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An Introduction to Sensor fusion
The document introduces sensor fusion, highlighting the limitations of single sensors, such as high uncertainty and low precision. It discusses various data fusion strategies, including fusion across sensors, attributes, domains, and time, along with input/output characteristics. A common representation format is outlined to improve data processing by normalizing values and aligning spatial and temporal data.
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An Introduction to Sensor fusion
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- 2. Why? Representation Certainty Accuracy Completeness Scenarios •Single Sensor cannot generate data at the desired granularity ?? • Single Sensor has high uncertainty in measurement ?? • Single Sensor precision is too low ?? • Sensor cannot be used to directly measure the desired parameter ?? • Single sensor generates incomplete picture of the situation?
- 3. Data Fusion Strategies Boudjemaaand Forbes Fusion Type • Fusion across sensors • Fusion across attributes • Fusion across domain • Fusion across time Durrant-Whyte Configuration • Complementary • Competitive • Cooperative Dasarathy (1994) Input/Output Characteristic • Data In -> Data Out • Data In -> Feature Out • Feature In -> Feature Out • Feature In -> Decision Out • Decision In – Decision Out
- 4. Common Representation Format •Converting local spatial data to common coordinate systems • Image registration Spatial Alignment • Converting local observation time to common time axis • Dynamic Time Wrapping Temporal Alignment • Values and uncertainties are normalized to a common scale • Binarization, Z-transform, Binning Sensor Value Normalization
- 5. Thanks you Author :Asit Deva Date : 10 April 2019
Editor's Notes
- #3 Representation – Sensors have a limitation to measure value once every 5 minutes. We use 5 sensors to measure values once every minute. Certainty – Sensor measures temperature with uncertainty of +/- 2 Fah. We use 10 sensors to measure the temperate at the same-time and then take the average of the reading. Accuracy – IMU measure speed but is not accurate. We use Kalman filter to fuse the data from IMU and Wheel encoder to get a more accurate reading. Completeness – Ammeter gives current drawn by the motor but fails to provide a complete scenario. We add a temperature sensor to add more information about the working environment.
- #4 Boudjemaa and Forbes Fusion Type Fusion across sensors: 5 temperature sensors measuring temperature of same object. Fusion across attributes: Different sensors measuring different parameters to deduce a final derived measurement Fusion across domains: When designing a temperature scale, data from different domains are fused to get a scale that fits all domains. Fusion across time: Current measurement fused with historical measurement. Durrant-Whyte Configuration Complementary: Sensors of different types work independently to create complete picture Competitive: Sensors measure the same property to help reduce uncertainty. Co-operative: Sensors work independently to measure different aspects to generate new information that no single sensor can generate independently Dasarathy (1994) Input/Output Characteristice DaI-DaO: Data In -> Data Out Filtering and Smoothing DaI-FeO: Data In -> Feature Out Feature generation FeI-FeO: Feature In -> Feature Out Feature fusion FeI-DeO: Feature In -> Decision Out Decision generation DeI-DeO: Decision In -> Decision Out Decision fusion
- #5 Common Representation Format Conversion of all sensor observations to a common format is a basic requirement for all multi-sensor data fusion systems. The reason for this is that only after conversion to a common format are the sensor observations compatible and sensor fusion may be performed. Based on three types of information - spatial, temporal and value - the process of converting the sensor observations into a common representational format involves the following three functions: Spatial Alignment. Transformation of the local spatial positions x to a common coordinate system. The process is known as spatial alignment and involves geo-referencing the location and field-of-view of each sensor. Temporal Alignment. Transformation of the local times t to a common time axis. The process is known as temporal alignment and is often performed using a dynamic time warping algorithm. Sensor Value Normalization. The sensor values y and their uncertainties Δy are normalized to a common scale. This process is often performed using robust statistics.