Lorentz Constraint Angle Estimation in Non-Gaussian Noise
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Solution Overview
Problem
Current target signal angle estimation methods are not robust in non-Gaussian noise environments and fail to accurately estimate signal angles due to off-grid issues and high computational complexity, especially when using sparse reconstruction models based on Gaussian noise assumptions.
Innovation Solution
The Lorentz constraint angle estimation method constructs an N-time slot received signal model using Intelligent Reconfigurable Surface to reflect target and interference signals, combines Lorentz norm and atomic norm for sparse reconstruction, and employs an augmented Lagrangian function with alternating direction mixed multiplier method for iterative updates, followed by multi-signal classification to analyze spectral peaks for angle estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sparse reconstruction models based on Gaussian noise assumptions are used, then the angle estimation process can be completed in shorter time with fewer sensors, but the robustness to non-Gaussian noise environment deteriorates
Solution Approach 1:
The patent changes the noise distribution parameter from Gaussian to non-Gaussian (alpha-stable distribution), fundamentally altering the statistical model to match the actual noise environment. This parameter change enables the estimator to maintain high reliability in non-Gaussian conditions while preserving computational efficiency through the use of characteristic function-based estimation methods.
Solution Approach 2:
The patent replaces traditional mechanical signal processing approaches (such as matched filtering and conventional spectral methods) with a statistical field-based approach using characteristic functions and probability distribution theory. This substitution allows for more efficient computation that is both faster and more robust to non-Gaussian noise interference.
2Measurement precision
If conventional angle estimation methods are used, then the system can operate in simple environments, but the measurement precision deteriorates in complex non-Gaussian noise environments
Solution Approach 1:
The patent converts the harmful non-Gaussian noise characteristics into a beneficial factor by explicitly modeling the noise using alpha-stable distribution. Instead of treating non-Gaussian noise as an adversary to be suppressed, the method embraces its statistical properties and uses them to design an estimator that is inherently robust, thereby converting the harmful noise environment into a manageable statistical model that improves measurement precision.
Solution Approach 2:
The patent introduces characteristic functions as an intermediary mathematical tool that bridges the gap between the observed signals contaminated by non-Gaussian noise and the underlying angle parameters. The characteristic function serves as a mediator that preserves the essential statistical properties while filtering out the harmful noise effects, enabling accurate parameter extraction even in severe noise conditions.
3Reliability
If robust noise suppression methods are applied, then the reliability in non-Gaussian environment improves, but the computational complexity increases
Solution Approach 1:
The patent extracts and isolates the essential statistical characteristics of non-Gaussian noise (alpha-stable distribution parameters) from the complex signal environment. By focusing computation only on estimating these key distribution parameters and using them to guide the angle estimation, the method achieves robustness without requiring computationally intensive processing of the entire signal spectrum, thereby reducing overall computational complexity.
Solution Approach 2:
The patent performs preliminary estimation of the alpha-stable noise distribution parameters before conducting the main angle estimation. This preliminary action characterizes the noise environment in advance, allowing the subsequent angle estimation to proceed with optimized parameters that reduce computational burden. By preparing the noise model beforehand, the method avoids repeated complex computations during the main estimation phase.
Data Source
AI summary
The disclosure provides a Lorentz constraint angle estimation method and a system in a non-Gaussian environment; the method includes the following steps: constructing an N-time slot received signal model based on a non-Gaussian noise environment to obtain a reflected signal; constructing a cost model based on Lorentz norm by a difference value between an actual received signal and the reflected signal, and performing an angle estimation by combining with an atomic norm to obtain a signal sparse reconstruction model; constructing an augmented Lagrangian function by the signal sparse reconstruction model, and carrying out the iterative update on the augmented Lagrangian function to obtain a reconstructed signal; and analyzing the reconstructed signal and searching spectral peaks globally to obtain spatial spectral peak points, and completing an angle estimation of the reconstructed signal.