Improving noisy sensor positions using accurate inter-sensor range measurements

In this paper, we consider the problem of improving noisy sensor positions using accurately measured inter-sensor distances. A novel two-step algorithm is proposed. In Step-1, the multidimentional scaling (MDS) technique is applied to the range measurements and a shifted, rotated and possibly reflected version of the true sensor positions is obtained. Step-2 converts the original problem into a generalized trust region sub-problem (GTRS) that can be solved globally via simple bisection searching. The proposed algorithm is shown to be asymptotically efficient. Simulations verify the efficiency of the proposed algorithm.

[1]  J. J. Moré Generalizations of the trust region problem , 1993 .

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

[3]  K. C. Ho,et al.  Alleviating Sensor Position Error in Source Localization Using Calibration Emitters at Inaccurate Locations , 2010, IEEE Transactions on Signal Processing.

[4]  K. C. Ho,et al.  TOA localization in the presence of random sensor position errors , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[5]  Wing-Kin Ma,et al.  Semi-Definite Programming Algorithms for Sensor Network Node Localization With Uncertainties in Anchor Positions and/or Propagation Speed , 2009, IEEE Transactions on Signal Processing.

[6]  Alfred O. Hero,et al.  Locating the Nodes , 2005 .

[7]  Moe Z. Win,et al.  On the robustness of ultra-wide bandwidth signals in dense multipath environments , 1998, IEEE Communications Letters.

[8]  Peter M. Schultheiss,et al.  Array shape calibration using sources in unknown locations-Part II: Near-field sources and estimator implementation , 1987, IEEE Trans. Acoust. Speech Signal Process..

[9]  Su Khiong Yong,et al.  A generic statistical-based UWB channel model for high-rise apartments , 2005 .

[10]  K. C. Ho,et al.  Refining inaccurate sensor positions using target at unknown location , 2012, Signal Process..

[11]  Anthony J. Weiss,et al.  Array shape calibration using sources in unknown locations-a maximum likelihood approach , 1989, IEEE Trans. Acoust. Speech Signal Process..

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

[13]  K. C. Ho,et al.  An Approximately Efficient TDOA Localization Algorithm in Closed-Form for Locating Multiple Disjoint Sources With Erroneous Sensor Positions , 2009, IEEE Transactions on Signal Processing.

[14]  Moe Z. Win,et al.  Cooperative Localization in Wireless Networks , 2009, Proceedings of the IEEE.

[15]  Michael R. Osborne,et al.  Numerical algorithms for constrained maximum likelihood estimation , 2003, The ANZIAM Journal.

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

[17]  Michael R. Osborne,et al.  Scoring with constraints , 2000, The ANZIAM Journal.

[18]  Jian Li,et al.  Exact and Approximate Solutions of Source Localization Problems , 2008, IEEE Transactions on Signal Processing.

[19]  Peter M. Schultheiss,et al.  Array shape calibration using sources in unknown locations-Part I: Far-field sources , 1987, IEEE Trans. Acoust. Speech Signal Process..

[20]  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.

[21]  Moe Z. Win,et al.  Ranging With Ultrawide Bandwidth Signals in Multipath Environments , 2009, Proceedings of the IEEE.

[22]  K. C. Ho,et al.  On the Use of a Calibration Emitter for Source Localization in the Presence of Sensor Position Uncertainty , 2008, IEEE Transactions on Signal Processing.

[23]  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.