Mr. Hongzhou Chai Profile

Hongzhou Chai

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SPIE Journal Paper | 7 August 2024

Accurate soil moisture retrieval using Cyclone Global Navigation Satellite System delay Doppler maps by random forest regression algorithm based on multi-characteristic variable combination

Naiquan Zheng, Hongzhou Chai, Qiankun Zhang, Zhihao Wang, Honglei Ma

JARS, Vol. 18, Issue 03, 034510, (August 2024) https://doi.org/10.1117/12.10.1117/1.JRS.18.034510

KEYWORDS: Education and training, Satellites, Soil moisture, Quality control, Data modeling, Reflection, Reflectivity, Doppler effect, Scattering, Reliability

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Soil moisture (SM) is a crucial meteorological parameter affecting agricultural production and ecosystems, so accurately capturing SM is significant for farm irrigation and ecosystem protection. The delay Doppler maps data of the Cyclone Global Navigation Satellite System (CYGNSS) contains characteristic information related to SM, providing a new observation method for SM observation. First, the CYGNSS data are matched to the soil moisture active and passive dataset of 36 km×36 km grid by the nearest neighbor method. Then, a random forest regression (RFR) model is constructed for training and prediction. It is found that the RFR model based on the combination of 13 characteristic variables can accurately predict SM. On the training set, the correlation coefficient (R) of the model is 0.966, the root mean square error (RMSE) is 0.026 cm3 cm−3, and the mean error (ME) is 0.000 cm3 cm−3. On the test set, the R of the model is 0.903, the RMSE is 0.041 cm3 cm−3, and the ME is −0.001 cm3 cm−3. Simultaneously, to study the advantages of RFR prediction, this study also compared it with multiple linear regression (MLR) predictions. The results show that the RFR algorithm has higher accuracy both in the training set and the test set. Compared with MLR, R increased by 26.3% in the training set and RMSE decreased by 59.4%. R increased by 17.1% in the test set, and RMSE decreased by 35.9%. This demonstrates that the RFR model with multi-characteristic variables has good reliability, making it an efficient way to use satellite remote sensing data for SM retrieval.

Proceedings Article | 23 January 2024 Paper

ARIMA assisted low-cost MEMS-INS/GNSS integrated navigation in GNSS denial environment

Ming Li, Hongzhou Chai

Proceedings Volume 12978, 1297811 (2024) https://doi.org/10.1117/12.3019426

KEYWORDS: Satellite navigation systems, Navigation systems, Error analysis, Signal filtering, Data modeling, Mathematical modeling, Autoregressive models, Tunable filters, Electronic filtering, Sensors

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Global Navigation Satellite System (GNSS) and low-cost Inertial Navigation System (INS) is commonly used in fields such as vehicle navigation. However, when the carrier is in a GNSS rejection environment, the navigation accuracy rapidly decreases and cannot meet the positioning accuracy requirements. Therefore, this paper uses the Autoregressive Integrated Moving Average model (ARIMA) to learn the positioning information of GNSS during normal operation, output predictive values in the case of GNSS unavailable, and fuse them with the MEMS-INS mechanical arrangement results through Kalman filtering. Vehicle experiments show that the proposed algorithm significantly improves the navigation performance compared to traditional GNSS/INS integrated navigation, reducing the maximum position error in the east and north directions by 76.4% and 69.2%, respectively, and increasing the root mean square error of position accuracy in the east, north, and up directions by an order of magnitude.

Proceedings Article | 23 January 2024 Paper

Usability analysis of smart tablets and smart phones in GNSS-IR surface height monitoring

Naiquan Zheng, Hongzhou Chai

Proceedings Volume 12978, 129780C (2024) https://doi.org/10.1117/12.3020008

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Given the difference between the external antenna and built-in antenna, taking Zhonghaida GNSS geodesic receiver, Honor 60 and Huawei MatePad Pro as examples, 10 hours of continuous observation data were collected on campus to verify the availability of smart tablets and smart phones in Global Navigation Satellite System Interferometric Reflection (GNSS-IR) surface height monitoring. Experiments were conducted with the elevation angle intervals of 5°-20°, 5°-25° and 5°-30°, respectively, to determine the best elevation angle interval in surface height monitoring. The experimental results showed that the optimum altitude angle range of the GNSS receiver, Honor 60 and Huawei MatePad Pro is 5°-30°. Considering the results of RMSE, this study believes that the monitoring accuracy of different SNR types is G-S1C>R-S1C>C-S2I. Moreover, the surface height monitoring effect of the Zhonghaida geodesic GNSS receiver is relatively good, and the RMSE value in the experiment of 5°-30° elevation angle range is kept within 10 cm, while that of Honor 60 and Huawei MatePad Pro is within 20 cm. It can be seen from their median values that the deviation between the retrieved surface height and the measured value of the three devices is kept between-5 cm-5 cm, thus verifying the availability of smart tablets and smart phones in GNSS-IR surface height monitoring. In a word, this study gives an example to demonstrate the application of low-cost smart equipment in GNSS-IR technology. Furthermore, it lays a foundation for its application in snow depth retrieval, sea level height monitoring and soil moisture detection in the future.

Proceedings Article | 19 January 2024 Paper

Data processing method of high dynamic gravity measurement for unmanned surface vehicle at sea

Minzhi Xiang, Hongzhou Chai, Xingtao Wu, Minqiang Tang, Haopeng Fan, Yong Zhang, Zhen Yang

Proceedings Volume 12980, 129801T (2024) https://doi.org/10.1117/12.3021099

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During high dynamic gravity measurements conducted on unmanned surface vehicle, the presence of low-frequency noise caused by the vertical and horizontal motion disturbances of the carrier in conjunction with the low-frequency excitation noise from the sensor, results in a direct mixture within the frequency band of the gravity signal. Admittedly, conventional filtering techniques such as finite impulse response (FIR) or infinite impulse response (IIR) filtering prove insufficient in eliminating the measurement noise, ultimately leading to a decrease in gravity measurement accuracy.In this regard, this paper proposes the use of the kalman smoothing method as a replacement for the traditional frequency domain low-pass filtering technique. This method allows for the identification of gravity anomaly information even in the presence of noise by employing optimal estimation methods.Given that the gravity measurement data is processed offline, this paper further utilizes the optimal fixed interval smoothing algorithm to process the gravity measurement data obtained from unmanned surface vehicle. This algorithm enhances the accuracy beyond what is achievable with traditional frequency domain low-pass filtering techniques. To validate the effectiveness of our proposed algorithm, we have conducted processing on real sea test data, confirming its efficacy.

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