空间二维相关K分布杂波建模与仿真

针对雷达杂波在空间二维相关的特点,提出了一种相关K分布杂波模型及仿真方法:首先提出了基于1-范数的空间相关性假设,然后利用修正的球不变随机过程(SIRP)法得到相关K分布随机场,最后,利用计算机仿真实验验证了该杂波建模方法的正确性。     

相关非高斯分布雷达杂波仿真与验证

雷达杂波的仿真是进行雷达性能评估的重要手段,也是雷达信号处理的一种重要课题。球不变随机过程(SIRP)是雷达杂波建模的主要方法之一,SIRP可以用来描述雷达杂波的幅度统计特性和时间相关特性,这是因为SIRP的幅度概率密度函数和协方差矩阵可以独立控制。在讨论SIRP的基本性质的前提下,分别针对CHI分布和K分布,给出了两种基于SIRP的相关雷达杂波仿真的方法。最后利用计算机仿真对雷达杂波的幅度概率分布和相关性进行了验证,证明了该方法的有效性。     

SIRP法相干相关K分布雷达杂波的建模与仿真

雷达相干相关杂波的产生多是由相应的非相干相关杂波的零记忆非线性变换法（Zero Memory Nonlinearity,ZMNL)加以推广得到的。ZMNL法的前提是已知非线性变换前后杂波的相关系数之间的非线性关系，而对于相干相关K分布杂波却难以找到这样一种适用的非线性关系，于是提出了于不变随机过程法（Spherically Invariant Random Processes,SIRP）的相干相关K分布杂波的仿真模型，以高新型谱为例进行了仿真，仿真结果证明了这种方法的有效性。     

高分辨率雷达复合高斯杂波的高效仿真

球不变随机过程（sIRP）是对高分辨率雷达的检测性能进行仿真分析所广泛采用的杂波模型。文中在总结SIRP主要理论结果的基础上,详细分析了参考文献[7]中给出的仿真方法对长“拖尾”的“尖”杂波仿真效率低的原因,提出了对杂波包络的概率密度函数（PDF）进行分割,用分割点上的PDF值来构造上界函数的新的仿真方法。仿真算例表明,改进的方法显著地提高了仿真的效率。     

时空二维相关K分布雷达海杂波建模与仿真

海杂波是影响雷达对海工作的重要因素,海杂波的精确建模与仿真是实现雷达性能检测,以及算法优化的先决条件。在分析海杂波幅度分布特性、时间和空间相关特性的前提下,利用Cholesky分解并结合球不变随机过程法（SIRP,spherically invariant random processes）,提出了一种用于生成时空二维相干相关K分布海杂波的仿真模型,仿真结果表明该方法能够生成满足时间和空间相关特性要求的二维海杂波,得到的数据可以直接作为雷达目标模拟器中的输入信号。     

天基雷达空时二维杂波建模与仿真

讨论了天基雷达空时二维杂波的建模和仿真方法,给出了空时杂波回波仿真公式并讨论了杂波单元的划分、径向速度和地面位置的计算等.仿真结果表明天基雷达杂波的严重距离模糊以及由地球自转引起的非平稳性会使得空时自适应处理的性能下降;最后指出了天基雷达杂波抑制对空时自适应处理的要求.     

基于SIRV高分辨率雷达杂波建模与仿真

高分辨率雷达杂波建模与仿真是评估雷达性能的重要手段。广义复合概率密度通常用来描述高分辨率雷达杂波。在广义复合分布中,将散斑分量和调制分量均采用广义伽马(GΓ)分布来表示。采用球不变随机矢量(SIRV)进行杂波建模的优点在于能够独立控制其相关性和一维边缘概率密度。对广义复合分布和SIRV建模方法进行了讨论,在此基础上,给出了相干相关的广义复合分布的SIRV模型,利用计算机进行了广义复合分布的杂波仿真,并对杂波仿真的统计特性与理论值进行了比较。仿真结果与理论分析相吻合,说明了该模型的有效性和普适性。     

雷达相关杂波的建模与仿真研究

为了研究杂波环境下的信号处理问题,需要对雷达杂波环境进行建模与仿真。文中归纳了雷达杂波的统计模型,包括幅度分布和功率谱模型,以及杂波的相应类型,并对服从一定幅度分布和功率谱分布的相关杂波序列产生方法进行了分析,给出了每种统计模型的仿真过程,最后根据杂波仿真的两种主要方法——无记忆非线性变换法ZMNL和球不变随机过程法SIRP对相关杂波进行了仿真,解决了仿真过程中成型滤波器的设计和特殊函数求值问题。仿真结果表明,用文中所述方法进行仿真可得到较好的结果。     

相关韦布尔杂波的建模与仿真

讨论了杂波序列的特性和杂波的一般产生方法，就目前常用的高斯相关序列和具有一定幅度分布特性的序列的产生方法进行了阐述，文中提出了用时域滤波法结合球不变随机过程法(SIRP)来产生相关韦布尔(Weibull)杂波的方法，并给出了计算机仿真结果。     

相参相关K分布机载雷达杂波仿真

为了研究机载雷达杂波环境下的信号处理问题,采用球不变随机过程法对相参相关K分布杂波进行了仿真,球不变随机过程法可以对杂波的边缘概率密度函数和自相关函数进行独立控制,并且高阶概率密度函数可以显示表达,从而克服了零记忆非线性变换法中非线性变换对自相关函数的影响。最后介绍了仿真模型和仿真方法,从而证明了该方法的有效性。     

非高斯相关杂波背景下雷达目标检测方法研究

在非高斯相关杂波背景下,基于MTD(Moving Target Detection)的雷达目标检测性能严重下降。针对该问题,根据Alpha稳定分布杂波模型、分数低阶统计量理论,以输出信杂比最大为准则,提出了一种适用于非高斯相关杂波背景的雷达目标检测方法。该方法通过分解信号分数低阶协方差矩阵,计算等效杂波分数低阶协方差矩阵特征向量,得到最佳滤波器系数。通过仿真和实测数据,对所提出方法的检测性能进行了验证,并且与基于MTD的检测方法进行了比较,结果表明,在非高斯相关杂波背景下,所提方法的检测性能明显优于传统的MTD。      

非高斯相关杂波背景下雷达目标统计检测方法

在非高斯相关杂波背景、参数随机或未知的情况下,通常难以甚至无法建立统计检测模型。针对此问题,基于粒子滤波方法,根据未知参数的概率分布函数抽取粒子,将复杂积分运算转化为求和运算求取似然函数,给出了一种雷达目标似然比检测的通用模型,较好地解决了非高斯相关杂波条件下无法得到检测统计量的问题。此外,以复SG-Alpha稳定分布作为非高斯相关杂波分布模型,给出了基于粒子滤波的检测统计模型,并对本文方法与传统的检测方法在高斯相关杂波和非高斯相关杂波背景下的检测性能进行了仿真比较,验证了算法的有效性。     

Modeling and simulation of compound clutter for coastal environment

This paper introduces a novel clutter power model of coastal compound and deduces the statistic characteristics and the arithmetics of model parameter estimation algorithm. Then, a novel coastal compound distributed model of the clutter amplitude is proposed and its statistic character- istics are studied. Subsequently, the nonlinear relation of autocorrelation function is deduced between the coastal compound clutter random sequences and the correlated Gaussian random sequences, which is the important prerequisite for the clutter simulation based on the ZMNL (Zero Memory NonLin- earity). Lastly, the advantage of the algorithm is demonstrated by a feasible simulation of the gen- eration of the random coastal compound clutter sequences.     

Generation of correlated log-normal clutter samples

A method is described for generating random log-normal vectors with the desired correlation matrix and specified parameters. Such vectors may represent samples of correlated clutter signal. The presented method makes use of the suitable nonlinear transformation of a random normal vector with correlated components.     

具有非高斯相关噪声的目标跟踪

为实现强杂波背景下视频的鲁棒跟踪,在常 用非线性系统模型的基础上引入柯 西高斯混合噪声模型,充分考虑了非高斯噪声前 后时刻的状态相关性,并以权重条件最小方差为标准,推导了非高斯相关噪声的最优建议分 布 函数,在粒子滤波框架内实现了非高斯相关噪声模型时系统状态的准确估计。在新算法的框 架内采用多特征自适应融合的方法,实现了强噪声背景下视频目标的鲁棒跟踪。实验结果表 明,本文方法扩展了粒子滤波的适用范围,有效提升了强噪声环境下视频目标跟踪的精度和 稳定性。     

Threshold detection in correlated non-Gaussian noise fields

Classical threshold detection theory for arbitrary noise and signals, based on independent noise samples, i.e., using only the first-order probability density of the noise, is generalized to include the critical additional statistical information contained in the (first-order) covariances of the noise. This is accomplished by replacing the actual, generalized noise by a “quasi-equivalent” (QE-)model employing both the first-order PDF and covariance. The result is a “near-optimum” approach, which is the best available to date incorporating these fundamental statistical data. Space-time noise and signal fields are specifically considered throughout. Even with additive white Gaussian noise (AWGN) worthwhile processing gains per sample (Γ^(c)) are attainable, often O(10-20 dB), over the usual independent sampling procedures, with corresponding reductions in the minimum detectable signal. The earlier moving average (MA) noise model, while not realistic, is included because it reduces in the Gaussian noise cases to the threshold optimum results of previous analyses, while the QE-model remains suboptimum here because of the necessary constraints imposed in combining the PDF and covariance information into the detector structure. Full space-time formulation is provided in general, with the important special cases of adaptive and preformed beams in reception. The needed (first-order) PDF here is given by the canonical Class A and Class B noise models. The general analysis, including the canonical threshold algorithms, correlation gain factors Γ^(c), detection parameters for the QE-model, along with some representative numerical results for both coherent and incoherent detection, based on four representative Toeplitz covariance models is presented     

Estimating the correlation coefficient of highly correlated clutter

A maximum-likelihood estimator for the pulse-to-pulse correlation coefficient r of Rayleigh-distributed clutter components is considered. For r greater than 0.9, an approximate computationally feasible estimator is derived. Results of Monte Carlo simulation indicate that the approximate estimator is only slightly inferior to the maximum-likelihood estimator.     

Clairvoyant and adaptive signal detection in non-Gaussian clutter:a data-dependent threshold interpretation

This paper addresses the problem of signal detection in correlated non-Gaussian clutter modeled as a spherically invariant random process. The optimum strategy to detect a constant signal, with either known or unknown complex amplitude, embedded in correlated Gaussian clutter is given by comparing the whitening-matched filter output with a fixed threshold. When the clutter is non-Gaussian, the performance of the matched filter sensibly degrades. The optimum strategy is the classical whitening-matched filter output compared with a data-dependent threshold. This interpretation provides a deeper insight into the structure of the optimum detector and allows us to single out a family of suboptimum detectors based on a polynomial approximation of the data-dependent threshold. They are easy to implement and have performance that is really close to the optimal. The adaptive implementation of the polynomial detectors is also investigated, and their performance is analyzed by means of Monte Carlo simulation for various clutter scenarios     

A radar application of a modified Cramer-Rao bound: parameterestimation in non-Gaussian clutter

In this paper, we derive a lower bound on the error covariance matrix for any unbiased estimator of the parameters of a signal composed of a mixture of spherically invariant random processes (SIRPs). The proposed approach represents a special case of the global Cramer-Rao bound for hybrid random and deterministic parameters estimation, and it is particularly useful when the data, conditioned on a vector of unwanted random parameters (nuisance parameters) with a priori known probability density function, can be modeled as a Gaussian vector. The case of signal composed of a mixture of K-distributed clutter, Gaussian clutter, and thermal noise belongs to this set, and it is regarded as a realistic radar scenario. In the radar problem considered here, this bound can be numerically computed in closed-form, whereas the computation of the true (marginal) Cramer-Rao bound turns out to be infeasible. The performance of some practical estimators are compared with it for two study cases