Wide-field infrared imaging systems are often constrained by detector size, cooling requirements, and payload limitations, leading to the need for multi-FOV detector sharing. However, conventional geometric multiplexing introduces severe spatial aliasing, which significantly degrades target localization performance. This paper proposes a polarization-encoded field-of-view multiplexing method for recovering spatial information from aliased detector measurements. The imaging plane is divided into multiple FOV regions, each assigned a distinct polarization state. After optical folding, the modulated sub-images are superimposed onto a common detector region. Six-channel polarization measurements are used to reconstruct pixel-wise Stokes vectors, and the spatial origin of each pixel is identified through polarization-domain similarity matching and target-level voting. MATLAB-based simulations were conducted using a nine-region multiplexing configuration. The proposed method achieves 97.3% pixel-level classification accuracy under ideal conditions and maintains over 95% accuracy at a noise level of σ = 0.02. The normalized Stokes reconstruction error is below 0.02, and stable performance is observed under polarization modulation deviations within ±10°. By introducing polarization as an additional encoding dimension, the proposed framework enables efficient separation of multiplexed spatial information without increasing detector resources, demonstrating its potential for compact wide-field infrared sensing applications.

Detector-multiplexed wide-field infrared imaging reuses detector area by folding several sub-fields of view onto a common focal-plane region, but the branch identity of an aliased small target is then unavailable from intensity alone. We propose a dual-domain optical code that combines a fixed Zemax-derived point-spread-function (PSF) library with a conditionally assigned linear-polarization codebook. To replace the earlier feature-level validation, we developed an image-level radiometric simulator that generates four analyzer images under a fixed incident-photon budget, applies mirror transfer, throughput loss, Poisson shot noise, background, read noise, and physical multi-target PSF superposition, and then re-extracts morphology and Stokes observables. The 1037 PSF samples were divided by sub-field into construction, calibration, and locked test sets of 623, 207, and 207 samples. The polarization-to-field assignment was selected by an exhaustive 9! search on the construction data, whereas the compound weight and separability threshold were fixed from calibration data only. In 25,000 locked target-level tests, the equal-weight-form compound decoder achieved 95.936% accuracy (95% confidence interval of 95.684–96.174%), compared with 10.836%, 45.564%, and 88.720% for intensity-only, shape-only, and polarization-only decoding. At the calibrated threshold, the effective capacities were five, four, and nine for the shape, polarization, and compound code spaces. The results are simulation-phase evidence and require experimental verification with measured infrared throughput and component tolerances.