Satellite Transponder Gain Control With ARIMA Prediction Under AWGN Jamming
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Solution Overview
Problem
Existing automatic gain control (AGC) schemes in satellite transponders are unable to respond quickly enough to rapid signal amplitude variations caused by additive white Gaussian noise (AWGN) jamming, leading to inefficiencies in satellite communications.
Innovation Solution
Implementing a model predictive automatic gain control (MPC-AGC) system using an autoregressive integrated moving average (ARIMA) model to predict future signal values, calculate signal averages, and adjust gain control values through a model predictive controller, with predefined lookup tables for maximum control capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If existing AGC schemes use current signal amplitude tracking errors to determine gain control values, then the system is simple to implement, but the response speed is slow and cannot keep up with rapid amplitude variations
Solution Approach 1:
The patent applies preliminary action by using an ARIMA model to predict future signal amplitude values before they actually occur. The controller proactively adjusts gain control values based on predicted amplitude variations rather than reacting after errors occur, enabling fast response to rapid amplitude changes while maintaining manageable system complexity through statistical modeling
Solution Approach 2:
The patent implements feedback by continuously monitoring actual signal amplitude and comparing it with predicted values from the ARIMA model. The controller uses this feedback information to adjust gain control values, creating a closed-loop system that adapts to real-time conditions while maintaining fast response through the predictive component
2Reliability
If frequency hopping is used to mitigate jamming, then anti-jamming capability is improved, but signal amplitude variations and channel noise uncertainty increase
Solution Approach 1:
The patent uses feedback mechanisms where the ARIMA model continuously adapts to observed signal characteristics and the controller adjusts gain control values based on actual amplitude measurements. This feedback loop compensates for amplitude variations introduced by frequency hopping, maintaining stable signal processing despite the inherent instability of frequency-hopped signals
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the ARIMA model parameters and gain control values in response to changing signal conditions. The system adapts its predictive parameters to match the current frequency hop pattern and signal characteristics, maintaining effective control despite the varying signal composition
Data Source
AI summary
The present disclosure provides a method for model predictive automatic gain control under additive white Gaussian noise jamming. The method includes predicting a plurality of consecutive signal values by an autoregressive integrated moving average model; calculating a signal average value of the plurality of consecutive signal values; calculating a gain control value using the signal average value of the plurality of consecutive signal values; if the gain control value is greater than a maximum control capability of a AGC processor, using the gain control value as a desired gain control value; or if the gain control value is equal to or less than the maximum control capability, using a minimum difference between an estimated amplitude and each of reference amplitudes in the LUT as the desired gain control value; and calculating a new AGC gain for a current time step according to the desired gain control value.