乘性噪声环境下基于符号秩统计量的非参伪码捕获方法

扩频系统常常工作在多径环境中,伴随着加性噪声的同时往往还存在着乘性噪声.该文提出了一种乘性噪声环境下基于符号秩统计量的非参伪码捕获方法,将伪码捕获等价为假设检验问题,利用局部最佳检测算法推导出了乘性噪声环境下基于符号秩的检测统计量,通过简化记分函数进一步给出了局部次佳秩检测器.将局部次佳秩检测器与局部最佳检测器和平方和检测器的性能进行了仿真对比,结果表明该文所提出的捕获方法在乘性噪声环境下检测性能接近于乘性噪声环境下的局部最佳检测器,而较平方和检测器则有较大幅度的提高.     

弱相关非高斯环境下基于局部最佳检测器的伪码捕获方法

该文为解决弱相关非高斯噪声环境下的伪码捕获问题,提出了一种基于局部最佳检测算法的伪码捕获方法,将伪码捕获等价为假设检验问题,将弱相关非高斯噪声建模为一阶滑动平均SS噪声模型,利用局部最佳检测算法推导出弱相关非高斯噪声环境下的伪码捕获检测统计量,在此基础上对检测统计量进行了简化,给出了其实现结构,并与传统的伪码捕获方法进行了性能仿真对比,仿真结果表明该文所提出的捕获方法在弱相关非高斯噪声环境下检测性能有较大幅度的提高,且非高斯噪声脉冲特性越明显,所设计的检测器优势越明显。     

一种基于自动删除算法的PN码捕获技术研究

针对多径干扰对频率选择性信道下伪码捕获性能的影响,提出了一种基于自动删除算法的PN码捕获方法。该串行相关捕获系统通过一个基于排序数据方差的自动删除单元平均恒虚警检测器来删除参考滑窗中的干扰样本,并且不需要多径干扰数目的先验信息。对系统的平均捕获时间及检测性能进行了计算与仿真,与传统的自适应捕获方法相比,改进后捕获算法的性能得到较大的改善。     

采用多相关峰值的PN码频域匹配滤波捕获实现方法

现有的伪噪声(PN)码捕获算法大多是基于相关峰值进行门限判决,而捕获性能依赖于门限实时估计的正确性。针对这种情况,该文提出了一种利用多次PN码相关峰值位置间的相互关系进行判决的算法,避免了对信道噪声的实时估计,并给出了基于频域匹配滤波的实现方法和算法复杂度分析,推导了所提算法的检测概率和虚警概率,并且使用计算机仿真验证了推导结果的正确性;结果表明,所提算法避免了噪声估计的开销,增强了PN码捕获的鲁棒性,提高了捕获性能,适合工程应用。      

基于部分匹配滤波的伪码捕获方法研究

研究了高斯白噪声环境下部分匹配滤波的伪码捕获方法的性能,推导了该方法的检测概率和虚警概率。基于部分匹配滤波的伪码捕获方法相对于传统的全匹配滤波法,既可减少处理时间,又能节省系统资源。并利用MATLAB工具对不同信噪比环境下的伪码捕获性能进行仿真,结果表明该方法在一定信噪比条件下是实用的。     

基于前置三阶相关检测的高性能直扩伪码捕获

为了提高无线电导航系统长伪码序列的检测概率,同时降低捕获时间,提出了一种带前置三阶相关检测的伪码捕获算法,即根据伪随机码和高斯噪声的三阶相关特性,首先实现本地伪码与接收伪码序列的码元相位同步,再用传统捕获方法进行伪码相位同步.理论分析和仿真结果表明,该方法能够有效提高捕获过程的检测概率,并降低捕获时间.     

一种局部最优的非高斯接收机设计

在超低频通信中,由于非高斯噪声的严重干扰,传统的基于高斯噪声的接收机的性能恶化.本文提出了一种在非高斯噪声环境下局部最优的接收机设计,具体推导了局部最优检测在非高斯噪声模型下的FFT快速实现算法,该算法充分利用了FFT算法在运算速度上的优势,大大降低了运算复杂度.非高斯噪声模型采用广泛应用的Class B统计物理模型.仿真实验表明,局部最优接收机性能明显优于传统的匹配滤波检测器,运算速度快且易于硬件设计实现,有很高的实用价值.     

基于FFT伪码捕获方法及性能

扩频伪码的捕获是扩频通信系统的核心关键技术。文章介绍了一种基于FFT得伪码捕获方法,并分析了该方法在高斯白噪声信道下PN码同步检测和虚警概率,从而进一步研究其解扩性能提供了理论依据。     

一种CDMA2000系统的PN码初始捕获算法

文中依据CDMA2000物理层标准,提出了一种针对移动台下行同步的伪噪声（PN）码相位捕获算法。本算法利用部分匹配滤波（PMF）后进行快速傅里叶变换（FFT）实现对PN码相位的可靠捕获,可以应用于有较大的频率偏移和较低的接收信号导频信噪比（Pilot Ec／N0）的环境下。并进一步对算法进行了性能仿真验证,仿真结果证明了本文算法相对传统差分算法有更好的捕获性能。     

非高斯噪声中信号的神经网络检测

论述了用多层感知器神经网络检测非高斯噪声中确知信号与随机信号的工作原理，网络结构和训练算法，讨论了几种非高斯噪声中信号的神经网络检测器性能。计算机仿真证明，在非高斯噪声条件下神经网络检测性能优于线性最佳匹配滤波器检测器和局部最佳检测器。     

Direct sequence code acquisition in multiplicative and non-Gaussian noises

A new detector for direct sequence spread spectrum code acquisition in multiplicative and non-Gaussian noises is proposed in this article. Modelling the acquisition problem as a hypothesis-testing problem, a detector is derived for multiplicative and non-Gaussian noises, based on the locally optimum detection technique. Numerical results show that the proposed detector can offer substantial performance improvement over the conventional schemes in multiplicative and non-Gaussian noises.     

PN code acquisition using signed-rank-based non-parametric detector in a multiplicative noise model

Multiplicative noise is known to be useful in modelling an environment that is difficult to describe with an additive noise model. In this article, signed-rank-based non-parametric detectors are used for pseudonoise (PN) code acquisition in multiplicative noise. First, a locally optimum (LO) detector based on the signs and ranks of observations is derived, and then the locally suboptimum rank (LSR) detector is proposed by using approximate score functions. The finite sample-size performance of the LSR detector is considered. Numerical results show that the LSR detector asymptotically has almost the same performance as the LO detector for multiplicative noise.     

Detection of multiplicative noise in stationary random processesusing second- and higher order statistics

This paper addresses the problem of detecting the presence of colored multiplicative noise, when the information process can be modeled as a parametric ARMA process. For the case of zero-mean multiplicative noise, a cumulant based suboptimal detector is studied. This detector tests the nullity of a specific cumulant slice. A second detector is developed when the multiplicative noise is nonzero mean. This detector consists of filtering the data by an estimated AR filter. Cumulants of the residual data are then shown to be well suited to the detection problem. Theoretical expressions for the asymptotic probability of detection are given. Simulation-derived finite-sample ROC curves are shown for different sets of model parameters     

Code acquisition for DS/SS communications in non-Gaussian impulsive channels

In this letter, we propose a new detector for code acquisition systems in non-Gaussian noise channels. Modeling the acquisition problem as a hypothesis testing problem, a detector is derived for non-Gaussian, symmetric /spl alpha/-stable noise, based on the locally optimum detection technique. Numerical results show that the proposed detector can offer robustness and substantial performance improvement over the conventional schemes in non-Gaussian channels.     

基于循环平稳的复调频信号检测性能研究

针对加性色噪声和乘性色噪声联合干扰下的雷达运动目标检测问题进行了研究.首先利用循环平稳信号处理原理,分析了加性噪声和乘性噪声联合干扰下的复调频信号的循环统计特性,在此基础上构造了基于二阶循环矩的检测统计量,推导了检测统计量的输出信噪比与观测噪声统计特性的关系.最后利用Monte-Carlo仿真试验得到检测性能的曲线,并与基于时频分布的Wigner-Hough检测器做了比较,仿真试验进一步验证了本文提出的二阶循环矩检测器性能要优于Wigner-Hough检测器.     

Data-adaptive algorithms for signal detection in sub-Gaussianimpulsive interference

We address the problem of coherent detection of a signal embedded in heavy-tailed noise modeled as a sub-Gaussian, alpha-stable process. We assume that the signal is a complex-valued vector of length L, known only within a multiplicative constant, while the dependence structure of the noise, i.e. the underlying matrix of the sub-Gaussian process, is not known. We implement a generalized likelihood ratio detector that employs robust estimates of the unknown noise underlying matrix and the unknown signal strength. The performance of the proposed adaptive detector is compared with that of an adaptive matched filter that uses Gaussian estimates of the noise-underlying matrix and the signal strength and is found to be clearly superior. The proposed new algorithms are theoretically analyzed and illustrated in a Monte-Carlo simulation     

Simulation Results for the Decision-Directed MAP Receiver for M-ary Signals in Mulitiplicative and Additive Gaussian Noise

Simulation results are presented for the error-rate performance of the recursive digital maximum a posteriori probability (MAP) detector for knownM-ary signals in multiplicative and additive Gaussian noise. The structure of the digital simulation of the optimum detector is generally described, with specific results obtained for a quaternary signal and 2500 digit per second transmission rate. The simulation is focused on the aeronautical multipath communication problem. Plots of detection error rate versus additive signal-to-noise ratio are given, with the power ratio of multiplicative process to desired signal as a parameter. Results are presented for the cases where the detector has perfect knowledge of the first- and second-order statistics of the multiplicative and additive processes and also where these statistics are estimated in near real time. For comparison, the error rates of conventional coherent and noncoherent digital MAP detectors are also obtained. It is shown that with nonzero multiplicative noise, the error rates of the conventional detectors saturate at a level that is irreducible for increasing additive signal-to-noise ratio. The error rate of the optimum detector having perfect statistical knowledge continues to decrease rapidly with increasing additive signal-to-noise ratio. In the absence of multiplicative noise, the conventional coherent detector and the optimum detector are shown to exhibit identical performance. Suboptimum detectors, having less than perfect statistical knowledge, yield error rates bounded below by the optimum detector rates and bounded above by the conventional detector rates.     

Unbiased homomorphic system and its application in reducing multiplicative noise

The problem of reducing the multiplicative noise corrupting a signal is discussed. A generalisation of the existing sampled function weighted order (SFWO) filter is proposed by relaxing the symmetry condition on the probability density function (PDF) of the noise. This generalised SFWO filter is then used within a homomorphic system to reduce the multiplicative noise. It is shown that the output from such a system is biased, and hence, a suitable bias compensation technique is suggested. An unbiased homomorphic system, whose design is based on the PDF of the corrupting multiplicative noise, is proposed to reduce the noise. Images generated by coherent imaging systems are always corrupted by speckle, a kind of multiplicative noise having a lognormal distribution. A filter called the mean median filter, to reduce additive white Gaussian noise, is first proposed and then used within the unbiased homomorphic system to reduce the speckle in images. The effectiveness of the various proposed algorithms is demonstrated and compared with that of some of the existing schemes through extensive simulations