Integrated sensing and communication (ISAC) can turn radio measurements into networked sensing services, but accurate position estimates alone do not ensure useful tracking. Missed targets, late acquisition, track interruptions and aging observations also shape the information available to an application. This article examines tracking of noncooperative unmanned aerial vehicles (UAVs) from a Third Generation Partnership Project (3GPP) perspective. It connects sensing-service requirements and network architecture to three information interfaces: receiver search regions, measurement context and track validity. A five-UAV case study in a 3GPP urban scenario and a ray-traced San Francisco scene illustrates the tradeoff between target coverage and extra outputs, the distinction between acquisition and ongoing continuity, and the value of testing a static mission envelope before adding adaptation. An observation-age example explains how a state predicted to the present can still rely on old evidence. The resulting lessons are to align receiver search with requested coverage, preserve measurement time and uncertainty, and assess coverage, ambiguity and timeliness together. We conclude by pointing out fruitful avenues for further work in sensing design, calibration and service validation.
The same radio interactions that complicate wireless communication can reveal what is happening around a network. Integrated sensing and communication (ISAC) turns these interactions into opportunities to observe unmanned aerial vehicles (UAVs), road vehicles, and industrial mobile robots. Yet a strong echo is not necessarily a reliable object observation: stationary background can conceal motion, reflections can create virtual objects, and one viewpoint can leave part of an object's motion unobserved. This tutorial explains how known cellular signals are used to estimate propagation, suppress stationary background, interpret reflected paths, and combine observations from different sites. UAV examples under a Third Generation Partnership Project (3GPP) stochastic channel model and ray tracing in a San Francisco urban scene illustrate the mechanisms through spatial layouts, channel profiles, and before/after range--Doppler maps. Local checks reduce false outputs, with some loss of detection. Cooperation improves position accuracy for UAVs detected by both a single site and cooperating sites. The examples show how signal resources, measurement reporting, and application needs shape sensing performance, providing practical guidance for sensing services in complex environments.