EE Seminar: High Resolution Direct Position Determination of Radio Frequency Sources

~~Speaker: Tom Tirer
M.Sc. student under the supervision of Prof. Anthony J. Weiss

Wednesday, April 6th, 2016 at 15:30
Room 011, Kitot Bldg., Faculty of Engineering

High Resolution Direct Position Determination of Radio Frequency Sources

Abstract

The most common methods for localization of radio frequency transmitters are based on two processing steps. In the first step, parameters such as angle of arrival or time of arrival are estimated at each base station independently. In the second step, the estimated parameters are used to determine the location of the transmitters. The direct position determination approach advocates using the observations from all the base stations together in order to estimate the locations in a single step. This single-step method is known to outperform two-step methods when the signal to noise ratio is low, and inherently overcomes the problem of associating estimated parameters with their relevant sources.
In the presented work, we propose a direct-position-determination-based method for localization of multiple emitters that transmit unknown signals. The method does not require knowledge of the number of emitters, and therefore the use of model order determination techniques is avoided. It is based on minimum-variance-distortionless-response considerations to achieve a high resolution estimator that requires only a two-dimensional search for planar geometry, and a three dimensional search for the general case.
We study two different scenarios. The first scenario is localization of stationary radio emitters using stationary, spatially separated sensor arrays, which is based on delays and angles of arrival. The second scenario is localization of stationary narrowband radio emitters using multiple moving receivers, which is based on Doppler frequency shifts. In both cases the proposed method shows superiority over other competing spectral-based localization methods. Under the assumption of independent, circular, complex Gaussian snapshots, we derive an analytical expression for the estimation mean square error, composed of variance and bias due to finite sample effects and asymptotic bias. We evaluate the performance of the advocated method and verify the usefulness of the theoretical expressions using extensive Monte Carlo simulations.