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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 typically modeled as a zero-mean random vector. Residual biases may cause inconsistent covariance estimates, misassociation, multiple track swaps, and redundant/spurious track generation; we therefore require some efficient mechanism for mitigating the effects of residual biases. We present here results based on the Schmidt-Kalman filter for mitigating the effects of residual biases. A key advantage of this approach is that it maintains the cross-correlation between the state and the bias errors, leading to a realistic covariance estimate. The current work expands on the work previously performed by Numerica through an increase in the number of bias terms used in a high fidelity simulator for air defense. The new biases considered revolve around the transformation from the global earth-centered-earth-fixed (ECEF) coordinate frame to the local east-north-up (ENU) coordinate frame. We examine not only the effect of bias mitigation for the full set of biases, but also analyze the interplay between the various bias components.

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