Linear prediction approach to direction estimation ofcyclostationary signals in multipath environment

We investigate the estimation of the directions-of-arrival (DOA) of closely spaced narrowband cyclostationary signals in the presence of multipath propagation. By exploiting the spatial and temporal properties of most communication signals, we propose a new cyclic forward-backward linear prediction (FBLP) approach for coherent signals impinging on a uniform linear array (ULA). In the proposed algorithm, the evaluation of the cyclic array covariance matrix is avoided, and the difficulty of choosing the optimal time lag parameter is alleviated. As a result, the proposed approach has two advantages: (1) the computational load is relatively reduced, and (2) the robustness of estimation is significantly improved. The performance of the proposed approach is confirmed through numerical examples, and it is shown that this approach is superior in resolving the closely spaced coherent signals with a small length of array data and at relatively low signal-to-noise ratio (SNR)     

Improved spatial smoothing techniques for DOA estimation ofcoherent signals

In the context of coherent signal classification, spatial smoothing is necessary for the application of the eigen-based direction of arrival (DOA) estimation methods. However, the currently known spatial smoothing algorithms not only reduce the effective aperture of the array, but also do not consider the cross correlations of the subarray outputs. An improved spatial smoothing algorithm which can fully utilize the correlations of the array outputs and produce a more stable estimate of the covariance matrix is presented. Simulation results are provided to verify the theoretical prediction. The superiority of this method over the conventional methods is obvious, especially when the SOSR (subarray to overall size ratio) is small     

Analysis of knee vibration signals using linear prediction

Clinical methods used at present for the diagnosis of cartilage pathology in the knee are invasive in nature, and carry some risks. There exists a need for the development of a safe, objective, noninvasive method for early detection, localization, and quantification of cartilage pathology in the knee. This paper investigates the possibility of developing such a method based on an analysis of vibrations produced by joint surfaces rubbing against one another during normal movement. In particular, the method of modeling by linear prediction is used for adaptive segmentation and parameterization of knee vibration signals. Dominant poles are extracted from the model system function for each segment based on their energy contributions and bandwidths. These dominant poles represent the dominant features of the signal segments in the spectral domain. Two-dimensional feature vectors are then constructed using the first dominant pole and the ratio of power in the 40-120 Hz band to the total power of the segment. The potential use of this method to distinguish between vibrations produced by normal volunteers and patients known to have cartilage pathology (chondromalacia) is discussed.     

A multistep linear prediction approach to blind asynchronous CDMAchannel estimation and equalization

A multistep linear prediction approach is presented for blind channel estimation, multiuser interference (MUI) suppression, and detection of asynchronous short-code direct sequence code division multiple access signals in multipath channels. Only the spreading code of the desired user is assumed to be known; its transmission delay may be unknown. We exploit the previously proposed multistep linear prediction approach for blind multiple-input multiple-output channel estimation in conjunction with the structure imposed by the desired user's spreading code sequence. With the knowledge of the desired user's code sequence, only the second-order statistics of the data are needed under certain sufficient conditions on the underlying multiuser MIMO transfer function. Based on the desired user's channel estimate, a linear minimum mean square error filter is designed for simultaneous equalization and MUI suppression. Three illustrative simulation examples are presented     

Zero-crossing estimation in signal generation of narrowband digital angle-modulated signals

It is shown that a zero-crossing estimator (ZCE), based on sinusoidal interpolation and realised with digital circuitry, can be used in place of a conventional DAC in signal generation of narrowband digital angle-modulated signals. An example of generating a GTFM signal with this ZCE is described.     

一种利用旋转结构的分布式信号DOA估计方法

某些领域中由于多径影响信号往往具有分布特性,此时传统波达方向(DOA)参数估计算法往往失效.针对此类问题提出了一种无需搜索的分布式信号DOA估计算法,该方法利用已知信号有用信息逐一估计分布信号在相邻阵元间产生的旋转因子,利用DOA旋转不变结构估计分布目标DOA参数.与点目标ESPRIT方法相比该方法能够有效克服多径扩展影响,不敏感于多径分布的具体形式,并在一定程度上抑制相位误差影响.仿真实验证明了其有效性.     

The linear prediction of deterministic signals

A method is presented for linear estimations of functionals of deterministic signals containing additive noise. The method is based on statistical decision theory and assumes discrete observations. In general terms a deterministic signal, with the noise subtracted, is a member of a class of functions with no probability distribution over the members of the class. In this paper the class is restricted to real one-dimensional functions parametrized by a real vector. The linear minimax estimate of the function to be estimated is proposed and the problem of computing it shown to be equivalent to a quadratic programming problem which can be solved exactly when the class of true signals is finite and sometimes when the class is infinite. In the latter case the problem can be solved approximately, subject to some mild restrictions on the signal. The exact algebraic solution is given for prediction of linear signals for up to three observations and is compared with the solution based on Wiener's theory.     

Simultaneous frequency and direction estimation of coherent signals

A new method is proposed in this paper for simultaneous frequency and directionestimation of coherent signals. The method is based on the rotational invariance techniques and uses an array triplet in motion to estimate the central frequencies and azimuths of coherent signals from narrowband sources. Without searching in the space of frequency-direction, the computational efficiency of the method is improved significantly. Simulation results in the typical examples demonstrate the performance of this new method.     

一种分布式目标波达方向估计方法

本文提出一种在多径情况下,利用广义阵列流形的特点,通过求解矩阵的广义特征值分解,不用进行谱峰搜索或多维参数搜索,直接由广义特征值得到分布式目标波达方向（DOA）估计的方法。该方法的估计性能对信号源的分布特性优于MUSIC方法。     

The performance of a direct-sequence spread-spectrum acquisition system using a linear prediction filter for narrowband interference suppression

The coarse acquisition performance of a direct sequence spread-spectrum receiver is analyzed when a linear prediction filter is used for narrowband interference suppression. We show that once an appropriate matching strategy is identified, the linear prediction filter can provide favorable performance when narrowband interference is present over a considerable range of both interference power and bandwidth. In addition, the presence of the filter dramatically improves the performance over the case where there is no filter, except when the interference bandwidth and the power are both small (i.e., when the processing gain provides sufficient interference immunity without the filter). If long spreading sequences are used with moderately sized observation windows, the acquisition performance can be severely degraded when a parallel acquisition scheme is used due to the linear predication filter. We show, however, that a slower serial receiver will provide reliable performance.This work was supported in part by the Office of Naval Research under contract ONR N00014-91-J-1234, the Army Research Office under contract ARO DAAL03-91-0071, and the NSF Center for Ultra-High Speed Circuits and Systems (ICAS).     

Analysis of the spatial filtering approach to the decorrelation ofcoherent sources

The recently proposed spatial filtering, which is developed to effectively decorrelate coherent signals, is analyzed. It has been shown that any set of distinct preliminary estimates of directions of arrival (DOAs) can be used to obtain a full rank source covariance matrix. In addition, a particular signal enhancement approach is developed to minimize the effects of the sensor noise. Statistical analysis of the spatial filtering and its enhanced version are also studied using the Monte Carlo method     

Method of estimation of appear direction of unknown shape signals

It is proposed a new method of build of direction-finding characteristic of antenna array to detect the direction of the signal source in condition of unknown signal shape. A comparative analysis of proposed method is carried out with numerical simulation.     

Modified linear prediction algorithm for narrowband interference suppression in universal mobile telecommunication system

Narrow band interference (NBI) deteriorates the quality of the spectrum, leading to a poorer performance of modern universal mobile telecommunication system (UMTS) spread spectrum systems. The linear prediction algorithm is one of the most significant techniques to overcome NBI and enhance the performance of UMTS systems. In this paper, a modified linear prediction algorithm is proposed for NBI suppression in a conditionally stationary environment. This modification improves the error energy estimation in the auto‐regression model of the linear prediction. The convergence of the proposed algorithm is evaluated, and its robustness is verified using Kullback–Leibler metrics for conditionally stationary NBI signals. Computer simulations are carried out, and the obtained results demonstrate the performance of the proposed algorithm and its compliance with UMTS protocols. Copyright © 2012 John Wiley & Sons, Ltd.     

Adaptive beamspace focusing for direction of arrival estimation of wideband signals

In this paper, we present an adaptive beamspace focusing technique for the direction of arrival (DOA) estimation of wideband signals. The proposed focusing scheme can perform coherent signal subspace transformation in the beamspace domain without preliminary DOA estimation or iteration. It can maintain low focusing error over a predefined sector-of-interest in the field-of-view (FOV) of the array while adaptively suppressing out-of-sector sources. The beamspace gain outside the sector-of-interest is controlled via additional constraints that provide robustness against moving or suddenly appearing out-of-sector sources. We formulate the adaptive beamspace design problem as a second-order cone program (SOCP) that can be solved efficiently using interior point methods. Numerical simulations are presented showing the superior performance of our approach compared to classical non-adaptive beamspace focusing techniques.     

A subspace method for direction of arrival estimation ofuncorrelated emitter signals

A novel eigenstructure-based method for direction estimation is presented. The method assumes that the emitter signals are uncorrelated. Ideas from subspace and covariance matching methods are combined to yield a noniterative estimation algorithm when a uniform linear array is employed. The large sample performance of the estimator is analyzed. It is shown that the asymptotic variance of the direction estimates coincides with the relevant Cramer-Rao lower bound (CRB). A compact expression for the CRB is derived for the ease when it is known that the signals are uncorrelated, and it is lower than the CRB that is usually used in the array processing literature (assuming no particular structure for the signal covariance matrix). The difference between the two CRBs can be large in difficult scenarios. This implies that in such scenarios, the proposed methods has significantly better performance than existing subspace methods such as, for example, WSF, MUSIC, and ESPRIT. Numerical examples are provided to illustrate the obtained results     

Robust linear prediction of speech signals based on orthogonalframework

A linear prediction is formulated via the orthogonal principle to facilitate the incorporation of various error minimisation criteria. A weighting function, which downplays extreme errors, is used to provide a robust estimate lying between the L₁ and L₂ criteria. Experiments based on synthetic vowels reveal that the proposed method outperforms the L₁ and L₂ estimates     

A unified approach to linear estimation problems for nonstationary processes

The linear least mean-square (LLMS) error estimation problem of a nonstationary signal corrupted by additive white noise is studied. The formulation of the problem is very general, in the sense that it deals with different estimation problems (smoothing, filtering, and prediction) involving correlation between the signal and the white noise and the possibility of estimating a linear operation (in quadratic mean) of the signal. The obtained solution is in the form of a suboptimum estimate and is derived by using the approximate series expansions for stochastic processes with the aim of solving the Wiener-Hopf equation in the general (nonstationary) case. The main characteristic of this new solution is that it can be computed efficiently using a recursive algorithm similar to the Kalman filter without requiring the signal to obey a state-space model.     

A multiuser approach to narrowband cellular communications

We compare three receivers for coded narrowband transmission affected by fading and co-channel interference. The baseline receiver is based on conventional diversity reception with maximal-ratio combining. A multiuser approach allows us to derive a maximum-likelihood multiuser receiver and its reduced-complexity suboptimal version. Finally, a decorrelating diversity receiver, which seeks a tradeoff between performance and complexity, is studied. Closed-form performance parameters are derived for all the proposed receivers in the case of coded coherent PSK and independent frequency nonselective Rayleigh fading     

High-resolution direction finding of non-stationary signals using matching pursuit

One of the main goals of time–frequency (TF) signal representations in non-stationary array processing is to equip multi-antenna receivers with the ability to separate sources in the TF domain prior to direction finding. This permits high-resolution direction-of-arrival (DOA) estimation of individual sources and of more sources than sensors. In this paper, we use linear decomposition of sensor data based on matching pursuit (MP). The leading atoms of the MP, which capture most of the source TF signatures, can be different for different sources and, as such, provide the desired source discrimination. The MP coefficients with high signal-to-noise ratio (SNR) and corresponding to the leading decomposition atoms are used to develop the MP-MUSIC DOA estimation for non-stationary source signals. We demonstrate the source discriminatory capability of the proposed technique using linear FM, nonlinear FM, and other non-stationary signals. Further, we compare MP-MUSIC performance with conventional MUSIC and the time–frequency MUSIC, which incorporates bilinear transforms.     

线性调频信号时/频差估计算法

提出了一种新的快速估计线性调频信号时/频差的算法.该算法将抽取的自模糊函数与Radon变换结合估计线性调频信号的调频率, 通过分数阶傅里叶变换估计出模糊函数脊线与频率轴交点位置, 应用解调频沿脊线搜索模糊函数峰值.对于接收信号中存在多分量的情况, 根据其模糊函数脊线位置的不同, 该算法能够分辨各分量信号, 并分别精确估计出各分量的时/频差.由于只需一维搜索模糊函数峰值, 并可用快速傅里叶变换实现, 该算法大大减少了运算量.仿真实验表明, 随着信噪比的提高, 该算法估计的时/频差均方误差逐渐逼近克拉美-罗下界.