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Traditional beamformers need to know the incident angle of the desired signal leading while its abili-ty to handle interference is limited.In this paper,the constrained steer vector of linearly constrained min-imum-variance(LCMV)beamformer is modified to make sidelobe null to direction of powerful jammer.Inaddition,the state-space concept is used to describe the anti-jammer filter,and Kalman filter algorithm isdeduced by building the observation model and measurement equation.The new method is more efficient oncomputation and more robust to survive environment with large scale variation in interference strength.Fi-nally,simulation results shows that the new approach can form the null with its depth in proportion to powerin direction of jammer,and has steady convergence process.The novel method can effectively improve thesignal-to-jammer-plus-noise power ratio(SJNR)of GPS signals to make the correlation peak easy to track.
Traditional beamformers need to know the incident angle of the desired signal leading while its abili-ty to handle interference is limited. In this paper, the constrained steer vector of linearly constrained min-imum-variance (LCMV) beamformer is modified to make sidelobe null to direction of powerful jammer. Addition, the state-space concept is used to describe the anti-jammer filter, and Kalman filter algorithm isdeduced by building the observation model and measurement equation. The new method is more efficient oncomputation and more robust to survive environment with large scale variation in interference strength. Fi-nally, simulation results shows that the new approach can form the null with its depth in proportion to power in direction of jammer, and has steady convergence process. The novel method can effectively improve the signal- jammer-plus-noise power ratio (SJNR) of GPS signals to make the correlation peak easy to track.