This paper presents a performance comparison of different estimation and prediction techniques applied to the problem of tracking multiple robots. The main performance criteria are the magnitude of the estimation or prediction error, the computational effort and the robustness of each method to non-Gaussian noise. Among the different techniques compared are the well known Kalman filters and their different variants (e.g. extended and unscented), and the more recent techniques relying on Sequential Monte Carlo Sampling methods, such as particle filters and Gaussian Mixture Sigma Point Particle Filter.

翻译：本文针对多机器人跟踪问题，对不同估计与预测技术的性能进行了比较。主要性能评估标准包括估计或预测误差的幅度、计算量以及各方法对非高斯噪声的鲁棒性。所比较的技术包括著名的卡尔曼滤波器及其不同变体（如扩展卡尔曼滤波和无迹卡尔曼滤波），以及基于序贯蒙特卡洛采样方法的较新技术，例如粒子滤波和高斯混合Sigma点粒子滤波。