Time-Frequency Filtering and Carrier-Phase Ambiguity Resolution for GPS-Based TSPI Systems in Jamming Environment
Abstract
In the project period, we developed a class of time-frequency filters based on the combination of the empirical-mode decomposition (EMD) method and a general blind-source separation (BSS) algorithm. We obtained evidence that the method is able to separate jamming from the GPS signal for JSR up to 45dB. A forefront research area in signal processing is particle filters. The idea is to evolve the probability distribution of a signal by using a large number of particles according to the system equations and some stochastic processes, which is similar to Monte-Carlo simulation in physics and chemistry. Motivated by the fact that particle filters have been used widely in various types of signal-processing tasks, we applied this technique to GPS positioning of moving objects in a jamming environment. In particular, we considered a class of regularized particle filters, suitable for estimating the position of a moving object (e.g., a car) equipped with some proper GPS C/A code receiver. Theoretically, a question of interest is how the estimation error depends on uncertainties in the velocity measurement of the car and on the noise level in the UPS signal. Our analysis of the covariance matrix constructed from simulated particles led to a formula relating this matrix to the covariance matrices of the velocity and of the position error from least-squares processing of UPS pseudoranges. The formula was verified by numerical simulations.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 15, 2007
- Accession Number
- ADA476580
Entities
People
- Ying-Cheng Lai
Organizations
- Arizona State University