【雷达与对抗】【2012.12】舰船雷达目标跟踪研究

舰船雷达目标跟踪研究"/>

【雷达与对抗】【2012.12】舰船雷达目标跟踪研究

本文为美国托莱多大学（作者：Nishatha Nagarajan）的电子工程硕士论文，共169页。

日益增长的能源需求最终促进了风力涡轮机的发展。风力涡轮机的建设具有若干潜在的影响，其中最重要的影响是由于碰撞和栖息地损失而增加的鸟类死亡率。从那时起，雷达被部署用来研究鸟类对风力涡轮机的行为。雷达采用目标跟踪的方式来准确有效地识别目标（鸟类）。为了提高雷达的跟踪效率，近年来发展了多种跟踪方法。最广泛使用的跟踪技术是卡尔曼滤波和粒子滤波。这些滤波器使用具有随机误差的数据，并估计系统当前状态的精确值。卡尔曼滤波器是一种线性估计器，它不依赖于一组过去的观测值，因此在实时应用中是高效的。粒子滤波器也称为序列蒙特卡罗方法，是一种非线性估计器，它使用一组具有不同权重的粒子进行估计。然而，粒子滤波器具有较长的计算时间。卡尔曼滤波器和粒子滤波器是经过多年研究发展起来的，为各种类型的系统创造了各种模型。

本文在跟踪中采用了用于信号重构的压缩匹配追踪（CoSaMP）算法的块迭代形式，称为BCoSaMP。与粒子滤波相比，该算法可以较少的运算时间获得相似或更好的性能。提出了采用卡尔曼滤波估计的BCoSaMP算法，与其它模型相比，在某些情况下降低了均方误差。

本文主要研究radR中的跟踪器模型，建立了基于线性数据和高斯噪声的卡尔曼滤波跟踪模型，该模型能够针对多种目标运动特性。针对非高斯噪声的非线性目标运动，设计了粒子滤波器。将基于稀疏数据的BCoSaMP模型应用于目标跟踪，并提出了一种修改的BCoSaMP算法，用卡尔曼滤波估计代替最小二乘估计。这些模型都采用不同的数据集进行了测试，并进行了比较分析。在radR系统中，通过仿真数据和舰船雷达数据对算法进行了测试，以比较所建立的跟踪器模型与传统方法的效果。在某些目标模拟航迹的情况下，混合算法比其它模型具有更好的性能。粒子滤波在舰船雷达数据的目标检测中表现出最高的检测概率。

The growing energy needs have eventually increased the development ofwind turbines. The constructions of wind turbines have several potentialimpacts of which the most significant factor is the increasing bird mortalityrates due to collision and habitat loss. Since then, radars have been deployedto study the behavior of birds towards wind turbines. Radarsemploy target tracking for identifying the targets (birds) accurately and efficiently.Several methods of tracking were developed to improve the tracking efficiencyof the radars over the years. Most widely used tracking techniques areKalman filter and particle filter. These filters use data with random errorsand estimate accurate values for the current state of the system. Kalman filteris a linear estimator which does not depend on a set of past observations andhence efficient in real time applications. The particle filter also known assequential Monte Carlo method is a nonlinearestimator which uses a set of particles with various weights for estimation.However, particle filters have high computation time. Kalman and particlefilters were developed over the years creating various models for various typesof systems. A block version of Compressive Matching Pursuit (CoSaMP) algorithmused in signal reconstruction called BCoSaMP was employed in tracking. It wasseen to give a similar or better performance than particle filter with lesscomputation time. The BCoSaMP algorithm with Kalman filterestimation was developed which reduces the mean square error as compared toother models in certain cases.

This thesis focuses on developing tracker models in radR. Kalmanfilter tracking model based on linear data and Gaussian noise that operatesover a variety of target motions and velocities is developed. Particle filteris designed for nonlinear target motion with non-Gaussian noise. BCoSaMP modelthat assumes data as sparse is applied for target tracking and a modifiedBCoSaMP which replaces least square estimation with Kalman filter estimationare also implemented. These models were tested with different data sets and acomparative analysis is performed. The algorithms are tested on simulated dataand marine radar data in radR to compare the effects of the developed tracker modelswith the conventional methods in radR. The hybrid algorithm is shown to have betterperformance over the other models in the case of simulated track for sometargets. Particle filter has the highest detection rate with marine radar data.

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