State estimators often provide self-assessed uncertainty metrics, such as covariance matrices, whose credibility is critical for downstream tasks. However, these self-assessments can be misleading due to underlying modeling violations like noise model mismatch (NMM) or system model misspecification (SMM). This letter addresses this problem by developing a unified, multi-metric framework that integrates noncredibility index (NCI), negative log-likelihood (NLL), and energy score (ES) metrics, featuring an empirical location test (ELT) to detect system model bias and a directional probing technique that uses the metrics' asymmetric sensitivities to distinguish NMM from SMM. Monte Carlo simulations reveal that the proposed method achieves excellent diagnosis accuracy (80-100%) and significantly outperforms single-metric diagnosis methods. The effectiveness of the proposed method is further validated on a real-world UWB positioning dataset. This framework provides a practical tool for turning patterns of credibility indicators into actionable diagnoses of model deficiencies.

翻译：状态估计器通常提供自评估的不确定性指标（如协方差矩阵），其可信度对下游任务至关重要。然而，由于噪声模型失配（NMM）或系统模型设定错误（SMM）等底层建模违规，这些自评估可能产生误导。本文通过开发一个统一的多指标框架来解决该问题，该框架整合了不可信指数（NCI）、负对数似然（NLL）和能量评分（ES）指标，并利用经验位置检验（ELT）检测系统模型偏差，以及通过指标的非对称敏感性区分NMM与SMM的定向探测技术。蒙特卡洛模拟表明，所提方法实现了出色的诊断准确率（80-100%），显著优于单指标诊断方法。基于实际UWB定位数据集的进一步实验验证了该方法的有效性。该框架为将可信度指标的模式转化为可操作模型缺陷诊断提供了实用工具。