Source localization using unique characterizations of multipath propagation in an urban environment

Abstract

This paper proposes a method for determining the location of GSM mobile transmitters. The process discussed here estimates the location of a source without the use of multilateration or LOS techniques. A Multipath Characteristic Database (MCD) containing the multipath signatures for each possible transmitter location in an area of interest is populated via ray-tracing software simulations. The multipath characteristics of interest are Angle of Arrival (AOA), and Time Difference of Arrival (TDOA). An analysis of an eigenstructure Joint Angle and Delay Estimations (JADE) is presented, and the properties of the MCD are discussed. Since the proposed method utilizes a simulated multipath signature database the need for a priori soundings from the area of interest is eliminated, thus making this location estimation system ideal for use in hostile territories.

Preprocessing algorithm for source localisation in a multipath environment

“…As a result, in the case of spherical wave propagation, the magnitude of the received snapshots must be properly equalised using the matrix G, which is defined in Equation (20).…”

“…A ray-tracing approach is presented in [19] but it requires a database for fingerprinting. A raytracing method that uses TDOA is proposed in [20], which requires a "Multipath Characteristic Database". The approach in [21] also uses a predefined database of template reflected signals.…”

Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning

“…We propose an approach to enhance TDOA localization Systems in urban scenario binding together a ray-tracing propagation tool to extract the multipath fingerprint and a machine learning framework to improve the precision even in NLOS situation. Contrary to similar approaches [ 1 ], where the NLOS measurements are not used in the position estimation, we build a multipath exploitation scheme that uses ray tracing and machine learning to give an extra layer of information using the buildings and obstacles in the environment. Despite the limitation of the primary results, in some scenarios, where the obstacles do not change their position, the channel impulse response estimation can be improved by repeatedly averaging the delays and power of the multipath components.…”

Localization of a noisy broadband surface target using time differences of multipath arrivals

“…Phelan et al (2012) demonstrated localization of a mobile radio transmitter in a complex urban environment using a two-dimensional (2-D) array by matching multipath arrival angles and delays with modeled values from a raytracer. 3 In underwater acoustics, theoretical studies have shown that multipath information can provide improved range estimation of an underwater target compared to techniques that only utilize wavefront curvature.…”

“…Phelan et al (2012) demonstrated localization of a mobile radio transmitter in a complex urban environment using a two-dimensional (2-D) array by matching multipath arrival angles and delays with modeled values from a raytracer. 3 In underwater acoustics, theoretical studies have shown that multipath information can provide improved range estimation of an underwater target compared to techniques that only utilize wavefront curvature. 4 Blanc-Benon (1995) compared the use of time differences of multipath arrivals (TDOMA) received by an array with matched field processing on the same array and found that time differences were less sensitive to modeling errors.…”