Hybrid GPS Accelerometer Speed Estimation
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Solution Overview
Problem
Existing methods for estimating vehicle speed using GPS and inertial sensors face challenges such as battery drain, inaccurate signal reception in tunnels, low sampling rates, and errors like bias and noise, leading to divergent speed estimation.
Innovation Solution
A system and method that record vehicle speed and acceleration using a combination of GPS and INS sensors, assign weights to forward and backward speeds, calculate corrected speeds, and determine a slope to estimate speed based on the trend, using either the corrected speed, minimum, or maximum of forward and backward speeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS sensor is used to measure speed, then speed estimation is possible, but battery drains and signal reception is poor in tunnels
Solution Approach 1:
The patent combines GPS sensor data with accelerometer data to create a hybrid speed estimation system. The processor integrates information from both sensors, using the accelerometer to fill gaps when GPS signals are unavailable or weak, thereby maintaining speed estimation accuracy while allowing the GPS sensor to operate at lower sampling rates, reducing battery consumption.
Solution Approach 2:
The accelerometer acts as an intermediary sensor that provides complementary information to the GPS sensor. When GPS signals are poor or unavailable, the accelerometer continues to provide motion data that can be processed to estimate speed, ensuring continuous operation without requiring the GPS sensor to run continuously, thus reducing battery drain.
2Use of energy by moving object
If GPS sensor operates at low sampling rate, then battery consumption is reduced, but speed estimation accuracy at granular level deteriorates
Solution Approach 1:
The system merges low-frequency GPS data with high-frequency accelerometer data. The GPS sensor operates at low sampling rates to conserve battery, while the accelerometer runs at high frequencies to capture granular motion details. The processor combines these data streams to produce accurate granular speed estimates without requiring the GPS sensor to operate continuously at high rates.
Solution Approach 2:
The system changes the sampling rate parameter of different sensors based on their capabilities and requirements. GPS operates at low sampling rates (e.g., 1 Hz) to save battery, while the accelerometer operates at high sampling rates (e.g., 50 Hz or 100 Hz) to capture detailed motion. The processor integrates these differently sampled data streams to achieve accurate granular speed estimation.
3Measurement precision
If accelerometer data is used to estimate speed, then granular speed estimation is possible, but errors such as bias and noise increase
Solution Approach 1:
The system uses feedback mechanisms where the processor continuously monitors accelerometer data for signs of bias or noise accumulation. When errors are detected, the system adjusts its processing algorithm or incorporates GPS corrections to counteract the drift, ensuring long-term reliability of speed estimates while maintaining granular resolution.
Solution Approach 2:
The system merges accelerometer-based speed estimation with GPS-based speed measurement. The GPS data serves as a reference to correct accumulated errors in accelerometer integration. By combining both sources, the system achieves granular speed estimation from the accelerometer while using GPS to periodically reset bias and reduce noise, preventing divergence over time.
Data Source
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AI summary
System and method for estimating speed of a vehicle is disclosed. Speed of a vehicle is recorded using a first sensor at a time interval of a plurality of time intervals. The first sensor can be a GPS sensor delivering velocity data. Further, an acceleration of the vehicle may be recorded using a second sensor at each sub-interval of the time interval. The second sensor can be a MEMS accelerometer. At each sub-interval, a forward speed and a backward speed of the vehicle are obtained based upon the acceleration at each sub-interval. The forward speed is determined by adding the integrated sub-interval acceleration values to the first sensor value present at the start of the time interval. The backward speed is obtained by subtracting the integrated sub-interval acceleration values from the first sensor value present at the end of the time interval. After obtaining the forward speed and the backward speed, a predefined weight may be assigned to the forward speed and the backward speed at each sub-interval. Subsequently, a corrected speed of the vehicle at each sub-interval is calculated from a weighted sum of the forward speed and the backward speed. Further, a change of speed during the time intervals is determined from the difference of the speed data of the first sensor. Based upon the slope, a choice can be made between different algorithms for determining the speed of the vehicle at each sub-interval, e.g using the weighted sum or the maximum or minimum value of the forward and backward speed.