scholarly journals Cognitive Radar Waveform Optimization Based on Mutual Information and Kalman filtering

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 653 ◽  
Author(s):  
Yu Yao ◽  
Junhui Zhao ◽  
Lenan Wu
Keyword(s):  
Mutual Information ◽  
Target Detection ◽  
Kalman Filtering ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Cognitive Radar ◽  
Scattering Coefficients ◽  
Waveform Optimization ◽  
New Strategy ◽  
Selection Of

A new strategy to optimizing the waveforms of cognitive radar under transmitted power constraint is presented. Our scheme is to enhance the performance of target estimation by minimizing the MSE (mean-square error) of the estimates of target scattering coefficients (TSC) based on Kalman filtering and then minimizing mutual information (MI) between the radar target echoes at successive time instants. The two steps are the optimal design of transmission waveform and the selection of a reasonable waveform from the ensemble for emission, respectively. The waveform design technique addresses the problems of target detection and parameter estimation in intelligent transportation system (ITS), where there is a need of extracting the features of target information obtained from different sensors. As the number of iterations increases, simulation results show better TSC estimation from the radar scene provided by the proposed approach as compared with the traditional waveform optimization algorithm. In addition, the proposed algorithm results in improved target detection probability.

Study on WSN Localization Algorithm and Simulation Model Based on Kalman Filtering Algorithm

2014 ◽  
Vol 945-949 ◽  
pp. 2380-2385
Author(s):  
Lian Zhou Gao
Keyword(s):  
Wireless Sensor Network ◽  
Simulation Model ◽  
Sensor Network ◽  
Kalman Filtering ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm

This paper studies on the algorithm to improve the location of Wireless Sensor Network (WSN) in Intelligent Transportation System (ITS). Considering multi-path effect in the localization, an improved RSSI algorithm is introduced in the localization algorithm. The localization results are analyzed under different density of beacon nodes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, to test the algorithm based on Kalman filtering algorithm, a simulation model of ITS is developed, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application.

An Extensive Review on Data Mining Methods and Clustering Models for Intelligent Transportation System

2018 ◽  
Vol 27 (2) ◽  
pp. 263-273 ◽  
Author(s):  
Sesham Anand ◽  
P. Padmanabham ◽  
A. Govardhan ◽  
Rajesh H. Kulkarni
Keyword(s):  
Data Mining ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Optimal Number ◽  
Clustering Methods ◽  
Trip Planning ◽  
Data Mining Techniques ◽  
Selection Of

AbstractData mining techniques support numerous applications of intelligent transportation systems (ITSs). This paper critically reviews various data mining techniques for achieving trip planning in ITSs. The literature review starts with the discussion on the contributions of descriptive and predictive mining techniques in ITSs, and later continues on the contributions of the clustering techniques. Being the largely used approach, the use of cluster analysis in ITSs is assessed. However, big data analysis is risky with clustering methods. Thus, evolutionary computational algorithms are used for data mining. Though unsupervised clustering models are widely used, drawbacks such as selection of optimal number of clustering points, defining termination criterion, and lack of objective function also occur. Eventually, various drawbacks of evolutionary computational algorithm are also addressed in this paper.

Short-Term Traffic Flow Forecasting Based on SVR with Improved Artificial Fish Swarm Algorithm

2015 ◽  
Vol 734 ◽  
pp. 508-514
Author(s):  
Ren Xiao Fang ◽  
Wei Hong Yao ◽  
Xu Dong Zhang
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Support Vector ◽  
Short Term ◽  
Artificial Fish Swarm Algorithm ◽  
Traffic Flow Forecasting ◽  
Search Mechanism ◽  
Artificial Fish Swarm ◽  
Selection Of

Real-time and accurate traffic flow forecasting is one of the key contents of Intelligent Transportation System. For the disadvantage of parameter selection of Support Vector Regression (SVR), an improved artificial fish swarm (IAFS) algorithm using the adaptive search mechanism was applied to optimize SVR. This method aimed at improving the prediction accuracy and extensibility of short-term traffic flow forecasting. Then a short-term traffic flow forecasting model based on IAFS-SVR was proposed. The results show that the proposed method has better prediction performance, and is suitable for short-term traffic flow forecasting.

Study on WSN Localization Algorithm and Simulation Model for Intelligent Transportation System

2014 ◽  
Vol 548-549 ◽  
pp. 1407-1414
Author(s):  
Zheng Feng Li ◽  
Lian Zhou Gao
Keyword(s):  
Simulation Model ◽  
White Noise ◽  
Kalman Filtering ◽  
Intelligent Transportation System ◽  
Gaussian White Noise ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Localization Algorithm ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm

This paper conducts research on the algorithm to improve the location of Wireless Sensor Network (WSN) in Intelligent Transportation System (ITS). The localization algorithm introduced an improved RSSI vehicle localization algorithm based on multi-path effect and Gaussian white noise. The localization results under different values of Gaussian white noise and different density of beacon nodes are analyzes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, a simulation model of ITS is developed to test the algorithm based on mixed noise and Kalman filtering algorithm, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application

Study on WSN Localization Algorithm and Simulation Model for Intelligent Transportation System

2014 ◽  
Vol 539 ◽  
pp. 867-873 ◽  
Author(s):  
Lian Zhou Gao
Keyword(s):  
Simulation Model ◽  
White Noise ◽  
Kalman Filtering ◽  
Intelligent Transportation System ◽  
Gaussian White Noise ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Localization Algorithm ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm

This paper conducts research on the algorithm to improve the location of Wireless Sensor Network (WSN) in Intelligent Transportation System (ITS). The localization algorithm introduced an improved RSSI vehicle localization algorithm based on multi-path effect and Gaussian white noise. The localization results under different values of Gaussian white noise and different density of beacon nodes are analyzes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, a simulation model of ITS is developed to test the algorithm based on mixed noise and Kalman filtering algorithm, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application

Moving Target Detection and Segmentation Method Research in Intelligent Transportation System

2012 ◽  
Vol 490-495 ◽  
pp. 951-955
Author(s):  
Yi Zhang ◽  
Hui Zhi Sun
Keyword(s):  
Target Detection ◽  
High Speed ◽  
Moving Objects ◽  
Intelligent Transportation System ◽  
Color Space ◽  
Intelligent Transportation ◽  
Experimental Results ◽  
Moving Target ◽  
Segmentation Method ◽  
Self Adaptation

This paper proposes a new algorithm for the detection of moving objects based on picture blocks and HVS color space of image differences techniques. The experimental results show that this algorithm can overcome the weakness of image background differencing and coterminous frame differencing method and suppress shadows effectively with advantages of self-adaptation and high speed. It can be used to detect the high-speed moving objects in the complex.

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