ON SOFTWARE IMPROVEMENT OF IMAGE CHANNEL SUPPRESSION IN SDR RECEIVERS WITH ANALOG CONVERSION TO A LOW INTERMEDIATE FREQUENCY

A method for digital signal processing in SDR receivers with analog conversion to a low intermediate frequency is proposed. In contrast to known systems, the proposed approach does not consider parasitic phase and amplitude distortions, but uses the direct method minimizing of the signal of the mirror reception channel. Generally speaking, this can be done simultaneously at several frequencies. It is shown that in computational terms, this is reduced to signal processing by an algorithm similar to a digital non-recursive filter, and to determine its coefficients, it is sufficient to solve a system of linear algebraic equations.

Gaussian Recursive Filter for Nonlinear Systems with Finite-step Correlated Noises and Packet Dropout Compensations

This paper is focused on the nonlinear state estimation problem with finite-step correlated noises and packet loss. Firstly, by using the projection theorem repeatedly, the mean and covariance of process noise and measurement noise in the condition of measurements before the current epoch are calculated. Then, based on the Gaussian approximation recursive filter (GASF) and the prediction compensation mechanism, one-step predictor and filter with packet dropouts are derived, respectively. Based on these, a nonlinear Gaussian recursive filter is proposed. Subsequently, the numerical implementation is derived based on the cubature Kalman filter (CKF), which is suitable for general nonlinear system and with higher accuracy compared to the algorithm expanded from linear system to nonlinear system through Taylor series expansion. Finally, the strong nonlinearity model is used to show the superiority of the proposed algorithm.