In a departure from the past, unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs) are increasingly needed for complementary cooperation in military, scientific, and commercial applications, because this is more efficient than standalone operations. Information sharing through acoustic underwater communication is vital for complementary cooperation between USVs and UUVs. Normally, since USVs have advantages in terms of wide operational boundaries compared to UUVs, they are efficient for tracking UUVs. In this paper, we suggest a UUV tracking algorithm for a USV. The tracking algorithm’s development consists of three main software models: an estimation based on an extended Kalman filter (EKF) with a navigation smoothing method, guidance based on multimode guidance, and re-searching based on a pattern. In addition, the algorithm provides a procedure for tracking UUVs in complex acoustic underwater communication environments. The tracking algorithm was tested in a simulated environment to check the performance of each method, and implemented with a USV system to verify its validity and stability in sea trials. The UUV tracking algorithm of the USV shows stable and efficient performance.

Terrain-referenced navigation (TRN) provides an alternative navigation method for environments with limited GPS availability. This paper proposes a vision-based TRN framework that employs stereo imagery and a rotation-invariant iterative closest point (ICP) algorithm to align reconstructed elevation maps with a terrain elevation database. In contrast to conventional ICP, which is sensitive to camera intrinsic errors, the proposed approach improves robustness at high altitudes. Its feasibility and effectiveness are demonstrated through full-scale flight tests using a Cessna aircraft equipped with an IMU, camera, and barometric altimeter. The results show that the proposed method consistently enhances positioning accuracy and robustness compared with a filter-based approach, particularly under challenging high-altitude conditions where image resolution is reduced. The algorithm proved capable of maintaining reliable performance across varying flight altitudes, demonstrating its robustness under high-altitude conditions. This study establishes the novelty of integrating rotation-invariant ICP with vision-based TRN and provides real-world validation through actual flight testing. The findings offer valuable implications for future research and potential applications in unmanned aerial vehicles and long-range guided systems, where passive and GPS-independent navigation is critical for mission success.

Terrain-referenced navigation (TRN) relies on nonlinear terrain matching, which can produce multimodal position uncertainty that should be retained during estimation. Accuracy can deteriorate when the relative probabilities of ground and canopy returns vary over time because a fixed measurement model cannot adapt to the changing return regime. To address this problem, an interacting multiple-model (IMM) extension of the Rao–Blackwellized point-mass filter (RBPMF) is developed, yielding an IMM-RBPMF that adapts between measurement hypotheses while retaining the non-Gaussian navigation posterior. The extension uses grid-resolved posterior mixing to remap complete mode-conditioned RBPMF representations onto a common grid; nonlinear-state point masses are combined using global IMM interaction probabilities, whereas conditional Gaussian components are combined using state-dependent local responsibilities. Monte Carlo simulations over rough and rough–flat–rough terrain compare the proposed filter with single-model and fixed-mixture RBPMFs. Relative to the best baseline for each metric, the filter reduces overall horizontal-position root-mean-square error (RMSE) by up to 44% and altitude RMSE by 53%, demonstrating improved robustness to ambiguous radar returns and weakly informative terrain.