1.1. Background
Accurate terrain mapping is important for enhancing the maneuverability and survivability of modern aircraft. As mission profiles increasingly involve low altitude operations to evade enemy detection, accurate and up-to-date terrain information becomes essential for maintaining safe clearance margins. To minimize radar detection during low-altitude missions, aircraft employ terrain-following and terrain-avoidance maneuvers that allow them to fly close to the terrain while avoiding enemies [
1,
2,
3]. These operations typically depend on onboard terrain databases, such as digital terrain elevation data (DTED), which provide gridded elevation maps of Earth’s surface [
4].
DTED has been widely adopted in navigation, targeting, and mission planning systems due to its ability to provide essential terrain data [
5]. However, conventional DTED is inherently static and often outdated, failing to reflect recent changes in terrain caused by environmental dynamics or natural disasters. This limitation makes it unsuitable for real-time decision making in dynamic environments. Moreover, reliance on pre-stored elevation maps limits adaptability in previously unmapped regions. These constraints underscore the need for real-time terrain mapping systems that can dynamically update elevation information during flight based on onboard sensor data.
1.2. Related Work
Recent terrain mapping studies can be broadly categorized into deterministic and probabilistic approaches. Deterministic methods often extend grid-based elevation maps through scan alignment and surface reconstruction [
6,
7]. However, these methods do not account for uncertainty and offer limited adaptability in dynamic environments. For example, LiDAR-based point cloud processing has been used for terrain mapping in autonomous trucks. These approaches generate 2.5D grid maps and mesh maps using Poisson reconstruction, with each map built from precise vehicle poses [
7]. A similar approach has been applied to differential-drive ground robots, where 2.5D elevation maps are used for path planning based on slope and roughness, under the assumption of accurate localization [
8]. Although they perform well in ideal scenarios, these methods may be sensitive to localization uncertainty, potentially affecting the terrain maps. Moreover, they typically operate under fixed scan patterns, lacking mechanisms to dynamically adapt sensing directions or update criteria in real-time. More recent work has introduced adaptive voxel grid representations that dynamically adjust resolution based on terrain structure, with demonstrations in both simulation and real-world environments [
9]. These methods mitigate sensor noise and pose uncertainty, but still rely on fixed scan schedules without feedback-driven sensing.
To address the limitations of deterministic terrain mapping, probabilistic models such as Gaussian process (GP), mixture models, and Bayesian regression have been extensively explored [
10,
11,
12]; while GP can adapt to local terrain smoothness, it often requires high computational resources, limiting its applicability in real-time onboard systems. Hybrid approaches such as GMM-GP and stochastic variational GP mapping improve model flexibility and uncertainty representation. However, these methods are typically applied to offline point clouds obtained from vision or sonar sensors [
13,
14]. They do not support real-time azimuth scan command adjustment or integration with radar-based terrain mapping. Recent extensions have attempted to improve the efficiency of GP-based mapping, such as multi-modal map building frameworks integrating SLAM with GP modeling [
15] and latent-field occupancy mapping with sensor FoV priors [
16]; while these studies demonstrate enhanced uncertainty representation, they are primarily tailored to LiDAR or vision data and remain unsuitable for lightweight radar-based real-time operation. Additional probabilistic approaches have addressed task-specific needs, such as slope-aware terrain estimation for legged robot locomotion [
17] and probabilistic multi-level surface modeling [
18]. Other studies consider localization uncertainty [
19] or implement radar-specific occupancy modeling [
20]. Neural elevation models have been proposed for terrain mapping and path planning [
21]. Although they offer probabilistic representation, they are typically limited to offline processing or non-radar data sources, and do not provide mechanisms for real-time applications.
Cluster-based methods, particularly the Gaussian Mixture Model (GMM), offer a trade-off between modeling fidelity and computational efficiency. GMM-based terrain maps have been applied to real-time information–theoretic exploration by quantifying expected information gain [
22]. These maps have also been used for distributed terrain estimation through mixture-model-based fusion across agents [
23], supporting compact probabilistic representations. However, in most existing approaches, posterior responsibility is often treated as a clustering metric or as a weighting factor in data fusion. In contrast, the proposed method uses posterior responsibility to control both grid updates and azimuth scan commands in a feedback loop.
Radar maintains reliable performance under fog, dust, and spray, where LiDAR performance often degrades [
20]. Previous work has extracted terrain point clouds from radar data cubes using PCFilt-94 filtering [
24], but typically relies on fixed azimuth scan commands.
The proposed method addresses these limitations by introducing a responsibility-based terrain mapping and scan adaptation mechanism, enabling real-time adjustment of both grid updates and scan direction not supported by prior terrain mapping studies.