In the electromagnetic silence environment, for the azimuth-only passive localization problem in the conical formation, it was concluded that there was a deviation between the ideal standard formation and the actual formation. These disturbance deviations should be eliminated to make the UAV reach the ideal formation state. Therefore, the adjustment model of individual UAV, the directed graph model of formation node and the MDP model of adjustment strategy are constructed. Based on the relevant factor model constructed, the azimuth-only passive localization model in conical formation is established and solved in MATLAB. Finally, experiments and analysis are carried out in the simulation environment, and the effectiveness and feasibility of the proposed algorithm are proved.

This paper proposes a distributed information‐fusion framework for cooperative environmental awareness in multi‐UAV systems. The framework addresses limited individual sensing coverage and incomplete, spatially discontinuous observations in cluttered or communication‐constrained scenarios by integrating a UAV control model, a directional perception model, and a feature‐level fusion architecture. Salient features from multiple platforms are extracted, normalized for translation, rotation, and scale, and matched to generate a consistent global map. Simulation results show that the proposed method improves situational consistency, reduces redundant data transmission, and enhances flight safety in unknown environments. These results support multi‐UAV applications in emergency response, environmental monitoring, and large‐area exploration.

Joint UAV–target assignment and route construction must account for service windows, payload limits, flight range, and the distance required to return to the depot. We study this problem in a static simulator and evaluate constraint-aware hierarchical assignment and routing (C-HAR), a deterministic constructive heuristic. At each decision step, C-HAR removes UAV–target pairs that fail the current feasibility checks and ranks the remaining pairs using target value, incremental travel, time-window slack, and predicted route imbalance. The evaluation uses 40 shared synthetic instances, independent route replay, Wilson intervals for audited event rates, and paired randomization tests. C-HAR obtained a feasible completed value of 0.941 ± 0.014, with no audited time-window or resource violations among 542 serviced targets. Its feasible-value difference from the equally screened Feasible-Greedy comparator was not statistically significant. The simulator uses fully observed, noise-free states; here, “deterministic” describes the decision rule and state update for a fixed generated instance, not the uncertainty of a physical UAV system. No trained neural policy or flight experiment is evaluated.

Dynamic task allocation in multi-unmanned-aerial-vehicle (multi-UAV) teams must account for uncertain task return, changing reachability, intermittent communication, and terminal-return energy. We develop UCDG-PD for one-task-per-UAV allocation. At each decision epoch, an obstacle-aware bipartite graph keeps only capability-compatible edges satisfying deadline, outbound-route, and task-to-terminal return-energy constraints. A lower-confidence bound converts an edge-level reliability requirement into deterministic admission. On the surviving graph, agents use an idle-augmented entropic assignment, dynamic average tracking of task demand, neighbor-mixed prices, and task-local leases to resolve conflicts without global edge sorting. We derive the chance-constraint reduction and prove uniqueness of the entropic relaxation, characterize consensus fixed points through KKT conditions, bound demand-tracking disagreement under jointly connected mixing, and establish binary lease feasibility. Controlled trajectory-level simulation models UAV motion, energy, task release and expiry, priority shocks, no-fly zones, stochastic links, and terminal returns. Across 28 common-seed trials, UCDG-PD achieves conditional execution success of 0.995, compared with 0.964 for risk-neutral PD and 0.987 for a centralized auction; successful-task coverage is 0.770 versus 0.838 for the auction. A 1600-residual held-out audit gives coverage of 0.944 (95% Wilson interval: 0.932–0.955) against a nominal value of 0.950. These results quantify the reliability–coverage–value trade-off under the defined trajectory-level simulation protocol.