多假设卡尔曼滤波的随机进化模型

2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2019-10-01 DOI:10.1109/SDF.2019.8916630

S. Handke, Joshua Gehlen

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引用次数: 0

摘要

提出了一种基于多假设跟踪的高机动目标随机化方法。利用当前演化模型中的加速度范围参数来设计各种运动模型。该方法将该参数随机化，以覆盖更大范围的机动特性。仿真结果表明，该方法具有更可靠的航迹连续性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Randomized Evolution Model for Multi Hypothesis Kalman Filter

A new randomized approach for highly maneuvering targets based on multi hypothesis tracking is presented. The acceleration range - a parameter in current evolution models is used to design various motion models. The approach randomises this parameter to cover a wider range of maneuver characteristics. Simulation shows that the performance of the new method results in a more reliable track continuity.

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2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)

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