乘性噪声环境下基于局部最佳检测器的伪码捕获方法

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乘性噪声环境下的伪码捕获检测统计量,文中给出了基于局部最佳检测算法的捕获结构,并与传统的平方和检测器进行了性能仿真对比,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较平方和检测器有较大幅度的提高,而在无乘性噪声的环境下检测性能只较传统的平方和检测器检测性能稍有降低。     

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。     

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。     

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。     

利用逆正态得分函数修正秩的非参量检测器

提出了2种新型基于修正秩检验的非参量检测器:广义符号修正秩检测器和Mann-Whitney修正秩检测器.通过Monte-Carlo仿真分析了这2种检测器对起伏雷达目标的检测性能,并分别与4种经典的非参量检测器:广义符号检测器、Mann-Whitney检测器、修正秩方检测器和Savage检测器的检测性能进行了比较.广义符号修正秩和Mann-Whitney修正秩检测器是通过设计秩统计量的逆正态得分函数来获得检验统计量,通过Monte-Carlo仿真实验得出比修正前的非参量检测器有更好的检测性能.这2种新型修正秩检测器对积累分布函数连续且概率密度函数服从对称分布的数据具有恒虚警性能.     

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.     

基于分段相关－视频积累方法的多驻留伪码捕获系统优化设计

针对载波多普勒下的伪码捕获问题,分析了基于分段相关-视频积累方法的多驻留伪码捕获系统的最优性能。推导了正交双通道检测器的检测概率和虚警概率表达式,给出了多驻留系统的捕获时间均值表达式;通过采用遗传算法对系统进行优化设计,增大了系统的多普勒容限,降低了捕获时间;分析了驻留级数、多普勒和载噪比等对捕获系统性能的影响。研究成果可用于指导扩频系统接收机伪码捕获模块的设计。     

强干扰环境下基于最大信干噪比准则的联合波束形成与伪码捕获方法

在阵列天线直接序列扩频遥测系统中,联合波束形成与伪码捕获可以最大限度的利用阵列增益提高低信噪比下的捕获性能.考虑强干扰环境下的大系统扩展比多目标测控,分析了基于NLMS权值搜索与伪码捕获方法的抗干扰容限,提出了基于最大信干噪比(SINR)准则的自适应波束形成与伪码捕获联合实现方法,根据伪码相位搜索的滑动相关运算结果估计信号功率,最大化输出SINR实现自适应波束形成.计算机仿真表明,最大化SINR波束形成与伪码捕获联合方法的捕获性能更优,在低信噪比和强干扰环境下可以有效实现空间自适应波束形成和快速伪码捕获,并已成功应用于某遥测系统.     

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.     

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 nonparametric detectorsin DS/SS systems

In this paper, signed-rank based nonparametric detectors are used for pseudonoise (PN) code acquisition in direct-sequence spread-spectrum (DS/SS) systems. We first derive the locally optimum rank (LOR) detector and then propose the locally suboptimum rank (LSR) and modified signed-rank (MSR) detectors using approximate score functions. We compare the single-dwell scheme without the verification mode using the proposed LSR and MSR detectors with that using the conventional squared-sum (SS) and modified sign (MS) detectors. From the simulation results, it is shown that the proposed LSR and MSR detectors perform better than the MS detector by about 2-3 dB and are nearly optimum     

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.     

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.     

Memoryless discrete-time signal detection in long-range dependent noise

The problem of designing optimum memoryless detectors for known signals in long-range dependent (LRD) noise is considered. In particular, under the performance criterion of asymptotic relative efficiency (ARE), optimum memoryless detection in LRD noise is investigated by exploiting the Hermite expansions. The detectors considered have the form of a nonlinearity followed by an accumulator and threshold comparator. It is shown that when the noise is LRD and Gaussian, all nonlinearities with Hermite rank one are asymptotically equivalent in terms of efficiency and are most powerful. Moreover, the optimum nonlinearities with Hermite rank greater than one are given by the corresponding Hermite polynomials. The case of non-Gaussian LRD noise that can be derived by nonlinear transformation of Gaussian noise is also considered. In this case, the globally optimum nonlinearity is very difficult to obtain in general. Instead, we proposed a suboptimal nonlinearity, which is given by a linear combination of the corresponding locally optimum detector and the inverse of the transform function generating the noise. Simulations show that the proposed detector outperforms the locally optimal detector for LRD non-Gaussian noise.     

Comparison of known signal detection schemes under a weaklydependent noise model

The authors consider the discrete-time signal detection problem under the presence of additive noise exhibiting weak dependence. They first propose a weakly dependent noise model, in which the additive noise is modelled as a moving average process. They derive the locally optimum, memoryless, and one-memory detector test statistics under the model. The asymptotic performance of the one-memory detector is compared with that of the locally optimum and memoryless detectors. Specific examples for the asymptotic performance comparison of these detectors are considered. The authors also investigate the finite sample-size performance of several detectors through Monte-Carlo simulation. It is observed that the one-memory detector can achieve almost optimum performance at the expense of only one memory unit under the weakly dependent noise model, and is rather insensitive to slight model change     

Locally optimum rank detection of correlated random signals inadditive noise

Nonparametric detection of a zero-mean random signal in additive noise is considered. The locally optimum detector based on signs and ranks of observations is derived, for good weak-signal detection performance under any specified noise probability density function. This detector is shown to have interesting similarities to the locally optimum detector for random signals. It may also be viewed as a generalization of the locally optimum rank detector for known signals. Examples of the test statistic of the detector are given for some specific noise probability density functions. Asymptotic and finite sample-size performance of the locally optimum rank detector is also considered