Derivation of CRLB for Wireless Capsule Endoscope Localization Using Received Signal Strength

This paper presents a theoretical analysis for location estimation of a wireless capsule endoscope (WCE) in the small-intestine region. Under three different shadowing scenarios by assuming standard deviation of the shadowing with the constant variance, parameter-dependent variance, or correlated and parameter-dependent variance, analytical formulas are derived for the Cramér-Rao lower bound (CRLB) for capsule positioning when distance and azimuth angle measurements are employed to estimate the location of the capsule. The CRLB analysis can be employed to quantify the best achievable location estimation performance. We also present some numerical results to show the CRLB for three scenarios. The results show that in the most realistic scenario, when the shadowing is spatially correlated and distance dependent and the standard deviation of noise is 7 dB, the maximum square root of CRLB is 3.8 mm. Moreover, the square root of CRLB experiences its minimum value of 2 mm, when the capsule is away from its first location about half of the capsule length. Overall, the result of this paper suggests that it is possible to use received signal strength such as measured by a radar system for location estimation of a WCE.

[1]  Eryk Dutkiewicz,et al.  Investigation of in-body path loss in different human subjects for localization of capsule endoscope , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[2]  Neal Patwari,et al.  Correlated link shadow fading in multi-hop wireless networks , 2008, IEEE Transactions on Wireless Communications.

[3]  T. Zwick,et al.  A model approach to the analytical analysis of stroke detection using UWB radar , 2013, 2013 7th European Conference on Antennas and Propagation (EuCAP).

[4]  Eryk Dutkiewicz,et al.  Investigation of radar approach for localization of gastro intestinal endoscopic capsule , 2014, 2014 IEEE Wireless Communications and Networking Conference (WCNC).

[5]  Zhenyu Wang,et al.  Simulating correlated shadowing in mobile multihop relay/ad-hoc networks , 2006 .

[6]  E.C. Fear,et al.  Tissue Sensing Adaptive Radar for Breast Cancer Detection—Experimental Investigation of Simple Tumor Models , 2005, IEEE Transactions on Microwave Theory and Techniques.

[7]  Sören Andersson,et al.  Optimizing Wireless Communication Systems , 2014 .

[8]  Bruno Clercks,et al.  MIMO Wireless Networks , 2014 .

[9]  S. Kay Fundamentals of statistical signal processing: estimation theory , 1993 .

[10]  R. Michael Buehrer,et al.  Received signal strength-based sensor localization in spatially correlated shadowing , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  R. Michael Buehrer,et al.  Location Estimation Using Differential RSS with Spatially Correlated Shadowing , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[12]  Ilangko Balasingham,et al.  An ultra wideband communication channel model for the human abdominal region , 2010, 2010 IEEE Globecom Workshops.

[13]  Kegen Yu,et al.  Ground-Based Wireless Positioning , 2009 .

[14]  Ainslie,et al.  CORRELATION MODEL FOR SHADOW FADING IN MOBILE RADIO SYSTEMS , 2004 .

[15]  Dawn M Sears,et al.  Frequency and clinical outcome of capsule retention during capsule endoscopy for GI bleeding of obscure origin. , 2004, Gastrointestinal endoscopy.

[16]  Thomas Zwick,et al.  Performance of an ultra wideband radar for detection of water accumulation in the human bladder , 2010, The 7th European Radar Conference.

[17]  Eryk Dutkiewicz,et al.  Antenna performance for localization of capsule endoscope , 2014, 2014 8th International Symposium on Medical Information and Communication Technology (ISMICT).