Adaptive Nonlinear Filter Coefficients for Stable Signal Tracking
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Solution Overview
Problem
Existing nonlinear filters are complex, expensive, and unstable, making them ineffective for implementing nonlinear relationships in signal processing systems.
Innovation Solution
An adaptive nonlinear filtering system that uses a thermometer code address generator to select input-dependent filter parameters from memory banks, allowing for the adaptation of filter coefficients based on feedback signals to track a target signal, thereby implementing nonlinear filter functions efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing nonlinear filters are used to implement nonlinear relationships, then filtering capability is provided, but device complexity increases and stability deteriorates
Solution Approach 1:
The filter is divided into multiple parallel branches, each handling a specific portion of the nonlinear filtering task. Each branch contains its own set of coefficients and processing elements, allowing the complex nonlinear operation to be broken down into manageable segments that can be processed independently and then combined.
Solution Approach 2:
The filter coefficients are made adaptive and time-varying rather than fixed. The system dynamically adjusts coefficients based on input signal characteristics and desired output, enabling the filter to adapt to different nonlinear relationships without requiring a completely different filter structure for each case.
2Adaptability or versatility
If existing nonlinear filters are used to implement nonlinear relationships, then filtering capability is provided, but manufacturing cost increases
Solution Approach 1:
The filter structure is designed to be universal, capable of implementing various nonlinear filtering functions through configuration of coefficients and branch selections rather than requiring dedicated hardware for each specific nonlinear function. This multi-functionality reduces manufacturing costs by using a single versatile platform.
Solution Approach 2:
The patent uses coefficient sets that can be stored and recalled, effectively copying filter characteristics rather than hardwiring each nonlinear function. This allows the same physical hardware to implement different nonlinear filtering behaviors by loading different coefficient sets, significantly reducing manufacturing complexity and cost.
3Adaptability or versatility
If existing nonlinear filters are used to implement nonlinear relationships, then filtering capability is provided, but reliability decreases
Solution Approach 1:
The filter incorporates feedback mechanisms where the output is compared with the target signal and the error is used to adjust coefficients in real-time. This feedback loop ensures the filter maintains stability and reliability by continuously correcting deviations from desired performance.
Solution Approach 2:
The system pre-calculates and stores multiple coefficient sets that correspond to different operating conditions and nonlinear relationships. By having pre-prepared coefficient sets, the filter can switch to appropriate pre-validated configurations rather than attempting real-time calculations that might introduce instability, thus cushioning against potential reliability issues.
Data Source
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AI summary
An adaptive nonlinear filtering system includes an adaptive filter module that is configured to generate relative location information pertaining to a relative location of an input signal within an input range; determine an input dependent filter parameter based at least in part on the relative location information; generate an output signal based at least in part on the input dependent filter parameter; and feed back a feedback signal that is generated based at least in part on the output signal and a target signal.