Chapter 30: Localization and Positioning

Learning Objectives

• Define TOA, TDOA, AOA, and RSSI measurement models and derive the geometric interpretation of range circles, hyperbolas, and bearing lines for position estimation
• Formulate position estimation as a nonlinear least-squares problem and derive iterative solutions including Gauss-Newton, weighted LS, and maximum likelihood estimators
• Compute the Cramer-Rao bound for position estimation (position error bound, PEB) and analyse how base station geometry, bandwidth, and antenna count affect positioning accuracy
• Describe 5G NR positioning methods (Multi-RTT, DL-TDOA, UL-AoA, DL-AoD) and their signal design including PRS and SRS for positioning
• Identify NLOS conditions and apply mitigation techniques including residual-based detection, robust estimation, and machine learning approaches
• Explain the radio-SLAM framework using factor graph formulation and describe how multipath can be exploited rather than mitigated for simultaneous localisation and mapping