Velocity Estimation Using Traction Speed for GPS Loss
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Solution Overview
Problem
Conventional velocity estimation systems for heavy machinery are inaccurate when GPS signals are lost for an extended period due to accumulating errors from low-cost MEMS accelerometers, leading to unreliable velocity readings.
Innovation Solution
A velocity estimation system that includes a locating device for generating location signals, a traction device speed sensor, and a controller that uses a Kalman filter to estimate velocity based on location changes and traction device speed when GPS signals are unavailable, ensuring accurate velocity calculation even without GPS data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS-based velocity estimation is used, then velocity accuracy is maintained under normal conditions, but velocity becomes inaccurate when GPS signals are lost for extended periods
Solution Approach 1:
The system changes the velocity estimation parameter source based on GPS availability. When GPS is available, it uses GPS-derived velocity; when GPS is lost, it switches to using traction device speed sensor data. This parameter substitution resolves the contradiction by maintaining reliable velocity estimation under different operational conditions.
Solution Approach 2:
The traction device speed sensor acts as an intermediary measurement source that provides alternative velocity data when the primary GPS-based system fails. This intermediary system ensures continuous reliable velocity estimation without complete dependency on GPS signals.
2Adaptability or versatility
If accelerometer-based velocity calculation is used, then velocity can be estimated without GPS, but errors accumulate over time due to accelerometer drift
Solution Approach 1:
The traction device speed sensor serves as a more reliable intermediary measurement source compared to accelerometers. It provides continuous velocity data without the time-dependent drift errors characteristic of accelerometer integration, enabling accurate GPS-independent velocity estimation.
Solution Approach 2:
The system replaces the mechanical integration of accelerometer data (which accumulates errors) with direct measurement from the traction device speed sensor. This substitution eliminates the error accumulation problem while maintaining the ability to estimate velocity without GPS.
3Measurement precision
If conventional positioning systems using Kalman filter with GPS and accelerometer are used, then velocity accuracy is maintained when GPS is available, but the system becomes complex and fails when GPS is lost
Solution Approach 1:
The system extracts and removes the problematic accelerometer component from the velocity estimation process, relying instead on the more reliable traction device speed sensor. This simplifies the system architecture while maintaining accuracy, eliminating the need for complex Kalman filtering to compensate for accelerometer drift.
Solution Approach 2:
The traction device speed sensor serves multiple functions: it provides velocity data for both GPS-assisted and GPS-independent operation modes. This multi-functionality reduces system complexity by eliminating the need for separate accelerometer-based estimation pathways and their associated complex error correction mechanisms.
Data Source
AI summary
A velocity estimation method and system is disclosed. The method may include receiving a location signal indicative of a location of the machine and estimating the velocity of the machine based on a change in the location of the machine over a period of time. The method may further include determining a loss of the location signal, detecting a traction device speed of the machine, and selectively estimating the velocity of the machine based on the traction device speed when the location signal is determined to be lost.