A wavelet pre-filter scheme for MDS tracking

In this paper we propose a low-complex non-parametric pre-filtering technique for Time of Arrival (TOA) based target tracking systems. The output of a wavelet transform computed on each one of the anchor-to-target links is used to estimate the noise and the dynamic for the corresponding link. Subsequently this information is used to adaptively decide on the reliability of pre-filtered data. To take full advantage of the pre-filtering scheme, it is used together with an Multidimensional Scaling (MDS)-based tracking algorithm previously proposed. Indeed, since both the pre-filtering scheme and tracking algorithm don´t rely on any a-priori knowledge, their combination results in a completely non parametric approach to the target tracking problem. The aforementioned technique is investigated and compared against a state variable formulation of the problem implemented through an Extended Kalman Filter (EKF). As a result, the MDS-based tracking algorithm with the wavelet pre-filtering scheme here proposed, while being completely non-parametric, it is shown to overcome the EKF algorithm under different target dynamics and noise conditions.