An analysis of advanced optimization algorithms for multiuser cooperative navigation

Abstract

Navigation in low signal-to-noise ratio (SNR) environments continues to be an extremely challenging problem for GNSS. Effects such as multipath fading, shadowing, jamming of the waveform not only greatly limit the navigation accuracy, but also increase the outage probability that occurs when the received signal level falls below the minimum threshold. Newer navigation approaches have focused on employing terrestrial signals that maintain higher signal power such as digital television (DTV), cellular, etc. These are referred to as signals of opportunity (SoOP). The focus of this research is using these signals in a time difference of arrival (TDOA) cooperative navigation network. A major problem arises with relation to convergence of the positioning algorithm for this type of network. To address this problem, our research studies this problem from a different perspective. Here we will pose the problem as a convex optimization problem and address it as such. The main idea being that if the problem is structured in this form, we can use this well understood area to develop algorithms for faster convergence with respect to cooperative navigation. As such, this research is a combination of analytical analysis, algorithm development, and simulation results that describe performance under certain conditions.

Composite data fusion algorithm for miniature vehicles building navigation base in formation flying

“…Article [17] presented the parallel estimation problem in terms of the full system dynamics and illustrated both time-varying and time-invariant solutions to the estimator design problem. Article [18] posed the fusion problem as a convex optimization problem and addressed it. Approximate dynamic programming was employed in [19] to implement cooperative navigation for formation that consists of heterogeneous autonomous vehicles.…”