Maximum Entropy Fuzzy Clustering Research Articles

On eigenvalue-based cooperative spectrum sensing using feature extraction and maximum entropy fuzzy clustering

On eigenvalue-based cooperative spectrum sensing using feature extraction and maximum entropy fuzzy clustering

Robust entropy-based symmetric regularized picture fuzzy clustering for image segmentation

Symmetric regularized picture fuzzy clustering is a new fuzzy clustering method, and it is difficult to choose its weighting fuzzy factor m and lacks certain robustness to noises or outliers. To this end, this paper proposes a robust entropy-based symmetric regularized picture fuzzy clustering with spatial information constraints for noisy image segmentation. The idea of maximum entropy fuzzy clustering is firstly introduced into symmetric regularized picture fuzzy clustering, and we can obtain an entropy-based symmetric regularized picture fuzzy clustering method to avoid selecting weighting fuzzy factors m. Meanwhile, it has clear physical meaning. Then, spatial neighborhood information of current clustering pixel is embedded into its clustering objective function, and a new robust fuzzy clustering algorithm for image segmentation is obtained to enhance the ability to suppress noise. In the end, the convergence of new robust clustering algorithm is proved by Zangwill theorem. Many experimental results show that the proposed algorithm can achieve higher segmentation performance than existing picture fuzzy clustering.

Interacting T-S fuzzy semantic model estimation for maneuvering target tracking

This paper proposes an interacting Takagi–Sugeno(T-S) fuzzy semantic model estimator (ITS-FSM) for maneuvering target tracking, and constructs a framework for a generic interacting T-S fuzzy semantic model to incorporate semantic information concerning the target. To adaptively calculate the transition probability matrix of the models, a probabilistic switching model based on semantic fuzzy sets is derived by using the degree of intersection between fuzzy sets, which enables switching from one semantic fuzzy set to another. We also propose an efficient kernel maximum entropy fuzzy clustering method to identify the premise parameters of the model. This enables the proposed algorithm to recursively estimate the premise parameters in case of a limited number of samples. Moreover, a modified extended forgetting factor recursive least squares (MEFRLS) estimator is used to identify the parameters of the T-S fuzzy semantic model. The results of experiments on three simulation datasets show that the proposed ITS-FSM algorithm is efficient, and is excellent at handling non-Gaussian noise.

Sensor selection based on maximum entropy fuzzy clustering for target tracking in large‐scale sensor networks

This study proposes a sensor selection approach based on maximum entropy fuzzy clustering to address the target tracking problem in large-scale sensor networks. The authors try to deal with this problem at two levels: (i) sensor-level tracking: data association problem and sensor-level tracking are carried out at the local level, and only the track outputs are transmitted to the fusion centre for data fusion; (ii) global-level fusion: two sensor selection strategies are adopted at the fusion centre, which seek to only choose a subset of reliable sensors for track-to-track fusion and bias registration. In addition, an improved sensor selection approach is proposed for data fusion in both sparse and dense target environments, and a new fuzzy membership reconstruction strategy is introduced for data association in dense target environments. Furthermore, the proposed sensor selection strategy is also effective in the presence of the possible changing sensor biases. Simulation results are given to evaluate the performance of the proposed approaches.

Fuzzy Quadrature Particle Filter for Maneuvering Target Tracking

In this paper, a novel fuzzy quadrature particle filter (FQPF) based on maximum entropy fuzzy clustering for maneuvering target tracking is proposed. The novelties of the fuzzy quadrature particle filter are in the update step in which the predicted and posterior probability density functions are approximated by introducing a set of quadrature point probability densities based on the Gauss–Hermite quadrature rule as a Gaussian. The particle and quadrature point weights can be adaptively estimated based on the weighting exponent and fuzzy membership degrees provided by a modified version of maximum entropy fuzzy clustering algorithm. Unlike the Gaussian particle filter (GPF) using the prior distribution as the proposal distribution, the new FQPF uses a set of modified quadrature point probability densities as the proposal distribution that can effectively enhance the diversity of samples and improve the approximate performance. Finally, simulation results are presented to demonstrate the versatility and improved performance of the fuzzy quadrature particle filter over other nonlinear filtering approaches, namely the unscented Kalman filter, quadrature Kalman filter, particle filter, and GPF, to solve maneuvering target tracking problems.

Bearings-only maneuvering target tracking based on fuzzy clustering in a cluttered environment

This paper proposes a novel bearings-only maneuvering target tracking algorithm based on maximum entropy fuzzy clustering in a cluttered environment. In the proposed algorithm, the interacting multiple model (IMM) approach is used to solve the maneuvering problem of target, and the false alarms generated by clutter are accommodated through a probabilistic data association filter (PDAF). To reduce the computational load, the association probability is substituted by fuzzy membership degree provided by a modified version of fuzzy clustering algorithm based on maximum entropy principle, and the “maximum validation distance” is also defined based on the discrimination factor, which enables the algorithm eliminate invalid measurements. Moreover, to avoid the unobservability problem of passive target tracking, a nonlinear measurement model of multiple passive sensors is formulated. Finally, simulation results show that the proposed algorithm has advantages over the conventional IMM-PDAF algorithm in terms of simplicity and efficiency.

Fuzzy Linear Discriminant Analysis-guided maximum entropy fuzzy clustering algorithm

Linear Discriminant Analysis (LDA) is a classical statistical approach for supervised feature extraction and dimensionality reduction, hard c-means (HCM) is a classical unsupervised learning algorithm for clustering. Based on the analysis of the relationship between LDA and HCM, Linear Discriminant Analysis-guided adaptive subspace hard c-means clustering algorithm (LDA–HCM) had been proposed. LDA–HCM combines LDA and HCM into a coherent framework and can adaptively reduce the dimension of data while performing data clustering simultaneously. Seeing that LDA–HCM is still a hard clustering algorithm, we consider the fuzzy extension version of LDA–HCM in this paper. To this end, firstly, we propose a new optimization criterion of Fuzzy Linear Discriminant Analysis (FLDA) by extending the value of membership function in classical LDA from binary 0 or 1 into closed interval [0, 1]. In the meantime, we present an efficient algorithm for the proposed FLDA. Secondly, we show the close relationship between FLDA and Maximum Entropy Fuzzy Clustering Algorithm (MEFCA): they both are maximizing fuzzy between-class scatter and minimizing within-class scatter simultaneously. Finally, based on the above analysis, combining FLDA and MEFCA into a joint framework, we propose fuzzy Linear Discriminant Analysis-guided maximum entropy fuzzy clustering algorithm (FLDA–MEFCA). LDA–MEFCA is a natural and effective fuzzy extension of LDA–HCM. Due to the introduction of soft decision strategy, FLDA–MEFCA can yield fuzzy partition of data set and is more flexible than LDA–HCM. We also give the convergence proof of FLDA–MEFCA. Extensive experiments on a collection of benchmark data sets are presented to show the effectiveness of the proposed algorithm.

Improved Estimate-to-Track Association Method for Track Continuity

The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on finite set statistics. However, the PHD filter keeps no record of target identities and hence does not produce track-valued estimates of individual targets. To solve this problem, an improved estimate-to-track association method for track continuity is proposed in this paper. Firstly, a multi-step prediction of current target states is made, and then the weighted labels are assigned to them according to the inertia. Secondly, the fuzzy membership degrees of the current state estimates belonging to the tracks are obtained by utilizing the maximum entropy fuzzy clustering. Finally, the tracks are maintained by integrating all this information. Different from the traditional estimate-to-track association method, the proposed algorithm does not update the track information by simply summing the log likelihood ratios between adjacent scans, but take the entire multi-scan information into account by the operations such as weighting and clustering. The simulation results show that the proposed algorithm can maintain target tracks more accurately, even when the targets cross each other, implying strong robustness and excellent performance of track continuity.

Maximum entropy fuzzy clustering with application to real-time target tracking

The problem of data association for target tracking in a cluttered environment is discussed. In order to deal with the problem of data association for real time target tracking, a novel data associ...

Maximum entropy fuzzy clustering with application to real-time target tracking

The problem of data association for target tracking in a cluttered environment is discussed. In order to deal with the problem of data association for real time target tracking, a novel data association method based on maximum entropy fuzzy clustering is proposed. Firstly, the candidate measurements of each target are clustered with the aid of the modified maximum entropy fuzzy clustering. Then the joint association probabilities are reconstructed by utilizing the fuzzy membership degree of the measurement belonging to the target. At the same time, in order to deal with the uncertainty of the measurements, a new weight assignment is introduced. Moreover, the characteristic of the discrimination factor is analyzed, and the influence of the clutter density on it is also considered, which enables the algorithm eliminate those invalidate measurements and reduce the computational load. Finally, the simulation results show that the proposed algorithms have advantages over the existing ones in terms of efficiency and low computational load.

Maximum entropy fuzzy clustering with application to real-time target tracking

The problem of data association for target tracking in a cluttered environment is discussed. In order to deal with the problem of data association for real time target tracking, a novel data association method based on maximum entropy fuzzy clustering is proposed. Firstly, the candidate measurements of each target are clustered with the aid of the modified maximum entropy fuzzy clustering. Then the joint association probabilities are reconstructed by utilizing the fuzzy membership degree of the measurement belonging to the target. At the same time, in order to deal with the uncertainty of the measurements, a new weight assignment is introduced. Moreover, the characteristic of the discrimination factor is analyzed, and the influence of the clutter density on it is also considered, which enables the algorithm eliminate those invalidate measurements and reduce the computational load. Finally, the simulation results show that the proposed algorithms have advantages over the existing ones in terms of efficiency and low computational load.