Blind Calibration is an exciting formulation for in-situ calibration in sensor networks. Laura Balzano (while at UCLA) and Robert Nowak (UW-Madison) wrote a paper for IPSN 2007 on this topic, and have since further studied the method.

Lipor, John, and Laura Balzano. 2014. “Robust Blind Calibration via Total Least Squares.” In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4244–48. ieee link

Balzano, Laura, and Robert Nowak. 2008. “Blind Calibration of Networks of Sensors: Theory and Algorithms.” In Networked Sensing Information and Control, edited by Venkatesh Saligrama, 9–37. Springer US. springer link

Balzano, Laura, and Robert Nowak. 2007. “Blind Calibration of Sensor Networks.” In Proceedings of the 6th International Conference on Information Processing in Sensor Networks, 79–88. acm link

Here are some documents related to the IPSN 2007 paper. For code for the 2014 robust version, please visit this link.

Slides from the EE ARR Talk: available here.

Calibration data from the NESL CVS (currently not available as of March 2018, working on finding a new link): available here.

Cold Air Drainage data used for the IPSN paper:

  1. Data I used to do the ground truth calibration
  2. Data I used to do the blind calibration
  3. A snippet of matlab code is available here to help you understand the file formats.
If you would like matlab code for a basic example of blind calibration by subspace matching, you can download it here.