KEWLS and KFF 2D Comparative Model

This Matlab model and the included results are submitted as reference for the paper ''. Presenting a comparative study of the Sequential Unscented Kalman Filter (SUKF), Least-squares (LS) Multilateration and standard Unscented Kalman Filter (UKF) for localisation that relies on sequentially received datasets. The KEWLS and KKF approach presents a novel solution using Linear Kalman Filters (LKF) to extrapolate individual sensor measurements to a synchronous point in time for use in LS Multilateration.

本 Matlab 模型及其附带结果作为论文的参考提交。本文提出了一种针对基于按顺序接收数据集进行定位的 Sequential Unscented Kalman Filter (SUKF)、Least-squares (LS) Multilateration 和标准 Unscented Kalman Filter (UKF) 的比较研究。KEWLS 和 KKF 方法提出了一种新颖的解决方案，采用线性 Kalman Filter (LKF) 对单个传感器测量值进行外推，以在同步时间点用于 Least-squares Multilateration。