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.
This work presents a new network centric architecture for multiple frame assignment (MFA) tracking. The architecture improves on earlier network tracking schemes by allowing trackers to broadcast decisions about their local soft-level associations, via the Soft Associated Measurement Reports (SAMRs). The SAMR may be followed by an "Oops" message, if the soft association was incorrect and must be revoked. We show, however, that such revocations are very rare in most scenarios. This paper discusses the implementation of the new algorithm and presents simulation results. Considerable improvements in the consistency of the air picture are demonstrated, owing to the the reduced latency in transmission of measurement-to-track associations. The earlier network architectures, namely, the Centralized MFA, the Replicated Centralized MFA, and the Network MFA on Local and All Data, are also discussed in this work, as they form the foundation for the "Oops" algorithm.