Adaptive Antenna Switching Pattern for Angle of Arrival
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Solution Overview
Problem
Existing direction finding algorithms, such as AoX algorithms, face accuracy issues due to settling delays in antenna switching patterns caused by inductive or capacitive elements, which affect the quality of I and Q signals used for angle of arrival calculations.
Innovation Solution
The method involves determining a preferred antenna switching pattern using neural networks trained with I and Q signals to minimize settling delays, allowing for more accurate angle of arrival calculations by selecting the optimal sequence of antenna elements for signal sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional antenna switching pattern is used, then the signal acquisition process is simple and fast, but the settling delays caused by inductive or capacitive elements reduce the quality of I and Q signals
Solution Approach 1:
The patent applies dynamics by making the antenna switching pattern adaptive rather than fixed. The system dynamically selects switching patterns based on real-time signal characteristics and angle of arrival estimates, allowing the multiplexer to optimize its switching behavior for each specific measurement scenario, thereby reducing settling delays while maintaining signal quality.
Solution Approach 2:
The patent changes the parameters of the antenna switching pattern based on signal conditions. By adjusting the switching sequence and timing parameters according to the estimated angle of arrival and signal characteristics, the system optimizes the balance between settling time and signal quality, allowing faster acquisition without sacrificing measurement precision.
2Measurement precision
If the antenna switching pattern is optimized for accuracy, then the quality of I and Q signals improves, but the complexity of determining the optimal pattern increases
Solution Approach 1:
The patent implements feedback by using the estimated angle of arrival and signal quality metrics to inform subsequent switching pattern selections. The system continuously monitors measurement outcomes and uses this feedback to refine its pattern selection strategy, improving accuracy while managing complexity through intelligent reuse of previous results.
Solution Approach 2:
The patent applies preliminary action by pre-characterizing different antenna switching patterns and their performance characteristics under various conditions. This pre-computed information is stored and retrieved during operation, avoiding the need for complex real-time optimization while still achieving high accuracy through informed pattern selection.
3Measurement precision
If multiple antenna switching patterns are evaluated, then the accuracy of angle of arrival calculation improves, but the time required for signal acquisition increases
Solution Approach 1:
The patent applies partial action by evaluating only a subset of possible antenna switching patterns rather than exhaustively testing all patterns. The system selects and evaluates only those patterns most likely to provide accurate measurements based on preliminary estimates, achieving high accuracy without the time penalty of complete evaluation.
Solution Approach 2:
The patent uses periodic action by alternating between different antenna switching patterns in a structured sequence. This periodic evaluation allows the system to gather sufficient data for accurate angle of arrival calculation while maintaining a predictable and efficient acquisition timeline, balancing accuracy requirements with acquisition speed.
Data Source
AI summary
A device and method for improving the accuracy of angle of arrival and departure computations is disclosed. The device and method rely on manipulation of the antenna switching pattern to achieve an improved calculation of arrival angle. In one embodiment, the device calculates an estimate angle of arrival using conventional methods. The device then determines which of a plurality of different antenna switching pattern yields the more accurate results at this estimated angle of arrival. The AoA measurement is then repeated using the preferred antenna switching pattern. In another embodiment, the device captures the amplitude and/or phase of the signal from each antenna element. The device then sorts these antenna elements and defines a preferred antenna switching pattern based on the sort list. The AoA measurement is then performed using the preferred antenna switching pattern. In another embodiment, neural networks may be utilized to determine the preferred antenna switching pattern.