A multi-sensors weighted data fusion method based on measurement traversal correction
1
Citation
0
Reference
10
Related Paper
Citation Trend
Abstract:
Abstract In the case of multi-sensors weighted data fusion with unknown prior knowledge, a weighted data fusion method based on measurement traversal correction is proposed. The fusion accuracy of multi-sensors data fusion is influenced by both the measurement data accuracy and the data fusion weights of sensors. The measurement data of sensors is corrected through analyzing the reliability of different time data measured by sensors. The fusion weights of multi-sensors data fusion are optimal through depply analyzing the influence of weight distribution on multi-sensors data fusion accuracy. Typical examples are used to validate the proposed fusion algorithm, and the result shows that the fusion result is satisfying, and the algorithm is theoretical and practical.Keywords:
Sensor Fusion
A measuring method of molten pool temperature which is influenced by various factors is designed based on multi sensor data fusion. With the time-varying measured object, an adaptive weighted fusion algorithm for searching the minimum standard deviation is proposed here. The corresponding weight is achieved by using sensors measurements through approach of self-adaption. The statistic characteristics of estimated standard deviation are also elaborated in this paper. A comparison is made between real-time processing of adaptive weighted fusion algorithm and traditional mean algorithm. The theoretical analysis and experimental results demonstrate that the method can measure molten pool temperature effectively.
Sensor Fusion
Statistic
Cite
Citations (1)
A measurement fusion function is defined and shown to decide the covariance of measurement fusion. If two multisensor data fusion systems have identical measurement fusion function, then the systems have identical covariances. The greater the function is, the less covariance the fusion system can obtain.
Sensor Fusion
Tracking (education)
Analysis of covariance
Cite
Citations (4)
Abstract In the case of multi-sensors weighted data fusion with unknown prior knowledge, a weighted data fusion method based on measurement traversal correction is proposed. The fusion accuracy of multi-sensors data fusion is influenced by both the measurement data accuracy and the data fusion weights of sensors. The measurement data of sensors is corrected through analyzing the reliability of different time data measured by sensors. The fusion weights of multi-sensors data fusion are optimal through depply analyzing the influence of weight distribution on multi-sensors data fusion accuracy. Typical examples are used to validate the proposed fusion algorithm, and the result shows that the fusion result is satisfying, and the algorithm is theoretical and practical.
Sensor Fusion
Cite
Citations (1)
To improve the efficiency of generating test data by using symbolic execution,a method combining forward traversal and backward traversal is proposed.After studying and comparing these advantages and disadvantages of forward traversal and backward traversal,an algorithm integrating the advantages of both of traversal methods is designed.It partitions a program into blocks,acting as a forward traversal on the whole while as backward traversal in every block.This method combines the advantages of checking infeasible paths as soon as possible in forward traversal and avoiding unnecessary assignments in backward traversal.The redundancy problems in forward traversal are solved.Finally the experimental result demonstrates this algorithm's feasibility and effectiveness.
Graph traversal
Depth-first search
Cite
Citations (0)
The track fusion is an important aspect in the multi-sensor data fusion.Because of the public noise,the track estimate errors from the different sensors are not independent in the state estimate fusion system.So the fusion problem becomes complex.This article researched the simple fusion,adaptive track fusion and weighted covariance fusion.The comparison of data fusion methods shows that adaptive track fusion and weighted covariance fusion is effective to multi-sensor data fusion.The simulation indicates that the algorithm has preferable fusion result.
Sensor Fusion
Cite
Citations (0)
Graph traversal
Cite
Citations (2)
Mining frequent traversal patterns is to discover the consecutive reference paths traversed by a sufficient number of users from Web logs. The previous approaches for mining frequent traversal patterns need to repeatedly scan the traversal paths and take a large amount of computation time to find frequent traversal patterns. However, the discovered frequent traversal patterns may become invalid or inappropriate when the databases are updated. We propose an incremental updating technique to maintain the discovered frequent traversal patterns when the user sequences are inserted into or the database. Our approach partitions the database into some segments and scans the database segment by segment. For each segment scan, the candidate traversal sequences that cannot be frequent traversal sequences can be pruned and the frequent traversal sequences can be found out earlier. Besides, the number of database scans can be significantly reduced because some information can be computed by our approach. The experimental results show that our algorithms are more efficient than other algorithms for the maintenance of mining frequent traversal patterns.
Graph traversal
Cite
Citations (8)
The purpose of data fusion is to produce an improved model or estimate of a system from a set of independent data sources. There are various multisensor data fusion approaches, of which Kalman filtering is one of the most significant. Methods for Kalman filter based data fusion include measurement fusion and state fusion. This paper gives a simple a review of fusion and state fusion, and secondly proposes new integrated method of state fusion based on fusion procedures at the prediction and update level. To illustrate application, a simple example is performed to evaluate the proposed method.
Sensor Fusion
Data set
Cite
Citations (0)