Rapid wildfire response requires UAV swarms to approach a fire region from dispersed initial states, coordinate their arrival with limited information exchange, and adapt terminal deployment to updated fireline geometry. This paper proposes a two-stage distributed guidance framework for time-coordinated fireline deployment. In Stage I, a regularized closing-time variable is combined with bounded-degree topological interaction, proximity-based collision avoidance, and persistent longitudinal speed regulation to reduce rendezvous-time dispersion. In Stage II, the updated fireline is discretized into service points and assigned using a geometry-aware structured strategy, followed by distance-aware terminal synchronization and local safety control. Conditional analysis establishes convergence of the reduced Stage-I coordination dynamics under admissible switching topologies and sufficient separation conditions for ideal Stage-II deployment. In the 12-UAV Stage-I comparison, the proposed method reduces the arrival-time spread to 1.34 s, compared with 16.12 s for fixed-topology cooperative guidance and 4.20 s without longitudinal timing correction. Stage-I tests are further extended to 100 UAVs, while Stage-II assignment is evaluated with up to 60 UAVs and maintains low computational cost. In matched three-dimensional tests involving measurement noise, wind disturbance, and bounded communication delay, the proposed method achieves the smallest terminal timing error among the compared strategies, with a mean Stage-II spread of 2.654 s over the four tested conditions. The results demonstrate effective and scalable time-coordinated deployment under the considered kinematic and communication conditions.

Directed inter-UAV Time-Differenced Carrier Phase (TDCP) couples an inter-node distance increment with a relative clock-bias increment, while phase differencing produces temporally correlated noise and near-planar formations retain a weak relative-height mode. We formulate a fixed-lag factor graph that combines reciprocal TDCP, Time-of-Arrival (TOA), Global Positioning System (GPS), clock dynamics, and calibrated navigation-frontend motion and altitude outputs. An invertible sum-and-difference transformation exposes geometry and clock-bias-increment-rate channels while full covariance propagation preserves the reciprocal-pair likelihood. Local information analysis shows that the reverse observation removes the single-direction geometry–clock-increment rank deficiency, and phase-level modeling yields the first-order moving-average covariance retained by block whitening. A causal motion-consistency test (MCT) compares the whitened geometry channel with an independent motion prediction before the current pair enters optimization; a flagged pair is assigned negligible information and its detector–estimator phase arc is reset. Implementation checks verify the exact four-bias-state residual, covariance-normalized reciprocal innovations, and operation under sparse single-direction ambiguity changes. Across ten paired runs at $p_s=5\%$, MCT detected all 770 injected events with a pair-level false-alarm rate of 0.169% and reduced the mean position RMSE by 96.6% relative to DCS.

This paper investigates joint deployment of unmanned aerial vehicle (UAV)-borne linear-array angle-of-arrival (AOA) sensors for localizing a target UAV in three-dimensional space. Since each sensing UAV carries a lightweight one-dimensional (1-D) AOA array, each measurement provides only one angular constraint, and its information contribution depends jointly on the UAV waypoint and array pointing direction. This leads to a coupled coordinate–orientation design problem that differs from conventional full-AOA deployment. We formulate a Cramér–Rao lower bound (CRLB)-based framework under A- and D-optimality criteria, covering both free-flight and constrained hovering regions. By exploiting the structure of the 1-D AOA Fisher information matrix, we show that, for fixed UAV coordinates, the orientation block can be exactly eliminated through a low-dimensional eigenproblem. The resulting reduced coordinate problem is then solved by a geometry-structured sequential quadratic programming (SQP) method, whose curvature model captures the radial and tangential sensitivities induced by line-of-sight geometry. Numerical simulations further validate the effectiveness of the proposed approach.