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In this paper, a novel maximum fuzzy correntropy Kalman filter (MFC-KF) algorithm is proposed to solve the problem that the effect of different samples on state estimation is uncertain in common correntropy. In the proposed algorithm, a new optimization criterion—the maximum fuzzy correntropy criterion with fuzzy correntropy based on fuzzy information theory—is used to optimize the Kalman filter, by reducing the effect of the common correntropy applying the same weight for all samples. Moreover, to apply the MFC-KF algorithm to bearings-only maneuvering target tracking, it is combined with the least-squares method for measurement conversion. Moreover, the kernel width is set adaptively. Simulations show that the proposed algorithm can track a target more accurately than the interactive multi-model extended Kalman filter (IMMEKF), the interactive multi-model unscented Kalman filter (IMMUKF), or the maximum correntropy Kalman filter (MCKF).

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.

In this paper, a novel Takagi-Sugeno (T-S) fuzzy model particle filtering algorithm (TSF-PF) based on fuzzy C-regression clustering is proposed for uncertainty modeling of the target dynamic model with non-Gaussian noise. In the proposed algorithm, a generic semantic framework of the T-S fuzzy model is constructed to incorporate spatial feature information of a target into the particle filter, in which the spatial feature information is characterized by several semantic fuzzy sets. Meanwhile, a fuzzy C-regression clustering method based on correntropy is proposed to adaptively identify the premise parameters of the T-S fuzzy model, which is used to adjust the weight of models, and a Kalman filter is used to identify the consequent parameters. And then an efficient importance density function is constructed by using the proposed T-S fuzzy model, which can efficiently improve the robust and diversity of the sampling particles. Furthermore, in order to improve the real-time performance of the proposed algorithm, two improved T-S fuzzy model particle filtering algorithms are presented. The simulation results show that the tracking performance of the proposed algorithms are better than that of the traditional interacting multiple model (IMM), interacting multiple model unscented Kalman filter (IMMUKF), interacting multiple model particle filter (IMMPF) and interacting multiple model Rao-Blackwellized particle filter (IMMRBPF). Particularly, the proposed algorithms can accurately track the maneuvering target when the moving direction abruptly changes or the prior information of the target dynamic model is inaccuracy.

In this paper, a novel intuitionistic fuzzy clustering algorithm based on feature selection (IFC-FS) for multiple object tracking is proposed. In the proposed algorithm, the neighborhood rough set is used to achieve the adaptive selection of the multiple object features of visual objects, which are applied to calculate the distance similarity measure between the objects and the observations. At the same time, in order to incorporate the local information of objects into the intuitionistic fuzzy clustering, the local information distances between objects and observations are estimated by using the optimal subpattern assignment metric based on the reference topology set, and a new intuitionistic fuzzy clustering based on maximum entropy principle is proposed by using the new similarity distance measure. Finally, the association probabilities among the objects and the observations are reconstructed by utilizing the intuitionistic fuzzy membership degrees. The experimental results show that the proposed algorithm can effectively improve the estimated accuracy and robustness of the association probabilities between the objects and the observations, and have the ability to track accurately multiple objects in the complex background and long-time occlusion environment.

In this paper, we propose a novel recursive Takagi-Sugeno (T-S) fuzzy semantic modeling approach for discrete state-space system. According to the information learning theoretic (ILT), the correntropy can capture the higher moments of the error probability distribution to deal with non-Gaussian noise. Considering the advantages of fuzzy theory and correntropy, fuzzy correntropy is constructed and a novel kernel fuzzy C-regression model clustering based on fuzzy correntropy is proposed to solve the premise parameter identification problem of the T-S fuzzy model. To the identification of the consequent part parameters of the T-S fuzzy model, a modified extended forgetting factor recursive least squares (MEFRLS) estimator is presented. Moreover, to evaluate the performance of the proposed fuzzy model, the proposed T-S fuzzy model is applied to solve the problem of maneuvering target tracking by incorporating the target feature semantic information. Finally, the experiment results show the proposed algorithm can effectively track a maneuvering target, and its performance is better than the exist algorithms, such as interacting multiple model Kalman filter (IMMKF), interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model particle filter (IMMPF) and interacting multiple model Rao-Blackwellized particle filter the (IMMRBPF).

The probability hypothesis density filter under glint noise is efficient for multi-object tracking. However, this filter is inapplicable to tracking the multiple maneuvering objects in case of low detection probability. In order to track the multiple maneuvering objects under glint noise more efficiently, we propose a novel multi-object Bayesian (MOB) filter for jump Markov system under glint noise by applying jump Markov system models and variational Bayesian approach to the MOB filter and develop an implementation of this filter. The developed implementation uses the Student's t-distribution to depict the glint noise, uses the Gaussian-Gamma distribution to represent the predicted state distribution and updated state distribution of each target, and uses the variational Bayesian method to acquire the approximate state distribution of each target. A comparison of the proposed MOB filter with the existing filters demonstrates that the proposed MOB filter is better at tracking the maneuvering objects under glint noise than the existing filters.

Considering the development of object detection based on deep learning framework in recent years, it has brought a new scope for multi-source fusion in the field of autonomous driving. In this paper, we propose a new architecture with a feature pyramid attention module to fuse the projected radar data and camera data. In the proposed algorithm, the detection model of YOLOv3 is employed by us and the feature pyramid module is extended with the input interface of the radar projection image and attention module. Additionally, in order to reduce the interference information from different scales of radar projected block, a new generation mechanism of radar projection images is introduced. Finally, the radar projection image is fused in feature pyramid layers with an attention module. The result shows that the proposed fusion algorithm outperforms better than image-only network for the nuScenes dataset. The code for this research will be made available to the public at: https://github.com/yuanliangxie/nuscenes_data_process.

This paper proposes a marginal multi-object Bayesian filter with multiple hypotheses to track multiple objects in the presence of object appearing and object disappearing, missed detection and clutter. This filter delivers the probability of existence and probability density function of each object. A mathematical model for searching K-best hypotheses is set up by the maximization of the generalized joint likelihood ratios of hypotheses, which results in a 2-dimensional assignment problem. The K-best hypotheses can be acquired by using the Murty algorithm to solve the 2-dimensional assignment problem. According to the K-best hypotheses, the existence probabilities and probability density functions of objects are formed. Furthermore, an implementation of this filter for a linear Gaussian system is developed and is extended to nonlinear observations. Experimental result demonstrates that the proposed filter outperforms other available filters at various numbers of clutter and different detecting probabilities.

The probability hypothesis density (PHD) filter and marginal distribution Bayes (MDB) filter are two efficient Bayes approaches for multi-target tracking. However, these two filters fail to provide the state estimation of a target during its initial times due to the poor capability of the two filters on the target detection. To enhance the capability of the MDB filter on the target detection, we present a method for the target detection based on the rule-based track initiation technique, and develop a multi-target Bayes filter with the target detection by applying this target detection method to the MDB filter. Simulation results indicate that this filter has a stronger detecting and tracking capability of the target than the existing PHD and MDB filters.

Aiming at the modeling uncertainty and nonGaussian characteristics of dynamic systems in maneuvering target tracking, a Takagi-Sugeno (T-S) modeling particle filter method via intuitionistic fuzzy decision (IFD-TSPF) is proposed. As for the Structure recognition of T-S model, the membership degree of multiple features are calculated by the ridge reduction distribution function, and then the intuitionistic fuzzy point operator is used to calculate the decision scores corresponding to the features. According to the principle of evidence decision, the appropriate premise variables are selected. Finally, the importance density function is constructed by the estimation results of the proposed T-S model, which can effectively reduce the particle degradation. Through explanation of simulation results, which show that the performance of our algorithm is better than interacting multi-model particle filter(IMMPF) and interacting multi-model Rao-Blackwellized particle filter(IMMRBPF). When the direction of the target is suddenly changed, it can effectively track the target.

For the problem of inaccurate or difficult to obtain statistical characteristics of non-Gaussian noise, an interacting T-S fuzzy modeling algorithm is proposed to incorporate spatial-temporal information into particle filtering. In the proposed method, feature information is characterized by multiple semantic fuzzy sets, and the model transition probabilities are estimated by using the fuzzy set transition probabilities, which can be derived by the closeness degrees between the fuzzy sets. Furthermore, the correntropy can capture the statistical information to solve the non-Gaussian noise, thus a kernel fuzzy C-regression means (FCRM) based on correntropy and spatial-temporal information is proposed to adaptively identify the premise parameters of T-S fuzzy model, and a modified strong tracking method is used to estimate the consequence parameters. By using the proposed interacting T-S fuzzy model, an efficient importance density function is constructed for the particle filtering algorithm. Finally, the simulation results show that the tracking performance of the proposed algorithm is effective in processing non-Gaussian noise.

Tracking multiple maneuvering (MM) targets is a well-known and challenging problem because of clutter and several uncertainties existing in target motion mode, target detection, and data association. An efficient solution to this problem is the Gaussian mixture probability hypothesis density (GM-PHD) filter for jump Markov system (JMS) models. However, this solution is inapplicable to circumstances where detection probability is low because the GM-PHD filter for JMS models requires a high detection probability. To address this problem, we propose a sequential multiple target (MT) Bayes filter for JMS models. To track MM targets that are switching among a set of linear Gaussian models, an implementation process of this filter for linear Gaussian jump Markov MT models is also developed. The conclusion that the novel filter is more efficient for tracking MM targets than the existing filter for JMS models in circumstances of low detection probability is validated by simulation results.

In the paper, a novel auxiliary truncated particle filtering for bearings-only maneuvering target tracking (ATPF-BOT) is proposed. In the proposed algorithm, a modified prior probability density function (PDF) is derived to solve the modeling uncertainty problem, which can simultaneously incorporate current measurement information and target characteristic information. Meanwhile, the proposal distribution is jointly designed by using the prior PDF and the modified prior PDF. Moreover, the proposal distribution is approximately calculated based on adaptive least square method so as to apply the ATPF algorithm for bearings-only maneuvering target tracking, and a practical algorithm is also developed. The experiment results show that the proposed algorithm is computationally efficient and successfully implemented in bearings-only target tracking systems.

In this paper, a novel multi-source vehicle detection algorithm based on deep learning is proposed. In the proposed algorithm, in order to detect the vehicle objects, a fast deep learning algorithm based on convolutional neural network (CNN) is utilized to detect the vehicle objects, and the radar is used to obtain the position information about the vehicle objects. At the same time, a coordinate transformation method from radar coordinate system to video pixel coordinate system is presented, then the video detections and radar infromation are integrated to improve the detection performance of vehicles Finally, the experiment results based on the real datasets show that the proposed algorithm is very effective for the vehicle object detection and tracking.

Particle filtering (PF) schemes are a set of simulation-based techniques relying on proposal distributions which have a crucial impact on their performance. In this paper, we introduce a novel constrained Extended Kalman particle filter (CEPF), the constrained Extended Kalman filter is used to generate the porposal distribution. The algorithm integrates the nonlinear state constraint information and the latest measurement information simultaneously into the dynamic system transition density. The proposed algorithm selects particles with higher likelihood to propagate into the next time ste by an efficient series of constraint optimization. The method can convergent theoretically and the simulation results show estimate advantage compared with other convention filters such as Extended Kalman Filter (EKF), Unscented Kalman Filer (UKF), Generic Particle Filter(GPF), particle filter with EKF proposal (EPF) and particle filter with UKF proposal (UPF).

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.

The marginal distribution Bayes (MDB) filter is an efficient approach for tracking an unknown and time-varying number of targets in the presence of clutter, noise, data association uncertainty, and detection uncertainty. This filter propagates the marginal distributions and existence probabilities of each target in the filter recursion, and it admits a closed-form solution for a linear Gaussian multi-target model. However, this closed-form solution is not general enough to accommodate nonlinear multi-target models. In this paper, we propose two implementations of the MDB filter to accommodate nonlinear multi-target models. The first is the first-order Taylor approximation MDB (FTA-MDB) filter which is based on the linearization technique of nonlinear function, and the second is the unscented transform MDB (UT-MDB) filter which is based on the unscented transform technique. Simulation results demonstrate that the proposed implementations are better on multiple targets tracking than the UK-PHD filter.

To investigate the prognostic significance of Follicular Lymphoma International Prognostic Index 2 (FLIPI2) in FL patients treated with rituximab maintenance.A tatol of 140 newly diagnosed FL patients who received Rituximab plus cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) chemotherapy in our department were retrospectively analyzed from December 2002 to December 2014. Among 140 patients with FL 122 patients achieved response, from them 56 patients received R maintenance (RM) every 2 months for median 8 times (RM group) while the rest 66 patients did not receive further anti-lymphoma treatment (non-RM group).There was no statistical difference in age, sex, pathologic grading, staging, FLIPI or FLIPI2 between RM and non-RM groups. The 2-year progression-free survival (PFS) of RM and non-RM groups were 89.7% and 77.6% (P=0.043) while the 2-year overall survival were 100% and 98.6% (P=0.131). FLIPI2 is a significant prognostic model either in the total cohort, RM or non-RM groups (P0.001 all). In subgroup analysis, RM was able to decrease disease progression in low and intermediate-risk group of FLIPI2, while the 2-year PFS of RM and non-RM groups in high-risk group were similar (55.6% vs 46.9%)(P=0.920).FLIPI2 presents robust prognostic significance either in RM or OBS patients, the patients in FLIPI2 low and intermediate-risk group may benefite from RM, but the role of RM in high-risk patients should be further to investigate.

High contents of curcusones A and B and trace amounts of spirocurcasone exist in the roots of Jatropha curcas. Here, a one-step semisynthesis method of spirocurcasone and pyracurcasone was built, not only resulted an increased yield of spirocurcasone but also produced pyracurcasone, which exhibited greater cytotoxicity compared to curcusones A and B. The plausible mechanism of the formation of pyracurcasone was proposed, and the proposed biogenetic origin for spirocurcasone by Taglialatela-Scafati was confirmed.

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.

For the conventional truncated unscented Kalman filtering (TUKF) algorithm requires the measurement to be a bijective function, a novel improved truncated unscented Kalman filtering is proposed. In the proposed algorithm, we linearize the bijective measurements function based on the statistical linear regression (SLR) in order to obtain the only inverse function of the measurement function. It is a modified algorithm which extends the range of practical application of the filtering problems. Finally, the experiments show that the performance of the proposed algorithm is better than the unscented Kalman filter (UKF) and the quadrature Kalman filter (QKF). This approach can efficiently deal with this problem that measurement functions are not bijective.

Four new phragmalin limonoids (chukfuransins A-D) were isolated from the twigs and leaves of Chukrasia tabularis. Chukfuransins A (1) and B (2) feature a unique C-15/C-20 linkage proposed to be built by a biogenetic pathway involving Michael addition. Chukfuransins C (3) and D (4) feature the C-15/C-21 linkage. Their structures and absolute configurations were established by NMR techniques and X-ray crystallographic analysis.

Although visual tracking have been greatly improved in the last decade, there are still many challenges that are not fully resolved. Of these challenges, occlusions, which can be long lasting, are often ignored. Under the framework of particle filtering, this paper uses the incremental principal component analysis subspace method to learn an orthogonal subspace, then gets the linear representation of target appearance. To avoid the tracking drift, an occlusion handling scheme was proposed. In this scheme, firstly, determinate state of the target by the variance of the reconstruction error, then predict the position of the target by velocity prediction method and particle filtering method. The experiments revealed that proposed algorithm is impactful to deal with occlusion.

In order to cope with the complex variation of target appearance during visual tracking, a robust tracking algorithm based on multi-scale kernelized least squares (KLS) is proposed. First, by showing that the dense sampling set of translated patches is circulant, using the well-established theory of circulant matrices, kernelized least squares is efficient computed with fast Fourier transform (FFT). Second, we propose a method to judge whether target and background changed significantly by the response variance of all candidate targets, and use it to improve the model updating. Meanwhile, robust scale estimation based on a scale pyramid representation is proposed to handle large scale variations. Extensive experimental results show that our approach outperforms state-of-the-art methods, while operating at real-time.

To improve the performance of multi-object tracking in the complex scenario with frequent occlusions and cluttered backgrounds, a novel online multi-object tracking algorithm based on fuzzy logic is proposed. In the proposed algorithm, firstly, the similarity measure of multiple features between the objects and the measurements are calculated, including the background-weighted color feature, histogram of oriented gradients feature, local binary pattern feature and spatial distance feature. Secondly, the fuzzy rule base is constructed by incorporating the expert knowledge, which can adaptively allocate the weight of each feature by using fuzzy logic. The association probabilities between objects and measurements are substituted by the weighted sum of multiple features' similarity measure, which can effectively improve the accuracy of data association. Experimental results using challenging public datasets demonstrate that the improved performance of the proposed algorithm, compared with other state-of-the-art tracking algorithms.

Previous analyses of the naphthomycin biosynthetic gene cluster and a comparison with known naphthomycin-type products from Streptomyces sp. CS have suggested that new products can be found from this strain. In this study, screening by LC-MS of Streptomyces sp. CS products formed under different culture conditions revealed several unknown peaks in the product spectra of extracts derived from oatmeal medium cultures. Three new naphthomycins, naphthomycins L (1), M (2), and N (3), and the known naphthomycins A (4), E (5), and D (6) were obtained. The structures were elucidated using spectroscopic data from 1D and 2D NMR and HRESIMS experiments.

Catalytic asymmetric formal total synthesis of (+)-dichroanone and (+)-taiwaniaquinone H has been achieved. Key step involved construction of all-carbon quaternary carbon by palladium-catalyzed conjugate addition of arylboronic acid to 3-methyl cyclohexenone. Furthermore, a new approach to build [6-5-6] tricyclic backbone via formyl introduction and subsequent aldol-type condensation was also explored. (C) 2014 Elsevier Ltd. All rights reserved.

Bayesian filter is an efficient approach for multi-target tracking in the presence of clutter. Recently, considerable attention has been focused on probability hypothesis density (PHD) filter, which is an intensity approximation of the multi-target Bayesian filter. However, PHD filter is inapplicable to cases in which target detection probability is low. The use of this filter may result in a delay in data processing because it handles received measurements periodically, once every sampling period. To track multiple targets in the case of low detection probability and to handle received measurements in real time, we propose a sequential measurement-driven Bayesian filter. The proposed filter jointly propagates the marginal distributions and existence probabilities of each target in the filter recursion. We also present an implementation of the proposed filter for linear Gaussian models. Simulation results demonstrate that the proposed filter can more accurately track multiple targets than the Gaussian mixture PHD filter or cardinalized PHD filter.

Total synthesis of (+/-)-brazilin has been accomplished. m-CPBA epoxidation of allyl alcohol 10 and epoxy opening reaction mediated by m-chlorobenzoic acid, formed in situ as a byproduct, gave advanced intermediate diol 14. O-alkylation and cyclization gave phenol 6 which enabled the formal synthesis of (+/-)-brazilein and (+/-)-brazilide A. (C) 2013 Elsevier Ltd. All rights reserved.

For the nonlinear non-Gaussian filtering problem of observation data in sparseness sampling environment, a novel auxiliary Gaussian sum quadrature particle filter (AGSQPF) based on target characteristics is proposed. In the proposed algorithm, the predicted and the posterior probability density function of target state are approximated by finite Gaussian mixtures based on Gauss-Hermite quadrature and the particle filtering. Moreover, the proposed algorithm can incorporate target speed, time interval and the latest observation information into the importance density function, which can effectively improve the performance. The simulation results show that the performance of the proposed algorithm is much better than Gaussian sum quadrature particle filter (GSQPF) for sparseness sampling environment.

Novel auxiliary truncated unscented Kalman filtering (ATUKF) is proposed for bearings-only maneuvering target tracking in this paper. In the proposed algorithm, to deal with arbitrary changes in motion models, a modified prior probability density function (PDF) is derived based on some auxiliary target characteristics and current measurements. Then, the modified prior PDF is approximated as a Gaussian density by using the statistical linear regression (SLR) to estimate the mean and covariance. In order to track bearings-only maneuvering target, the posterior PDF is jointly estimated based on the prior probability density function and the modified prior probability density function, and a practical algorithm is developed. Finally, compared with other nonlinear filtering approaches, the experimental results of the proposed algorithm show a significant improvement for both the univariate nonstationary growth model (UNGM) case and bearings-only target tracking case.

Tracking the target that maneuvers at a variable turn rate is a challenging problem. The traditional solution for this problem is the use of the switching multiple models technique, which includes several dynamic models with different turn rates for matching the motion mode of the target at each point in time. However, the actual motion mode of a target at any time may be different from all of the dynamic models, because these models are usually limited. To address this problem, we establish a formula for estimating the turn rate of a maneuvering target. By applying the estimation method of the turn rate to the multi-target Bayes (MB) filter, we develop a MB filter with an adaptive estimation of the turn rate, in order to track multiple maneuvering targets. Simulation results indicate that the MB filter with an adaptive estimation of the turn rate, is better than the existing filter at tracking the target that maneuvers at a variable turn rate.

Spirocurcasone (II) and pyracurcasone (III) are prepared stereospecifically in one step from available curcusone.

For the nonlinear and non-Gaussian filtering problem of target tracking, a novel Gaussian sum quadrature particle filter(GSQPF) based on Gauss-Hermite quadrature and Gaussian sum particle filter is proposed. In the proposed algorithm, according to the advantage of Gaussian-Hermite quadrature points in the nonlinear approximation and the diversity of quadrature points, we introduce a set of quadrature point probability densities to approximate the important density function, the filtering and prediction densities are approximated as finite Gaussian mixtures. Because of the advantage of Gaussian mixture and the particle filtering, it can effectively improve the performance. The simulations show that the presented filter can outperform both Gaussian sum particle filter(GSPF) and quadrature particle filter(QPF).

In this paper, a new data association algorithm based on intuitionistic fuzzy clustering for multi-target tracking in cluttered environment was proposed. In the proposed algorithm, the joint association probabilities in JPDAF are reconstructed by utilizing the intuitionistic fuzzy membership degree of the measurement belonging to the target. In order to compute the intuitionistic fuzzy membership degree, a new intuitionistic fuzzy clustering method is proposed. At the same time, to deal with the uncertainty of the measurements, a new weight assignment is introduced. Finally, the simulation results show that the proposed algorithm is effective, and the performance of tracking is higher than the JPDAF algorithm.

This paper proposes a fast and stable automatic mosaic method of the unmanned aerial vehicle (UAV) images based on the Kalman filter. Firstly, the features of the unmanned aerial vehicle Images are analyzed. Then, a Kalman filter was proposed for predicting the search area of feature points after analyzing the movement model of the overlap areas in the images. The Kalman filter helps to find the useful feature points in the specific areas within a short time. Following the analysis result, the detail steps of the method are finally presented. The experimental results show that the proposed method not only ensure the successful execution of automatic mosaic for the UAV video images, but also can reduce the time-cost.

In this paper, a new algorithm for multiple maneuvering target tracking is proposed. The proposed algorithm which is based on separating the multiple maneuvering target tracking into three parts-the data association, the estimation of the single target dynamic model and the estimation of the single target tracking subproblems conditional on the data association and the target dynamic model. Where the data assciation subproblem can be solved by the fuzzy data assciation, the single target dynamic model by the Rao-Blackwellized particle filter (MMRBPF) and the single target tracking by Kaiman filter or extend Kaiman filter. Finally, the experiment results show that the proposed algorithm can effectively track multiple manuvering targets.

To solve the problem of initiating tracks for multi-target in dense clutters environment, a Mean shift track initiation algorithm based on Hough transform is proposed. In the algorithm, firstly, hough transform is applied to transform observation points from input space, referred to as feature space into curves in a special parameter space; then a Mean shift clustering algorithm is executed to cluster the items gained in the parameter space, and the problem of peak seeking is also solved adaptively. Furthermore, a fuzzy influential factor, which is based on the vote number of accumulation matrix and distance between items in the parameter space and clustering center, is defined to design kernel function of Mean shift; thus clutters are removed more effectively. Experimental results show that proposed algorithm has high detection accuracy and can initiate tracks effectively.

In this paper, a new adaptive alpha - beta filter is proposed for maneuvering target tracking based on fuzzy logic. Firstly, we analyzed that the change of course angle and the measurement residual could reflect the maneuverability of the target real time, then the two parameters are used as the input variables of the fuzzy logic, and the fuzzy logic rules of the maneuverability are designed to calculate the parameters of the alpha - beta filter. The experiment results show a higher performance in maneuvering targets tracking problems and the method could be easily applied to real project.

In this paper, a new target tracking algorithm based on Gauss-Hermite quadrature is proposed in passive sensor array. Firstly, the quadrature Kalman filter (QKF) that used statistical linear regression (SLR) to linearize a nonlinear function through a set of Gauss-Hermite quadrature points is analyzed for passive target tracking. The performance of the filter is more accurate than the extended Kalman filter (EKF), the pseudo linear kalman filter (PLKF) and the unscented Kalman filter (UKF) in nonlinear dynamic system. Secondly, in order to avoid the unobservability problem of passive target tracking, a nonlinear measurement model of multiple passive sensors is founded, and the algorithm can deal with the case of non-Gaussian noise. Finally, the simulation results show that the proposed algorithm is effective, and its performance is superiority over above methods.