Detection and localization of wide band interference in the global positioning system L1 band using cross correlation techniques

Society is getting more and more dependent on the usage of global navigational satellite systems (GNSS). The signals recieved from the satellites are very weak and therefore sensitive to interference. If the GNSS signal gets interfered in an area, there is a potential risk for economic damages or even loss of life. When something is interfering with the signal, then the interference needs to be detected and the source localized in order to ensure the functionality of GNSS in the area. There is a number of ways that the source of interference can be located. This report focuses on a method that uses time difference of arrival (TDOA) for estimation of the position. Collection of data is made by a number of independent front ends with unsynchronized clocks that sample the GNSS signal and sends it to a central server.The server processes the collected data with a software based GNSS receiver and calculates the doppler frequency for each satellite as well as extracting the navigational data from the data. The navigational data is then used to syncronize the different datastreams in time. Correlation is made between the datasets in order to detect interference. When interference is detected, the difference in time is calculated between the correlation peak of the satellite signal and the peak from the interference. This gives the TDOA between the front ends. After TDOA has been calculated between a number of different front ends, localization of the interference source is performed by estimation of the intersection between of the hyperbolic lines that is created from the TDOA. The main focus has been investigating the characteristics of the cross-correlation function (CCF) for determining its behaviour for a GPS signal with and without interference. In comparison with other methods such as measurement of the AGC and calculation of the C/No, the CCF based method has been proven to be more sensitive but also much more computationally expensive.Validerat; 20101217 (root