TOA sensor network self-calibration for receiver and transmitter spaces with difference in dimension

We study and solve the previously unstudied problem of finding both transmitter and receiver positions using only time of arrival (TOA) measurements when there is a difference in dimensionality between the affine subspaces spanned by receivers and transmitters. Anchor-free TOA network calibration has uses both in radio, radio strength and sound applications, such as calibrating ad hoc microphone arrays. Using linear techniques and requiring only minimal number of receivers and transmitters, an algorithm is constructed for general dimension p for the lower dimensional subspace. Degenerate cases are determined and partially characterized as when receivers or transmitters inhabit a lower dimensional affine subspace than was given as input. The algorithm is further extended to overdetermined cases in a straightforward manner. Utilizing the minimal solver, an algorithm using the Random Sample Consensus (RANSAC) paradigm has been constructed to simultaneously solve the calibration problem and remove severe outliers, a common problem in TOA applications. Simulated experiments show good performance for the minimal solver and the RANSAC-like algorithm under noisy measurements. Two indoor environment experiments using microphones and speakers give a RMSE of 2.35 cm and 3.95 cm on receiver and transmitter positions compared to computer vision reconstructions.