  For radio-based time-of-arrival (TOA) positioning systems applied in harsh
environments, obstacles in the surroundings and on the vehicle itself will
block the signals from the anchors, reduce the number of available TOA
measurements and thus degrade the localization performance. Conventional
multi-antenna positioning technique requires a good initialization to avoid
local minima, and suffers from location ambiguity due to insufficient number of
TOA measurements and/or poor geometry of anchors at a single epoch. A new
initialization method based on semidefinite programming (SDP), namely MEMA-SDP,
is first designed to address the initialization problem of the MEMA-TOA method.
Then, an iterative refinement step is developed to obtain the optimal
positioning result based on the MEMA-SDP initialization. We derive the
Cramer-Rao lower bound (CRLB) to analyze the accuracy of the new MEMA-TOA
method theoretically, and show its superior positioning performance over the
conventional single-epoch and multi-antenna (SEMA) localization method.
Simulation results in harsh environments demonstrate that i) the new MEMA-SDP
provides an initial estimation that is close to the real location, and
empirically guarantees the global optimality of the final refined positioning
solution, and ii) compared with the conventional SEMA method, the new MEMA-TOA
method has higher positioning accuracy without location ambiguity, consistent
with the theoretical analysis.
