Data Fusion Algorithm for Asynchronous Radar Networks under Blanket Jamming

2012 ◽  
Vol 157-158 ◽  
pp. 1446-1452 ◽  
Author(s):  
Yan Li Zhao ◽  
Xiao Zun Ma ◽  
Xiang Dong Gao ◽  
Lei Yun ◽  
Shu Qin Fu
Keyword(s):  
Data Fusion ◽  
Target Tracking ◽  
State Vector ◽  
Processing Technique ◽  
High Accuracy ◽  
Observation Data ◽  
Fusion Algorithm ◽  
Batch Mode ◽  
Radar Networks ◽  
Simulation Results

Owing to the incompleteness and asynchronicity of the observation data, it is hard for the asynchronous netted radar to track its target effectively under intense blanket jamming. This paper presents a kind of data fusion algorithm for the asynchronous sensors based on optimal linearization approach, providing a solution to the problem of target tracking of radar networks under blanket jamming. First, the optimal linearization processing technique of the observation equation is derived. Then, based on this, the state vector of the target is initially estimated in batch mode. Finally, the filtering is processed in sequence. The simulation results show that the data fusion algorithm presented in the paper can track the target in high accuracy.

scholarly journals Oil exploration oriented multi-sensor image fusion algorithm

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 188-196
Author(s):  
Zhang Xiaobing ◽  
Zhou Wei ◽  
Song Mengfei
Keyword(s):  
Wavelet Transform ◽  
Data Fusion ◽  
Image Fusion ◽  
High Accuracy ◽  
Signal Decomposition ◽  
Oil Exploration ◽  
Complex Wavelet Transform ◽  
Fusion Algorithm ◽  
New Type ◽  
Priority Function

AbstractIn order to accurately forecast the fracture and fracture dominance direction in oil exploration, in this paper, we propose a novel multi-sensor image fusion algorithm. The main innovations of this paper lie in that we introduce Dual-tree complex wavelet transform (DTCWT) in data fusion and divide an image to several regions before image fusion. DTCWT refers to a new type of wavelet transform, and it is designed to solve the problem of signal decomposition and reconstruction based on two parallel transforms of real wavelet. We utilize DTCWT to segment the features of the input images and generate a region map, and then exploit normalized Shannon entropy of a region to design the priority function. To test the effectiveness of our proposed multi-sensor image fusion algorithm, four standard pairs of images are used to construct the dataset. Experimental results demonstrate that the proposed algorithm can achieve high accuracy in multi-sensor image fusion, especially for images of oil exploration.

Unscented Particle Filter Algorithm for Ballistic Target Tracking

2011 ◽  
Vol 130-134 ◽  
pp. 369-372
Author(s):  
Jun Wei Zhao ◽  
Ming Jun Zhang ◽  
Yong Gang Yan ◽  
Yong Peng Yan
Keyword(s):  
Convergence Rate ◽  
Particle Filter ◽  
Target Tracking ◽  
State Vector ◽  
The State ◽  
Convergence Speed ◽  
Matlab Software ◽  
Unscented Particle Filter ◽  
Simulation Results ◽  
Particle Filter Algorithm

At present, the ballistic Target tracking has a higher demand in convergence rate and tracking precision of filter algorithm. In the paper, a filter algorithm was improved based on particle filter. The algorithm was carried out from the aspects such as particle degradation and particle diversity lack. A novel ballistic coefficient parameter model was built, and was expanded to the state vector for filtering. Finally, the improved algorithm was simulated by MATLAB software. The simulation results show that the algorithm can obtain better convergence speed and tracking precision.

Filtering Performance of Digital Alternate Array on Target Tracking

2014 ◽  
Vol 644-650 ◽  
pp. 4269-4272
Author(s):  
Dong Kai Cao ◽  
Jun Li ◽  
Hong Ming Liu
Keyword(s):  
Kalman Filter ◽  
Measurement Error ◽  
Data Fusion ◽  
Target Tracking ◽  
Measurement Data ◽  
New Method ◽  
Present Moment ◽  
Simulation Results ◽  
Novel Model ◽  
Error Filtering

For target tracking of agile digital array, we propose a novel model called alternate array. The model means that total array is divided into vertical subarray at the present moment, and vertical subarray is transformed into horizontal subarray at the next moment, then the subarray comes to the first subarray divisions, and so on. Alternate array gives rise to differences of statistical characters of measurement error. Filtering performance is influenced by the feature of measurement data. In the paper, we come up with a new method based on Kalman filter (KF) to solve the difficulty. The new method merges pre-processing and data fusion thought together. Simulation results demonstrate the validity of the proposed algorithm.

Research of Asynchronous Multi-Type Sensors Data Fusion

2010 ◽  
Vol 142 ◽  
pp. 16-20
Author(s):  
Y. Qin ◽  
Xue Hui Wang ◽  
Ming Jun Feng ◽  
Zhen Zhou ◽  
L.J. Wang
Keyword(s):  
Data Fusion ◽  
Target Tracking ◽  
Tracking System ◽  
Control Process ◽  
Good Control ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Control Effect ◽  
Fusion Algorithm ◽  
Multi Target Tracking

A data fusion algorithm was established for estimating the state of target tracking system with multi-type sensor. Through Kalman filter regarding the multi-sensors to computer goal estimated value, it can obtain estimation value of goal at moment. And mean square deviation of fusion estimation value was smaller than single sensor's mean square deviation. The simulation results indicated that synchronisms data fusion method was effective to the multi-target tracking problem. Asynchronous multi-sensor fusion process can obtain good control effect in the practice control process.

Unscented Particle Filter Algorithm for Ballistic Target Tracking

2014 ◽  
Vol 686 ◽  
pp. 359-362
Author(s):  
Yan Zhai ◽  
Xiao Bo Guo ◽  
Yong Gang Yan
Keyword(s):  
Convergence Rate ◽  
Particle Filter ◽  
Target Tracking ◽  
State Vector ◽  
The State ◽  
Convergence Speed ◽  
Matlab Software ◽  
Unscented Particle Filter ◽  
Simulation Results ◽  
Particle Filter Algorithm

At present, the ballistic Target tracking has a higher demand in convergence rate and tracking precision of filter algorithm. In the paper, a filter algorithm was improved based on particle filter. The algorithm was carried out from the aspects such as particle degradation and particle diversity lack. A novel ballistic coefficient parameter model was built, and was expanded to the state vector for filtering. Finally, the improved algorithm was simulated by MATLAB software. The simulation results show that the algorithm can obtain better convergence speed and tracking precision.

AN APPROACH TO SOLVE THE PROBLEM OF DATA FUSION FOR MULTI-TARGET TRACKING USING FUZZY LOGIC

2020 ◽  
pp. 16-24
Author(s):  
Dang Quang Hieu
Keyword(s):  
Fuzzy Logic ◽  
Data Fusion ◽  
Target Tracking ◽  
Joint Probability ◽  
Identification Algorithm ◽  
Fuzzy Data ◽  
Fusion Algorithm ◽  
Logic Simulation ◽  
Multi Target Tracking ◽  
Better Than

This paper presents a multitarget data fusion (identification) algorithm called the Fuzzy data fusion algorithm (FDFA) for radar target tracking. This approach is formulated using the Kalman filter and FDF, the algorithm is accomplished by using fuzzy logic. Simulation results for cluttered conditions show that the proposed algorithm's performance is better than the probability data fusion (PDF) and the joint probability data fusion (JPDF) filters, which were presented in [2-4, 6].

Study on Data Fusion Algorithm of TCAS/ADS-B Integrated Surveillance System Based on the Present Statistical Model

2013 ◽  
Vol 427-429 ◽  
pp. 1311-1314
Author(s):  
Ya Jun Xu
Keyword(s):  
Data Fusion ◽  
Statistical Model ◽  
Collision Avoidance ◽  
Surveillance System ◽  
Position Error ◽  
Integrated System ◽  
Fusion Algorithm ◽  
Simulation Results ◽  
Collision Avoidance System ◽  
The Difference

The difference between Traffic Alert and Collision Avoidance System and Automatic Dependent Surveillance Broadcast surveillance principle was compared. A TCAS/ ADS-B integrated surveillance system based on the present statistical model was built. Using the data fusion algorithm, the optimal local track of TCAS and ADS-B ,as well as the optimal fused track of the integrated system were estimated. The simulation results show that the maximum optimal fused estimated position error is 100m, it is certificated that the integrated system can improve track estimates accuracy, improve the surveillance precision of TCAS.

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