Cramér-Rao Bound Analysis for Localization in α-μ Fading IoT Environments

The range-based localization approaches using received signal strength (RSS) are inaccurate due to randomness in received signal power arises from shadowing and multipath effects. By modeling multipath effects using a generalized fading environment, such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha {-}\mu $ </tex-math></inline-formula> distribution improves localization accuracy while maintaining low model complexity. We derive estimator’s localization error bound for model considering shadowing and multipath effects as Gaussian and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha {-}\mu $ </tex-math></inline-formula> distribution, respectively. We also compare the location estimator performance to assess potential areas of algorithm improvement and parameters on which CRB depends. Furthermore, this letter also provides parameters that influence localization performance based on experiments performed on two real-world testbeds.