One line of thinking is that measurement fusion at the system level provides enough value to warrant the cost of communicating the measurement level information to the system level. Another line of thinking is that in general the tracks generated at the source are sufficient to convey the information needed at the system level. There is no argument that measurements provide theoretically more information, but is the cost worth the possible extra value? This paper builds on our previous work that began to explore this issue with a straightforward simulation setup. In the previous paper it was shown that measurement fusion at the system level is better than just source track fusion at the system level. Those results were obtained when the update rate of measurements at the source were the same rate as the source tracks sent. In reality, the source tracks are produced at the full measurement rate, and the full measurement rate is not likely to be sent to the system level. Thus, this paper looks at disparate rates at the source level versus the system level. That is, the tracks produced at the source level will have a higher update rate than the rate at which measurements are sent to the system level to fuse.

In addition to the typical random errors that vary between consecutive measurements, the measurements for most all sensors used for target tracking include bias errors that remain relatively fixed during a target tracking episode and are typically characterized by an a priori mean and covariance. Since the bias errors are approximately fixed during a tracking episode, those errors violate the typical assumption of the measurement errors being white noise. Inflating the measurement covariance of the random errors by adding the bias covariance gives track covariances that poorly represent the true errors. The Schmidt-Kalman filter can be used to prevent the track covariances from becoming artificially too small. However, the Schmidt-Kalman filter produces a track covariance that encompasses the random and bias errors. In this paper, the authors formulate the target tracking as a least-square estimation (LSE) problem and show that the track covariance due to the bias errors can be isolated from the track covariance due the random errors. The authors utilize Monte Carlo simulations to verify and illustrate the accuracy of isolation of the bias and random covariances.