Model-Based Estimation Techniques Applied To Global Navigation Satellite System Jammers

Abstract

Model-based estimation techniques have been developed and applied to data collected from real Global Navigation Satellite System (GNSS) jammers. Lowpower civilian GNSS jammers pose a growing threat to the integrity of GNSS timing and navigation, and the present effort develops various countermeasures for these devices. The use of illegal civilian GNSS jammers has grown in recent years out of concern for personal privacy, sometimes on the part of innocents, but often in support of unauthorized or illegal activities. These civilian jammers are commonly referred to as personal privacy devices (PPDs). The effects of these PPDs are not limited to the individual user; they disrupt GNSSenabled equipment in a radius of 100m-1000m or more around each device. GNSS systems are being further integrated into many aspects of our society; therefore, the rise in PPD use portends trouble for various pieces of civilian infrastructure. PPD use can be discouraged through more rigorous enforcement of spectrum interference laws. These enforcement actions will require specially designed equipment, and in particular, algorithms to be run by that equipment: algorithms that detect, acquire, track, and geolocate these PPDs. Six contributions are made to the body of knowledge on PPDs and the model-based algorithms related to PPD signal detection, acquisition, and tracking, and to PPD geolocation. However, many of these contributions can be generalized to additional non-PPD signals. The first contribution is a survey of the signal characteristics of 18 different. The second contribution is a sensible chirp-style signal model for the PPDs. The third contribution is a sensitive PPD chirp-style signal detection algorithm that has been extensively optimized for low computational burden. The fourth contribution is a two-part FFT-based signal acquisition procedure that can rapidly acquire a full state estimate of the target PPD using the data provided by the signal detection algorithm. The fifth contribution is a signal tracking Kalman filter for estimating the states of the received PPD signal. The sixth contribution is a time-of-arrival geolocation algorithm that enables low-bandwidth inter-receiver array communication. All of the developed algorithms have been verified on real PPD data collected in a laboratory or in the field.