Clutter Component Updating via Dimensional Reduction for Joint Sensing
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Solution Overview
Problem
Existing communication systems face challenges in real-time clutter removal, especially in environments where clutter components change over time, which hinders effective joint communication and sensing operations.
Innovation Solution
An apparatus and method that transform multi-dimensional echo signal representations into one-dimensional signals, utilizing a clutter matrix representation and singular value decomposition to determine and update clutter components, enabling efficient clutter removal by stacking and smoothing matrices to track meaningful clutter subspaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional clutter removal methods are used, then clutter components can be determined, but the computational complexity is high and real-time processing is difficult
Solution Approach 1:
The patent segments the clutter removal process into distinct stages: (1) obtaining measurement results from sensing operations, (2) transforming multi-dimensional echo signals into one-dimensional signals, (3) determining clutter components using a clutter matrix representation, and (4) updating clutter components dynamically. This segmentation allows each stage to be optimized independently, reducing overall computational complexity while maintaining real-time processing capability.
Solution Approach 2:
The patent transforms multi-dimensional representations of echo signals into one-dimensional echo signals. This dimensionality reduction simplifies the data structure and significantly reduces the computational burden of subsequent clutter component determination operations, enabling real-time processing without sacrificing essential clutter information.
2Measurement precision
If clutter components are updated frequently to track environmental changes, then clutter removal accuracy improves, but computational overhead increases
Solution Approach 1:
The patent implements dynamic updating of clutter components based on environmental changes. The system determines second clutter components using a clutter matrix representation that incorporates previously determined clutter components, allowing the clutter model to adapt to changing environments. This dynamic approach maintains high accuracy while avoiding unnecessary computations in stable environments.
Solution Approach 2:
The patent uses feedback from previously determined clutter components to inform subsequent clutter component determination. The clutter matrix representation incorporates historical clutter information, allowing the system to refine its clutter model iteratively. This feedback mechanism improves accuracy over time while reducing computational overhead by building upon existing knowledge rather than recalculating from scratch.
3Loss of information
If multi-dimensional echo signal representations are processed directly, then complete information is retained, but processing time and computational resources increase
Solution Approach 1:
The patent transforms multi-dimensional representations of echo signals into one-dimensional echo signals through a carefully designed transformation process. This dimensionality reduction maintains the essential information needed for clutter component determination while dramatically reducing the computational complexity and processing time required for subsequent operations.
Data Source
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AI summary
Solutions enabling updating clutter components that can be used in clutter removal when joint communication and sensing, for example, are disclosed. To update clutter components by second clutter components, sets of measurement results of sensing of an environment of which clutter components have been determined are obtained (201). Then, per a set, multi-dimensional representations of echo signals received as measurement results of the set are transformed (202) at least partly into one-dimensional echo signals. The second clutter components are determined (203) based at least on corresponding one-dimensional echo signals and a clutter matrix representation that comprises elements obtained from previously determined clutter components of the environment.