The use of consider covariance analysis in choosing data weights to reduce the effect of errors in model parameters on satellite orbit determination accuracy

Choosing data weights to reduce the effect of errors in model parameters on satellite orbit determination accuracy is studied. The effect of these errors is evaluated using consider covariance analysis. A derivation of consider covariance analysis is given. In consider covariance analysis, errors are modeled as random variables.;Three weighted least squares (WLS) filters are investigated. The first filter uses a scalar weight for each batch of data. Values of the weights are found that reduce the trace of the consider covariance, tr(;The next filter investigated is the SRCC filter. This filter is similar to the RCC filter, except that each scalar data point is assigned a scalar weight. The data are processed one scalar data point at a time, and the weights are solved for analytically. The SRCC filter is found to be unpredictable and unreliable.;The final filter investigated is the minimum consider covariance (MCC) filter. The MCC filter uses a weight matrix that is a full matrix in order to obtain the absolute minimum tr(...