Learned representations in intelligent sensing systems are often evaluated by reconstruction fidelity or downstream prediction accuracy, but these criteria do not specify which latent distinctions are justified by the sensing process. In sensor-conditioned environments, nuisance factors can change measurements without changing the scene, while distinct scenes may be indistinguishable under limited sensing capability. This paper formulates sensor-conditioned representation correctness as preserving sensing-supported scene distinctions while suppressing nuisance-induced and sensor-unsupported variation. We introduce the scene-relevant observation quotient, a representation target induced by sensing-supported distinguishability after nuisance canonicalization, and develop Observation-Quotient Tucker-Structured Autoencoding (OQ-TSAE), a scene-nuisance factorized framework with diagnostics for false distinction, false merge, nuisance sensitivity, and latent ordering consistency. Experiments on a controlled benchmark show that quotient-consistent supervision improves representation-correctness diagnostics over reconstruction-oriented, metric-learning, and contrastive-learning baselines. Sensitivity, perturbation, and ablation studies show the importance of quotient-aligned supervision, reliable quotient relations, and quotient geometry. Complementary real-radar experiments show that a reconstruction-only OQ-TSAE variant retains competitive downstream utility, robustness under observation degradation, and low seed-to-seed variability. These results suggest that sensor-conditioned representations should be evaluated not only by predictive utility, but also by whether their latent geometry preserves sensing-justified scene distinctions.

翻译：智能传感系统中的学习表征常通过重构保真度或下游预测精度进行评估，但这些标准并未明确哪些潜在区分是传感过程所合理支撑的。在传感器条件化环境中，干扰因素可能在不改变场景的情况下改变测量值，而不同场景在有限传感能力下可能难以区分。本文形式化定义了传感器条件化表征的正确性，即保留传感支持的场景区分，同时抑制干扰诱导及传感器不支持的变异。我们提出场景相关观测商——在干扰规范后由传感支持的可区分性导出的表征目标，并开发观测商塔克结构自编码框架，该场景-干扰因子化框架可诊断错误区分、错误合并、干扰敏感度及潜在排序一致性。在受控基准上的实验表明，商一致性监督相较于重构导向、度量学习及对比学习基线，能改进表征正确性诊断。敏感性、扰动及消融研究揭示了商对齐监督、可靠商关系及商几何的重要性。补充真实雷达实验表明，纯重构的OQ-TSAE变体在保持竞争性下游效用的同时，兼具观测退化下的鲁棒性与低种子间变异性。这些结果表明，传感器条件化表征不仅应通过预测效能评估，更需考量其潜在几何是否保留了传感所证实的场景区分。