A Fast Time-delay Calculation Method in TWR Detection Through N Layers Medium

Author(s):  
Lei WenTai ◽  
Ding Xing ◽  
Zhang Qi
2017 ◽  
Vol 89 (2) ◽  
pp. 297-303 ◽  
Author(s):  
Zhiwei Kang ◽  
Xin He ◽  
Jin Liu ◽  
Tianyuan Tao

Purpose The authors proposed a new method of fast time delay measurement for integrated pulsar pulse profiles in X-ray pulsar-based navigation (XNAV). As a basic observation of exact orientation in XNAV, time of arrival (TOA) can be obtained by time delay measurement of integrated pulsar pulse profiles. Therefore, the main purpose of the paper is to establish a method with fast time delay measurement on the condition of limited spacecraft’s computing resources. Design/methodology/approach Given that the third-order cumulants can suppress the Gaussian noise and reduce calculation to achieve precise and fast positioning in XNAV, the proposed method sets the third-order auto-cumulants of standard pulse profile, the third-order cross-cumulants of the standard and the observed pulse profile as basic variables and uses the cross-correlation function of these two variables to estimate the time delay of integrated pulsar pulse profiles. Findings The proposed method is simple, fast and has high accuracy in time delay measurement for integrated pulsar pulse profiles. The result shows that compared to the bispectrum algorithm, the method improves the precision of the time delay measurement and reduced the computation time significantly as well. Practical implications To improve the performance of time delay estimation in XNAV systems, the authors proposed a novel method for XNAV to achieve precise and fast positioning. Originality/value Compared to the bispectrum algorithm, the proposed method can improve the speed and precision of the TOA’s calculation effectively by using the cross-correlation function of integrated pulsar pulse profile’s third-order cumulants instead of Fourier transform in bispectrum algorithm.


2005 ◽  
Vol 15 (06) ◽  
pp. 445-455 ◽  
Author(s):  
HAZEM M. EL-BAKRY ◽  
QIANGFU ZHAO

This paper presents a new approach to speed up the operation of time delay neural networks. The entire data are collected together in a long vector and then tested as a one input pattern. The proposed fast time delay neural networks (FTDNNs) use cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented time delay neural networks is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.


2011 ◽  
Vol 59 (8) ◽  
pp. 2057-2062 ◽  
Author(s):  
Francesco Benedetto ◽  
Gaetano Giunta
Keyword(s):  

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