A novel adaptive interference mitigation approach based on space time processing for global navigation system receiver arrays

Abstract

This paper proposes a novel adaptive interference mitigation approach based on space time processing for global navigation system receiver arrays. The novel approach combines two adaptive filters, Normalized Least Mean Square (NLMS) algorithm and reduced-rank Multistage Nested Wiener Filter (MNWF), for interference mitigation, and shares the information of different channels' characters under various interference environments. Furthermore, the proposed filter adaptive exchanges their weights according to acquisition results and MSE (Mean Square Error). It is demonstrated that the proposed mixed double adaptive filter provides both advantages at the aspects of higher convergence speed and lower complexity for navigation receiver arrays than the other traditional adaptive array algorithms via simulations.

A Mixing Vector Based an Affine Combination of Two Adaptive Filters for Sensor Array Beamforming

“…They exhibit faster convergence rate, better tracking capability, and lower computational complexity than full rank techniques. Several reduced-rank methods have been proposed in the last decade, such as auxiliary vector filter (AVF), conjugate gradient reduced-rank filter (CGRRF) [21], multistage nested Wiener filter (MNWF) [22] and its modified approaches applied in a wide area of adaptive array beamforming [23][24][25]. Many important results on how to improve the convergence rate and/or how to reduce the computational complexity of reduced-rank adaptive filters have been obtained in the literature (see, e.g., [26,27]).…”