Lattice structure adaptive IIR notch filter based on least square kurtosis

A new variable step-size algorithm for a second-order lattice form structure adaptive infinite impulse response （IIR） notch filter to detection and estimation frequency of sinusoids in Gaussian noises is proposed. Utilizing least square kurtosis of output signals as a cost function, the new gradient-based algorithm to update frequency of the adaptive IIR notch filter and the new variable step-size algorithm are given. The computer simulation results show that the proposed algorithm has better ability in suppressing colored Gaussian noises and better accuracy in estimating parameters at low SNR than previous algorithms.

TIME-VARYING AR MODELING AND ADAPTIVE IIR NOTCH FILTER FOR ANTI-JAMMING DSSS RECEIVER

Using Time-Varying AR (TVAR) model and adaptive notch filter is a new method for the non-stationary jammer suppression in Direct Sequence Spread Spectrum (DSSS). The performance of TVAR model for Instantaneous Frequency (IF) estimation will be affected by some factors such as basis functions. Focusing on this problem, the optimal basis function of TVAR model for the IF estimation of the LFM signal is obtained in this paper. Besides the depth and width of notching, the phase properties of notch filter affect the Signal-to-Interference plus-Noise Ratio (SINR) of correlation output to the narrowband jammer suppression in DSSS, in response to the problem the closed solution of correlation output SINR improvement has been derived when a single frequency jammer passes through direct IIR notch filter, and its performance has been compared with those of five coefficient FIR filters. Later, a novel method for LFM jammer suppression based on Fourier basis TVAR model and direct IIR notch filter is proposed. The simulation results show the effectiveness of the proposed method.

Opposition-based differential evolution for IIR system identification problem

Nature inspired optimization algorithms have made substantial step towards solving of various engineering and scientific real-life problems.Success achieved for those evolution-ary optimization techniques are due to simplicity and flexibility of algorithm structures.In this paper,optimal set of filter coefficients are searched by the evolutionary optimiza-tion technique called Opposition-based Differential Evolution(ODE)for solving infinite impulse response(IIR)system identification problem.Opposition-based numbering con-cept is embedded into the primary foundation of Differential Evolution(DE)technique metaphorically to enhance the convergence speed and the performance for finding the optimal solution.The population is generated with the evaluation of a solution and its opposite solution by fitness function for choosing potent solutions for each iteration cycle.With this competent population,faster convergence speed and better solution quality are achieved.Detailed and balanced search in multidimensional problem space is accomplished with judiciously chosen control parameters for mutation,crossover and selection adopted in the basic DE technique.When tested against standard benchmark examples,for same order and reduced order models,the simulation results establish the ODE as a competent candidate to others in terms of accuracy and convergence speed.