FIR Filter Self-Configuration Using Genetic Optimization
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Solution Overview
Problem
Existing finite impulse response (FIR) filters require manual adjustment and human intervention for configuration changes, which is impractical due to the large number of supported configurations, especially in versatile computers used in environments like aircraft avionics, where automatic adaptation is necessary for optimal performance.
Innovation Solution
A method involving a genetic algorithm to automatically determine a chain of sinusoidal band-stop filters with configuration parameters, using a combination of objective and constraint functions to optimize filter performance across various configurations without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of FIR filter parameters is used, then filtering performance can be optimized for specific configurations, but the process becomes impractical due to the large number of supported configurations
Solution Approach 1:
The system performs self-configuration by automatically determining optimal FIR filter parameters for each supported configuration using determination means, eliminating the need for manual adjustment. The filter adapts to different configurations autonomously based on input parameters such as sampling frequency and signal characteristics.
Solution Approach 2:
The invention changes the approach from manually setting filter parameters to automatically determining them through computational means. The determination means calculates optimal parameters (cut-off frequencies, filter order) based on configuration inputs, enabling rapid adaptation across hundreds of configurations without human intervention.
2Adaptability or versatility
If versatile computers with software-configurable interfaces are used, then device size is reduced and flexibility is increased, but the FIR filter must be precisely tuned for each configuration which is no longer feasible manually
Solution Approach 1:
The invention replaces the mechanical/manual process of filter tuning with an automated computational system. The determination means uses software-based algorithms to calculate optimal filter parameters, substituting human operators with automated processing capable of handling hundreds of configurations efficiently.
Solution Approach 2:
The determination means serves multiple functions: it accepts various input parameters (sampling frequency, signal type, configuration ID), performs optimization calculations, and outputs appropriate filter parameters for diverse configurations. This universal tool replaces the need for configuration-specific manual tuning procedures.
3Extent of automation
If automatic determination of FIR filter parameters is implemented, then flexibility for new configurations is improved without human intervention, but complex computational processes are required
Solution Approach 1:
The system performs preliminary calculations and determinations of optimal filter parameters before actual filter operation. The determination means pre-computes parameters based on configuration inputs, so when a configuration change occurs, the filter is already optimized and ready for immediate deployment without runtime adjustments.
Data Source
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AI summary
The present invention relates to a method for determining a finite impulse response filter, comprising the following steps: - generation (120) of a plurality of individuals with configuration parameters; - cyclic implementation (130) of the following substeps in relation to each current population of configuration parameters: - evaluation (131) of each individual by an objective function; - evaluation (132) of each individual by at least one constraint function; - selection (133) of a group of individuals from among all individuals based on their evaluations by the objective function and by the constraint function, and formation of the parent individuals; - crossover (134) of the parent individuals to form a new population; - mutation (135) of the individuals of the new population; - verification (136) of a stopping criterion