Functional Wiener Filter for Nonlinear Signal Processing
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Solution Overview
Problem
Existing filtering techniques, such as the Wiener-Hopf method and finite impulse response filters, are computationally inefficient and consume significant power, and are limited to linear parameter solutions, making them unsuitable for nonlinear signal processing and applications in devices with processing and power constraints like IoT devices.
Innovation Solution
A functional Wiener filter is generated using a reproducing kernel Hilbert space (RKHS) and a correntropy measure, enabling nonlinear signal processing with minimal computational complexity and power consumption, suitable for deployment in devices with constraints, such as FPGAs and ASICs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If linear filtering techniques (Wiener-Hopf method, finite impulse response filters) are used, then the filtering implementation is simple, but the computational efficiency is poor and power consumption is high
Solution Approach 1:
The patent transforms the filtering approach from linear parameter space to nonlinear feature space through kernel mapping. By changing the parameter representation using reproducing kernel Hilbert space theory, the system achieves both computational efficiency and nonlinear modeling capability, resolving the contradiction between implementation simplicity and computational efficiency.
2Ease of manufacture
If linear filtering techniques are used, then the implementation is straightforward, but the applicability to nonlinear signal processing is limited
Solution Approach 1:
The patent applies kernel mapping to transform data from the original linear space to a higher-dimensional nonlinear feature space. This dimensional transformation enables the filter to capture nonlinear relationships while maintaining the mathematical framework of linear filtering, thus achieving both straightforward implementation and nonlinear signal processing capability.
3Adaptability or versatility
If conventional filtering methods are used in IoT devices, then device compatibility is maintained, but processing capability and power consumption are problematic
Solution Approach 1:
The patent extracts and utilizes only the essential statistical properties (second-order statistics) of the signal for filtering operations. By focusing on extracting only the necessary information rather than processing the entire signal complexity, the method reduces computational load and power consumption while maintaining compatibility with IoT device constraints.
4Reliability
If existing filtering techniques are used, then the solution is well-established, but computational resources required are excessive
Solution Approach 1:
The patent replaces the traditional mechanical computation approach of conventional filtering with a mathematical transformation approach based on reproducing kernel Hilbert space theory. This substitution eliminates the need for complex iterative computations and large computational resources while maintaining the reliability and established theoretical foundation of filtering methods.
Data Source
AI summary
Various embodiments of the present disclosure provide methods, apparatuses, and computer program products for functional nonlinear Wiener-based signal filtering, with which an estimate of a target signal may be produced. Various embodiments involve generation and/or implementation of a functional Wiener filter for continuous time series data filtering, such as signal prediction or signal denoising. In various embodiments, the functional Wiener filter is configured through a reproducing kernel Hilbert space employing a similarity measure that embeds signal statistical information, such as the correntropy measure. With this, the functional Wiener filter is uniquely applicable to the space of nonlinear mappings.