TOF Distance Estimation with Accelerometer Step Detection
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Solution Overview
Problem
The accuracy of distance estimation in remote vehicle control systems is compromised in noisy environments due to multiple signal paths caused by reflection sources, leading to inaccurate TOF signal measurements.
Innovation Solution
A system and method that incorporates a time-of-flight subsystem, an accelerometer, and filtering techniques to generate a stable distance estimate by detecting human steps and adjusting the distance signal based on TOF and accelerometer data, using smoothing filters and step detection algorithms to refine the distance estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If TOF signal is used for distance measurement in noisy environments with multiple reflection sources, then distance estimation can be obtained, but measurement precision deteriorates due to multiple signal paths
Solution Approach 1:
The patent introduces an accelerometer as an intermediary sensor to detect human steps, which serves as a mediator to validate and correct TOF distance measurements. The accelerometer data acts as an intermediate verification layer that helps distinguish valid distance changes from noise caused by reflections, thereby improving measurement precision in noisy environments.
Solution Approach 2:
The system implements feedback by continuously monitoring accelerometer data and using it to adjust or validate TOF distance measurements. When the accelerometer detects human steps that correspond to expected distance changes, it confirms the TOF measurement; when there's a mismatch, it flags potential noise from reflections, creating a feedback loop that improves distance estimation accuracy.
2Measurement precision
If filtering techniques are applied to TOF signal to improve accuracy, then measurement precision improves, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent merges TOF distance measurement with accelerometer-based step detection into a unified distance estimation system. By combining data from both sensors and processing them together through a integrated filtering algorithm, the system achieves improved measurement precision without proportionally increasing device complexity, as the processing is consolidated rather than duplicated.
Solution Approach 2:
The filtering system is designed to handle multiple functions: it processes TOF signals, validates them against accelerometer data, detects human steps, and corrects distance measurements. This multi-functional approach allows a single processing module to perform what would otherwise require separate systems, thereby improving precision while controlling complexity.
3Measurement precision
If step detection algorithm is implemented to adjust distance estimate, then measurement precision improves by filtering out noise, but difficulty of detecting and measuring increases due to need for multiple sensor correlations
Solution Approach 1:
Instead of trying to directly detect and filter noise from TOF signals, the system inverts the approach by using accelerometer data to infer valid distance changes. Rather than analyzing the TOF signal for noise patterns, it checks whether accelerometer-detected steps correspond to expected distance changes, making the detection process simpler and more reliable.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the accuracy of distance estimation by filtering out noise and accurately determining human steps, thereby enhancing the performance of remote vehicle control systems in noisy environments.
Implementation Method 1
The TOF subsystem generates a TOF distance signal by periodically transmitting a TOF signal between the mobile node and the base station and measuring the time taken for transmission of the TOF signal therebetween
Implementation Method 2
The mobile node includes an accelerometer for generating an accelerometer signal. The filter detects a human step based on variances in the accelerometer signal
Data Source
AI summary
A method of estimating a distance between a mobile unit and a vehicle includes providing a time of flight subsystem including circuitry incorporated in the mobile unit and circuitry incorporated in the vehicle, and generating a time of flight distance signal by periodically transmitting a time of flight signal between the mobile unit and the vehicle and measuring the time taken for transmission of the time of flight signal therebetween. A travel sensor is disposed at the mobile unit and generates a travel sensor signal. A value of a distance estimate signal is initialized based on the time of flight distance signal. A movement of the mobile unit is determined based on variance in the travel sensor signal. The initialized value of the initialized distance estimate signal is changed or increased or decreased based upon determination of movement of the mobile unit.