The localization of multiple wireless agents via, for example, distance and/or bearing measurements is challenging, particularly if relying on beacon-to-agent measurements alone is insufficient to guarantee accurate localization. In these cases, agent-to-agent measurements also need to be considered to improve the localization quality. In the context of particle filtering, the computational complexity of tracking many wireless agents is high when relying on conventional schemes. This is because in such schemes, all agents’ states are estimated simultaneously using a single filter. To overcome this problem, the concept of multiple particle filtering (MPF), in which an individual filter is used for each agent, has been proposed in the literature. However, due to the necessity of considering agent-to-agent measurements, additional effort is required to derive information on each individual filter from the available likelihoods. This is necessary because the distance and bearing measurements naturally depend on the states of two agents, which, in MPF, are estimated by two separate filters. Because the required likelihood cannot be analytically derived in general, an approximation is needed. To this end, this work extends current state-of-the-art likelihood approximation techniques based on Gaussian approximation under the assumption that the number of agents to be tracked is fixed and known. Moreover, a novel likelihood approximation method is proposed that enables efficient and accurate tracking. The simulations show that the proposed method achieves up to 22% higher accuracy with the same computational complexity as that of existing methods. Thus, efficient and accurate tracking of wireless agents is achieved.

经由例如距离和/或方位测量来定位多个无线代理具有挑战性，特别是如果仅依靠信标到代理的测量不足以保证精确定位的话。在这些情况下，还需要考虑代理之间的测量以提高定位质量。在粒子过滤的情况下，当依赖常规方案时，跟踪许多无线代理的计算复杂度很高。这是因为在这样的方案中，使用单个滤波器同时估计所有代理的状态。为了克服这个问题，文献中已经提出了多颗粒过滤（MPF）的概念，其中对每个试剂使用单独的过滤器。但是，由于必须考虑代理之间的测量，需要进行额外的努力才能从可用的可能性中得出有关每个单独的过滤器的信息。这是必需的，因为距离和方位测量值自然取决于两种代理的状态，在MPF中，这两种代理的状态由两个单独的滤波器估算。由于通常无法通过分析得出所需的可能性，因此需要一个近似值。为此，在假定要跟踪的主体数量是固定且已知的前提下，这项工作扩展了基于高斯近似的最新技术似然近似技术。此外，提出了一种新颖的似然近似方法，该方法使得能够进行有效且精确的跟踪。仿真表明，所提出的方法在与现有方法相同的计算复杂度下，可实现高达22％的更高精度。因此，