A Unified ISR World Model: Vossels, Voxels, Mipvols, and Reinforcement Learning

Abstract

Greg Passmore*

ISR enterprises must integrate measurements from many sensing modalities, radar, EO/IR, LiDAR, SIGINT, acoustics, cyber, weather, and jamming, each with its own resolution, structure, and format. Traditional systems often discard richness by reducing inputs to tracks, maps, or occasional images, leaving little for machine learning to exploit. This paper introduces Vossels (Volumetric Singular Spectral Elements), which were designed to address sensor discordance by storing raw sensor data in a unified atomic structure, ensuring that the full information content is preserved for artificial intelligence and reinforcement learning.

A Vossel encodes (x, y, z, t, λi , aj ) as a singular volumetric spectral element, capturing space, time, spectral channels, and auxiliary attributes. Integrated into voxel grids and mipvol hierarchies, Vossels support multiresolution storage, retrieval, and time-series segmentation. Mipvols accelerate access by adapting resolution to projected target size, increasing speed without discarding data relevant to the task. This provides consistent, resolution-preserving storage that eliminates modality-specific silos, simplifies the mental model, and creates a centralized repository optimized for AI exploitation.

Because raw data is retained rather than simplified, reinforcement learning agents can operate directly on the richness of ISR streams, identifying correlations across modalities and scales. The memory manager and frustum extractor keep massive datasets tractable by keeping local neighborhoods in GPU VRAM or AI context windows while paging global volumes efficiently. This allows adaptive policies to evolve—learning when to refine, when to approximate, and how to balance fidelity with performance.

This paper defines the Vossel structure, the supporting voxel/mipvol hierarchy, and the mathematics of extraction and fusion. Kriging interpolation is presented for high-fidelity value estimation, view frustum extraction for adaptive retrieval, and multi- modal fusion strategies for co-registration. By design, Vossels enable AI and reinforcement learning to exploit ISR data in its full richness, transforming integration from siloed processes into a coherent, self-optimizing system.

HTML PDF