共查询到18条相似文献,搜索用时 62 毫秒
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K分布杂波中分布式目标的Rao检测 总被引:1,自引:0,他引:1
该文针对高分辨雷达体制下,点目标分裂成分布式目标所带来的检测问题,提出了基于Rao检测的分布式目标自适应检测算法。将分布式目标建模为子空间信号,目标不仅在距离维上扩展同时也在Doppler频率维上扩展。Rao检测算法只需对H0假设条件下的未知参数进行最大似然估计,在构造检测器的过程中运用两步法检测策略,有效地减少了计算量和复杂度。最后,用Monte Carlo仿真了该算法的检测性能,并与以前的检测器相比较验证了新提出的检测器对分布式目标在K分布杂波中的有效性。 相似文献
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针对非高斯非均匀海杂波背景下雷达海面目标检测性能改善的问题,该文基于海杂波的先验知识提出了一种自适应Rao雷达目标检测方法。首先将海杂波的纹理分量和散斑协方差矩阵分别建模为逆高斯随机变量和逆复Wishart分布的随机矩阵,然后基于Rao检验和未知参数估计,设计了一种匹配海杂波特性的雷达目标自适应Rao检测方法。通过理论推导和实验验证了所提检测方法对杂波平均功率和协方差均值矩阵具有恒虚警特性。仿真数据和实测数据实验结果表明,在非高斯非均匀环境下所提检测方法优于已有检测方法,并且具有良好的鲁棒性。 相似文献
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复合高斯杂波下距离扩展目标的OM-GLRT 总被引:1,自引:2,他引:1
广义似然比检测(Generalized Likelihood Ratio Test,GLRT)是解决复合高斯杂波下距离扩展目标检测问题的一种有效方法,而当目标速度未知时,对于毫米波等高频雷达而言,速度估计误差将造成方向矢量(steering vector)失配,从而导致GLRT性能的严重下降.此时,如何设计最佳的GLRT检测器就成为一个优化问题.本文在分析方向矢量失配对GL-RT影响的基础上提出了一种最优匹配GLRT(Optimum Matched GLRT,OM-GLRT)方法.仿真结果表明,OM-GLRT能有效地实现对速度未知目标的检测. 相似文献
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期望最大化(Expectation Maximization,EM)算法是求解参数最大似然估计(MLE)的最优迭代算法,但若参数初始化不恰当,会使估计值落入"初值陷阱",导致错误的参数估计值.为此,本文提出了估计高斯混合噪声参数的矩 - EM算法,即先求参数的矩估计,并用矩估计值初始化参数,再通过EM迭代算法估计参数.在此基础上,经高斯化滤波,导出了高斯混合噪声背景下未知幅度弱信号的Rao检验统计量.仿真结果表明,矩 - EM算法可以更准确地估计噪声参数;基于矩 - EM算法的Rao检测性能优于基于EM算法的Rao检测性能. 相似文献
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本文研究复合高斯杂波环境中的距离扩展目标的自适应检测问题。有色杂波采用参数未知的自回归(AR)过程描述。结合Wald检测准则,仅需对H1假设条件下的未知参数进行最大似然估计,给出了一种新的基于参数化模型的扩展目标检测器——参数化Wald检测器。该检测器的检验统计量可解释为首先针对各个待测单元分别计算检验统计量,然后将所有待测单元的输出进行非相参累加,其对杂波的随机功率起伏具有恒虚警率(CFAR)特性。相比于常规的基于协方差矩阵的检测方法,参数化检测算法的执行过程不需要依赖辅助数据,仅利用待测扩展目标数据即可实现自适应处理,有效缓解了训练压力并降低了计算量。仿真实验表明,所提出的参数化Wald检测器的检测性能优于之前提出的参数化广义似然比检测器的性能。 相似文献
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Performance analysis of GLRT-based adaptive detector for distributed targets in compound-Gaussian clutter 总被引:2,自引:0,他引:2
The problem of adaptive detection for spatially distributed targets in compound-Gaussian clutter is studied. We first derive the optimum NP detector and suboptimum two-step GLRT detector. For the two-step detection strategy, we also introduce three covariance matrix estimation strategies and evaluate their CFAR properties and complexity issues. Next, the numerical results are presented by means of Monte Carlo simulation strategy. In particular, the simulation results highlight that the performance loss due to adaptively estimating the texture is negligible, and that the loss due to adaptively estimating covariance matrix largely depends on the estimation algorithm, the number of the secondary data vectors and the number of the scatterers. 相似文献
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将距离扩展目标建模为子空间信号,用球不变模型模拟K分布杂波,提出了广义Wald检测算法。该算法是对待检测距离单元进行非相参积累,对杂波的纹理分量而言具有CFAR特性。首先对各个待检测距离单元分别检测,其输出的统计量是“白化”后的信号向信号子空间投影的能量和其向与信号子空间正交的噪声子空间投影的能量的比值来计算的,然后将各个待检测距离单元输出的统计量进行累加,形成最终的检验统计量。为了验证其有效性,通过Monte Carlo仿真了该算法的检测性能,并与文献[5]提出的自适应Wald检测器进行比较。 相似文献
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Signal detection of known (within a complex scaling) rank one waveforms in non-Gaussian distributed clutter has received considerable attention. We expand on published solutions to consider the case of rank one waveforms that have some unknown parameters, i.e., signal amplitude, initial phase, Doppler shift, and Doppler rate of change. The contribution of this paper is the derivation and performance analysis of two joint estimators of Doppler shift and Doppler rate-the chirp embedded in correlated compound-Gaussian clutter. One solution is based on the maximum likelihood (ML) principle and the other one on target signal second-order cyclostationarity. The hybrid Cramer-Rao lower bounds (HCRLBs) and a large sample closed-form expression for the mean square estimation error (only for the Doppler shift) are also derived. Numerical examples are provided to show the behavior of the proposed estimator under different non-Gaussian clutter scenarios 相似文献
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This paper deals with the problem of detecting distributed targets in the presence of partially homogeneous Gaussian disturbance with unknown covariance matrix. Since no uniformly most powerful test exists for the problem at hand, we devise and assess two detection strategies based on the Rao test, and the Wald test respectively. Remarkably both tests ensure the constant false alarm rate (CFAR) property with respect to both the structure of the covariance matrix as well as the power level. A preliminary performance assessment, conducted by resorting to simulated data, also in comparison to previously proposed detectors, has confirmed the effectiveness of the newly proposed detection algorithms. 相似文献
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This paper handles the problem of detecting signals with known signature and unknown or random amplitude and phase in the presence of compound-Gaussian disturbance with known spectral density. Two alternative approaches are investigated: the Neyman-Pearson criterion and the generalized likelihood ratio strategy. The first approach leads to a hardly implementable detector but provides an upper bound for the performance of any other detector. The generalized likelihood ratio strategy, instead, leads to a canonical detector, whose structure is independent of the disturbance amplitude probability density function. Based on this result, the threshold setting, which is itself independent on both the noise distribution and the signal parameters, ensures a constant false alarm rate. Unluckily, this receiver requires the averaging of infinitely many components of the received waveform. This is not really a drawback since a close approximation can be found for a practical implementation of the receiver. The performance analysis shows that the generalized likelihood ratio test (GLRT) detector suffers a quite small loss with respect to the optimum Neyman-Pearson receiver (less than 1 dB in the case of random amplitude) and largely outperforms the conventional square-law detector 相似文献