In this letter, we analyze the performance trade-off in distributed integrated sensing and communication (ISAC) networks. Specifically, with the aid of stochastic geometry theory, we derive the probability of detection of that of the coverage given user number. Based on the analytical derivations, we provide a quantitative description of the performance limits and the performance trade-off between sensing and communication in a distributed ISAC network under the given transmit power and bandwidth budget. Extensive simulations are conducted and the numerical results validate the accuracy of our derivations.

Maneuvering-target tracking has always been an important and challenge work because the unknown and changeable motion-models can easily lead to the failure of model-driven target tracking. Recently, many neural network methods are proposed to improve the tracking accuracy by constructing direct mapping relationships from noisy observations to target states. However, limited by the coverage of training data, those data-driven methods suffer other problems, such as weak generalization abilities and unstable tracking effects. In this paper, a digital twin system for maneuvering-target tracking is built, and all kinds of simulated data are created with different motion-models. Based on those data, the features of noisy observations and their relationship to target states are found by two specially designed neural networks: one eliminates the observation noises and the other one predicts the target states according to the noise-limited observations. Combining the above two networks, the state prediction method is proposed to intelligently predict targets by understanding the information of motion-model hidden in noisy observations. Simulation results show that, in comparison with the state-of-the-art model-driven and data-driven methods, the proposed method can correctly and timely predict the motion-models, increase the tracking generalization ability and reduce the tracking root-mean-squared-error by over 50% in most of maneuvering-target tracking scenes.

Space–air–ground integrated networks (SAGINs) are expected to support ubiquitous communication and sensing in next-generation wireless systems. In this paper, we develop a stochastic-geometry-based analytical framework for a three-tier integrated sensing and communication (ISAC) network composed of ground base stations (BSs), unmanned aerial vehicles (UAVs), and low-earth-orbit (LEO) satellites. In the proposed architecture, ground BSs are spatially distributed according to an independent homogeneous Poisson point process (HPPP), UAVs perform sensing and uplink transmission with horizontal locations modeled by an HPPP, and LEO satellites provide relay/backhaul support with ground projections following an independent HPPP. Analytical expressions for the hop-wise communication coverage probability and the single-UAV sensing detection probability are derived. The end-to-end coverage probability is rigorously formulated as a joint event, and a tractable product-form expression is obtained by exploiting the independence of the three-tier spatial processes. Monte Carlo simulations validate the analysis and illustrate how satellite relaying, UAV deployment parameters, and propagation conditions affect communication reliability and sensing detectability. The proposed framework provides useful benchmark-level insights for the design of SAGIN-enabled ISAC systems and establishes analytical building blocks for future studies on cooperative sensing fusion.