Multiple model PHD filter for tracking sharply maneuvering targets using recursive RANSAC based adaptive birth estimation

An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles.The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper,we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.

Maneuvering target tracking using threshold interacting multiple model algorithm

To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model （TIMM） is proposed. This algorithm is based on the interacting multiple model （IMM） method and applies a threshold controller to improve tracking accuracy. It is also applicable to other advanced algorithms of IMM. In this research, we also compare the position and velocity root mean square （RMS） errors of TIMM and IMM algorithms with two different examples. Simulation results show that the TIMM algorithm is superior to the traditional IMM alzorithm in estimation accuracy.

Fast density peak-based clustering algorithm for multiple extended target tracking

The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influence for the tracking results of different partitions is analyzed, and the form of the most informative partition is obtained. Then, a fast density peak-based clustering (FDPC) partitioning algorithm is applied to the measurement set partitioning. Since only one partition of the measurement set is used, the ET-PHD filter based on FDPC partitioning has lower computational complexity than the other ET-PHD filters. As FDPC partitioning is able to remove the spatially close clutter-generated measurements, the ET-PHD filter based on FDPC partitioning has good tracking performance in the scenario with more clutter-generated measurements. The simulation results show that the proposed algorithm can get the most informative partition and obviously reduce computational burden without losing tracking performance. As the number of clutter-generated measurements increased, the ET-PHD filter based on FDPC partitioning has better tracking performance than other ET-PHD filters. The FDPC algorithm will play an important role in the engineering realization of the multiple extended target tracking filter.

Efficient 2-D MUSIC algorithm for super-resolution moving target tracking based on an FMCW radar

Frequency modulated continuous wave(FMCW)radar is an advantageous sensor scheme for target estimation and environmental perception.However,existing algorithms based on discrete Fourier transform(DFT),multiple signal classification(MUSIC)and compressed sensing,etc.,cannot achieve both low complexity and high resolution simultaneously.This paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW radar.Firstly,we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching strategy.Secondly,we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data,which improves the efficiency of continuous target tracking.These two approaches have improved the algorithm efficiency by nearly 2-4 orders of magnitude without losing accuracy and resolution.Simulation experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios.

WSN Mobile Target Tracking Based on Improved Snake-Extended Kalman Filtering Algorithm

A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.

Multiple extended target tracking algorithm based on Gaussian surface matrix

In this paper, we consider the problem of irregular shapes tracking for multiple extended targets by introducing the Gaussian surface matrix（GSM） into the framework of the random finite set（RFS） theory. The Gaussian surface function is constructed first by the measurements, and it is used to define the GSM via a mapping function. We then integrate the GSM with the probability hypothesis density（PHD） filter, the Bayesian recursion formulas of GSM-PHD are derived and the Gaussian mixture implementation is employed to obtain the closed-form solutions. Moreover, the estimated shapes are designed to guide the measurement set sub-partition, which can cope with the problem of the spatially close target tracking. Simulation results show that the proposed algorithm can effectively estimate irregular target shapes and exhibit good robustness in cross extended target tracking.

Tracking Target Identification Model Based on Multiple Algorithms

In view of current situation of bad data synchronization, image blurring and tracking station stability in tracking target identification, a kind of tracking target identification model based on multiple algorithms was put forward, firstly establishing the image degradation model, using the wavelet algorithm for image preprocessing, doing image edge segmentation by using Robert algorithm after pretreatment, then using the maximum variance threshold method for image threshold segmentation, then extracting target features from the segmented image, and finally using the ABS algorithm to finish target tracking. Experiments proved the proposed model practical and effective.

Target Tracking Using the Interactive Multiple Model Method

Aim To develop a practical target tracking algorithm for different motion modes. Methods After creation of the new model, it was implemented by computer simulation to prove its performance and compared with the often-used current statistical model. Results The simulation results show that the new IMM (interactive multiple model) have low tracking error in both maneuVering segment and non^Inaneuwi segment while the current statistical model bas muCh higher tracking error in non-maneuvering segment. Conclusion In the point of trackintaccuracy, the new IMM method is much better than the current acceleration method. It can develop into a practical target hacking method.

A multiple template approach for robust tracking of fast motion target

Target tracking is very important in computer vision and related areas. It is usually difficult to accurately track fast motion target with appearance variations. Sometimes the tracking algorithms fail for heavy appearance variations. A multiple template method to track fast motion target with appearance changes is presented under the framework of appearance model with Kalman filter. Firstly, we construct a multiple template appearance model, which includes both the original template and templates affinely transformed from original one. Generally speaking, appearance variations of fast motion target can be covered by affine transformation. Therefore, the affine tr templates match the target of appearance variations better than conventional models. Secondly, we present an improved Kalman filter for approx- imate estimating the motion trail of the target and a modified similarity evaluation function for exact matching. The estimation approach can reduce time complexity of the algorithm and keep accuracy in the meantime. Thirdly, we propose an adaptive scheme for updating template set to alleviate the drift problem. The scheme considers the following differences： the weight differences in two successive frames; different types of affine transformation applied to templates. Finally, experiments demonstrate that the proposed algorithm is robust to appearance varia- tion of fast motion target and achieves real-time performance on middle/low-range computing platform.

Labeled box-particle CPHD filter for multiple extended targets tracking

In multiple extended targets tracking, replacing traditional multiple measurements with a rectangular region of the nonzero volume in the state space inspired by the box-particle idea is exactly suitable to deal with extended targets, without distinguishing the measurements originating from the true targets or clutter.Based on our recent work on extended box-particle probability hypothesis density(ET-BP-PHD) filter, we propose the extended labeled box-particle cardinalized probability hypothesis density(ET-LBP-CPHD) filter, which relaxes the Poisson assumptions of the extended target probability hypothesis density(PHD) filter in target numbers, and propagates not only the intensity function but also cardinality distribution. Moreover, it provides the identity of individual target by adding labels to box-particles. The proposed filter can improve the precision of estimating target number meanwhile achieve targets' tracks. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.

Method for Underwater Target Tracking Based on an Interacting Multiple Model

According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference （FIMM） is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model （CA）, the Singer mode~, and the nearly constant speed horizontal-turn model （HT） in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm.

Multiple Targets Tracking Using Kinematics in Wireless Sensor Networks

Target tracking is considered as one of the cardinal applications of a wireless sensor network. Tracking multiple targets is more challenging than tracking a single target in a wireless sensor network due to targets’ movement in different directions, targets’ speed variations and frequent connectivity failures of low powered sensor nodes. If all the low-powered sensor nodes are kept active in tracking multiple targets coming from different directions of the network, there is high probability of network failure due to wastage of power. It would be more realistic if the tracking area can be reduced so that less number of sensor nodes will be active and therefore, the network will consume less energy. Tracking area can be reduced by using the target’s kinematics. There is almost no method to track multiple targets based on targets’ kinematics. In our paper, we propose a distributed tracking method for tracking multiple targets considering targets’ kinematics. We simulate our method by a sensor network simulator OMNeT++ and empirical results state that our proposed methodology outperforms traditional tracking algorithms.

Multi-Bernoulli Filter for Tracking Multiple Targets Using Sensor Array

This paper presents a multi-Bernoulli filter for tracking the direction of arrival(DOAs)of time-varying number of targets using sensor array.Our method operates directly on the measurements of sensor array and does not require any detection.Firstly,more information is reserved and compared with the after-detection measurements using a finite set of detected points.It can significantly improve the tracking performance,especially in low signal-to-noise ratio.Secondly,it inherits the advantages of the multi-Bernoulli approximation which models each of the targets individually.This allows more accurate multi-target state estimation,especially when targets cross.The proposed filter does not need clustering step and simulation results showcase the improved performance of the proposed filter.

Tracking Algorithm Based on Improved Interacting Multiple Model Particle Filter

Measurements are always interfered with glint noise in a radar target tracking system, which makes the performance of traditional filtering fall sharply and even divergent.Against this problem, a new Interactive Multiple Model Particle Filter (IMMPF) algorithm is proposed for target tracking by introducing PF into Interactive Multiple Model (IMM).Different from the general method to select importance density function from PF, the particles are extracted from observation likelihood function within depending on observation noises.Observation noise is modelled, and the latest observation is fused, then the target can be effectively tracked.Finally, the optimized method is simulated with respect to bearings-only tracking of maneuvering target in a glint noise environment.Compared with the existing filtering algorithms, it turns out that the developed filtering algorithm is more efficient and closer to the real-time tracking requirement of high maneuvering targets.

Target Tracking Algorithm Based on Meanshift and Kalman Filter

Directed at the problem of occlusion in target tracking,a new improved algorithm based on the Meanshift algorithm and Kalman filter is proposed.The algorithm effectively combines the Meanshift algorithm with the Kalman filtering algorithm to determine the position of the target centroid and subsequently adjust the current search window adaptively according to the target centroid position and the previous frame search window boundary.The derived search window is more closely matched to the location of the target,which improves the accuracy and reliability of tracking.The environmental influence and other influencing factors on the algorithm are also reduced.Through comparison and analysis of the experiments,the modified algorithm demonstrates good stability and adaptability,and can effectively solve the problem of large area occlusion and similar interference.

Maneuvering target tracking algorithm based on cubature Kalman filter with observation iterated update

Reasonable selection and optimization of a filter used in model estimation for a multiple model structure is the key to improve tracking accuracy of maneuvering target.Combining with the cubature Kalman filter with iterated observation update and the interacting multiple model method,a novel interacting multiple model algorithm based on the cubature Kalman filter with observation iterated update is proposed.Firstly,aiming to the structural features of cubature Kalman filter,the cubature Kalman filter with observation iterated update is constructed by the mechanism of iterated observation update.Secondly,the improved cubature Kalman filter is used as the model filter of interacting multiple model,and the stability and reliability of model identification and state estimation are effectively promoted by the optimization of model filtering step.In the simulations,compared with classic improved interacting multiple model algorithms,the theoretical analysis and experimental results show the feasibility and validity of the proposed algorithm.

Maneuvering target tracking algorithm based on CDKF in observation bootstrapping strategy

The selection and optimization of model filters affect the precision of motion pattern identification and state estimation in maneuvering target tracking directly.Aiming at improving performance of model filters,a novel maneuvering target tracking algorithm based on central difference Kalman filter in observation bootstrapping strategy is proposed.The framework of interactive multiple model(IMM) is used to realize identification of motion pattern,and a central difference Kalman filter(CDKF) is selected as the model filter of IMM.Considering the advantage of multi-sensor fusion method in improving the stability and reliability of observation information,the hardware cost of the observation system for multiple sensors is adopted,meanwhile,according to the data assimilation technique in Ensemble Kalman filter(En KF),a bootstrapping observation set is constructed by integrating the latest observation and the prior information of observation noise.On that basis,these bootstrapping observations are reasonably used to optimize the filtering performance of CDKF by means of weight fusion way.The object of new algorithm is to improve the tracking precision of observed target by the multi-sensor fusion method without increasing the number of physical sensors.The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

Dynamic alliance target tracking based on genetic algorithms in wireless sensor networks

Dynamic alliance(DA),namely,virtual corporations (VCs),is an enterprise management method. It means a temporary union formed by some independent commercial processes or corporations.Here, genetic algorithms(GA) is applied to the research of nodes DA selection optimization in wireless sensor networks(WSN) target tracking(TT) problem.The detailed optimized selection method is presented in the paper and a typical simulation is conducted to verify the effectiveness of our model.

Track Association for Dynamic Target Tracking System Based on AP Algorithm

Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system.A multi-sensor data association is proposed based on aftinity propagation(AP)algorithm.The proposed method needs an initial similarity,a distance between any two points,as a parameter,therefore,the similarity matrix is calculated by track position,velocity and azimuth of track data.The approach can automatically obtain the optimal classification of uncertain target based on clustering validity index.Furthermore,the same kind of data are fused based on the variance of measured data and the fusion result can be taken as a new measured data of the target.Finally,the measured data are classified to a certain target based on the nearest neighbor ideas and its characteristics,then filtering and target tracking are conducted.The experimental results show that the proposed method can effectively achieve multi-sensor and multi-target track association.

ALGORITHMS FOR TRACKING MANEUVERING TARGET WITH PHASED ARRAY RADAR

Several typical algorithms for tracking maneuvering target with phased array radar are studied in this paper. The constant gain filter with multiple models is analyzed. A typical method for adaptively controlling the sampling interval is modified. The performance of the single model and multiple model estimator with uniform and variable sampling interval are evaluated and compared. It is shown by the simulation results that it is necessary to apply the adaptive sampling policy based on the multiple model method when the maneuvering targets are tracked by the phased array radar since saving radar resources is more important. The adaptive algorithms of variable sampling interval are better than the algorithms of variable model. The adaptive policy to determine the sampling interval based on multiple model are superior than those based on the single model filter, because IMM estimator can adapt to the maneuver more quickly and the prediction covariance of IMM is the more sensitive and more reliable index than residual to determine the sampling interval. With IMM based method, lower sampling interval is required for a certain accuracy.