Ultra-Wideband Angle Estimation Using Neural Channel Responses
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Solution Overview
Problem
Existing methods for estimating angle of arrival (AoA) and angle of departure (AoD) of wireless signals are complex, expensive, and power-hungry, making them unsuitable for efficient and cost-effective use in ultra-wideband communication and radar systems.
Innovation Solution
A method and device that utilize a receiver antenna with an angle-dependent transfer function, combined with a neural network to derive angle information from the channel impulse response of ultra-wideband radio signals, reducing the need for mechanically and electronically complex components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple transmitter or receiver antennas with beamforming or phase difference measurement are used, then angle estimation accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the angle-dependent characteristics from the channel impulse response itself, rather than requiring complex antenna arrays. By analyzing the inherent angle information embedded in the CIR signal, the system achieves angle estimation without needing multiple antennas or mechanical rotation, thus reducing device complexity while maintaining measurement precision
Solution Approach 2:
The patent replaces mechanical rotation systems with signal processing-based angle estimation. Instead of physically rotating antennas to scan for angle information, the system uses neural networks to extract angle data from the received signal's channel impulse response, eliminating mechanical components and reducing overall system complexity
2Measurement precision
If rotational or mechanically movable directional antennas are used, then angle information is obtained, but device complexity and power consumption increase
Solution Approach 1:
The patent substitutes mechanical rotation with a stationary antenna combined with neural network-based signal analysis. The angle information is extracted computationally from the channel impulse response rather than through physical antenna movement, eliminating the power consumption associated with mechanical actuators while maintaining angle estimation capability
Solution Approach 2:
The patent introduces a neural network as an intermediary between the received signal and the angle estimation output. This computational mediator processes the channel impulse response to extract angle information, replacing the need for mechanical scanning systems and reducing power consumption while achieving the same measurement objective
3Measurement precision
If multiple antennas with phase difference measurement are used, then angle estimation is improved, but manufacturing cost and device complexity increase
Solution Approach 1:
The patent extracts angle information directly from the channel impulse response of a single antenna system, eliminating the need to manufacture and deploy multiple antennas. By taking out the angle-dependent characteristics that are inherently present in the CIR signal, the system achieves angle estimation with simpler, less expensive hardware
Solution Approach 2:
The patent uses a neural network trained on angle information to create a computational model that replicates the angle estimation function. Instead of physically copying antenna elements to achieve angle diversity, the system creates a virtual model through machine learning that processes the signal to extract angle data, reducing manufacturing costs while maintaining estimation accuracy
Data Source
AI summary
The invention relates to a method and device for estimating angle information (50) of a received ultra-wideband wireless signal. Upon reception of a wireless signal emitted from a transmitting device (20) with known sounding sequence, the receiving device (10) estimates the channel impulse response (CIR), selects a portion of the channel impulse response (CIR), and estimates angle information (50) given the angle-dependent antenna transfer functions of either the transmitting device (20), the receiving device (10), or both. For this, the selected portion of the channel impulse response of the signal is fed into a neural network (73) which outputs an angle information probability distribution for the ultra-wideband wireless signal (50).