Developing the fuzzy c-means clustering algorithm based on maximum entropy for multitarget tracking in a cluttered environment

Chen, Xiao; Li, Yaan; Yu, Jing; Li, Yuxing

doi:10.1117/1.jrs.12.016019

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…Aiming at the multitarget tracking (MTT) problem in the clutter environment, the multitarget tracking algorithm represented by the probabilistic data association [15] uses tracking gate to distinguish measurement and clutter and preliminarily solves the correlation problem between measurement and target state. Reference [16] proposes a multitarget tracking algorithm based on maximum entropy fuzzy C-means clustering joint probabilistic data association, which avoids confirmation matrix splitting and reduces the computational load of the JPDA algorithm. However, due to the large number of members in the cluster, the data association is very difficult, and only relying on the confirmation matrix for measuring data association is difficult to meet the requirements of tracking accuracy.…”

Section: Introductionmentioning

confidence: 99%

Multiradar Joint Tracking of Cluster Targets Based on Graph-LSTMs

et al. 2022

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The cluster target brings a serious challenge to the traditional multisensor multitarget tracking algorithm because of its large number of members and the cooperative interaction between members. Using multiradar joint tracking cluster target is an alternative method to solve the problem of cluster target tracking, but it inevitably brings the problem of radar-target assignment and tracking information fusion. Aiming at the problem of radar-target assignment and tracking information fusion, a joint tracking method based on graph-long short-term memory neural nets (Graph-LSTMs) is proposed. Firstly, we use multivariable stochastic differential equations (SDE) to model the cooperative interaction of cluster members and transform the derived state space model of cluster members into the same form as the constant velocity (CV) motion model, and the target state equation of cluster which can be used for Bayesian filtering iteration is established. Secondly, based on the detection relationship between radars and cluster members, we introduce the detection confirmation matrix and propose a radar-target assignment method to achieve multiple measurements of single member and detection coverage of all cluster members. Then, each radar uses δ-GLMB filter to estimate the motion state of the assigned targets. Finally, on the basis of spatial discretization, the labels of multiple estimates of cluster member states are obtained. We use the designed Graph-LSTMs to learn the cooperative relationship between target states to fuse the labels and obtain better tracking effect. The experimental results show that the proposed method effectively simulates the cluster motion and realizes the joint estimation of cluster target motion state by multiradar. Our method makes up for the defect that a single radar cannot stably track adjacent multiple targets and achieves better estimation fusion effect than the expectation-maximization (EM) algorithm and mean method.

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Section: Introductionmentioning

confidence: 99%

Multiradar Joint Tracking of Cluster Targets Based on Graph-LSTMs

et al. 2022

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“…Some scholars integrate information entropy theory with parameter extraction techniques to investigate characteristics of underwater object. Also in multi target tracking the maximum entropy theory has been combined with JPDA method [ 27 ]. Hence, information entropy theory has also important research value in underwater passive target tracking.…”

Section: Introductionmentioning

confidence: 99%

Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target

Ali

Khan

Raja

et al. 2021

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In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model.

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“…Information entropy theories are also used to estimate single target states and multiple targets states. The fuzzy c-means clustering method based on maximum information entropy combined with PDA is proposed in [28], which uses a value optimized by the maximum information entropy to represent the measurement-to-target association probability. The multiple target tracking problem has also been solved by the maximum entropy intuitionistic fuzzy data association algorithm [29], cross entropy [30], maximum-fuzzyentropy-based Gaussian clustering algorithm [31], entropy distribution and game theory based on the random finite set probability hypothesis density (PHD) method [32], maxi-mum entropy fuzzy based on the fire-fly and PF [33], and the distributed cross-entropybased δ-generalized labeled multi-Bernoulli (δ-GLMB) filter [34].…”

Section: Introductionmentioning

confidence: 99%

Passive Tracking of Multiple Underwater Targets in Incomplete Detection and Clutter Environment

Ali

et al. 2021

Entropy

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A major advantage of the use of passive sonar in the tracking multiple underwater targets is that they can be kept covert, which reduces the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the complexity of data association between measurements and targets make the problem of underwater passive multiple target tracking challenging. To deal with these problems, the cardinalized probability hypothesis density (CPHD) recursion, which is based on Bayesian information theory, is developed to handle the data association uncertainty, and to acquire existing targets’ numbers and states (e.g., position and velocity). The key idea of the CPHD recursion is to simultaneously estimate the targets’ intensity and the probability distribution of the number of targets. The CPHD recursion is the first moment approximation of the Bayesian multiple targets filter, which avoids the data association procedure between the targets and measurements including clutter. The Bayesian-filter-based extended Kalman filter (EKF) is applied to deal with the nonlinear bearing and Doppler measurements. The experimental results show that the EKF-based CPHD recursion works well in the underwater passive multiple target tracking system in cluttered and noisy environments.

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Developing the fuzzy c-means clustering algorithm based on maximum entropy for multitarget tracking in a cluttered environment

Cited by 7 publications

References 21 publications

Multiradar Joint Tracking of Cluster Targets Based on Graph-LSTMs

Multiradar Joint Tracking of Cluster Targets Based on Graph-LSTMs

Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target

Passive Tracking of Multiple Underwater Targets in Incomplete Detection and Clutter Environment

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