A laser inertial measurement unit (LIMU) is a navigation subsystem for rockets, airplanes, and other systems, and its reliability is highly demanding. Maintenance and selection based on performance evaluation are effective ways to improve the reliability of LIMUs. Therefore, this study presents a performance evaluation method for LIMUs under the non-equivalent priority of indicators. First, the LIMUs’ properties and expert knowledge ambiguity are analyzed, and the difficulties in the performance evaluation of LIMUs are described in detail, which are the difficulty in embedding expert knowledge, the explosion of rule combinations, and the non-equivalent priority of indicators, respectively. Second, to address the problems of difficulty in embedding expert knowledge and rule explosion, a hierarchical indicator system is constructed by synthesizing the historical information and test information, and a hierarchical evaluation model is built further. The “or operation” is introduced into the Belief Rule Base (BRB) model to realize the rule formulation and fusion in the case of non-equivalent priority of indicators, and the novel BRB-non-equal priority model is unified with the BRB model in the form of reasoning through the rational design of parameters. Third, an artificial bee colony algorithm is used to optimize the parameters of the hierarchical evaluation model, which can integrate test data and expert knowledge. Finally, using the performance evaluation of 8 LIMUs as a case, the feasibility and superiority of the performance evaluation method are validated.

The electromagnetic interference emanating from on-board electronic (OBE) equipment can substantially degrade the measurement precision of aeromagnetic fields. To address this issue, this paper proposes a novel and systematic compensation method. First, based on the Pearson correlation coefficient (PCC) and the marginal compensation contribution, a stepwise forward electrical feature selection strategy is designed to provide a compact and reliable input set for the compensation model. Subsequently, an adaptive denoising approach is introduced, which employs iterative correlation coefficient comparisons to determine the appropriate wavelet decomposition level. This enables effective noise reduction for various electrical features while maintaining their essential local features during reconstruction. Furthermore, partial least squares (PLS) is introduced for parameter estimation as a robust alternative to conventional ridge regression. By constructing latent variables, PLS effectively alleviates the ill-conditioning of the model, thereby enhancing compensation accuracy and anti-interference capability. Experimental results across various altitudes and flight conditions show that the proposed method outperforms conventional linear models and common deep learning models in terms of robustness and generalization during aircraft maneuvers. The proposed method provides aeromagnetic data support for precision-critical downstream applications, such as geomagnetic navigation and magnetic anomaly detection.