Sensorifuusio

Sensorifuusio is the process of merging data from multiple sensors to produce more accurate, reliable, and comprehensive information than would be possible with any single sensor. By combining complementary measurements, sensorifuusio reduces uncertainty, improves state estimation, and supports robust decision making in dynamic environments. It is widely used in robotics, autonomous vehicles, aerospace, industrial automation, and wearable health technologies.

Sensorifuusio can be performed at different levels: raw data fusion, where the original sensor signals are merged; feature fusion, which combines extracted attributes; and decision fusion, which merges the conclusions drawn by separate detectors. Common algorithms include Kalman filters and their variants (extended and unscented) for linear or near-linear systems, particle filters for nonlinear problems, and probabilistic approaches such as Bayesian networks. Machine learning and deep learning methods increasingly handle high-dimensional sensor data, sometimes in end-to-end fashions.

A typical system synchronizes sensors in time and space, calibrates them, and accounts for varying sampling rates and noise characteristics. Key challenges include drift, latency, sensor failures, data alignment, computational cost, and explainability. Evaluation often uses metrics such as accuracy, robustness to outliers, and latency.

Origins trace to aerospace and guidance systems in the mid-20th century with the Kalman filter. The term sensorifuusio began to be used in the late 20th century, as advances in robotics and AI broadened its scope. Related topics include data fusion, Kalman filtering, and multi-sensor perception.