Abstract There are several techniques implemented, in an underwater target tracking environment, for the nonlinear dynamic systems in Gaussian and non-Gaussian environments. It is assumed with non-Gaussian distribution to make the problem part of the non-Gaussian distribution, and is measured in terms of calculations of plenty of scenarios simulated to validate the potential of the sub-optimal filter.This research is further carried out by considering two categories of non-Gaussian noises i.e.a mixture of Gaussian noises and shot noise. To evaluate tracking in Gaussian and non-Gaussian noises, the suboptimal filters, Extended Kalman filter, and Unscented Kalman filter (UKF algorithms are considered. Gaussian noise is a statistical noise having probability density function equal to the normal distribution function. The suboptimal filters, Extended Kalman filter, and Unscented Kalman filter (UKF) algorithms are considered to evaluate tracking in Gaussian and non-Gaussian noises. To make further evaluation of the above said algorithms, they are compared with theoretical Cramer-Rao lower bound. The efficiency of UKF is in terms of percentage of non-Gaussian noise corrupted measurements, for which solution is obtained within a short time. The application of Monte-Carlo method at this simulations trapped accurate results.

中文翻译：

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高斯和非高斯环境下水下目标跟踪技术的实现

摘要 在水下目标跟踪环境中，针对高斯和非高斯环境中的非线性动态系统实现了多种技术。假设非高斯分布使问题成为非高斯分布的一部分，并通过大量模拟场景的计算进行衡量，以验证次优滤波器的潜力。 这项研究进一步进行了考虑两类非高斯噪声，即高斯噪声和散粒噪声的混合。为了评估高斯和非高斯噪声中的跟踪，次优滤波器、扩展卡尔曼滤波器和无迹卡尔曼滤波器（考虑了 UKF 算法。高斯噪声是一种概率密度函数等于正态分布函数的统计噪声。次优滤波器，扩展卡尔曼滤波器和无迹卡尔曼滤波器 (UKF) 算法被认为是评估高斯和非高斯噪声中的跟踪。为了进一步评估上述算法，将它们与理论的 Cramer-Rao 下界进行比较。UKF 的效率以非高斯噪声破坏测量的百分比表示，在短时间内获得解决方案。蒙特卡罗方法在该模拟中的应用捕获了准确的结果。在短时间内获得解决方案。蒙特卡罗方法在该模拟中的应用捕获了准确的结果。在短时间内获得解决方案。蒙特卡罗方法在该模拟中的应用捕获了准确的结果。