Multisensor data fusion is extensively used to merge data from heterogeneous sensors in a smart environment. However, sensors provide noisy and uncertain information which is a big challenge for researchers. Since uncertainty in the data is a central constraint for data fusion and decision-making systems. Dempster-Shafer’s evidence theory is an appropriate method for modeling and fusing uncertain information. In this paper, a novel data fusion scheme is proposed based on the modified belief entropy of the basic probability assignments (BPAs) to quantify the uncertainty in the information and fused them by Dempster-Shafer evidence theory. The proposed DFUDS (data fusion based on measuring uncertainty in Dempster-Shafer) scheme considers the available redundant information in the body of evidence (BoEs). The BoEs obtained from the sensor data are processed by proposed belief entropy, and fuse all pieces of evidence by Dempster’s rule of combination to transfer the conflicting data into decision-making results. Extensive computer simulation results show that the proposed scheme outperforms in terms of the degree of uncertainty, evidence, reasoning, and decision accuracy under active contexts of the smart environment.

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Multisensor Data Fusion Based on Modified Belief Entropy in Dempster–Shafer Theory for Smart Environment

In intelligent traffic monitoring, speed measuring millimeter waves (MMW) radar is one of the most commonly used tools for traffic enforcement. In traffic enforcement field, the radar must provide the evidence of each vehicle belongs to which lane. In this paper, we propose a novel kernel line segment adaptive possibilistic c-means clustering algorithm (KLSAPCM) for lane determination of vehicles. Firstly, the raw measurement data is preprocessed using the extracting method of data adjacent lane centerlines. Secondly, according to the improved minimum radius data search method, outliers are removed and the proposed KLSAPCM algorithm is initialized. Finally, the accuracy of lane determination has been improved by the proposed KLSAPCM clustering algorithm based on adaptive kernel line segment that conforms to the shape features of the measurement data in the actual scene. The experiment results for multiple scenes were: the KLSAPCM algorithm is compared with the DBSCAN, the $k$ -means, the FCM, the PCM, the AMPCM, and the APCM algorithms on real measurement datasets, and the results highlight the classification rate of the proposed algorithm. Meanwhile, the proposed algorithm gets a good real-time performance and strong robustness for some sparse moving vehicle scene applications.

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Lane Determination of Vehicles Based on a Novel Clustering Algorithm for Intelligent Traffic Monitoring

The problem of duplicate track determination called “Track-to-Track association” occurs when a target is reported by different sensors, and it is regarded as one of the most important challenges in distributed multi-sensor tracking systems. The present study aimed to propose a density-based fuzzy clustering method for solving the track-to-track association problem in distributed multi-sensor tracking systems. Unlike the previously published solutions, the proposed method does not need any information about the number of targets, due to the use of the density-based clustering approach. Proposed method has low computational overhead and can be used in real-time tracking systems. In addition, the proposed method uses the maximum entropy approach to determine the membership degree of single target related tracks and combines them. This paper presents three scenarios including sensors with complete and incomplete overlapping by considering the bias and a different number of sensors and targets for evaluating the proposed method based on the Monte Carlo simulation. The results indicate the improvement of the efficiency in comparison with the FTF approach. The efficiency of proposed method's results is close to the results of Bayesian minimum mean square error criterion that gives best possible results.

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An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System

Differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems: State-of-the-art and perspectives

The performance of track initialization methods based on real-time filtering depends heavily on the state estimation accuracy of the track head, which cannot be accurately obtained in many cases. This paper proposes the joint optimization problem of multi-target track initialization, in which the data association and track parameters of targets are obtained simultaneously. To this end, the target trajectory is first modeled as a weighted sum of a set of continuous time basis functions, and the corresponding track initialization is to determine discrete-value decision related to data association and continuous-value estimate related to function weights (i.e., track parameters). Such binary optimization is further transformed into the equivalent quadratic concave optimization of the data association vector by track parameter elimination, while each target definitely corresponds to one of the local minima that satisfy the target existence condition. In implementation, a modified normal rectangular algorithm is presented to obtain such minima, instead of the global minimum gotten by the standard normal rectangular algorithm. Finally, simulation results show the effectiveness of the proposed algorithm.

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A Concave Optimization-Based Approach for Joint Multi-Target Track Initialization

The outliers remove, the classification of effective measurements, and the weighted optimization method of the corresponding measurement are the main factors that affect the positioning accuracy based on range-based multi-target tracking in wireless sensor networks. In this paper, we develop an improved weighted least-square algorithm based on an enhanced non-naive Bayesian classifier (ENNBC) method. According to the ENNBC method, the outliers in the measurement data are removed effectively, dataset density peaks are found quickly, and remaining effective measurements are accurately classified. The ENNBC method improves the traditional direct classification method and took the dependence among continuous density attributes into account. Four common indexes of classifiers are used to evaluate the performance of the nine methods, i.e., the normal naive Bayesian, flexible naive Bayesian (FNB), the homologous model of FNB (FNB
 ROT
), support vector machine, k-means, fuzzy c-means (FCM), possibilistic c-means, possibilistic FCM, and our proposed ENNBC. The evaluation results show that ENNBC has the best performance based on the four indexes. Meanwhile, the multi-target tracking experimental results show that the proposed algorithm can reduce the root-mean-squared error of the position compared with the extended Kalman filter. In addition, the proposed algorithm has better robustness against large localization and tracking errors.

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Xinxin He

Papers

Measurement Data Fusion Based on Optimized Weighted Least-Squares Algorithm for Multi-Target Tracking