scholarly journals Target Tracking in WSN using Time Delay Neural Network

2017 ◽  
Vol 2 (2) ◽  
pp. 16-22
Author(s):  
Jayesh Himmatbhai Munjani ◽  
Maulin Joshi
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Time Delay ◽  
Transition Matrix ◽  
State Transition ◽  
State Transition Matrix ◽  
Prediction Algorithm ◽  
Multiple Model ◽  
Target Movement ◽  
Model Free

Energy efficient tracking is a challenging application of resource constrained wireless sensor network. Prediction based schemes play a vital role in energy saving by reducing an avoidable communication. Efficient tracking can be achieved only if state transition matrix used in filter closely resembles the target movement. Kalman filter has been widely used as prediction algorithm but fails in case of maneuvering target because of state transition matrix mismatch. To make tracking algorithm model free, Time delay neural network based prediction algorithm is proposed in this paper. Performance of Time Delay neural network (TDNN) is compared with Kalman filter and Interacting multiple model filter in terms of mean square error. Results shows that TDNN outperforms both the filters.

scholarly journals A Simplified Kalman Filter for Integrated Navigation System with Low-Dynamic Movement

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Xixiang Liu ◽  
Jian Sima ◽  
Yongjiang Huang ◽  
Xianjun Liu ◽  
Pan Zhang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Transition Matrix ◽  
State Transition ◽  
Random Noise ◽  
Oscillation Amplitude ◽  
State Transition Matrix ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Simplified Algorithm

In the integrated navigation system with inertial base, the update frequency of Strapdown Inertial Navigation System (SINS) is always higher than those of aided navigation systems; thus updating inconsistency among subsystems becomes an issue. The analysis indicates that the state transition matrix in Kalman filter is essentially a function of carrier motion. Based on this understanding, a simplified Kalman filter algorithm for integrated navigation is designed for those carriers with low-dynamic motions. With this simplified algorithm, when the filter is without aided information updating, only calculation and accumulation on state transition matrix are executed, and when the filter is with updating, normal time and measurement update are done based on the averaged state transition matrix. Thus the calculation load in the simplified algorithm will be significantly lessened. Furthermore, due to cumulative sum and average operation, more accurate state transition matrix and higher fusion accuracy will arrive for the smoothing effect on random noise of carrier motion parameters. Simulation and test results indicate that when the carrier is with a low-dynamic motion, the simplified algorithm can complete the data fusion of integrated system effectively with reduced computation load and suppressed oscillation amplitude of state vector error.

Numerical Algebra Solution: A New Algorithm for the State Transition Matrix

2017 ◽  
Vol 60 (12) ◽  
pp. 2620-2629 ◽  
Author(s):  
Wenfeng Nie ◽  
Tianhe Xu ◽  
Yujun Du ◽  
Fan Gao ◽  
Guochang Xu
Keyword(s):  
Transition Matrix ◽  
State Transition ◽  
The State ◽  
State Transition Matrix ◽  
Numerical Algebra

scholarly journals The prediction for development of COVID-19 in global major epidemic areas through empirical trends in China by utilizing state transition matrix model

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhong Zheng ◽  
Ke Wu ◽  
Zhixian Yao ◽  
Xinyi Zheng ◽  
Junhua Zheng ◽  
...  
Keyword(s):  
South Korea ◽  
Matrix Model ◽  
Transition Matrix ◽  
State Transition ◽  
State Transition Matrix ◽  
World Health ◽  
Daily Increment ◽  
The World ◽  
Increment Rate ◽  
Health Organization

Abstract Background Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to its high transmissibility. We aimed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from China. Methods Data from reports released by the National Health Commission of the People’s Republic of China (Dec 31, 2019 to Mar 5, 2020) and the World Health Organization (Jan 20, 2020 to Mar 5, 2020) were extracted as the training set and the data from Mar 6 to 9 as the validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death were collected and analyzed. We analyzed the data above through the State Transition Matrix model. Results The optimistic scenario (non-Hubei model, daily increment rate of − 3.87%), the cautiously optimistic scenario (Hubei model, daily increment rate of − 2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of − 1.50%) were inferred and modeling from data in China. The IFP of time in South Korea would be Mar 6 to 12, Italy Mar 10 to 24, and Iran Mar 10 to 24. The numbers of cumulative confirmed patients will reach approximately 20 k in South Korea, 209 k in Italy, and 226 k in Iran under fitting scenarios, respectively. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be earlier than predicted above. Conclusion The end of the pandemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to curb the development of COVID-19.

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