BSSOSS

Bayesian Spatial Surveillance and Object Sensing System (BSSOSS) is a probabilistic, distributed sensing framework designed to monitor large spatial regions by fusing heterogeneous sensor data to detect and track objects of interest. The system applies Bayesian inference to maintain a dynamic belief map of object presence, state, and motion over time, integrating inputs from cameras, radar, lidar, acoustic sensors, and other sources.

Its architecture typically includes a network of sensing nodes, a central or hierarchical fusion engine, and a policy layer for tasking and data handling. Nodes perform local feature extraction and transmit probabilistic observations; the fusion engine uses probabilistic data association, Kalman or particle filtering, and occupancy-grid representations to generate object tracks, occupancy maps, and confidence measures. The design supports both centralized and distributed processing and emphasizes privacy and security controls.

In operation, data are synchronized and calibrated to align in time and space. The system outputs include object tracks with state estimates, spatial heatmaps indicating the probability of presence, and alerting signals or risk assessments for human operators or automated decision systems.

Applications span security and defense, border and critical-infrastructure surveillance, traffic monitoring, industrial automation, and disaster-response coordination. Limitations include computational demands, sensitivity to sensor quality and calibration, challenges in data association across heterogeneous modalities, and concerns about privacy and governance.

Origin and status: The concept has appeared mainly in academic and industry research as a framework for multi-sensor fusion; there is no single standard implementation. Variants focus on specific sensor sets or mission profiles, reflecting broader trends in probabilistic sensor fusion.