Bernoulli filter based algorithm for joint target tracking and classification in a cluttered environment

This paper addresses the problem of joint tracking and classification (JTC) of a single extended target with a complex shape. To describe this complex shape, the spatial extent state is first modeled by star-convex shape via a random hypersurface model (RHM), and then used as feature information for target classification. The target state is modeled by two vectors to alleviate the influence of the high-dimensional state space and the severely nonlinear observation model on target state estimation, while the Euclidean distance metric of the normalized Fourier descriptors is applied to obtain the analytical solution of the updated class probability. Consequently, the resulting method is called the “JTC-RHM method.” Besides, the proposed JTC-RHM is integrated into a Bernoulli filter framework to solve the JTC of a single extended target in the presence of detection uncertainty and clutter, resulting in a JTC-RHM-Ber filter. Specifically, the recursive expressions of this filter are derived. Simulations indicate that: (1) the proposed JTC-RHM method can classify the targets with complex shapes and similar sizes more correctly, compared with the JTC method based on the random matrix model; (2) the proposed method performs better in target state estimation than the star-convex RHM based extended target tracking method; (3) the proposed JTC-RHM-Ber filter has a promising performance in state detection and estimation, and can achieve target classification correctly.

The Bernoulli filter is an exact Bayesian filter for target tracking under the framework of random finite sets. It has been implemented by using Gaussian mixture (GM) and sequential Monte Carlo (SMC) techniques for joint target detection and tracking. However, a single model is not enough to accommodate a maneuvering target that switches between a set of motion models. In this paper, we extend the Bernoulli filter to switching motion models, and propose a multiple-model (MM) Bernoulli filter for a maneuvering target. Then we give a GM implementation of the MM Bernoulli filter for linear Gaussian models. Finally, a numerical example is presented to verify the effectiveness of the GM-MM Bernoulli filter for a maneuvering target.

The high computational complexity of existing joint tracking and classification (JTC) algorithms hampers their application. After presenting a new description of the JTC problem--simultaneous tracking and classification (STC) instead of JTC, we derive two STC algorithms in both exact and approximate forms by applying Hayes' rule to the target state probability density function (pdf) and target class probability mass function (pmf) simultaneously under the assumption that the kinematic and attribute measurement processes are conditional independent. The mutual information exchange between tracker and classifier of the proposed STC algorithms is introduced by defining the simultaneous pdf-pmf of target state and class, the dependence of kinematic measurement on target class, the dependence of attribute measurement on target state and target model, class-dependent kinematic model sets, and class-dependent flight envelopes, etc. The proposed STC algorithms have four distinctive features. First, they have a modularized structure, i.e., they explicitly integrate a multiple-model filter and a Bayesian classifier. Second, the approximate versions, which follow easily from the proposed STC algorithms thanks to their modularized structure, have a closed form with a lower computational complexity and are more suitable for real-time applications. Third, the proposed exact STC algorithms are derived without the hidden approximation made in some existing multiple-model based JTC algorithms. Fourth, one of the proposed STC algorithms has the potential to further reduce the computational load since it has no redundant motion models. Simulation results suggest that the proposed STC algorithms provide a hopeful solution to a class of STC problems.

Methods dealing with the problem of Joint Tracking and Classification (JTC) are abundant, among which Simultaneous Tracking and Classification (STC) provides a modularized scheme solving tracking and classification subproblems simultaneously. However, there is no explicit hard decision on the class label but only soft decision (class probability) is provided. This does not fit many practical cases, in which a hard decision is urgently needed. To solve this problem, this paper proposes a Hard decision-based STC (HSTC) method. HSTC takes all the decision error rate, timeliness, and estimation error into account. Specifically, for decision, the sequential probability ratio test is adopted due to its nice properties and also the adaptability to our situation. For estimation, by utilizing the two-way information exchange between the tracker and the classifier, we propose flexible three tracking schemes related to decision. The HSTC tracking result is divided into three parts according to the time of making the hard decision. In general, the proposed HSTC method takes advantage of both SPRT and STC. Finally, two illustrative JTC examples with hard decision verify the effectiveness of the the proposed HSTC method. They show that HSTC can meet the demand of the problem, and also has the performance superiority in both decision and estimation.

Adjusting for Selection Effects in Epidemiologic Studies

The Bernoulli filter is a general, Bayes-optimal solution for tracking a single disappearing and reappearing target, using a single sensor whose observations are corrupted by missed detections and a general, known clutter process. Like virtually all target-tracking algorithms it presumes a hidden Markov model (HMM) structure on the sensor and target. Pieczynski's pairwise Markov model (PMM) relaxes this assumption, thereby addressing correlated sensor noise and non-Markovian target motion. In an earlier paper, we derived a “PMM Bernoulli filter” that obeys PMM rather than restrictive HMM sensor/target statistics. This paper generalizes both the HMM and PMM Bernoulli filters to the case of multiple, possibly correlated sensors, resulting in a general, Bayes-optimal single-target tracker for complexly correlated sensors.

Target tracking is a hot topic in the field of computer vision. It uses the context information of video or image sequence to model the appearance and motion information of the target, so as to predict the motion state of the target and calibrate the position of the target. From the perspective of building models, target tracking algorithms can be divided into generative models and discriminant models; From the number of tracking targets, it can be divided into single target tracking and multi-target tracking. Target tracking integrates the theories and algorithms of image processing, machine learning, optimization and other fields. It is the premise and foundation to complete higher-level image understanding.The basic task of target tracking is to give the initial position of the target in a video sequence and continuously track and locate the target in each subsequent frame. In this process, a priori conditions such as the color, shape and size of the target will not be provided, that is, the tracking algorithm can track only by learning the target in the first frame. The traditional solution is to calculate the project similarity offline and save it in the system for algorithm call, but it can not make full use of the latest scoring data to reflect the change of user interest. To solve the above problems, a target tracking item similarity incremental update mechanism suitable for online applications is proposed, so that the recommendation system can update the similarity data between the corresponding item and other items in real time after the current user submits the item score, so that the recommendation system can recommend based on the latest item similarity data, To adapt to changes in user interests.

In real world multiple extended target tracking problems, the presence of merged measurements is a frequently occurring phenomenon, however, most existing tracking algorithms in the literature assume that each target generates independent measurements. When this measurement merging phenomenon occurs, it increases the computational complexity of the tracking algorithms. Recently, the conditional joint decision and estimation (CJDE) algorithm based on the generalized Bayes risk was proposed to solve problems of joint detection, tracking and classification (JDTC) of targets. In this paper, we develop a principled Bayesian solution to the important problem involving inter-dependent decision and estimation conditioned on data based on the theory of random finite sets (RFS), and a tractable implementation based on the recently proposed generalized labeled multi-Bernoulli (GLMB) filter. The performance of the proposed technique is demonstrated by simulation of a multi-target bearings-only tracking scenario, where measurements become merged due to finite resolution effects.

This paper presents an energy-aware, sleep scheduling algorithm called SSMTT to support multiple target tracking sensor networks. SSMTT leverages the awakening result of interfering targets to save the energy consumption on proactive wake-up communication. For the alarm message-miss problem introduced by multiple target tracking, we present a solution that involves scheduling the sensor nodes' sleep pattern. We compare SSMTT against three sleep scheduling algorithms for single target tracking: the legacy circle scheme, MCTA, and TDSS. Our experimental evaluations show that SSMTT achieves better energy efficiency than handling multiple targets separately through single target tracking algorithms.

A multi-object Bayes filter analogous to the single-object Bayes filter can be derived using Finite Set Statistics for the estimation of an unknown and randomly varying number of target states from random sets of observations. The joint target-detection and tracking (JoTT) filter is a truncated version of the multi-object Bayes filter for the single target detection and tracking problem. Despite the success of Finite-Set Statistics for multi-object Bayesian filtering, the problem of multi-object smoothing with Finite Set Statistics has yet to be addressed. I propose multi-object Bayes versions of the forward-backward and two-filter smoothers and derive optimal non-linear forward-backward and two-filter smoothers for jointly detecting, estimating and tracking a single target in cluttered environments. I also derive optimal Probability Hypothesis Density (PHD) smoothers, restricted to a maximum of one target and show that these are equivalent to their Bayes filter counterparts.

Though there are a no. of methods for target tracking described in literature like Kalman filtering, extended Kalman filtering, Bayesian approach, IMM-PDA, ML-PDA, particle filters, random set theory, covariance intersection, neuro-fuzzy methods, tracking through genetic algorithms and so on, the goal has always been to bring adaptivity to tackle the changing situations. Since, no one sensor can perform well in all the conditions, Multi-sensor adaptive processing has been the inherent focus. This paper presents a brief account of the target tracking algorithms developed till date and to be developed in future and brings out the main development trends. As a novel way of presentation, a Boston Consulting Group (BCG) matrix analysis has been performed and the algorithms have been classified in four classes i.e. Question marks, stars, cash cows and dogs. It has been applied to the radar target tracking algorithms. The evolution and further discussion about future trends clearly show a shift towards knowledge based adaptivity and sensor fusion. Though a number of papers have come out bringing complete account of target tracking algorithms but their presentation format does not provide a way of their practical utilization in the system development. The mathematical formulations are complex and mixing is too much for a non-expert or even a system manager to take decisions. Thus a need was felt to provide a suitable format to the decision makers and provide the non-expert a balanced simple account of the algorithms. Further, a knowledge based perspective has been brought out well in this paper. Knowledge based theme though shown in target tracking here is not limited but applies to other areas of radar, ATR, air traffic control & collision avoidance, network centric warfare etc. also. Latest knowledge based research has been incorporated in a broader sense to cover ANNs, CI, fuzzy etc. also.

Interactive service robots are ready to serve us both in public and private places soon. This paper presents a multisensor framework to the simultaneous tracking and recognition of humans so that a service robot can implement reception tasks interactively. Both a laser range scanner and a camera have been deployed in the framework. By fusing range and visual data, the robot can detect and identify applicants in the university environment in order to provide dedicated services. The robustness of the robot perception is increased by the joint tracking and recognition of face, clothes and height, implemented using a bank of filters and probabilistic data association. Experiments in the university open days demonstrate the effectiveness of the proposed solution.

This paper addresses the problem of data fusion for joint multiple manoeuvring target tracking and classification where centralised versus decentralised architectures are contrasted. The proposal advocates the use of a hybrid approach combining a particle filter (PF) like method to deal with system nonlinearities, Fitzgerald's cheap joint probabilistic data association filter (CJPDAF) for the purpose of data association/target estimation problems and multiple model filter (MMF) to handle the issue of target manoeuvrability, yielding CJPDA-MMPF algorithm. The feasibility and the performances of the proposal have been demonstrated using complex scenarios involving a set of Monte Carlo simulations dealing with four crossing targets with different operations modes. Constant velocity model and constant turn rate model were employed to generate the manoeuvres. The results of the simulations demonstrate the feasibility and the superiority of the centralised fusion architecture approach when combined with multiple model particle filter and cheap joint data association filter.

This paper presents a simultaneous localization,mapping (SLAM) and object tracking(OT) method based on interacting multiple model (IMM) filter to achieve simultaneous estimation of robot and object's trajectories in unknown environments. The proposed approach integrates IMM based object tracking and EKF-based SLAM. Since the implementation of IMM, the method can help the robot to track object and estimate its motion model, which improves the accuracy of object localization. Moreover, this approach can help robot to track the object that have more ability to escape, and improve it practicality. Simulation and real robot experiment results validate the effectiveness of the proposed method in robot and target localization and mapping. The results also show the accuracy of target motion model estimation and the system ability to track maneuvering target.

The trajectory estimation of a highly maneuvering target is a challenging problem and has practical applications. The interacting multiple model (IMM) filter is a well-established filtering algorithm for the trajectory estimation of maneuvering targets. In this study, we present an overview of IMM filtering algorithms for tracking a highly-maneuverable fighter aircraft using an air moving target indicator (AMTI) radar on another aircraft. This problem is a nonlinear filtering problem due to nonlinearities in the dynamic and measurement models. We first describe single-model nonlinear filtering algorithms: the extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF). Then, we summarize the IMM-based EKF (IMM-EKF), IMM-based UKF (IMM-UKF), and IMM-based CKF (CKF). In order to compare the state estimation accuracies of the IMM-based filters, we present a derivation of the posterior Cramér-Rao lower bound (PCRLB). We consider fighter aircraft traveling with accelerations 3g, 4g, 5g, and 6g and present numerical results for state estimation accuracy and computational cost under various operating conditions. Our results show that under normal operating conditions, the three IMM-based filters have nearly the same accuracy. This is due to the accuracy of the measurements of the AMTI radar and the high data rate.