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Zhiqiang Yao

Publications and source records attributed to Zhiqiang Yao.

3 recordsLinked to original sources

Efficient and High-Accuracy Ray Tracing in Discretized Ionospheric Models

High-frequency (HF) ray tracing in complex ionospheric media generally involves a fundamental trade-off between path accuracy and computational efficiency, which directly affects practical applications such as over-the-horizon radar, ionospheric monitoring, and HF skywave communication systems. This paper presents RTM-GD, a ray-tracing framework that combines Hamiltonian ray integration with a continuously differentiable Galerkin--Difference (GD) interpolation strategy. Under discretized ionospheric grid conditions, the electron density is reconstructed as a $C^1$-continuous function within each grid cell, yielding an everywhere differentiable electron-density field for stable numerical integration and improved propagation-path accuracy. Numerical simulations and validations using measured HF oblique sounding data are conducted under diverse conditions, including different ionospheric states, low- and high-elevation angles, multiple operating frequencies, and both ordinary (O) and extraordinary (X) wave modes. Results show that RTM-GD consistently achieves sub-kilometer RMSE in both group-path and ground-distance metrics and sub-0.01-degree azimuth deviation relative to Richardson extrapolation, while reducing computational time by 98\%. Compared with Catmull--Rom interpolation, RTM-GD reduces the RMSEs of ray parameters by approximately one order of magnitude with less than 4\% additional computational cost. Measured-data validation based on ionogram synthesis further shows that the mean relative group-path error remains within 7\%, confirming reliable reproduction of practical HF oblique propagation characteristics. Overall, RTM-GD provides an accurate and computationally efficient framework for HF ray tracing in discretized ionospheric environments.

physics.ao-ph↗

GNSS Measurement-Based Context Recognition for Vehicle Navigation using Gated Recurrent Unit

Recent years, people have put forward higher and higher requirements for context-adaptive navigation (CAN). CAN system realizes seamless navigation in complex environments by recognizing the ambient surroundings of vehicles, and it is crucial to develop a fast, reliable, and robust navigational context recognition (NCR) method to enable CAN systems to operate effectively. Environmental context recognition based on Global Navigation Satellite System (GNSS) measurements has attracted widespread attention due to its low cost because it does not require additional infrastructure. The performance and application value of NCR methods depend on three main factors: context categorization, feature extraction, and classification models. In this paper, a fine-grained context categorization framework comprising seven environment categories (open sky, tree-lined avenue, semi-outdoor, urban canyon, viaduct-down, shallow indoor, and deep indoor) is proposed, which currently represents the most elaborate context categorization framework known in this research domain. To improve discrimination between categories, a new feature called the C/N0-weighted azimuth distribution factor, is designed. Then, to ensure real-time performance, a lightweight gated recurrent unit (GRU) network is adopted for its excellent sequence data processing capabilities. A dataset containing 59,996 samples is created and made publicly available to researchers in the NCR community on Github. Extensive experiments have been conducted on the dataset, and the results show that the proposed method achieves an overall recognition accuracy of 99.41\% for isolated scenarios and 94.95\% for transition scenarios, with an average transition delay of 2.14 seconds.

eess.SP↗

Horizontal Pyramid Matching for Person Re-identification

Despite the remarkable recent progress, person re-identification (Re-ID) approaches are still suffering from the failure cases where the discriminative body parts are missing. To mitigate such cases, we propose a simple yet effective Horizontal Pyramid Matching (HPM) approach to fully exploit various partial information of a given person, so that correct person candidates can be still identified even even some key parts are missing. Within the HPM, we make the following contributions to produce a more robust feature representation for the Re-ID task: 1) we learn to classify using partial feature representations at different horizontal pyramid scales, which successfully enhance the discriminative capabilities of various person parts; 2) we exploit average and max pooling strategies to account for person-specific discriminative information in a global-local manner. To validate the effectiveness of the proposed HPM, extensive experiments are conducted on three popular benchmarks, including Market-1501, DukeMTMC-ReID and CUHK03. In particular, we achieve mAP scores of 83.1%, 74.5% and 59.7% on these benchmarks, which are the new state-of-the-arts. Our code is available on Github

cs.CV↗