Parallel Prism Filter Network for Stopband and Memory Tradeoffs
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Solution Overview
Problem
Existing Prism filter systems face limitations such as suboptimal stop band attenuation, high memory storage requirements for long filters, and a tradeoff between filtering performance and dynamic response.
Innovation Solution
The development of new Prism low pass filter structures and techniques employing multiple Prism filters in parallel, allowing for improved tradeoff between filtering performance and dynamic response, and enabling the creation of filters with desirable passband flatness and stopband attenuation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the filter length is increased to improve stop band attenuation, then filtering performance is improved, but memory storage requirements increase
Solution Approach 1:
The filter is divided into multiple layers, where each layer contains a set of Prisms. This segmentation allows the filter to achieve high stop band attenuation through the cumulative effect of multiple layers without requiring each individual Prism to be excessively long, thereby managing memory requirements through structured organization.
Solution Approach 2:
The patent implements a nested structure where layers are contained within the filter system, and Prisms are contained within layers. This nested organization allows efficient memory management by grouping related computational elements together, enabling the system to handle long filter requirements through hierarchical structuring rather than flat expansion.
2Manufacturing precision
If the filter length is increased to improve stop band attenuation, then filtering performance is improved, but the number of floating point operations increases
Solution Approach 1:
By segmenting the filter into layers with multiple Prisms per layer, the computational workload is distributed across modular units. Each Prism processes a portion of the frequency spectrum, and their combined output achieves the desired stop band attenuation without requiring a single excessively long computational sequence.
Solution Approach 2:
The filter structure allows dynamic adjustment of the number of layers and Prisms per layer based on performance requirements. This dynamic configuration enables optimization of the tradeoff between computational cost and filtering performance, allowing the system to achieve high stop band attenuation only when necessary rather than always using maximum computational resources.
3Speed
If the filter length is reduced to improve dynamic response, then response speed is improved, but filtering performance deteriorates
Solution Approach 1:
The segmented layer structure allows different parts of the filter to serve different functions: earlier layers can be configured for faster response while later layers provide enhanced attenuation. This segmentation enables the filter to achieve both fast dynamic response and high filtering performance by optimizing each layer's contribution rather than requiring uniform filter length throughout.
Solution Approach 2:
Different layers can be configured with different numbers of Prisms and different characteristic frequencies, allowing local optimization of filter properties. Regions of the filter can be tailored for speed while other regions are optimized for attenuation, achieving both fast dynamic response and high filtering performance through non-uniform local configuration.
4Manufacturing precision
If multiple distinct band pass filter designs are instantiated for spectral analysis, then filtering performance is improved, but device complexity increases
Solution Approach 1:
The layered Prism filter structure is designed to be universally applicable across different frequency ranges and filtering requirements. By adjusting the number of layers, Prisms per layer, and characteristic frequencies, the same fundamental architecture can perform multiple filtering functions, replacing the need for numerous distinct filter designs and reducing overall system complexity.
Solution Approach 2:
The filter structure allows dynamic reconfiguration for different spectral analysis requirements by adjusting layer parameters and Prism characteristics. This dynamic adaptability enables a single filter design to replace multiple fixed designs, reducing device complexity while maintaining filtering performance across various applications.
Data Source
AI summary
A filter system for filtering an input signal comprises a network of Prism filters including at least one cosine Prism filter and at least one sine Prism filter. The network comprises a first branch (210) in parallel with a second branch, (220) each branch arranged to receive the input signal as an input, the first branch comprising the cosine Prism filter/s (211), the second branch comprising the sine Prism filter/s (221). The network of Prism filters is arranged to generate an output signal based on a combination of an output of the first branch with an output of the second branch. A method of designing a convolutional filter is also provided, comprising inputting a test signal into a filter system to generate an impulse response of the filter system and generating a convolutional filter based on the impulse response.