Data Reception Analysis of the AIS on board the <i>TianTuo-3</i> Satellite

Li, Shiyou; Chen, Xiaoqian; Li-hu, Chen; Zhao, Yong; Sheng, Tao; Bai, Yuzhu

doi:10.1017/s0373463316000916

Cited by 19 publications

(12 citation statements)

References 15 publications

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“…Vodas et al [21] proposed a ship accident prediction and judgment model based on the AIS data. Li et al [22] proposed a method to improve the AIS data collection equipment. Through the detailed analysis of the data received by the AIS receiver in Skynet 3's debugging stage, receiving capability was improved.…”

Section: Related Workmentioning

confidence: 99%

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

et al. 2020

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Timely and accurate depth estimation of a shallow waterway can improve shipping efficiency and reduce the danger of waterway transport accidents. However, waterway depth data measured during actual maritime navigation is limited, and the depth values can have large variability. Big data collected in real time by automatic identification systems (AIS) might provide a way to estimate accurate waterway depths, although these data include no direct channel depth information. We suggest a deep neural network (DNN) based model, called DDTree, for using the real-time AIS data and the data from Global Mapper to predict waterway depth for ships in an accurate and timely way. The model combines a decision tree and DNN, which is trained and tested on the AIS and Global Mapper data from the Nantong and Fangcheng ports on the southeastern and southwestern coast of China. The actual waterway depth data were used together with the AIS data as the input to DDTree. The latest data on waterway depths from the Chinese maritime agency were used to verify the results. The experiments show that the DDTree model has a prediction accuracy of 91.15%. Therefore, the DDTree model can provide an accurate prediction of waterway depth and compensate for the shortage of waterway depth monitoring means. The proposed hybrid DDTree model could improve marine situational awareness, navigation safety, and shipping efficiency, and contribute to smart navigation.

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Section: Related Workmentioning

confidence: 99%

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

et al. 2020

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“…Notice that for most ground business, there would be large geographical distribution variances of SCTV. Take air traffic monitoring as an example, the air traffic SCTV data is downloaded from TianTuo-3 (National University of Defense Technology, Changsha, China) as shown in Figure 2 (National University of Defense Technology developed and launched TianTuo-3 micro-satellite in May 2014, which achieves worldwide collection of the air traffic SCTV data [26]). It can be seen that aircraft traffic distribution is dense in population agglomeration while scarce in vast ocean regions.…”

Section: Satellite Coverage Traffic Volume Modelingmentioning

confidence: 99%

An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction

Zhang

Yao³

et al. 2019

Applied Sciences

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The satellite constellation network is a powerful tool to provide ground traffic business services for continuous global coverage. For the resource-limited satellite network, it is necessary to predict satellite coverage traffic volume (SCTV) in advance to properly allocate onboard resources for better task fulfillment. Traditionally, a global SCTV distribution data table is first statistically constructed on the ground according to historical data and uploaded to the satellite. Then SCTV is predicted onboard by a data table lookup. However, the cost of the large data transmission and storage is expensive and prohibitive for satellites. To solve these problems, this paper proposes to distill the data into a surrogate model to be uploaded to the satellite, which can both save the valuable communication link resource and improve the SCTV prediction accuracy compared to the table lookup. An effective surrogate ensemble modeling method is proposed in this paper for better prediction. First, according to prior geographical knowledge of the SCTV distribution, the global earth surface domain is split into multiple sub-domains. Second, on each sub-domain, multiple candidate surrogates are built. To fully exploit these surrogates and combine them into a more accurate ensemble, a partial weighted aggregation method (PWTA) is developed. For each sub-domain, PWTA adaptively selects the candidate surrogates with higher accuracy as the contributing models, based on which the ultimate ensemble is constructed for each sub-domain SCTV prediction. The proposed method is demonstrated and testified with an air traffic SCTV engineering problem. The results demonstrate the effectiveness of PWTA regarding good local and global prediction accuracy and modeling robustness.

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“…The Micro and Nano Satellite Center of the Chinese National University of Defense Technology launched the first Chinese ADS-B satellite, TianTuo-3 , to Sun-synchronous Low Earth Orbit (LEO) at a height of 496 km on 20 September 2015 (Li et al, 2017). The ADS-B payload on TianTuo-3 can receive four million messages transmitted by aircraft over 24 hours.…”

Section: Global Aircraft Detection Analysismentioning

confidence: 99%

“…Many institutions and countries have been involved in the development of space-based ADS-B owing to its global, continuous, low-latency and high-performance surveillance. More than a dozen satellites with ADS-B receivers have been launched, such as the GomX (GomX-1, GomX-2, GomX-3 and GomX-4 designed by the GomSpace Company) series satellites (Alminde et al, 2012; Gerhardt et al, 2017), STU-2 (Shanghai Technology University) satellite (Wu et al, 2016), TianTuo-3 (Li et al, 2017) and Iridium-NEXT satellites. Live data from ADS-B receivers on the Iridium-NEXT constellation of 66 satellites with 100% global coverage, including over the North Pole and every ocean, will offer a worldwide ADS-B service (Garcia et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-beamforming for Space-based ADS-B

Li-hu

et al. 2018

J. Navigation

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We investigate a strategy to address the problem of low aircraft detection probability of space-based Automatic Dependent Surveillance-Broadcast (ADS-B). A nineteen-element hexagonal array and adaptive multi-beamforming method is proposed. This method aims to adjust the beam pattern dynamically to reduce signal collision and enlarge coverage. With the focus on the mission requirement of global aircraft detection by 2020, the appropriate gain and direction of beams are studied and designed in detail. Theoretical analysis and simulations show that this adaptive multi-beam antenna can greatly improve flight detection probability at an average reporting interval from 10 seconds to 5 seconds when compared with the traditional fixed multi-beam antenna. The results of this work show that the design of a 19-element antenna along with the adaptive multi-beamforming method can be considered as a building block of future space-based ADS-B.

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Data Reception Analysis of the AIS on board the TianTuo-3 Satellite

Cited by 19 publications

References 15 publications

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction

Adaptive Multi-beamforming for Space-based ADS-B

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