Ultra-wideband (UWB) time-of-flight (TOF)-based ranging information in a non-line-of-sight (NLOS) environment can display significant forward errors, which directly affect positioning performance. NLOS has been a major factor limiting the improvement of UWB positioning accuracy and its application in complex scenarios. Therefore, in order to weaken the influence of the indoor complex environment on the NLOS environment of UWB and to further improve the performance of positioning, in this paper, we first analyze the factors and characteristics of NLOS formation in an indoor environment. The NLOS is divided into fixed NLOS influenced by spatial structure and dynamic random NLOS influenced by human occlusion. Then, the anchor LOS/NLOS information map is established by making full use of indoor spatial a priori information. On this basis, a robust adaptive extended Kalman filtering algorithm based on the anchor LOS/NLOS information map is designed, which is not only effectively able to exclude the influence of spatial NLOS, but can also optimize the random error. The proposed algorithm was validated in different experimental scenarios. The experimental results show that the positioning accuracy is better than 0.32 m in complex indoor NLOS environments.

Machine learning is frequently used to detect multipath and non-line-of-sight signals in indoor pseudolite systems. Signal information redundancy and how to find algorithm’s optimal hyperparameters pose significant challenges to this task. To this end, a Bi-layer Optimization Scheme (BOS) is proposed in this paper. In the first layer, a result-data-driven principal component analysis adjustment strategy is proposed. This strategy eliminates the correlation among the feature parameters of the original pseudolite signals and constructs a feature space with optimal dimensionality. It’s contributing to reducing information redundancy in signals. In the second layer, an enhanced Dung Beetle Optimizer is proposed. The algorithm incorporates the Good Point Set, Opposition-Based Learning, and Cauchy-Gauss Mutation strategies, and has been demonstrated to achieve faster convergence and better global optimization capability. It is employed for the adaptive selection of hyperparameters. With BOS optimization, the classification accuracy of the Support Vector Machine algorithm improved by 6.0% and 6.1% on the two datasets, respectively, while the classification precision of line-of-sight signals improved by an average of 12.3%. This confirms the applicability and practical value of the BOS in indoor pseudolite systems.

Pseudolite (PL) positioning technology is one of the effective methods to achieve high-precision indoor positioning. The Distributed Array Pseudolite System (DAPLS) is a ground-based augmentation architecture designed to provide high-precision positioning in GNSS-denied or indoor environments. However, maintaining the stability and integrity of pseudolite signals in distributed deployments remains a significant challenge. To address this, a Pseudolite Monitoring Station (PMS) was developed for real-time signal observation, performance evaluation, and anomaly detection. The proposed PMS integrates a multi-channel front-end, signal-processing engine, and monitoring algorithms capable of continuous assessment across three hierarchical levels: Signal Quality Monitoring (SQM), Receiver Processing Monitoring (RPM), and Measurement Quality Monitoring (MQM). To integrate multi-domain monitoring results, a Composite Quality Index (CQI) model is introduced, combining normalized sub-scores through weighted fusion to reflect overall system integrity. A comprehensive Signal Quality Assessment (SQA) framework is further introduced, including four dimensions of evaluation: constellation status, time reference, spatial coordinate reference, and signal anomaly detection. An indoor DAPLS experiment was conducted within a laboratory-level test field. The system comprised three pseudolite transmitter arrays (six transmitters each) and a central monitoring station. Experimental results showed stable synchronization within ±5 ns, coordinate accuracy within 0.2 m, and consistently high signal quality. The monitoring station effectively detected minor signal distortions and synchronization deviations, confirming its diagnostic precision and robustness. This study demonstrates a complete monitoring and evaluation framework for DAPLS, enabling both system-level quality assurance and signal integrity monitoring. The proposed PMS and SQA methods provide essential tools for future deployment of pseudolite-based indoor positioning and timing systems.

Machine learning algorithms demonstrate strong efficacy in detecting interference signals in indoor pseudolite systems. Nonetheless, their dependence on multiple signal parameters limits practical applicability, as some devices may lack the capability to acquire all required parameters. To address this issue, this study proposes a self-adaptive decomposition and dimensionality reduction (SDDR) scheme that relies solely on the carrier-to-noise ratio (C/N₀) parameter. The proposed scheme consists of two modules. In the first module, a dung beetle optimization (DBO) algorithm improved in population diversity and exploration capability is integrated with variational mode decomposition (VMD) to adaptively extract C/N₀ signal features and construct a multidimensional dataset. In the second module, a principal component analysis (PCA) strategy based on maximum likelihood estimation (MLE) is introduced to assess the intrinsic dimensionality of the multidimensional dataset and dimensionality reduction, thereby suppressing redundant components and enhancing model generalization. Validation on two pseudolite datasets demonstrates that C/N₀ signals processed by the SDDR method are more suitable for machine learning-based detection. Specifically, the average detection accuracy of support vector machines (SVMs) and long short-term memory (LSTM) networks improves by 8.4% and 6.1% on the entire dataset, while their average detection accuracy for line-of-sight (LOS) signals increases by 13.0% and 13.2%, respectively.

Indoor pseudolite positioning systems can provide GNSS-like ranging signals in GNSS-denied environments, but their signal quality is easily degraded by indoor multipath, attenuation, antenna displacement, blockage, and receiver tracking instability. This study proposes a multi-source signal quality assessment method for indoor pseudolite positioning based on monitoring-station observations. An experimental platform was established with pseudolite signal generation, transmitting antenna deployment, receiver-side acquisition, and GNSS-SDR processing. Three manually controlled degradation scenarios were designed: transmitting antenna displacement, signal attenuation, and signal blockage. Signal-, correlation-, tracking-, and observation-domain indicators were extracted, including C/N0, EVM, SCB-related indicators, tracking errors, lock indicators, pseudorange residuals, carrier-phase residuals, Doppler residuals, and observation-domain C/N0 residuals. These indicators were converted into degradation-oriented residuals, robustly normalized, and fused into an Indoor Pseudolite Quality Index (IPQI). Experimental results show that different indicators exhibit distinct responses to different degradation mechanisms. The proposed Indoor Pseudolite Quality Index further distinguishes transient antenna displacement, sustained signal attenuation, and intermittent signal blockage, demonstrating its effectiveness for integrated signal quality monitoring and scenario-level degradation assessment in indoor pseudolite positioning systems.