An Improved Method for Localization of Wireless Capsule Endoscope Using Direct Position Determination

A novel methodology for localization of wireless-capsule-endoscope (WCE) by using the direct-position-determination (DPD) method is proposed. A WCE enables the diagnose of gastrointestinal tract disorders in a non-invasive visual manner. Conventional methods proposed for WCE localization are not optimal in terms of positioning accuracy since they include two disjoint stages: i) estimation of initial parameters such as direction, time, or time-difference of arrival (DOA, TOA, TDOA), and ii) localization performed by intersecting the loci provided by initial parameters. Moreover, most of these methods can localize only one signal transmitter. In contrast, the considered DPD is a single-step method which processes data from all sensor elements simultaneously and can resolve several (theoretically one less than sensors number) co-channel signal sources in a medical ward. More precisely, the proposed method can concurrently localize a WCE and some beacons attached to the patient’s body, which can supersede the use of micro-electromechanical-systems (MEMS). In addition, the considered DPD can locate some WCEs for multiple patients at the same time. To do this, DPD seeks for the peaks of a spatial profit function (SPF) which is based on the statistical cumulants of the signals observed at the array sensors placed around the medical ward. Here, we propose the classical DPD (CDPD) as a coarse but fast, and the generalized DPD (GDPD) as a fine and high resolution method for an improved localization of WCE. Also, the proposed technique can be easily extended to any other indoor applications, where the dimensions of the whole monitoring area and receiving array are about some carrier wavelengths. The superiority of CDPD over DOA-based WCE localization technique, and GDPD over CDPD is verified through comprehensive numerical analysis. We have also drawn the Cramer-Rao lower bound (CRLB) as a benchmark for performance analysis of GDPD. Moreover, we introduced a tighter lower bound on the localization mean square error (MSE) achieved with multiple group arrays (MGLB), corresponding to CDPD and DOA-based methods, to show the superiority of GDPD over these techniques