Sensor Fusion MCQs January 8, 2026August 10, 2024 by u930973931_answers 40 min Score: 0 Attempted: 0/40 Subscribe 1. What is the primary goal of sensor fusion? (A) Generating new sensors (B) Increasing the size of sensor data (C) Reducing the number of sensors used (D) Combining data from multiple sensors to achieve more accurate and reliable information 2. Which of the following is a common technique used in sensor fusion? (A) Principal Component Analysis (B) K-means Clustering (C) Kalman Filter (D) Histogram Equalization 3. What does a “Kalman Filter” estimate in sensor fusion? (A) The state of a system by combining measurements and predictions (B) The color of an object (C) The position of a sensor (D) The resolution of an image 4. In sensor fusion, what is “data association”? (A) Enhancing the quality of sensor data (B) Matching measurements from different sensors to the same object or event (C) Generating new sensor types (D) Reducing the number of sensors 5. Which algorithm is used for nonlinear sensor fusion? (A) Principal Component Analysis (B) K-means Clustering (C) Support Vector Machine (D) Extended Kalman Filter 6. What does “sensor calibration” involve? (A) Generating new sensor types (B) Increasing the size of sensor data (C) Reducing the number of sensors (D) Adjusting sensor measurements to ensure accuracy 7. Which type of sensor fusion combines data from sensors with different types? (A) Heterogeneous Sensor Fusion (B) Homogeneous Sensor Fusion (C) Data Normalization (D) Data Reduction 8. In sensor fusion, what is “fusion level”? (A) The calibration of sensors (B) The type of sensors used (C) The resolution of sensor data (D) The stage at which data from sensors is combined, such as raw data, feature level, or decision level 9. What is “decision fusion” in the context of sensor fusion? (A) Reducing the size of sensor data (B) Adjusting sensor measurements (C) Generating new sensor types (D) Combining decisions or classifications made by different sensors or algorithms 10. Which method is commonly used for sensor fusion in autonomous vehicles? (A) Data Normalization (B) Sensor Fusion with Lidar and Radar (C) Histogram Equalization (D) K-means Clustering 11. What is “feature fusion” in sensor fusion? (A) Enhancing image quality (B) Matching measurements from different sensors (C) Generating new sensors (D) Combining features extracted from multiple sensors to enhance analysis 12. Which technique is used to handle uncertainty in sensor fusion? (A) Bayesian Filtering (B) K-means Clustering (C) Principal Component Analysis (D) Histogram Equalization 13. What does “sensor fusion” aim to improve in a robotic system? (A) The cost of sensors (B) The speed of data transmission (C) The size of the robot (D) Accuracy and robustness of the robot’s perception and navigation 14. In sensor fusion, what is the “raw data level”? (A) Combining sensor data at the most basic level before any processing (B) Combining processed features from sensors (C) Generating new sensor types (D) Enhancing image quality 15. What is the purpose of “multi-sensor data fusion” in surveillance systems? (A) Increasing the size of sensor data (B) Reducing the number of sensors used (C) Generating random sensor data (D) Enhancing the ability to track and detect targets using various sensor types 16. Which approach is used for sensor fusion in GPS and IMU systems? (A) Histogram Equalization (B) Principal Component Analysis (C) K-means Clustering (D) Complementary Filter 17. What is the “decision level” fusion in sensor fusion? (A) Combining raw data from sensors (B) Combining decisions or outputs from different sensors or algorithms (C) Enhancing image quality (D) Generating new sensors 18. In sensor fusion, what is “data preprocessing”? (A) Combining sensor data (B) Preparing and cleaning sensor data before fusion (C) Generating new sensor types (D) Enhancing image quality 19. Which technique is used to fuse data from sensors with different time resolutions? (A) Histogram Equalization (B) Feature Extraction (C) Data Normalization (D) Time Synchronization 20. What is “sensor redundancy” in sensor fusion? (A) Using multiple sensors to provide backup and improve reliability (B) Reducing the number of sensors (C) Generating new sensor types (D) Increasing the size of sensor data 21. Which algorithm is commonly used for sensor fusion in robotic navigation? (A) Particle Filter (B) K-means Clustering (C) Principal Component Analysis (D) Histogram Equalization 22. What is “fused estimation”? (A) Estimating system states using data from multiple sensors (B) Adjusting sensor measurements (C) Generating new sensors (D) Enhancing image quality 23. What is “data fusion at the feature level”? (A) Enhancing image quality (B) Combining raw sensor data (C) Generating new sensors (D) Combining features extracted from multiple sensors 24. Which approach is used for fusion in multi-modal sensor systems? (A) Histogram Equalization (B) Principal Component Analysis (C) K-means Clustering (D) Multi-View Fusion 25. What does “sensor calibration” ensure in a fusion system? (A) Accurate and consistent sensor measurements (B) Reducing the number of sensors (C) Generating new sensors (D) Enhancing image quality 26. What is “cross-sensor calibration”? (A) Reducing the size of sensor data (B) Enhancing image quality (C) Generating new sensor types (D) Adjusting measurements from different types of sensors to ensure consistency 27. Which method is used for “data fusion in time series data”? (A) Histogram Equalization (B) K-means Clustering (C) Principal Component Analysis (D) Kalman Filtering 28. What is “sensor fusion in the data level”? (A) Generating new sensors (B) Combining processed features from sensors (C) Combining raw data from multiple sensors before further processing (D) Enhancing image quality 29. In sensor fusion, what is “data fusion strategy”? (A) The method or approach used to combine data from multiple sensors (B) Generating new sensors (C) Enhancing image quality (D) Reducing the number of sensors 30. What is “sensor fusion for object tracking”? (A) Enhancing image quality (B) Reducing the number of sensors (C) Generating new sensor types (D) Combining data from multiple sensors to improve the accuracy of tracking objects 31. What is “fusion algorithm”? (A) An algorithm for enhancing image quality (B) An algorithm for data normalization (C) An algorithm for generating new sensors (D) An algorithm used to combine data from multiple sensors 32. Which technique is used for “sensor fusion in robotics”? (A) Histogram Equalization (B) K-means Clustering (C) Principal Component Analysis (D) Extended Kalman Filter 33. What is “sensor fusion in IoT systems”? (A) Combining data from various sensors in Internet of Things (IoT) applications (B) Generating new sensor types (C) Reducing the number of sensors (D) Enhancing image quality 34. Which approach is used for “real-time sensor fusion”? (A) Histogram Equalization (B) Data Normalization (C) K-means Clustering (D) Stream Processing 35. What does “fused data” refer to? (A) Data with increased size (B) Data from a single sensor (C) Data that has been combined from multiple sensors to provide a more accurate result (D) Data that has not been processed 36. What is “multi-sensor integration”? (A) Enhancing image quality (B) Generating new sensors (C) Reducing the number of sensors (D) Combining data from different types of sensors to improve overall system performance 37. Which technique is used for “sensor fusion in autonomous driving”? (A) Data Normalization (B) Sensor Fusion with Lidar and Radar (C) Principal Component Analysis (D) Histogram Equalization 38. What is “sensor fusion in smart grids”? (A) Generating new sensors (B) Combining data from different sensors to enhance the management of electrical grids (C) Reducing the number of sensors (D) Enhancing image quality 39. What does “sensor fusion for health monitoring” involve? (A) Combining data from various health sensors to improve patient monitoring (B) Generating new sensor types (C) Reducing the number of sensors (D) Enhancing image quality 40. What is “sensor fusion in smart cities”? (A) Generating new sensors (B) Integrating data from multiple sensors to manage city infrastructure and services (C) Reducing the number of sensors (D) Enhancing image quality