Lagrange basis function:
TVAR Model of non-stationary jammerThe TVAR modelling of non-stationary sequence x( n) with order P is[7]: modelingl", and the TVAR modeling coefficients can be estimated by RLS, then the convergence is increased and the computation complexity is decreased'"! Shan proposed the FM jammer suppression in DSSS by using TVAR modeling and five coefficients FIR notch filters, and the time basis function of TVAR modeling is also used!", Besides the time basis function, there are Fourier basis function and Lagrange basis function. For LFM jammer suppression, a better basis function of TVAR is selected through simulation in this paper. The closed solution of output SINR improvement of correlation, after a narrow band jammer passes through direct IIR notch filter, is derived, and its narrow band jammer suppression performance is compared with those of optimal Wiener FIR filters. Based on the analysis, the techniques to suppression FM jammer in DSSS based on Fourier basis function TVAR modeling and direct IIR notch filters is proposed in this paper.(1)where e(n) is stationary white noise with zero mean and variance 0'2, and {aj(n),i == 1,2,.· .,p} are TVAR coefficients. The TVAR coefficients are modelled as linear combinations ofa set of basis time function {Uk (n), k ==1,2,···,q}: q a;(n) =La;kuk(n)(2) k=O where {Uk (n), k == 1,2, .. " q} are any basis functions, q is the order of basis, and a ik are polynomial functions of time n . The basis function used in paper [7] is:The basis function is the power of time, and it can be called time basis. Besides the time basis function, the Fourier basis function and Lagrange basis function are generally used [8].Fourier basis function:{ uk(n) =cos(mnk) (k is even)uk(n) =sin(mnk) Ck is odd)