Abstract
Wireless body sensor networks based on ultra-wideband radio have recently received much research attention due to its wide applications in health-care, security, sports and entertainment. Accurate localization is a fundamental problem to realize the development of effective location-aware applications above. In this paper the problem of constrained state estimation for individual localization in wireless body sensor networks is addressed. Priori knowledge about geometry among the on-body nodes as additional constraint is incorporated into the traditional filtering system. The analytical expression of state estimation with linear constraint to exploit the additional information is derived. Furthermore, for nonlinear constraint, first-order and second-order linearizations via Taylor series expansion are proposed to transform the nonlinear constraint to the linear case. Examples between the first-order and second-order nonlinear constrained filters based on interacting multiple model extended kalman filter (IMM-EKF) show that the second-order solution for higher order nonlinearity as present in this paper outperforms the first-order solution, and constrained IMM-EKF obtains superior estimation than IMM-EKF without constraint. Another brownian motion individual localization example also illustrates the effectiveness of constrained nonlinear iterative least square (NILS), which gets better filtering performance than NILS without constraint.
Highlights
In recent years, ultra-wide-band (UWB) technologies have drawn great interest in the wireless community [1]
Our contribution is that we propose the constrained interacting multiple model extended kalman filter (IMM-Extended Kalman Filter (EKF)) algorithm for UWB based individual localization, exploit second-order nonlinear state constraints providing better approximation for higher order nonlinearities and demonstrate the effectiveness of the new method on an individual localization example, compared with the unstrained interactive multiple model (IMM)-EKF
In order to solve non-linear tracking problems with behavior pattern of target changing with time, e.g., the measurement metric is based on IR-UWB TOA, interacting multiple model extended Kalman filter (IMM-EKF) is applied in this paper to perform individual localization
Summary
Ultra-wide-band (UWB) technologies have drawn great interest in the wireless community [1]. The major sources of this impairment are multi-path propagation and potential non-line-of-sight conditions To overcome these problems, we investigate the improvement in the positioning accuracy by taking the a priori knowledge about geometry among the on-body nodes into account. From a practical point of view, the prior knowledge of the on-body nodes’ distance can be obtained by letting the user deploy the nodes within a reasonably given area (e.g., drawn on a specific piece of clothes, typically on the torso) This information can be interpreted as additional reference measurements, which improves the localization accuracy in the view of information fusion. In this paper, considering the priori knowledge about geometry among the on-body nodes, a novel constrained state estimation for individual localization is presented.
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