Towghi N, Javidi B
Department of Electrical and Computer Engineering, University of Connecticut, Storrs 06269-2157, USA.
J Opt Soc Am A Opt Image Sci Vis. 2001 Aug;18(8):1844-52. doi: 10.1364/josaa.18.001844.
We develop algorithms to detect a known pattern or a reference signal in the presence of additive, disjoint background, and multiplicative white Gaussian noise with unknown statistics. The presence of three different types of noise processes with unknown statistics presents difficulties in estimating the unknown parameters. The standard methods such as expected-maximization-type algorithms are iterative, and in the framework of hypothesis testing they are time-consuming, because corresponding to each hypothesis one must estimate a set of parameters. Other standard methods such as setting the gradient of the likelihood function with respect to the unknown parameters will lead to a nonlinear system of equations that do not have a closed-form solution and require iterative methods. We develop an approach to overcome these handicaps and derive algorithms to detect a known object. We present new methods to estimate unknown parameters within the framework of hypothesis testing. The methods that we present are direct and provide closed-form estimates of the unknown parameters. Computer simulations are used to show that for the images tested, the receivers that we have designed perform better than existing receivers.
我们开发算法以在存在加性、不相关背景以及具有未知统计特性的乘性高斯白噪声的情况下检测已知模式或参考信号。存在三种具有未知统计特性的不同类型噪声过程给估计未知参数带来了困难。诸如期望最大化类型算法等标准方法是迭代的，并且在假设检验框架下它们很耗时，因为对于每个假设都必须估计一组参数。其他标准方法，例如相对于未知参数设置似然函数的梯度，将导致一个没有闭式解且需要迭代方法的非线性方程组。我们开发一种方法来克服这些障碍并推导用于检测已知对象的算法。我们提出在假设检验框架内估计未知参数的新方法。我们提出的方法是直接的，并提供未知参数的闭式估计。计算机模拟用于表明，对于所测试的图像，我们设计的接收器比现有接收器性能更好。
