Low-Sample Performance of Reduced-Rank Power Minimization Based Jammer Suppression for GPS

When wideband and narrowband interferences in a GPS system are stationary, a large number of data samples may be obtained to get a good estimate of the interference. However, the jamming environment may be one in which the narrowband jammers have the ability to change frequencies dynamically or the rapid dynamics of the aircraft during maneuvering causes arrival angles of wideband jammers to change. In either type of jamming environment, an interference suppression algorithm will only be effective if it can rapidly converge with a small sample size. We investigate the performance of reduced-rank interference suppression algorithms under conditions of low sample support. It is demonstrated that the Multi-Stage Nested Wiener Filter (MSNWF) outperforms other reduced-rank techniques in terms of suppressing both wideband and narrowband jammers under conditions of low sample support due to the optimal choice of the reduced-rank subspace effected by the MSNWF.