Physical Sciences and Mathematics Commons^™

Mitigation Of Biases Using The Schmidt-Kalman Filter - Art. No. 66990q, Randy Paffenroth, Roman Novoselov, Scott Danford, Marcio Teixeira, Stephanie Chan, Aubrey Poore

Randy C. Paffenroth

Fusion of data from multiple sensors can be hindered by systematic bias errors. This may lead to severe degradation in data association and track quality and may result in a large growth of redundant and spurious tracks. Multi-sensor networks will generally attempt to estimate the relevant bias values (usually, during sensor registration), and use the estimates to debias the sensor measurements and correct the reference frame transformations. Unfortunately, the biases and navigation errors are stochastic, and the estimates of the means account only for the "deterministic" part of the biases. The remaining stochastic errors are termed "residual" biases and are …