Modified joint probabilistic data association with classification-aided for multitarget tracking

Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.

Study on the Hungarian algorithm for the maximum likelihood data association problem

A specialized Hungarian algorithm was developed here for the maximum likelihood data association problem with two implementation versions due to presence of false alarms and missed detections. The maximum likelihood data association problem is formulated as a bipartite weighted matching problem. Its duality and the optimality conditions are given. The Hungarian algorithm with its computational steps, data structure and computational complexity is presented. The two implementation versions, Hungarian forest （HF） algorithm and Hungarian tree （HT） algorithm, and their combination with the naYve auction initialization are discussed. The computational results show that HT algorithm is slightly faster than HF algorithm and they are both superior to the classic Munkres algorithm.

Study on data association algorithm of multi-passive-sensor location system

Aiming at three-passive-sensor location system, a generalized 3-dimension （3-D） assignment model is constructed based on property information, and a multi-target programming model is proposed based on direction-finding and property fusion information. The multi-target programming model is transformed into a single target programming problem to resolve, and its data association result is compared with the results which are solved by using one kind of information only. Simulation experiments show the effectiveness of the multi-target programming algorithm with higher data association accuracy and less calculation.

Data association based on target signal classification information

In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information （TSCI） should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA （joint probabilistic data association）. The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.

New data association technique based on ACO with directional information considered

Due to the advantages of ant colony optimization （ACO） in solving complex problems, a new data association algorithm based on ACO in a cluttered environment called DACDA is proposed. In the proposed method, the concept for tour and the length of tour are redefined. Additionally, the directional information is incorporated into the proposed method because it is one of the most important factors that affects the performance of data association. Kalman filter is employed to estimate target states. Computer simulation results show that the proposed method could carry out data association in an acceptable CPU time, and the correct data association rate is higher than that obtained by the data association （DA） algorithm not combined with directional information.

Novel Grouped Probability Data Association Algorithm for MIMO Detection

To bridge the performance gap between original probability data association （PDA） algorithm and the optimum maximum a posterior （MAP） algorithm for multi-input multi-output （MIMO） detection, a grouped PDA （GP-PDA） detection algorithm is proposed. The proposed GP-PDA method divides all the transmit antennas into groups, and then updates the symbol probabilities group by group using PDA computations. In each group, joint a posterior probability （APP） is computed to obtain the APP of a single symbol in this group, like the MAP algorithm. Such new algorithm combines the characters of MAP and PDA. MAP and original PDA algorithm can be regarded as a special case of the proposed GP-PDA. Simulations show that the proposed GP-PDA provides a performance and complexity trade, off between original PDA and MAP algorithm.

Loopy belief propagation based data association for extended target tracking

The data association problem of multiple extended target tracking is very challenging because each target may generate multiple measurements.Recently,the belief propagation based multiple target tracking algorithms with high efficiency have been a research focus.Different from the belief propagation based Extended Target tracking based on Belief Propagation(ET-BP)algorithm proposed in our previous work,a new graphical model formulation of data association for multiple extended target tracking is proposed in this paper.The proposed formulation can be solved by the Loopy Belief Propagation(LBP)algorithm.Furthermore,the simplified measurement set in the ET-BP algorithm is modified to improve tracking accuracy.Finally,experiment results show that the proposed algorithm has better performance than the ET-BP and joint probabilistic data association based on the simplified measurement set algorithms in terms of accuracy and efficiency.Additionally,the convergence of the proposed algorithm is verified in the simulations.

Multi-target pig tracking algorithm based on joint probability data association and particle filter

In order to evaluate the health status of pigs in time,monitor accurately the disease dynamics of live pigs,and reduce the morbidity and mortality of pigs in the existing large-scale farming model,pig detection and tracking technology based on machine vision are used to monitor the behavior of pigs.However,it is challenging to efficiently detect and track pigs with noise caused by occlusion and interaction between targets.In view of the actual breeding conditions of pigs and the limitations of existing behavior monitoring technology of an individual pig,this study proposed a method that used color feature,target centroid and the minimum circumscribed rectangle length-width ratio as the features to build a multi-target tracking algorithm,which based on joint probability data association and particle filter.Experimental results show the proposed algorithm can quickly and accurately track pigs in the video,and it is able to cope with partial occlusions and recover the tracks after temporary loss.

Distributed interacted multisensor joint probabilistic data association algorithm based on D-S theory

In order to resolve the multisensor multiplied maneuvering target tracking problem, this paper presents a distributed interacted multiple model multisensor joint probabilistic data association algorithm （DIMM-MSJPDA）. First of all, the interacted multiple model joint probabilistic data association algorithm is applied to each sensor, and then the state estimation, estimation covariance, model probability, combined innovation, innovation covariance are delivered to the fusion center. Then, the tracks from each sensor are correlated and the D-S evidence theory is used to gain the model probability of an identical target. Finally, the ultimate state estimation of each target is calculated according to the new model probability, and the state estimation is transmitted to each sensor. Simulations are designed to test the tracking performance of DIMM-MSJPDA algorithm. The results show that the use of DIMM-MSJPDA algorithm enables the distributed multisensor system to track multiplied maneuvering targets and its tracking performance is much better than that of IMMJPDA algorithm.

New optimal algorithm of data association for multi-passive-sensor location system

In dense target and false detection scenario of four time difference of arrival （TDOA） for multi-passive-sensor location system, the global optimal data association algorithm has to be adopted. In view of the heavy calculation burden of the traditional optimal assignment algorithm, this paper proposes a new global optimal assignment algorithm and a 2-stage association algorithm based on a statistic test. Compared with the traditional optimal algorithm, the new optimal algorithm avoids the complicated operations for finding the target position before we calculate association cost; hence, much of the procedure time is saved. In the 2-stage association algorithm, a large number of false location points are eliminated from candidate associations in advance. Therefore, the operation is further decreased, and the correct data association probability is improved in varying degrees. Both the complexity analyses and simulation results can verify the effectiveness of the new algorithms.

DATA FUSION ALGORITHM BASED ON STATE AND ATTRIBUTE PARAMETER

A new data fusion algorithm is presented. The new algorithm has two steps. First, three basic probability assignments dependent on different attribute parameters with Demspter fusion rule are processed. Using the fusion results, one can calculate the evidence interval of the proposition that “the return is from target”. Then based on the magnitude of the center of the evidence interval, one can reject some false alarms, so as to cut down the number of clutters accepted by the filter gate. Second, the attribute parameter likelihood function(APLF, for short) and kinematic measurement likelihood function are used to form a joint likelihood function. A method is also proposed for calculating APLF. As for APLF, it is found and proved that there are differences between similar targets and dissimlar targets. By using the differences, one can distinguish adjacent targets more efficiently. In a word, the technique presented in this paper is an integrated adaptive data association fusion algorithm. The advantages of the algorithm are discussed and demonstrated via single and multiple targets tracking simulations. In simulation, the target maneuver, the presence of clutter and the varying of parameters are taken into consideration.

Hydraulic metal structure health diagnosis based on data mining technology

In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.

A Mechanism for Dynamic and Consistent Product Information Association

The dynamic and consistent information association among vtrious application activities in the full life cycle of a product is a key to the assurance of the cooperation among different application domains. In order to establish and maintain the association, a design-process-based product association model was proposed. This model takes advantage of the generic naming mechanism, the private protocol for history-based form feature modeling, on which the Data Association Protocol is built. Hence the model can provide the way of constructing and maintaining the information linkage among different product developing stages naturally and dynamically while keeping the privacy of the feature coding. A case study illustrates the utilities of the model in the data linking between design model and process planning model.

A Study of Using Synthetic Data for Effective Association Knowledge Learning

Association,aiming to link bounding boxes of the same identity in a video sequence,is a central component in multi-object tracking(MOT).To train association modules,e.g.,parametric networks,real video data are usually used.However,annotating person tracks in consecutive video frames is expensive,and such real data,due to its inflexibility,offer us limited opportunities to evaluate the system performance w.r.t.changing tracking scenarios.In this paper,we study whether 3D synthetic data can replace real-world videos for association training.Specifically,we introduce a large-scale synthetic data engine named MOTX,where the motion characteristics of cameras and objects are manually configured to be similar to those of real-world datasets.We show that,compared with real data,association knowledge obtained from synthetic data can achieve very similar performance on real-world test sets without domain adaption techniques.Our intriguing observation is credited to two factors.First and foremost,3D engines can well simulate motion factors such as camera movement,camera view,and object movement so that the simulated videos can provide association modules with effective motion features.Second,the experimental results show that the appearance domain gap hardly harms the learning of association knowledge.In addition,the strong customization ability of MOTX allows us to quantitatively assess the impact of motion factors on MOT,which brings new insights to the community.

A split target detection and tracking algorithm for ballistic missile tracking during the re-entry phase

In the re-entry phase of a ballistic missile,decoys can be deployed as a mean to overburden enemy defenses.This results in a single track being split into multiple track-lets.Tracking of these track-lets is a critical task as any miss in the tracking procedure can become a cause of a major threat.The tracking process becomes more complicated in the presence of clutter.The low detection rate is one of the factors that may contribute to increasing the difficulty level in terms of tracking in the cluttered environment.This work introduces a new algorithm for the split event detection and target tracking under the framework of the joint integrated probabilistic data association(JIPDA)algorithm.The proposed algorithm is termed as split event-JIPDA(SE-JIPDA).This work establishes the mathematical foundation for the split target detection and tracking mechanism.The performance analysis is made under different simulation conditions to provide a clear insight into the merits of the proposed algorithm.The performance parameters in these simulations are the root mean square error(RMSE),confirmed true track rate(CTTR)and confirmed split true track rate(CSTTR).

Vessel fusion tracking with a dual-frequency high-frequency surface wave radar and calibrated by an automatic identification system

High-frequency surface wave radar（HFSWR） and automatic identification system（AIS） are the two most important sensors used for vessel tracking.The HFSWR can be applied to tracking all vessels in a detection area,while the AIS is usually used to verify the information of cooperative vessels.Because of interference from sea clutter,employing single-frequency HFSWR for vessel tracking may obscure vessels located in the blind zones of Bragg peaks.Analyzing changes in the detection frequencies constitutes an effective method for addressing this deficiency.A solution consisting of vessel fusion tracking is proposed using dual-frequency HFSWR data calibrated by the AIS.Since different systematic biases exist between HFSWR frequency measurements and AIS measurements,AIS information is used to estimate and correct the HFSWR systematic biases at each frequency.First,AIS point measurements for cooperative vessels are associated with the HFSWR measurements using a JVC assignment algorithm.From the association results of the cooperative vessels,the systematic biases in the dualfrequency HFSWR data are estimated and corrected.Then,based on the corrected dual-frequency HFSWR data,the vessels are tracked using a dual-frequency fusion joint probabilistic data association（JPDA）-unscented Kalman filter（UKF） algorithm.Experimental results using real-life detection data show that the proposed method is efficient at tracking vessels in real time and can improve the tracking capability and accuracy compared with tracking processes involving single-frequency data.

Dense Mapping From an Accurate Tracking SLAM

In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requires a highperformance GPU to accelerate computation. This paper proposes a dense mapping approach which can remove outliers and obtain a clean 3D model using a CPU in real-time. The dense mapping approach processes keyframes and establishes data association by using multi-threading technology. The outliers are removed by changing detections of associated vertices between keyframes. The implicit surface data of inliers is represented by a truncated signed distance function and fused with an adaptive weight. A global hash table and a local hash table are used to store and retrieve surface data for data-reuse. Experiment results show that the proposed approach can precisely remove the outliers in scene and obtain a dense 3D map with a better visual effect in real-time.

A multi-target tracking algorithm based on Gaussian mixture model

Since the joint probabilistic data association(JPDA)algorithm results in calculation explosion with the increasing number of targets,a multi-target tracking algorithm based on Gaussian mixture model(GMM)clustering is proposed.The algorithm is used to cluster the measurements,and the association matrix between measurements and tracks is constructed by the posterior probability.Compared with the traditional data association algorithm,this algorithm has better tracking performance and less computational complexity.Simulation results demonstrate the effectiveness of the proposed algorithm.

Tracking a maneuvering target in clutter with out-of-sequence measurements for airborne radar

There are many proposed optimal or suboptimal al- gorithms to update out-of-sequence measurement（s） （OoSM（s）） for linear-Gaussian systems, but few algorithms are dedicated to track a maneuvering target in clutter by using OoSMs. In order to address the nonlinear OoSMs obtained by the airborne radar located on a moving platform from a maneuvering target in clut- ter, an interacting multiple model probabilistic data association （IMMPDA） algorithm with the OoSM is developed. To be practical, the algorithm is based on the Earth-centered Earth-fixed （ECEF） coordinate system where it considers the effect of the platform＇s attitude and the curvature of the Earth. The proposed method is validated through the Monte Carlo test compared with the perfor- mance of the standard IMMPDA algorithm ignoring the OoSM, and the conclusions show that using the OoSM can improve the track- ing performance, and the shorter the lag step is, the greater degree the performance is improved, but when the lag step is large, the performance is not improved any more by using the OoSM, which can provide some references for engineering application.