A Statistical Inference Technique for GPS Interference Detection

Narrowband interference can severely degrade the performance of GPS receivers. Detecting the interference and then characterizing it can lead to its removal. Knowledge that can be useful is the location of the source or direction of arrival of the interference (spatial-directional characteristics) and/or the time specification, frequency, and power of the interference (temporal-spectral characteristics). This work focuses on the second type. In previous work power spectral density fluctuation detection technique has been introduced [1], and statistical inference has been used to detect narrowband CW interference in the GPS signal band after being captured by the RF front end. In this technique, an assessment window of data is first divided into blocks and then analyzed to obtain the statistical parameters of the signal without interference. Then presence of interference is tested by comparing these parameters as a null hypothesis with the parameters extracted from a later window. The effect of the assessment window size on the level of confidence of this test has been already analyzed also in [1]. In this paper, using properties of the fast Fourier transform (FFT) and the effects of the number of FFT points on the test statistic mean and variance, we show that an appropriate data block size (for a specific window size) can be selected to achieve an optimum solution to detect "minimum power detectable interference." The algorithm to find the optimal solution is introduced. The theoretical result is supported by real experiments. Finally this algorithm is employed to detect a real GPS interference signal generated by a TV transmitter in Sydney, Australia.