基于卡尔曼和粒子滤波的雷达目标跟踪

Peter Rohal, J. Ochodnicky
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引用次数: 13

摘要

在数据处理器中对雷达数据进行自适应处理是指采用自适应滤波、认知滤波、智能滤波或智能滤波对雷达数据进行处理。过滤器或所谓的过滤器组用于过滤。我们可以观察、跟踪和显示雷达捕获的目标,包括这些滤波器。这些滤波器还用于处理跟踪目标(距离,航向,飞行水平,速度,测量误差可以通过适当的计算最小化,以便尽可能准确地估计速度,加速度和定位。跟踪是基于传感器测量处理数据的关联和融合来更新实时状态和信息的过程。在本文中,我们创建了一个粒子过滤器,可以观察二维空间中随机飞行物体的轨迹。将经典卡尔曼滤波与现代粒子滤波进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Radar target tracking by Kalman and particle filter
Adaptive processing of radar data in a data processor is the processing of radar data using adaptive, cognitive, intelligent or smart filtering. Filters or so-called filter banks are used for filtration. We can observe, track and display targets captured by radar with these filters included. These filters also serve to process the tracked targets (distance, course, flight level, speed, measurement errors can be minimized by appropriate calculations in order to estimate the speed, acceleration and positioning as accurately as possible. Tracking is the process of updating real-time statuses and information based on the correlation and fusion of processed data from measurement using sensors. In this article we created a Particle filter that can observe the trajectory of randomly flying object in 2D space. The classic Kalman filter with the modern Particle filter is compared.
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