MultiSensor

A multisensor system is one that collects data from multiple sensors and combines it to improve perception, interpretation, and decision making. By fusing information from different modalities, multisensor configurations can compensate for weaknesses in any single sensor, enhance accuracy, and provide greater resilience to occlusions, noise, and environmental variation.

Multisensor setups commonly involve heterogeneous sensors such as cameras, LiDAR, radar, acoustic sensors, and chemical or thermal sensors, as well as arrays of similar sensors used for redundancy. The data streams are synchronized and calibrated to enable meaningful fusion, and the placement and field of view of each sensor influence overall performance.

Data fusion techniques range from early fusion, which combines raw data at the sensor level, to late fusion, which merges decisions or features after initial processing. Statistical methods such as Kalman filters or Bayesian networks, as well as machine learning approaches, are used to estimate states, detect anomalies, and classify events.

Applications include autonomous vehicles and mobile robotics for environment perception, unmanned aerial systems for navigation and cargo delivery, industrial automation and process monitoring, smart buildings and environmental surveillance, and medical devices that track multiple physiological signals.

Benefits of multisensor systems include improved accuracy and robustness, extended coverage, and fault tolerance through redundancy. Challenges involve data synchronization, sensor calibration, bandwidth and power requirements, real-time processing demands, and handling conflicting or missing data.