一种新的FSK最佳相干解调器

本文讨论在信号各频率振幅受到的衰减不一致并存在有色加性高斯噪声(IGDF-ACGN)信道条件下,FSK信号的最佳相干解调问题。基于矢量表示系统模型的方法,导出了最佳解调器的结构。从理论分析和计算机模拟实验结果都证明了所导得的最佳解调器,不仅在IGDF-ACGN信道条件下是最佳的,而且在加性高斯白噪声(AWGN)信道条件下也是最佳的。文中还导出了相应的误码率公式,给出了误码率数值计算和计算机模拟曲线。     

基于MMSE准则的最佳相干FSK解调器

 本文提出用MMSE准则 设计最佳相干FSK解调器.在信道噪声分布未知条件下,导出了该解调器的结构.并给出了分析和计算机模拟实验结果,证明了该解调器在AWGN信道条件下与普通FSK解调器误码性能相同,在信号各个频率振幅受到的衰减不一致,并且为ACGN信道条件下误码性能优于普通FSK解调器,与文献[2]基于MAP准则的改进型FSK解调器的误码性能十分接近;在信道中的噪声为其它分布(如均匀分布)情况下,也有类似上述的结果.     

新型MFSK最佳相干解调器

本文推广了作者在1999年提出的方法和结论[1],讨论了在信号各频率振幅受到的衰减不一致并存在有色加性高斯噪声(IGDF-ACGN)信道条件下,M进制FSK(MFSK)信号的最佳相干解调问题。导出了最佳解调器的结构,说明了该解调器中有关参数的估计方法,以及实现时遇到的矢量、矩阵运算的有关快速算法。从理论和计算机模拟实验两方面说明了所导得的最佳解调器,不仅在IGDF-ACGN信道条件下是最佳的,而且在加性高斯白噪声(AWGN)信道条件下也是最佳的。文中给出了误码率的计算机模拟曲线。     

高斯色噪声下的相干信号 DOA 高分辨率估计*

针对高斯有色噪声下的DOA估计问题,提出一种基于高阶累积量稀疏表示的DOA估计方法。该方法利用四阶累积量矩阵中的第一列生成最小冗余向量,利用扩展阵列的最小冗余导向矢量构造过完备字典。然后利用L1范数作为稀疏约束条件,建立最小冗余向量的稀疏模型进行DOA估计。该方法将求解四阶累积量的次数从M4次降为M2-M+1次。同时又能充分利用四阶累积量的优点,对高斯有色噪声具有良好的抑制能力,并使阵列孔径得到了扩展,估计信号个数能大于阵元数目。仿真实验和理论分析验证了该方法比MUSIC-like和MUSIC算法具有更好的性能,不需要任何处理可以直接应用到相干信号。     

Suboptimum coherent detection of orthogonal phase-modulated signals

A suboptimum scheme for coherent demodulation of orthogonal phase-modulated signals in the additive white Gaussian noise channel is proposed and analyzed in terms of bit error probability. It is analytically shown that the suboptimum scheme provides the bit error performance competitive to the conventional optimum scheme, while requiring much less implementation complexity.     

Adaptive energy detection of quasi-deterministic signals in the presence of the non-Gaussian noise

Analytical expressions for the parameters of the adaptive energy detection of the quasi-deterministic radio signals in the presence of the Lichter noise are derived. The effect of the random amplitude distribution of radio signals on the decision statistics of detection is analyzed.     

Direction finding in the presence of colored noise by candidateidentification

A method is developed for identifying correct angles of arrivals from a set of candidate angles that contains spurious angles and the true ones, under the assumption that the colored noise is an autoregressive process and that a uniform linear array of sensors is used. The procedure is based on a relation derived for the higher-order reflection coefficients corresponding to the autocorrelation sequence of the signal plus noise. It is shown that the higher-order reflection coefficients of a set of plane waves impinging on a uniform linear array of sensors, in the presence of an unknown spatially autoregressive colored noise process, are equal to an order independent linear combination of the values of the Fourier transform of the corresponding optimal linear least squares normalized prediction error filter at the electric phase angles of the plane waves. The magnitudes of the coefficients are then used to decide whether a given candidate direction of arrival corresponds to an actual plane wave or not. The estimation of candidate angles is also briefly discussed     

On estimating spectral moments in the presence of colored noise

Let{q^(1) (t)}, the signal, be a complex Gaussian process corrupted by additive Gaussian noise{q^(2) (t) }. Observations onp(t)q(t)andp(t) q^(2) (t)are assumed to be available wherep(t)is a smooth weighting function andq = q^(1) + q^(2). Using the Fourier transform of the samples ofp(t)q(t)andp(t) q^(2) (t), estimators are derived for estimating the mean frequency and spectral width of the unknown power spectrum of the unweighted signal process. The means and variances of these statistics are computed in general, and explicitly for nontrivial practical examples. Asymptotic formulas for the moment estimators as a function of the number of realizations, frequency resolution, signal-to-noise ratio and spectral width, and consistency of the estimators are some of the results that are discussed in detail.     

Parameter estimation of autoregressive signals from observations corrupted with colored noise

This paper presents a new method for estimation of the parameters of a noisy autoregressive (AR) signal using observations corrupted with colored noise. This method is an improved least-squares (ILS) based method that combines low-order and high-order Yule–Walker equations. The performance of the proposed method is illustrated using computer simulations.     

On the detection of a signal in colored noise

A modeling approach is used in the detection of a random signal in colored noise. The received sequence is modeled as a regressive/autoregressive time series, and the presence or absence of the desired signal is determined through a hypothesis testing procedure. The test is based on the construction of anF-statistic using likelihood functions. The statistic can be easily incorporated into the computation of the probability of a false alarm, such as required in the processing of radar signals. Results based on simulated data and actual radar data are presented.This research was supported by NSERC Grant No. A3635.     

Instantaneous frequency estimation of polynomial FM signals usingthe peak of the PWVD: statistical performance in the presence ofadditive gaussian noise

The peak of the polynomial Wigner-Ville distribution (PWVD) has been previously proposed as an estimator of the instantaneous frequency (IF) for a monocomponent polynomial frequency modulated (FM) signal. In this paper, we evaluate the statistical performance of this estimator in the case of additive white Gaussian noise and provide an analytical expression for the variance. We show that for a given PWVD order, the estimator performance can be improved by a proper choice of the kernel coefficients in the PWVD. A performance comparison between the PWVD based IF estimator and another previously proposed one based on the high-order ambiguity function (HAF) is also provided, Simulation results show that for a signal-to-noise ratio larger than 3 dB, the proposed sixth-order PWVD outperforms the HAF in estimating the IF of a third- or fourth-order polynomial phase signal, evaluated at the central point of the observation interval     

非均匀噪声背景下混合信号DOA估计算法

针对非均匀噪声背景下非相关信源与相干信源并存时波达方向(DOA)估计问题，提出了基于迭代最小二乘和空间差分平滑的混合信号DOA估计算法。首先，该算法利用迭代最小二乘方法得到噪声协方差矩阵估计，然后对数据协方差矩阵进行“去噪”处理，利用子空间旋转不变技术实现非相关信源DOA估计；其次，基于空间差分法消除非相关信号并构造新矩阵进行前后向空间平滑，利用求根MUSIC算法估计相干信源DOA。相比于传统算法，该算法能估计更多的信源数，在低信噪比情况下DOA估计性能更优越。仿真实验结果验证了该算法的有效性。      

Optimal color filters in the presence of noise

The effect of noise on the number of effective channels (color filters) used to record a color image is investigated. Transmittances of color filters are calculated that minimize the mean square error that occurs when estimating, from the recorded data, the colors in the image under a collection of viewing illuminants. Since the results indicate that a significant improvement in color correction accuracy is achieved by using four channels, there is good reason to consider using four-tuples for representation of colorimetric information.     

Parameter estimation of autoregressive signals in presence of colored AR(1) noise as a quadratic eigenvalue problem

In this paper, we consider the problem of parameter estimation of autoregressive (AR) signals from observations corrupted with colored AR(1) noise. The proposed method is based on Yule-Walker equations. We express these equations as a quadratic eigenvalue problem and then the parameters of the signal and noise are estimated by solving this eigenvalue problem. We also apply the proposed method to the problem of sinusoidal frequency estimation in colored noise. The performance of the proposed algorithm is evaluated by computer simulation examples.     

干扰环境下的信号调制识别

针对干扰环境提出了一种基于独立成分分析(ICA)和支持矢量机(SVM)的通信信号调制识别算法。算法利用多天线接收技术,采用独立成分分析方法寻求观测样本矢量的统计独立分量,设计了以二值支持矢量机(SVM)为基础的多值分类器,该算法具有较高的训练速度和较好的分类性能。仿真表明,当信干比大于10 dB、移动速度小于12 m/s时,算法的总体正确识别率超过0.9。干扰环境下的信号调制识别研究具有较重要的实际工程应用意义。     

基于SystemView的2FSK调制解调系统的仿真设计与分析

SystemView是一种适用于通信系统设计与仿真分析的软件工具,可以对通信系统的工作过程进行实时仿真分析。本文采用SystemView构建2FSK调制解调仿真系统。通过使用SystemView分析窗口观察信号的波形和频谱,我们能直接观测到信号在传输过程中的波形和频谱的变化,这有助于我们更深刻地理解2FSK调制解调系统的工作原理和各个设备的功能。     

Optimal transform coding in the presence of quantization noise

The optimal linear Karhunen-Loeve transform (KLT) attains the minimum reconstruction error for a fixed number of transform coefficients assuming that these coefficients do not contain noise. In any real coding system, however, the representation of the coefficients using a finite number of bits requires the presence of quantizers. We formulate the optimal linear transform using a data model that incorporates the quantization noise. Our solution does not correspond to an orthogonal transform and in fact, it achieves a smaller mean squared error (MSE) compared to the KLT, in the noisy case. Like the KLT, our solution depends on the statistics of the input signal, but it also depends on the bit-rate used for each coefficient. Especially for images, based on our optimality theory, we propose a simple modification of the discrete cosine transform (DCT). Our coding experiments show a peak signal-to noise ratio (SNR) performance improvement over JPEG of the order of 0.2 dB with an overhead less than 0.01 b/pixel.     

Signal detection in the presence of multiple-access noise

In this paper we address the mutual interference problems that arise as a result of many independent terminals simultaneously accessing a common channel. "Random code" bounds are used to show the existence of large signal sets for which any one signal can be reliably detected by matched filters despite the mutual interference. Two types of signal sets are considered: sets with a unique signature for every terminal and sets drawn from a small collection of distinct waveforms. The latter sets permit reductions in receiver complexity.     

Sampling designs for the detection of signals in noise

Sampling designs for the detection of sure signals in Gaussian noise are considered. Both deterministic and random sampling schemes, using optimal and nonoptimal detectors, are presented and their performance is studied. The analytical results are supplemented by comparison of performance for small and large Sample size for some representative processes including the Gauss-Markov and Wiener processes.     

基于脉冲位置调制的无线光通信多级编码调制及其多阶段解调译码算法

针对无线光通信脉冲位置调制(PPM)与信道编码的结合应用,提出了一种多级编码调制方案,利用多级编码的多级标签结构,将PPM符号所对应的分组比特分拆到不同子码通道的码字中,同时结合PPM的信号集分割和极大似然检测解调方法,推导了方案的多阶段解调译码算法。在弱湍流大气条件下的仿真分析表明,PPM多级编码调制相对单级编码调制在误码率10-6下获得0.85 dB以上的增益,且可实现对不同重要程度信息段的不等差错保护。在分量码码型给定的条件下,按纠错能力逐级配置分量码并采用所推导的多阶段解调译码,可为系统提供更好差错性能,优于直接的并行译码方法。