping can be used in the detection and parameter estimation of signals which are unknown and embedded in noise.     

Results on principal component filter banks: colored noisesuppression and existence issues

We have made explicit the precise connection between the optimization of orthonormal filter banks (FBs) and the principal component property: the principal component filter bank (PCFB) is optimal whenever the minimization objective is a concave function of the subband variances of the FB. This explains PCFB optimality for compression, progressive transmission, and various hitherto unnoticed white-noise, suppression applications such as subband Wiener filtering. The present work examines the nature of the FB optimization problems for such schemes when PCFBs do not exist. Using the geometry of the optimization search spaces, we explain exactly why these problems are usually analytically intractable. We show the relation between compaction filter design (i.e., variance maximization) and optimum FBs. A sequential maximization of subband variances produces a PCFB if one exists, but is otherwise suboptimal for several concave objectives. We then study PCFB optimality for colored noise suppression. Unlike the case when the noise is white, here the minimization objective is a function of both the signal and the noise subband variances. We show that for the transform coder class, if a common signal and noise PCFB (KLT) exists, it is, optimal for a large class of concave objectives. Common PCFBs for general FB classes have a considerably more restricted optimality, as we show using the class of unconstrained orthonormal FBs. For this class, we also show how to find an optimum FB when the signal and noise spectra are both piecewise constant with all discontinuities at rational multiples of π     

New formulation of GNSS acquisition with CFAR detection

This paper deals with the analysis of the acquisition process performed by a global navigation satellite system (GNSS) receiver with a pilot and data channel or in case of GNSS hybrid receiver. Signal acquisition decides the presence or absence of GNSS signal by comparing signal under test with a fixed threshold and provides a code delay and a Doppler frequency estimation, but in low signal conditions or in a noisy environment; acquisition systems are vulnerable and can give a high false alarm and low detection probability. Firstly, we introduce a cell‐averaging‐constant false alarm rate (CFAR) then a data‐pilot cell‐averaging‐CFAR detector fusion based to deal with these situations. In this context, we use a new mathematical derivation to develop a closed‐form analytic expressions for the probabilities of detection and false alarm. The performances of the proposed detector are evaluated and compared with a non‐CFAR case through an analytical and numerical results validated by Mont Carlo simulations. Copyright © 2016 John Wiley & Sons, Ltd.     

基于希尔波特-黄变换的冲击无线电信号检测

冲击无线电信号具有低截获、瞬态特性。本文提出采用希尔波特-黄变换(HHT)方法从干扰和噪声中提取微弱的冲击无线电信号。通过HHT可以得到冲击无线电信号的时频谱,并与传统的小波时频谱和Wigner-Ville分布进行了比较。仿真表明：在信噪比低达-10dB的情况下进行冲击无线电信号检测,HHT方法优于传统的小波分析和Wigner-Ville分布。     

ECG beat detection using filter banks

The authors have designed a multirate digital signal processing algorithm to detect heartbeats in the electrocardiogram (ECG). The algorithm incorporates a filter bank (FB) which decomposes the ECG into subbands with uniform frequency bandwidths. The FB-based algorithm enables independent time and frequency analysis to be performed on a signal. Features computed from a set of the subbands and a heuristic detection strategy are used to fuse decisions from multiple one-channel beat detection algorithms. The overall beat detection algorithm has a sensitivity of 99.59% and a positive predictivity of 99.56% against the MIT/BIH database. Furthermore this is a real-time algorithm since its beat detection latency is minimal. The FB-based beat detection algorithm also inherently lends itself to a computationally efficient structure since the detection logic operates at the subband rate. The FB-based structure is potentially useful for performing multiple ECG processing tasks using one set of preprocessing filters     

Multi-resolution dyadic wavelet denoising approach for extraction of visual evoked potentials in the brain

More powerful techniques need to be developed to extract small and weak visual evoked potentials (VEPs) from the spontaneous cerebral electric activity EEG. The authors present a wavelet decomposition algorithm suitable for identification and detection of very weak VEPs. The cross-correlation analysis between Daubechies wavelet (i.e. dbN) functions /spl psi//sub N/(t), for N=4, 5 ,...,10 and a representative noiseless VEP signal is performed to choose the proper wavelet function, say that with maximum correlation coefficient (highest resemblance) with respect to the representative VEP signal sequence. In this way, the specific choice of the best wavelet prototype function is no longer arbitrary for the application of obtaining pattern reversal VEPs. Extensive clinical experiments have demonstrated that the multiresolution wavelet analysis method can identify and estimate the peak latency of VEP signal well, with only a much reduced trial of ensemble averaging (EA) required. The major advantages of the wavelet transform are that it can 'zoom-in' to time discontinuities, and that orthonormal bases, localised in time and frequency, can be constructed. With this zoom-in property of the wavelet analysis, the irregularities or abnormalities of signals can easily be detected. Also the characteristics of EP signals can be captured by means of wavelet analysis, which can be further used for the detection and recognition of the abnormalities in the brain.     

基于压缩感知的偏振鬼成像系统

偏振鬼成像系统结合了强度和偏振探测,扩展了鬼成像系统的信息量,可以进行有效的目标识别和探测。常规相关偏振鬼成像系统需要大量采样数,且复原结果信噪比低。为此提出基于缩感知的偏振鬼成像系统,利用系统获取物体的强度和偏振参数,采用压缩感知算法来反演获取物体的强度和偏振信息。利用仿真实验,采用具有相同反射率但不同偏振特性的物体进行研究,结果表明采用压缩感知算法可以在很少的采样数下获取高质量的物体强度和偏振信息,提高了系统的实时性,并与相关算法进行了对比。最后采用图像融合算法对强度和偏振信息进行了融合,通过融合信息可以有效地进行多种物体的识别。     

Iterative design and detection of a DFE in the frequency domain

Error-propagation phenomena and computational complexity of the filters' design are important drawbacks of existing decision-feedback equalizers (DFE) for dispersive channels. In this paper, we propose a new iterative block DFE (IBDFE) which operates iteratively on blocks of the received signal. Indeed, a suitable data-transmission format must be used to allow an efficient implementation of the equalizer in the frequency domain, by means of the discrete Fourier transform. Two design methods are considered. In the first method, hard detected data are used as input of the feedback, and filters are designed according to the correlation between detected and transmitted data. In the second method, the feedback signal is directly designed from soft detection of the equalized signal at the previous iteration. Estimators of the parameters involved in the IBDFE design are also derived. From performance simulations on a wireless dispersive fading channel, we observed that the IBDFE outperforms existing DFEs. Moreover, the IBDFE exhibits a reduction of the computational complexity when compared against existing schemes, both in signal processing and in filter design.     

室内穿墙场景下的无源人体目标检测算法

穿墙场景下,由于墙体造成信号严重衰减,接收信号中目标反射信号的能量大幅下降,接收信号淹没在收发机直射信号和室内家具反射信号中,难以检测墙后目标。针对上述问题,该文提出一种新颖的基于多维信号特征融合的穿墙多人体目标检测算法(TWMD)。先对接收到的信道状态信息(CSI)进行预处理以消除相位误差和幅值噪声,再利用CSI的时序相关性和子载波相关性从相关系数矩阵中提取多维信号特征,最后使用BP神经网络完成特征与检测结果之间的映射。实验结果表明,该算法在玻璃墙、砖墙和混凝土墙环境的识别精度分别在0.98, 0.90, 0.85以上。根据所统计的4000个各类样本的检测结果,与现有基于单一信号特征的检测算法相比,该文算法在对不同数量运动目标的检测上,获得了平均0.45的精度提升。     

Solving a class of optimum multiuser detection problems withpolynomial complexity

We identify a class of optimum multiuser detection problems which can be solved with polynomial complexity in the number of users. The identification is based on transforming a quadratic 0-1 programming problem into an equivalent problem in graph theory. For a synchronous direct sequence code-division multiple access (CDMA) system, the result translates to designing a set of pseudorandom codes with the property that the cross correlation between every pair of codes in the set over one symbol period is nonpositive. We give two sets of codes with good correlation properties that fall within this class. Finally, we derive a bound on the cardinality of a signal set in an n-dimensional space, having the property that the cross correlation between every pair of signals in the set is nonpositive