The paper tackles sensor design for large physical systems when you have an accuracy target but don't know what sensors you need. Instead of designing sensors first and checking accuracy later, the authors flip the problem: given an error budget, they synthesize the measurement likelihood function that enforces that budget while adding minimal information beyond what the system dynamics already provide. They do this using maximum-entropy optimization (among all posteriors meeting the accuracy constraint, pick the one closest to the prior), then back out the implied sensor characteristics.
Main takeaways:
- Classical sensor design requires knowing sensor models upfront; this approach inverts the flow by starting with accuracy requirements
- The method constructs a measurement likelihood via constrained maximum-entropy optimization: enforce the error budget while minimizing added information
- Works with various distance metrics (Wasserstein, KL divergence, moment constraints) and provides convex or convex-relaxed formulations
- A two-layer design architecture connects abstract accuracy budgets to concrete sensor placement, precision, and configuration choices