This paper presents a new approach to GPS-based navigation which offers significant improvement in antijam capability over traditional designs.The algorithms may be implemented at low cost in software in existing and future GPS receivers using, as inputs, postcorrelation I and Q data and, optionally, raw data from other sensors. Traditional systems are not optimal at high jammer-to-signal (J/S) ratios as a consequence of modular design, use of traditional fixed-gain or gainscheduled tracking loops, and use of artificial moding logic. The approach described here employs a nonlinear filter that operates efficiently at all J/S levels. Filter gains continuously adapt to changes in the J/S environment, and the error covariance propagation is driven directly by measurements to enhance robustness under high jamming and dynamics conditions. Extended-range correlation may be optionally included to increase the code tracking loss-of-lock threshold under high jamming scenarios. Computational complexity is comparable to an extended Kalman filter.Results of hardware-in-the-loop simulations are presented which demonstrate improvements of 15 dB or more in antijam capability relative to traditional designs.

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