On Using the Relative Configuration to Explore Cooperative Localization

Cooperative localization differs from conventional localizations in using the measurements between the unknown nodes, which provide the relative location information of the nodes. This paper investigates cooperative localization by adopting the concept of relative configuration that describes the “shape” of the node network, without considering its absolute location, orientation, and/or scaling. Since the relative configuration is a non-Euclidean object, we introduce the Procrustes coordinates as a coordinate representation, suggest using the relative error as a coordinate independent error metric, and then derive the Cramér-Rao lower bound (CRLB) and a CRLB-type bound for the Procrustes coordinates and the relative error respectively. Three applications of the relative configuration are demonstrated: the first one gives the CRLB analysis for anchor-free localization; the second one discusses the optimal minimally constrained system (MCS) for deriving the absolute locations; and the third one refers to the anchor selection with consideration of anchor location uncertainty. These applications show the advantages of using the relative configuration to investigate cooperative localization.

[1]  Ping Zhang,et al.  Anchor selection with anchor location uncertainty in wireless sensor network localization , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  P. Schönemann,et al.  Fitting one matrix to another under choice of a central dilation and a rigid motion , 1970 .

[3]  Yunhao Liu,et al.  Beyond Trilateration: On the Localizability of Wireless Ad Hoc Networks , 2009, IEEE/ACM Transactions on Networking.

[4]  B. Green THE ORTHOGONAL APPROXIMATION OF AN OBLIQUE STRUCTURE IN FACTOR ANALYSIS , 1952 .

[5]  B. C. Ng,et al.  On the Cramer-Rao bound under parametric constraints , 1998, IEEE Signal Processing Letters.

[6]  Wheeler Ruml,et al.  Improved MDS-based localization , 2004, IEEE INFOCOM 2004.

[7]  B. R. Badrinath,et al.  Ad hoc positioning system (APS) , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[8]  Randolph L. Moses,et al.  On optimal anchor node placement in sensor localization by optimization of subspace principal angles , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Stephen G. Kobourov,et al.  Force-Directed Approaches to Sensor Localization , 2006, ALENEX.

[10]  Frankie K. W. Chan,et al.  Accurate Distributed Range-Based Positioning Algorithm for Wireless Sensor Networks , 2009, IEEE Transactions on Signal Processing.

[11]  Xiaoli Li,et al.  Selective Anchor Placement Algorithm for Ad-Hoc Wireless Sensor Networks , 2008, 2008 IEEE International Conference on Communications.

[12]  Brian M. Sadler,et al.  Maximum-Likelihood Estimation, the CramÉr–Rao Bound, and the Method of Scoring With Parameter Constraints , 2008, IEEE Transactions on Signal Processing.

[13]  Mani B. Srivastava,et al.  On the Error Characteristics of Multihop Node Localization in Ad-Hoc Sensor Networks , 2003, IPSN.

[14]  Randolph L. Moses,et al.  On the Relative and Absolute Positioning Errors in Self-Localization Systems , 2008, IEEE Transactions on Signal Processing.

[15]  Brian D. O. Anderson,et al.  Rigidity, computation, and randomization in network localization , 2004, IEEE INFOCOM 2004.

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

[17]  La-or Kovavisaruch,et al.  Source Localization Using TDOA and FDOA Measurements in the Presence of Receiver Location Errors: Analysis and Solution , 2007, IEEE Transactions on Signal Processing.

[18]  Ying Zhang,et al.  Localization from connectivity in sensor networks , 2004, IEEE Transactions on Parallel and Distributed Systems.

[19]  T. Shores Applied Linear Algebra And Matrix Analysis , 1999 .

[20]  Ligang Liu,et al.  An as-rigid-as-possible approach to sensor network localization , 2010, TOSN.

[21]  Bill Jackson,et al.  Graph theoretic techniques in the analysis of uniquely localizable sensor networks , 2009 .

[22]  Anuj Srivastava,et al.  Statistical Shape Analysis , 2014, Computer Vision, A Reference Guide.

[23]  Tolga Eren,et al.  Cooperative localization in wireless ad hoc and sensor networks using hybrid distance and bearing (angle of arrival) measurements , 2011, EURASIP J. Wirel. Commun. Netw..

[24]  Mónica F. Bugallo,et al.  Sensor self-localization with beacon position uncertainty , 2009, Signal Process..

[25]  S. Umeyama,et al.  Least-Squares Estimation of Transformation Parameters Between Two Point Patterns , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

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

[27]  K. S. Arun,et al.  Least-Squares Fitting of Two 3-D Point Sets , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  K. C. Ho,et al.  Successive and Asymptotically Efficient Localization of Sensor Nodes in Closed-Form , 2009, IEEE Transactions on Signal Processing.

[29]  G. Giorgetti,et al.  Wireless Localization Using Self-Organizing Maps , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[30]  John W. Fisher,et al.  Nonparametric belief propagation for self-localization of sensor networks , 2005, IEEE Journal on Selected Areas in Communications.

[31]  Kin K. Leung,et al.  Self-Organized, Scalable GPS-Free Localization of Wireless Sensors , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[32]  Koen Langendoen,et al.  Distributed localization in wireless sensor networks: a quantitative compariso , 2003, Comput. Networks.

[33]  Erik D. Demaine,et al.  Anchor-Free Distributed Localization in Sensor Networks , 2003 .