sensor calibration

This work addresses the critical challenge of jointly designing sensor query rates and noise covariance under resource and cost constraints to meet prescribed trajectory estimation accuracy requirements. It presents the first formalization of this problem as a unified optimization model, leveraging semidefinite programming (SDP) within the Kalman filter error covariance framework to simultaneously optimize measurement scheduling and noise parameters. The proposed approach efficiently determines whether a given accuracy target is achievable and, when feasible, synthesizes a corresponding implementation strategy. Experimental validation demonstrates that the computed sensor configurations consistently attain the desired accuracy in both simulated and real-world scenarios, while also reliably identifying infeasible accuracy demands.

Sensor measurements are often corrupted by outliers and non-Gaussian noise, leading conventional state estimators to produce biased and unreliable estimates. This work proposes an adaptive joint state and covariance estimation framework that uniquely integrates robust loss functions with covariance estimation. By combining norm-aware adaptive robust losses, iteratively reweighted least squares for state updates, and minimum weighted covariance determinant estimation within a block coordinate descent scheme, the method achieves self-tuning estimation without manual parameter tuning. Experimental results demonstrate that the approach accurately recovers inlier covariance in both Monte Carlo simulations and real-world ultra-wideband localization scenarios, achieving state estimation accuracy that matches or surpasses existing baseline methods.

In long-term IoT sensor deployments, aging-induced drift severely degrades data quality, while limited access to ground-truth measurements exacerbates calibration challenges. To address this, we propose a unified probabilistic drift correction and uncertainty-driven calibration scheduling framework. First, we model sensor dynamic response using Gaussian process regression, enabling explicit quantification of measurement uncertainty. Second, we formulate an adaptive scheduling optimization framework that uses real-time uncertainty as feedback, jointly optimizing calibration accuracy and resource constraints. Unlike conventional methods reliant on abundant ground-truth labels, our approach operates effectively under sparse supervision. Evaluated on dissolved oxygen sensors in field deployments, the drift correction alone reduces mean squared error by over 20% on average; when integrated with optimal calibration scheduling, the reduction reaches up to 90%. This significantly enhances the reliability and sustainability of long-term environmental monitoring.

In ensemble-based data assimilation, sampling errors induce covariance underestimation and variance loss, degrading uncertainty quantification. To address this, we propose two distance-agnostic, machine learning–driven localization methods tailored for tabular data and integrated within the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) framework. Leveraging ensemble tree models—specifically XGBoost and Random Forest—we directly learn spatial correlation structures from state–observation relationships, eliminating assumptions about geographic distance, auxiliary numerical simulations, or manual hyperparameter tuning. Our key contribution is the first fully data-driven, nonparametric localization approach, which markedly mitigates parameter variance attenuation and enhances reliability in uncertainty quantification. Experiments demonstrate improved covariance estimation accuracy, substantial reduction in input-variable variance loss, better data–model consistency, and sustained computational efficiency and numerical stability—even with small ensemble sizes.

This study addresses the coordination of UAV tracking and transmission in multi-operator integrated sensing and communication (ISAC) systems under data localization constraints by proposing a dual-timescale framework. At the slow timescale, federated learning calibrates uncertainty while sharing only model parameters to preserve privacy. At the fast timescale, covariance intersection fusion and predictive beamforming enable real-time coordination, supported by established exact rank-one recovery of communication covariance matrices and finite-interval bounds on local uncertainty. This approach effectively mitigates overconfidence and enhances uncertainty consistency. Compared with purely communication-oriented schemes, the proposed method reduces tracking root mean square error (RMSE) by approximately 31% while increasing power consumption by less than 2%.

This study addresses the problem of measurement error correction in target studies containing only a single error-prone proxy variable by leveraging repeated measurement data from an external heterogeneous source. To identify the error distribution, we propose a conditional transportability assumption. Methodologically, we construct a unified spectral theory framework accommodating both diffuse and finite atomic spectra, and develop a data fusion estimator that integrates conditional deconvolution with orthogonal correction. Theoretically, we establish consistency and convergence rate bounds for the proposed estimator. Empirically, an analysis of NHANES data demonstrates that accounting for source-target heterogeneity substantially alters existing conclusions, thereby validating the effectiveness of the proposed approach.

研究通过Allan方差校准法在卡尔曼滤波框架下设置IMU参数，解决了复杂工作条件下IMU参数难以有效调整的问题。

研究通过已实现的观测协议数据评估未部署协议的预测价值问题，提出了一种基于潜在协方差结构的识别理论来解决此问题。

Industrial prediction and soft sensing often fail due to field data suffering from bias, latency, or seemingly plausible yet unreliable measurements. This work proposes a large language model (LLM)-guided Measurement Credibility Correction (MCC) method that, for the first time, leverages semantic information from process documentation to construct an external reference—requiring neither numerical correlations, fault labels, nor explicit process equations—for lightweight pre-inference correction. MCC translates document semantics into reference signals compatible with numerical models and integrates them at the front end of the prediction pipeline. Evaluated on multiple real-world industrial tasks, MCC reduces average relative MAE by 30.7% on authentic test data and by 80.3% under controlled contamination, while adding only 0.5–2.0k online parameters and incurring a maximum inference latency of 0.089 ms per step.

This work addresses regression prediction from heterogeneous, noisy sensor data in the absence of ground-truth labels by proposing the Neural Conjugate Aggregation Model (NCAM). NCAM integrates neural networks with conjugate Gaussian inference within a hierarchical Bayesian framework to unsupervisedly learn each sensor’s bias and reliability, enabling uncertainty decomposition and posterior aggregation for the target variable. To mitigate structural non-identifiability, the model incorporates sensor anchoring and variance regularization. Coupled with locally adaptive Monte Carlo conformal prediction, NCAM yields heteroscedastic prediction intervals that simultaneously offer Bayesian interpretability and finite-sample coverage guarantees. Experiments demonstrate that NCAM significantly outperforms baseline methods—including mean aggregation, probabilistic PCA, and Kalman filtering—on both synthetic and real-world air quality datasets, while providing well-calibrated uncertainty estimates.