Localization is a fundamental enabler technology for many applications, like vehicular networks, IoT, and even medicine. While Global Navigation Satellite Systems solutions offer great performance, they are unavailable in scenarios like indoor or underwater environments, and, for large networks, the instrumentation cost is prohibitive. We develop a localization algorithm from ranges and bearings, suitable for generic mobile networks. Our algorithm is built on a tight convex relaxation of the Maximum Likelihood position estimator. To serve positioning to mobile agents, a horizon-based version is developed accounting for velocity measurements at each agent. To solve the convex problem, a distributed gradient-based method is provided. This constitutes an advantage over centralized approaches, which usually exhibit high latency for large networks and present a single point of failure. Additionally, the algorithm estimates all required parameters and effectively becomes parameter-free. Our solution to the dynamic network localization problem is theoretically well-founded and still easy to understand. We obtain a parameter-free, outlier-robust and trajectory-agnostic algorithm, with nearly constant positioning error regardless of the trajectories of agents and anchors, achieving better or comparable performance to state-of-the-art methods, as our simulations show. Furthermore, the method is distributed, convex and does not require any particular anchor configuration.

中文翻译：

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根据范围、方位和速度测量对移动主体网络进行最大似然定位

本地化是许多应用的基本推动技术，例如车辆网络、物联网，甚至医学。虽然全球导航卫星系统解决方案提供了出色的性能，但它们在室内或水下环境等场景中不可用，而且对于大型网络来说，仪器成本过高。我们根据范围和方位开发了一种定位算法，适用于通用移动网络。我们的算法建立在最大似然位置估计器的紧凸松弛基础上。为了向移动代理提供定位服务，开发了基于水平的版本来考虑每个代理的速度测量。为了解决凸问题，提供了一种基于分布式梯度的方法。这比集中式方法具有优势，集中式方法通常会在大型网络中表现出高延迟并存在单点故障。此外，该算法估计所有必需的参数并有效地变得无参数。我们对动态网络定位问题的解决方案在理论上是有根据的并且仍然易于理解。正如我们的模拟所示，我们获得了一种无参数、异常值鲁棒且与轨迹无关的算法，无论智能体和锚点的轨迹如何，其定位误差几乎恒定，从而实现了比最先进的方法更好或相当的性能。此外，该方法是分布式的、凸的并且不需要任何特定的锚配置。