Towards Terrain-Based Navigation Using Side-Scan Sonar
Towards Terrain-Based Navigation Using Side-Scan Sonar
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DOI：
10.23919/fusion52260.2023.10224175
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发表时间：
2023-06
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影响因子：
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通讯作者：
Ellen Davenport;Junsu Jang;Florian Meyer
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文献类型：
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作者：
Ellen Davenport;Junsu Jang;Florian Meyer
本文介绍了一种基于地形的侧扫声纳（SSS）数据的统计模型和序贯贝叶斯估计方法。所提出的方法依赖于从SSS的接收到的ping中提取的斜距测量。特别是，将倾斜距离测量纳入地标导航约束的GPS拒绝环境中的自主平台的位置和高度误差。建议的导航滤波器包括一个预测步骤的基础上的无迹变换和更新步骤，依赖于粒子滤波。SSS测量模型旨在捕获SSS数据的高度非线性性质，同时在基于粒子的更新步骤中保持合理的计算要求。对于我们的数值结果，我们假设一个场景与表面车辆进行SSS和罗盘测量。模拟场景与我们当前的硬件平台一致。我们还讨论了所提出的方法可以扩展到自主水下航行器（AUV）在一个简单的方式，为什么SSS传感器和指南针的组合是特别适合于小型自主平台。
This paper introduces a statistical model and corresponding sequential Bayesian estimation method for terrain-based navigation using sidescan sonar (SSS) data. The presented approach relies on slant range measurements extracted from the received ping of a SSS. In particular, incorporating slant range measurements to landmarks for navigation constrains the location and altitude error of an autonomous platform in GPS-denied environments. The proposed navigation filter consists of a prediction step based on the unscented transform and an update step that relies on particle filtering. The SSS measurement model aims to capture the highly nonlinear nature of SSS data while maintaining reasonable computational requirements in the particle-based update step. For our numerical results, we assume a scenario with a surface vehicle that performs SSS and compass measurements. The simulated scenario is consistent with our current hardware platform. We also discuss how the proposed method can be extended to autonomous underwater vehicles (AUVs) in a straightforward way and why the combination of SSS sensor and compass is particularly suitable for small autonomous platforms.