Position estimation and tracking of an autonomous mobile sensor using received signal strength

In this paper, an algorithm for approximating the path of a moving autonomous mobile sensor with an unknown position location using Received Signal Strength (RSS) measurements is proposed. Using a Least Squares (LS) estimation method as an input, a Maximum-Likelihood (ML) approach is used to determine the location of the unknown mobile sensor. For the mobile sensor case, as the sensor changes position the characteristics of the RSS measurements also change; therefore the proposed method adapts the RSS measurement model by dynamically changing the pass loss value alpha to aid in position estimation. Secondly, a Recursive Least-Squares (RLS) algorithm is used to estimate the path of a moving mobile sensor using the Maximum-Likelihood position estimation as an input. The performance of the proposed algorithm is evaluated via simulation and it is shown that this method can accurately determine the position of the mobile sensor, and can efficiently track the position of the mobile sensor during motion.

[1]  R.L. Moses,et al.  Locating the nodes: cooperative localization in wireless sensor networks , 2005, IEEE Signal Processing Magazine.

[2]  Alfred O. Hero,et al.  Relative location estimation in wireless sensor networks , 2003, IEEE Trans. Signal Process..

[3]  Ahmad Hashemi-Sakhtsari,et al.  Target tracking by time difference of arrival using recursive smoothing , 2005, Signal Process..

[4]  Lixia Zhang,et al.  Recursive position estimation in sensor networks , 2001, Proceedings Ninth International Conference on Network Protocols. ICNP 2001.

[5]  Richard P. Martin,et al.  The limits of localization using signal strength: a comparative study , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[6]  G. Carter.  Time delay estimation for passive sonar signal processing , 1981 .

[7]  Donald F. Towsley,et al.  Mobility improves coverage of sensor networks , 2005, MobiHoc '05.

[8]  Brian D. O. Anderson,et al.  Wireless sensor network localization techniques , 2007, Comput. Networks.

[9]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[10]  LiXinrong.  RSS-Based Location Estimation with Unknown Pathloss Model , 2006 .

[11]  Azzedine Boukerche,et al.  Localization systems for wireless sensor networks , 2007, IEEE wireless communications.

[12]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[13]  F. Gustafsson,et al.  Mobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements , 2005, IEEE Signal Processing Magazine.

[14]  Tong Liu,et al.  Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks , 1998, IEEE J. Sel. Areas Commun..

[15]  Rudolf Mathar,et al.  Location tracking of mobiles in cellular radio networks , 1999 .

[16]  Xinrong Li,et al.  RSS-Based Location Estimation with Unknown Pathloss Model , 2006, IEEE Transactions on Wireless Communications.

[17]  Simon Haykin,et al.  Adaptive filter theory (2nd ed.) , 1991 .

[18]  Andrey V. Savkin,et al.  Radar Target Tracking via Robust Linear Filtering , 2007, IEEE Signal Processing Letters.

[19]  D. McCrady,et al.  Mobile ranging using low-accuracy clocks , 2000 .

[20]  A.H. Sayed,et al.  Network-based wireless location: challenges faced in developing techniques for accurate wireless location information , 2005, IEEE Signal Processing Magazine.

[21]  K.J.R. Liu,et al.  Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs , 2005, IEEE Signal Processing Magazine.

[22]  Mohamed F. Younis,et al.  Sink repositioning for enhanced performance in wireless sensor networks , 2005, Comput. Networks.

[23]  Azzedine Boukerche,et al.  Localization systems for wireless sensor networks , 2007, IEEE Wireless Communications.

[24]  Gianluca Mazzini,et al.  Localization in sensor networks with fading and mobility , 2002, The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications.