Biologically Inspired Sensor Fusion
Abstract
Accurate Automation Corporation (AAC) has developed a novel neural network-based sensor fusion system, inspired by the architecture of biological sensor fusion systems. The project performed research into the nature by which information from multiple sensors is fused by the central nervous system and developed a biological model for the process. Based upon this model we developed a system which fuses two or more sensor signals to generate a fused signal with an improved confidence of target existence and position. The system includes gain, control and fusion units, and also include an integration unit. The integration unit receives signals generated by two or more sensors, and generates integrated signals based on the sensor signals. The integration unit performs temporal and weighted spatial integration of the sensor signals, to generate respective sets of integrated signals supplied to the gain control and fusion units. The gain control unit uses a preprogrammed function to map the integrated signals to an output signal that is scaled to generate a gain signal supplied to the fusion unit. The fusion unit uses a preprogrammed function to map its received integrated signals and the gain signal, to a fused signal that is the output of the system. The weighted spatial integration increases the fused signal's sensitivity to near detections and suppresses response to detections relatively distant in space and time, from a detection of interest. The gain control and fusion functions likewise suppress the fused signal's response to low-level signals, but enhances response to high-level signals. In addition, the gain signal is generated from signals integrated over broad limits so that, if a detection occurred near in space or time to a detection of interest, the gain signal will cause the fused signal to be more sensitive to the level of the detection of interest.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 26, 1999
- Accession Number
- ADA391621
Entities
People
- Joel Davis
- Richard Akita
- Robert Pap