This paper addresses the issue of target tracking in indoor multipath environment using Time-of- Arrival (TOA) measured at multiple reference devices (RDs) with the assistance of inaccurate floor plane map. Due to the inaccuracy, the map features are estimated jointly with the target state, which is called map assisted simultaneous localization and mapping (MASLAM). To solve the problem, the multisensor single cluster probability hypothesis density (SC-PHD) filter is used to recursively estimate the state of mobile device (MD) and map features. In indoor environment, the received signal at RD consists of line-of-sight (LOS) path, single reflections, multiple reflections and diffraction paths. By treating the multiple reflections and diffraction paths as clutter, and modelling the LOS path and single reflections separately, a new update formula of multisensor SC-PHD filter for map features is derived. In addition, to mitigate the extremely high computation load of multisensor SC-PHD filter, a data association (DA) process is introduced. Furthermore, a particle filter-Gaussian mixture (PF-GM) implementation of the proposed multisensor SC-PHD filter is presented. Results based on TOA measurement data in the typical office area with 3 RDs show that the multisensor SC-PHD MA-SLAM scheme achieves an excellent target tracking performance with 90% of the estimation error smaller than 0.5m.

This paper presents a novel tracking localization scheme that requires only a single dominant non-line-of-sight (NLOS) path and its corresponding scattering point. The position of the scattering point arising out of the dominant one bounce NLOS scattering path can be estimated using the proposed two-step elliptical Lagrange constrained optimization method if the initial position of the mobile device (MD) can be estimated or when the MD transits from line-of-sight (LOS) to a NLOS region. With the dominant NLOS path and corresponding scattering point, the subsequent trajectory of the MD can be estimated. Simulation and empirical data through channel measurement experimentation in two different multipath environments have shown that our proposed NLOS localization scheme surpassed existing NLOS localization schemes by significant margins, especially at high TOA and AOA noise due to, e.g., low bandwidth and low signal-to-noise ratio (SNR).

This paper presents a comprehensive Non Line of Sight (NLOS) localization scheme and a least square estimator that leverages on the bi-directional estimation of the Angle of Arrival (AOA) and Time of Arrival (TOA) of signals exchanged between mobile and reference devices. The proposed localization scheme requires two or more signal paths which can be either Line of Sight (LOS) signals or Non-line of Sight (NLOS) multipath signals that undergo one bound scattering. Our multipath selection scheme is shown to be able to discard multiple bound scattering paths with a high degree accuracy. We used empirical data obtained through experimentation in a real environment to analyse the performance of our proposed localization scheme, and to compare it to the existing methods. The results of this experiment show that the proposed localization scheme that just uses two signal paths, is able to outperform the existing localization schemes in both LOS and extreme NLOS situations where all reference devices are in NLOS with the mobile device. This localization approach is very useful in multipath environments where it may not always be possible to have at least three reference devices in LOS with the mobile device.