Hierarchical Fusion Architecture Research Articles

Robust Hierarchical Data Fusion Scheme for Large-Scale Sensor Network

The advanced driver assistant system (ADAS) requires the collection of a large amount of information including road conditions, environment, vehicle status, condition of the driver, and other useful data. In this regard, large-scale sensor networks can be an appropriate solution since they have been designed for this purpose. Recent advances in sensor network technology have enabled the management and monitoring of large-scale tasks such as the monitoring of road surface temperature on a highway. In this paper, we consider the estimation and fusion problems of the large-scale sensor networks used in the ADAS. Hierarchical fusion architecture is proposed for an arbitrary topology of the large-scale sensor network. A robust cluster estimator is proposed to achieve robustness of the network against outliers or failure of sensors. Lastly, a robust hierarchical data fusion scheme is proposed for the communication channel between the clusters and fusion center, considering the non-Gaussian channel noise, which is typical in communication systems.

The Preliminary Research on Health Management of Stratospheric Airship

The stratospheric airship, with long-endurance and quasi-static capability, is suitable to carry out special mission such as communications relay, hot spots monitoring, etc. In order to improve the safety performance of the stratospheric airship and keep the robustness of mission, health management (HM) system is introduced, the foundation of which is the logic architecture. Based on the analysis of three options about the logic architecture used in spacecraft, hierarchical fusion architecture is selected, the functional modules in which are designed in detail. To solve urgent faults, moreover, the autonomous intelligent control system (AICS) is introduced. Meanwhile, monitoring mission is taken as an example to discuss the mission health management strategy based on laser communication.

TH‐C‐BRC‐04: Hierarchical Real‐Time Respiratory Motion Prediction: A Divide‐and‐Conquer Scheme for Application‐Oriented Optimality

Purpose: Motion management applications differ in their measurement rates and prediction length requirement: it is unlikely to find a Universityersally optimal predictor. This study develops a divide‐and‐conquer scheme, by partitioning the operation region and assigning proper predictor to each sub‐ region. Such hierarchical fusion architecture achieves application‐dependent optimal prediction performance. Method and Materials: We parameterize the overall operation region of real‐time prediction in terms of (measurement rate, prediction length) to parameterize the configuration and requirement of various motion management systems. The region of study is set to be [5Hz, 30Hz]*[200ms,600ms], spanning the full scope from non‐ tomographic to tomographic measurement, and for both gating and tracking controls. Four predictor candidates are examined: (a) adaptive linear (b) interactive multiple linear (MLM) (c) KDE (d) local circular motion (LCM); and it is determined that LCM is optimal for low measurement rates and short prediction lengths, while KDE is superior for medium‐to‐high measurement rate and medium‐to‐long prediction length. An operation‐ region specific hybrid model is developed and compared with the individual predictors. Prediction performance is quantified by nRMSE between predicted value and retrospective observations. Normalization with data deviation ensures meaningful population‐based statistics. Results: The proposed method outperforms all individual benchmark methods uniformly. The nRMSE was controlled to be under 0.15 for 200ms prediction and 0.4 for 600ms prediction, translating to sub‐mm prediction accuracy for typical respiratory motions. Conclusion: We have developed a hybrid predictor that adapts to specific motion management applications. This hybrid predictor combines the simplicity of the LCM model under low data rate condition, and the flexibility of the nonparametric KDE approach for long prediction lengths. The general methodology of divide‐and‐conquer extend to incorporate other predictors naturally, should they offer better performance in a sub‐region. The resulting hierarchical predictor uniformly outperforms individual methods, and provides an ideal motion management module for motion‐adaptive radiotherapy.