A multi-source information fusion method for ship target recognition based on bayesian inference and evidence theory

2021 ◽  
pp. 1-16
Author(s):  
Yu Zhang ◽  
Qunli Xiao ◽  
Xinyang Deng ◽  
Wen Jiang
Keyword(s):  
Bayesian Inference ◽  
Situation Awareness ◽  
Target Recognition ◽  
Evidence Theory ◽  
Distance Matrix ◽  
Target Type ◽  
Source Information ◽  
The Difference ◽  
Ship Recognition ◽  
Dynamic Time

The ship target recognition (STR) is greatly related to the battlefield situation awareness, which has recently gained prominence in the military domains. With the diversification and complexity of military missions, ship targets are mostly performed in the form of formations. Therefore, using the formation information to improve the accuracy of the ship target type recognition is worth studying. To effectively identify ship target type, we in this paper jointly consider the ship dynamic, formation, and feature information to propose a STR method based on Bayesian inference and evidence theory. Specifically, we first calculate the ship position distance matrix and the directional distance matrix with the Dynamic Time Warping (DTW) and the difference-vector algorithm taken into account. Then, we use the two distance matrices to obtain the ship formation information at different distance thresholds by the hierarchical clustering method, based on which we can infer the ship type. Thirdly, formation information and other attribute information are as nodes of the Bayesian Network (BN) to infer the ship type. Afterward, we can convert the recognition results at different thresholds into body of evidences (BOEs) as multiple information sources. Finally, we fuse the BOEs to get the final recognition. The proposed method is verified in simulation battle scenario in this paper. The simulation results demonstrate that the proposed method achieves performance superiority as compared with other ship recognition methods in terms of recognition accuracy.

Network Security Situation Awareness Based on Multi-Source Data Fusion

2014 ◽  
Vol 989-994 ◽  
pp. 4885-4888 ◽  
Author(s):  
Gang Chen ◽  
Jun Ping Cai ◽  
Jun Yang
Keyword(s):  
Network Security ◽  
Situation Awareness ◽  
Situational Awareness ◽  
Evidence Theory ◽  
System Level ◽  
Source Information ◽  
Security Research ◽  
Data Source ◽  
Source Level ◽  
Level Fusion

Network security situation awareness is an effective way to analysis security situation of complex network.The concept and model of network security situational awareness was introduced.A new model of network security situation awareness was proposed. Considering the characteristics of multi-source information in network security research, a security situation awareness algorithm based on information fusion was adopted. This algorithm advanced modified D-S evidence theory, gets the values of security situation awareness of network by data source level fusion, host-level fusion and system-level fusion. The results can reflect the general security state of network.

Urban heat island estimation from improved selection of urban and rural stations by DTW algorithm

2021 ◽  
Author(s):  
Yonghong Hu ◽  
Gensuo Jia ◽  
Jinlong Ai ◽  
Yong Zhang ◽  
Meiting Hou ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Urban Climate ◽  
Global Scale ◽  
Heat Island ◽  
Climate Effect ◽  
Urban Heat ◽  
The Difference ◽  
Temperature Records ◽  
Dynamic Time ◽  
Selection Of

Abstract Typical urban and rural temperature records are essential for the estimation and comparison of urban heat island effects in different regions, and the key issues are how to identify the typical urban and rural stations. This study tried to analyze the similarity of air temperature sequences by using dynamic time warping algorithm (DTW) to improve the selection of typical stations. We examined the similarity of temperature sequences of 20 stations in Beijing and validated by remote sensing, and the results indicated that DTW algorithm could identify the difference of temperature sequence, and clearly divide them into different groups according to their probability distribution information. The analysis for station pairs with high similarity could provide appropriate classification for typical urban stations (FT, SY, HD, TZ, CY, CP, MTG, BJ, SJS, DX, FS) and typical rural stations (ZT, SDZ, XYL) in Beijing. We also found that some traditional rural stations can’t represent temperature variation in rural surface because of their surrounding environments highly modified by urbanization process in last decades, and they may underestimate the urban climate effect by 1.24℃. DTW algorithm is simple in analysis and application for temperature sequences, and has good potentials in improving urban heat island estimation in regional or global scale by selecting more appropriate temperature records.

scholarly journals Research on Improvement of the Combination Method for Conflicting Evidence Based on Historical Data

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 762
Author(s):  
Shuai Yuan ◽  
Honglei Wang
Keyword(s):  
Decision Rule ◽  
Historical Data ◽  
Evidence Theory ◽  
Weighted Average ◽  
Combination Method ◽  
Final Decision ◽  
Evidence Based ◽  
Combination Methods ◽  
Conflicting Evidence ◽  
The Difference

In a multi-sensor system, due to the difference of performance of sensors and the environment in which the sensor collects evidence, evidence collected will be highly conflicting, which leads to the failure of D-S evidence theory. The current research on combination methods of conflicting evidence focuses on eliminating the problem of "Zadeh paradox" brought by conflicting evidence, but do not distinguish the evidence from different sources effectively. In this paper, the credibility of each piece of evidence to be combined is weighted based on historical data, and the modified evidence is obtained by weighted average. Then the final result is obtained by combining the modified evidence using D-S evidence theory, and the improved decision rule is used for the final decision. After the decision, the system updates and stores the historical data based on actual results. The improved decision rule can solve the problem that the system cannot make a decision when there are two or more propositions corresponding to the maximum support in the final combination result. This method satisfies commutative law and associative law, so it has the symmetry that can meet the needs of the combination of time-domain evidence. Numerical examples show that the combination method of conflict evidence based on historical data can not only solve the problem of “Zadeh paradox”, but also obtain more reasonable results.

scholarly journals Bayesian Inference for the Difference of Two Proportion Parameters in Over-Reported Two-Sample Binomial Data Using the Doubly Sample

Stats ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 111-120 ◽  
Author(s):  
Dewi Rahardja
Keyword(s):  
Bayesian Inference ◽  
Closed Form ◽  
Bayesian Approach ◽  
Interval Estimation ◽  
Real Data ◽  
Simulation Studies ◽  
Posterior Distributions ◽  
Binomial Data ◽  
The Difference ◽  
Data Subject

We construct a point and interval estimation using a Bayesian approach for the difference of two population proportion parameters based on two independent samples of binomial data subject to one type of misclassification. Specifically, we derive an easy-to-implement closed-form algorithm for drawing from the posterior distributions. For illustration, we applied our algorithm to a real data example. Finally, we conduct simulation studies to demonstrate the efficiency of our algorithm for Bayesian inference.

scholarly journals SCEDS: protein fragments for molecular replacement inPhaser

2013 ◽  
Vol 69 (11) ◽  
pp. 2216-2225 ◽  
Author(s):  
Airlie J. McCoy ◽  
Robert A. Nicholls ◽  
Thomas R. Schneider
Keyword(s):  
Normal Modes ◽  
Low Frequency ◽  
Distance Matrix ◽  
Triaxial Ellipsoid ◽  
Molecular Replacement ◽  
Elastic Network Model ◽  
Protein Fragments ◽  
Template Model ◽  
Invariant Regions ◽  
The Difference

A method is described for generating protein fragments suitable for use as molecular-replacement (MR) template models. The template model for a protein suspected to undergo a conformational change is perturbed along combinations of low-frequency normal modes of the elastic network model. The unperturbed structure is then compared with each perturbed structure in turn and the structurally invariant regions are identified by analysing the difference distance matrix. These fragments are scored with SCEDS, which is a combined measure of the sphericity of the fragments, the continuity of the fragments with respect to the polypeptide chain, the equality in number of atoms in the fragments and the density of Cαatoms in the triaxial ellipsoid of the fragment extents. The fragment divisions with the highest SCEDS are then used as separate template models for MR. Test cases show that where the protein contains fragments that undergo a change in juxtaposition between template model and target, SCEDS can identify fragments that lead to a lowerRfactor after ten cycles of all-atom refinement withREFMAC5 than the original template structure. The method has been implemented in the softwarePhaser.

Identification of the D-S Evidence Conflict Based on Multi-Source Information

2013 ◽  
Vol 347-350 ◽  
pp. 2191-2196
Author(s):  
Feng Nan ◽  
Yang Li
Keyword(s):  
Information Fusion ◽  
Distance Matrix ◽  
Source Information ◽  
Independent Evidence ◽  
Information Distance ◽  
Convergence Results ◽  
Conflicting Evidence ◽  
Conflict Identification ◽  
Paper Construct ◽  
Systematic Identification

TO solve the applying constraints problem of D-S evidence conflict in multi-information fusion and achieve the systematic identification of evidence conflict,we introduce the K-L information on the distance function to describe the characteristics of the conflict between the evidence in the paper, construct the distance matrix defined the level of conflict of independent evidence in the whole system. Simulating results show that: the effective conflict identification of K-L information distance can complete the application constraint of D-S theorys synthetic rule, obtaining the optimized convergence results for synthesis of normal conflicting evidences, isolating the highly conflicting evidence.

Determination of Basic Probability Assignment Based on Recognition Sequence

2014 ◽  
Vol 1049-1050 ◽  
pp. 1171-1175
Author(s):  
Yan Fei Chen ◽  
Xue Zhi Xia ◽  
Kui Tu
Keyword(s):  
Target Recognition ◽  
Evidence Theory ◽  
Practical Application ◽  
Recognition Sequence ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Recognition Result ◽  
Basic Probability ◽  
Simulation Results

In some practical application of target recognition with sensors, the sensors will give the recognition sequence of targets, which is more detailed than the single recognition result. How to properly construct the basic probability assignment by the recognition sequence becomes the key to successful application of evidence theory. For the recognition sequence of the target recognition results of general sensor is incomplete, and the importance of the types in the recognition sequence is in descending order, this paper proposes a method to construct weights of recognition sequence, the basic probability assignments constructed by the weights are closer to the real recognition results. Simulation results show that this method is more reasonable and effective than the method of contrast.

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