Urban unmanned aerial vehicle (UAV) navigation operates under tight bandwidth, compute, and latency budgets. Interference and building blockage cause rapid link fluctuations, making interference-aware path planning on grid signal-to-interference-plus-noise ratio (SINR) maps essential for reliable communication. In this paper, we formulate the problem as a joint update–navigation decision: map uncertainty triggers on-demand partial refreshes that are co-optimized with motion under a bandwidth budget. We introduce UT-Grid, an uncertainty-triggered grid-update framework, that refreshes conditionally triggered upon necessity to reduce overall map-update traffic, and MoE-D3QN, a Dueling Double DQN with sparse Mixture-of-Experts (Top-1 routing) that activates a single expert per step, cutting per-decision active parameters and FLOPs while matching or surpassing comparable dense D3QN planners. In an urban simulation with multi-source interference, the framework outperforms static-map, periodic-refresh, and dense D3QN baselines, increasing reaching probability and path efficiency while markedly reducing communication overhead.

Reliable positioning services are extremely important for users in mountainous environments. However, in such environments, the service reliability of conventional wireless positioning technologies is often disappointing due to frequent non-line-of-sight (NLoS) propagation and poor geometry of available anchor nodes. Hence, we propose a unmanned aerial vehicle (UAV)-enabled positioning system that utilizes UAV’s mobility to overcome the above challenges. In this article, we first analyze and model the major causes of service failures in the proposed system. In particular, a geometry-based NLoS probability model is established based on the digital elevation models (DEMs) of realistic terrain for reliability analysis. Subsequently, we propose a reliability-prediction method and derive the corresponding metric to evaluate the system’s ability to provide reliable positioning services. Moreover, we also develop a voting-based method for the further enhancement of service reliability. Monte Carlo simulations show that in mountainous environments, the proposed reliability-prediction method could achieve a prediction accuracy that is at least 36.8$\%$higher than that of the existing technique. In addition, in the experiments conducted in two typical valley scenarios, the proposed reliability-enhancement method improves the service reliability of the proposed system by 23$\%$and 29$\%$, respectively. These numerical results demonstrate the strong potential of the proposed system and methods for reliable positioning.