How does the vehicle designer decide which navigation solutions to employ and how to configure them? This section describes a framework for making these decisions based on applications of estimation theory to the problem of estimating position based on noisy measurements. This model enables designers to predict the performance of candidate designs based on their quantitative performance metrics. Using this analysis framework we present the answer to particular questions often asked when designing and deploying a range-based positioning system:
• What is the "best" geometry of the fixed acoustic nodes and mobile nodes in an LBL network? What is the sensitivity of the system precision with respect to changes in this geometry?
• What is the relative importance of geometry vis-a-vis range precision in an LBL network?
• What is the best range-based configuration (geometry and update rate) to integrate with dead-reckoning solutions.
To quantify these tradeoffs we propose metrics for positioning precision based on standard terrestrial positioning problems. We use the Cramer Rao lower bound (CRLB) to frame the question in a way that affords thorough analysis. Based on this framework, we articulate particular design tradeoffs, e.g., how design choices affect precision of the position estimate.
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