Why are a priori observation weights used in adjustment of GNSS data?

• A. To reflect known precision before processing.
• B. To discard low-quality observations.
• C. To update weights after each adjustment.
• D. To speed up convergence of the solution.

Answer: (A)

A priori observation weights encode what you already know about the expected precision of each GNSS observation before solving. In GNSS data, different measurements and satellites have different noise characteristics, and these can vary with signal type, atmosphere, multipath, hardware, and geometry. By assigning weights equal to the inverse of the anticipated variances, the adjustment gives more influence to the observations you trust more and less to the noisier ones. This leads to a more accurate solution and realistic formal uncertainties because the normal equations are built on those prior error assumptions. It’s not about discarding data or updating weights during the solution—those are separate practices. The upfront weighting is the reason this approach is used.