Target Tracking Problem Research Articles

Multiple extended target tracking by truncated JPDA in a clutter environment

Data association is a crucial part of target tracking systems with clutter measurements. In general, its complexity increases sharply with a number of targets and measurements. Recently, high-resolution sensors have given rise to extended target tracking problems and more than one measurements can emerge from each target, making the association problems more complex. In this study, a tractable algorithm based on the Gaussian process measurement model and truncated joint probabilistic data association technique is proposed for multiple extended target tracking in the presence of measurement origin uncertainty. Based on the marginal association probabilities, the calculation amount is effectively reduced by truncating the association events with low probabilities in the shortest path problem. The effectiveness of the proposed algorithm is verified by the test of multiple extended targets tracking and compared with the linear-time joint probabilistic data association as well as the algorithm on random finite sets. Simulation results show that the proposed algorithm can track multiple extended targets accurately, which is significant in high-resolution radar tracking systems.

Robust Standoff Target Tracking with Finite-Time Phase Separation Under Unknown Wind

This paper investigates robustness and finite-time stability issues for the phase separation problem in standoff target tracking. First, via a new angle, existing results concerning the stability of the standoff tracking of a stationary target with no wind by a single unmanned aerial vehicle (UAV) are reconsidered and extended; furthermore, a new compact condition for global asymptotic stability is provided. On the basis of the above, the phase separation problem is reexamined within the coordinated standoff tracking context, and globally finite-time stable distributed guidance laws are obtained that can treat simultaneously the convergence toward the desired standoff radius, the desired heading, and the proper angular difference between the tracking team of UAVs. In the sequel, the case of a moving target with wind is accounted for, and robust stability properties regarding the UAV convergence to the guidance vector field are shown. In the analysis, the concepts of total stability, input to state stability, as well as uniformly ultimately bounded stability are used. Finally, distributed guidance laws establishing robust global finite-time stability even in the case of both unknown target and wind velocity are proposed. The proposed guidance laws can track successfully even a maneuvering target as well as a slightly accelerating target but not forever, of course. Simulations demonstrate the feasibility and efficiency of the guidance laws.

A Bernoulli Track-Before-Detect Filter for Interacting Targets in Maritime Radar

This article is devoted to the problem of multitarget tracking in the maritime environment using Bernoulli track-before-detect (TBD) filtering. Detection and tracking of weak targets in sea clutter using high-resolution airborne radar is notoriously challenging compared to classical target tracking problems. Furthermore, the measured amplitude in range-azimuth cells that are in the vicinity of closely spaced (interacting) targets represents a superposition of individual target contributions, demanding a special joint treatment. This article proposes a novel solution for multitarget tracking using Bernoulli TBD for maritime radar and covers cases when targets are both far apart and closely spaced. Comparisons with existing TBD algorithms are used to verify the efficacy of the proposed Bernoulli TBD algorithm with realistic sea clutter simulations used to create challenging scenarios with variable target strengths.

BP neural Network-Kalman filter fusion method for unmanned aerial vehicle target tracking

The poor real-time performance and target occlusion occurred easily when the UAV was tracking the target. In this paper, a target tracking method based on the Back Propagation neural network fusion Kalman filter algorithm was developed to solve the position prediction problem of the UAV target tracking in real time. Firstly, the target tracking algorithm was used to acquire the center position coordinates of the target on the onboard computer, and then the coordinate difference matrix was constructed to train the BP neural network in real time. Secondly, when the target was occluded by the obstacles judged by the Bhattacharyya coefficient, the BP neural network fusion Kalman filter algorithm was used to accurately predict the center position coordinates of the occluded target. Then the flight speed of UAV was calculated by the deviation between the coordinates of the target and the image center. Finally, the velocity command was sent to the UAV by the onboard computer. The experimental results shown that the target position predicted by BP neural network fusion Kalman filter algorithm was more accurate and robust in predicting the center position coordinates of the target, and the UAV can track the moving target on the ground stably.

Gaussian Variational State Estimation for Nonlinear State-Space Models

In this paper, the problem of state estimation, in the context of both filtering and smoothing, for nonlinear state-space models is considered. Due to the nonlinear nature of the models, the state estimation problem is generally intractable as it involves integrals of general nonlinear functions and the filtered and smoothed state distributions lack closed-form solutions. As such, it is common to approximate the state estimation problem. In this paper, we develop an assumed Gaussian solution based on variational inference, which offers the key advantage of a flexible, but principled, mechanism for approximating the required distributions. Our main contribution lies in a new formulation of the state estimation problem as an optimisation problem, which can then be solved using standard optimisation routines that employ exact first- and second-order derivatives. The resulting state estimation approach involves a minimal number of assumptions and applies directly to nonlinear systems with both Gaussian and non-Gaussian probabilistic models. The performance of our approach is demonstrated on several examples; a challenging scalar system, a model of a simple robotic system, and a target tracking problem using a von Mises-Fisher distribution and outperforms alternative assumed Gaussian approaches to state estimation.

Research on underwater target tracking based on Gaussian Hermitian Kalman Particle filter algorithm

The underwater target tracking environment usually has the characteristics of strong nonlinearity and non-Gaussian. The target tracking problem usually uses the nonlinear filtering algorithm, which combines the nonlinear measurement model with the linear system dynamics. The primary goal of target tracking is to extract accurate information about the real-time state of the target from the noise nonlinear observation obtained by the sensor. A new Gaussian Hermitt Kalman particle filter algorithm (GHKF-PF) is used to improve the tracking accuracy of the particle filter algorithm (PF). GHKF-PF uses the GHKF sampling method to sample particles from the posterior distribution of the target, calculates the mean and covariance for each particle, and uses the mean and variance to guide sampling. In the simulation experiment, the uniform linear motion model of underwater 3D target in the environment of Gaussian mixture noise (GMN) is established, and the application of GHKF-PF algorithm in 3D space is realized, and the accuracy is higher than that of PF. In order to further verify the effectiveness of the algorithm, a six-dimensional uniform acceleration motion model is established and compared with the extended particle filter (EPF) algorithm and the unscented particle filter (UPF) algorithm. The simulation results show that the performance of the GHKF-PF algorithm is similar to that of the UPF algorithm and better than the EPF algorithm.

Robust Visual Tracking Based on a Modified Flower Pollination Algorithm

In this study, a target tracking algorithm based on the flower pollination algorithm (FPA) is proposed. This method solves the problem of robust visual target tracking in different complex tracking scenes with the good global and local optimisation ability of the FPA. Meanwhile, with the aim of solving the problem of invalid background feature interference and the loss of effective features caused by the fixed scale of tracking frame in traditional tracking methods, a scale adaptive adjustment model of tracking frame is proposed. Considering that the FPA has good global and local optimization ability at simultaneously, the position update equation of the FPA is introduced as the main optimization method of target tracking. In addition, considering that the traditional FPA is similar to classical swarm intelligence algorithm (such as the particle swarm optimization algorithm), it also faces the problems of a high probability of falling into local extrema, a low efficiency of late convergence speed and a high probability of early maturity. Therefore, this work proposes the GTFPA, an advanced FPA based on the gravitational search algorithm (GSA) and mutation mechanism via a trigonometric function. We qualitatively, quantitatively and statistically compare the proposed method with other classical general tracking methods through two datasets, OTB2015 and VOT2018, which contain hundreds of video sequences and more than ten tracking scenes and can effectively test the success rate, accuracy and stability of the trackers. The results of a large number of tracking experiments in a variety of complex tracking scenarios prove that the proposed GTFPA tracker performs well with regards to efficiency, accuracy and robustness.

Multi-Feature Fusion Target Re-Location Tracking Based on Correlation Filters

Target tracking has been a research hotspot in computer vision, and the correlation filtered target tracking algorithm has the benefits of low computational complexity and fast speed. Still, the tracking effect is not good when dealing with complicated circumstances. This paper proposes a multi-feature fusion target repositioning tracking algorithm for the target tracking problem in complex environments. First, a multi-feature weighted fusion algorithm is presented. Since each feature has different advantages in different environments, we combine HOG, CN, ULBP, and image edge features and use the weighted coefficient method to adaptively fuse each feature component. Second, to address the target occlusion problem, an occlusion judgment mechanism is introduced, and the target is re-located by fusion weighted filtering. Third, the scale pool is established, and the scale filter is trained by the classification search method. Finally, an adaptive model update strategy is proposed. We conduct comparison experiments with current mainstream algorithms on the publicly available datasets OTB-2015, VOT2018, UAV123, and TColor-128, respectively, and the experimental results show that our proposed algorithm is more robust in complex scenarios.

Robust Adaptive Self-Structuring Neural Network Bounded Target Tracking Control of Underactuated Surface Vessels.

This paper studies the target-tracking problem of underactuated surface vessels with model uncertainties and external unknown disturbances. A composite robust adaptive self-structuring neural-network-bounded controller is proposed to improve system performance and avoid input saturation. An extended state observer is proposed to estimate the uncertain nonlinear term, including the unknown velocity of the tracking target, when only the measurement values of the line-of-sight range and angle can be obtained. An adaptive self-structuring neural network is developed to approximate model uncertainties and external unknown disturbances, which can effectively optimize the structure of the neural network to reduce the computational burden by adjusting the number of neurons online. The input-to-state stability of the total closed-loop system is analyzed by the cascade stability theorem. The simulation results verify the effectiveness of the proposed method.

Recursive Secure Filtering Over Gilbert-Elliott Channels in Sensor Networks: The Distributed Case

This paper is concerned with the recursive secure filtering problem for a class of discrete-time systems subject to unreliable communication due to the security vulnerability of sensor networks. The unreliable communication, caused probably by denial-of-service cyber-attacks, is described by the well-known Gilbert-Elliott model. The addressed nonlinearities are applicable for some of the most investigated stochastic nonlinear models, including the well-known state-dependent multiplicative noises as special cases. The aim of this paper is to design a novel distributed filter that uses the information not only from the individual node itself but also from its neighboring nodes according to the given topology. In order to improve the security of designed filter, a χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> detector is utilized to detect abnormal innovations. By means of the failure and recovery rates of the Gilbert-Elliott channels, sufficient conditions are established to ensure the existence of an upper bound on the estimation error covariance, and then the desired filter parameters are designed by minimizing the trace of such an upper bound. The asymptotic boundedness of the estimation error covariance is subsequently investigated. Finally, a simulation example on the target tracking problem is employed to verify the effectiveness and the security of the proposed filtering scheme.

Distributed Auxiliary Particle Filtering With Diffusion Strategy for Target Tracking: A Dynamic Event-Triggered Approach

This paper investigates the particle filtering problem for a class of nonlinear/non-Gaussian systems under the dynamic event-triggered protocol. In order to avert frequent data transmission and reduce the communication overhead, a dynamic event-triggered transmission mechanism is adopted to decide whether the data should be transmitted or not. We first consider a scenario where all sensor nodes selectively transmit their newly obtained measurements to a central node, and a full likelihood function at the central node is derived by fusing the transmitted measurements and the information embodied in the non-triggered measurements. Based on the derived full likelihood function, a centralized auxiliary particle filtering algorithm is proposed to select those particles that are more likely to match the current measurement information. Next, based on the diffusion strategy, a distributed auxiliary particle filtering algorithm is further developed, where the local measurements and the local posteriors (approximated by the Gaussian mixture models) are exchanged among neighboring nodes under the dynamic event-triggered communication strategy. Finally, the effectiveness of the proposed filtering schemes is demonstrated via Monte Carlo simulations in a target tracking problem with received-signal-strength sensors.

Particle Filtering for Nonlinear/Non-Gaussian Systems With Energy Harvesting Sensors Subject to Randomly Occurring Sensor Saturations

In this paper, the particle filtering problem is investigated for a class of nonlinear/non-Gaussian systems with energy harvesting sensors subject to randomly occurring sensor saturations (ROSSs). The random occurrences of the sensor saturations are characterized by a series of Bernoulli distributed stochastic variables with known probability distributions. The energy harvesting sensor transmits its measurement output to the remote filter only when the current energy level is sufficient, where the transmission probability of the measurement is recursively calculated by using the probability distribution of the sensor energy level. The effects of the ROSSs and the possible measurement losses induced by insufficient energies are fully considered in the design of filtering scheme, and an explicit expression of the likelihood function is derived. Finally, the numerical simulation examples (including a benchmark example for nonlinear filtering and the applications in moving target tracking problem) are provided to demonstrate the feasibility and effectiveness of the proposed particle filtering algorithm.

Distributed Iterative FIR Consensus Filter for Multiagent Systems Over Sensor Networks

For the target-tracking problem, full state of the target may not be available since it may be expensive or impossible to obtain. Thus, the state needs to be reconstructed or estimated only according to measured inputs and outputs. The impossible case that all followers can measure the target directly yields the study of distributed methods, thus reducing the communication and computation resource while resulting in more robustness. This article confronts these problems by addressing a distributed iterative finite impulse response (DIFIR) consensus filter for leader-following systems. A solution to the underlying problem is obtained by involving a distributed measurement model wherein not only the neighbors' estimates are applied but also the directed measurement data are used, and expressed by a computationally efficient iterative algorithm. Applying this DIFIR strategy, it is shown that the leader's estimates by all followers reach H∞ consensus, whose value is the local unbiased estimates of the leader. Then, the result is extended to multiagent systems whose leader has unknown inputs. Incorporating the input estimates, a new DIFIR is proposed. Finally, examples are given to illustrate the consistency and robustness of the developed new design techniques.

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles (UAVs). In this paper, aiming to address the control problem of maneuvering target tracking and obstacle avoidance, an online path planning approach for UAV is developed based on deep reinforcement learning. Through end-to-end learning powered by neural networks, the proposed approach can achieve the perception of the environment and continuous motion output control. This proposed approach includes: (1) A deep deterministic policy gradient (DDPG)-based control framework to provide learning and autonomous decision-making capability for UAVs; (2) An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning; and (3) An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV’s control model built based on MN-DDPG. The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV’s flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.

Multisensor decentralized nonlinear fusion using adaptive cubature information filter

In nonlinear multisensor system, abrupt state changes and unknown variance of measurement noise are very common, which challenges the majority of the previously developed models for precisely known multisensor fusion techniques. In terms of this issue, an adaptive cubature information filter (CIF) is proposed by embedding strong tracking filter (STF) and variational Bayesian (VB) method, and it is extended to multi-sensor fusion under the decentralized fusion framework with feedback. Specifically, the new algorithms use an equivalent description of STF, which avoid the problem of solving Jacobian matrix during determining strong trace fading factor and solve the interdependent problem of combination of STF and VB. Meanwhile, A simple and efficient method for evaluating global fading factor is developed by introducing a parameter variable named fading vector. The analysis shows that compared with the traditional information filter, this filter can effectively reduce the data transmission from the local sensor to the fusion center and decrease the computational burden of the fusion center. Therefore, it can quickly return to the normal error range and has higher estimation accuracy in response to abrupt state changes. Finally, the performance of the developed algorithms is evaluated through a target tracking problem.

Joint beam selection and resource allocation for cognitive multiple targets tracking in MIMO radar with collocated antennas

Compared with conventional phased array radar, the collocated multiple-input multiple-out (C-MIMO) radar is envisioned to offer greater freedom in waveform design and can simultaneously manage different beams to track multiple targets in the simultaneous multi-beam (SM) working mode. In this study, aiming at the multiple targets tracking (MTT) problem, a joint beam selection integrated with power and bandwidth allocation (JSPBA) scheme for the C-MIMO radar is proposed in the SM mode. By incorporating the modified particle filter, the predicted conditional Cramer–Rao lower bound (PC-CRLB) is calculated, which gives a measure of the best achievable performance for targets tracking. Then, the optimisation model is established with the aim of improving the worst case of PC-CRLB to achieve better performance of the worst-case tracking. Next, a three-step solution method is proposed to solve the JSPBA problem by converting the non-convex problem into a series of convex problems. At last, after the solutions are fed back to the control centre to guide the beam generation in the next tracking epoch, a cognitive tracking system is established. Simulation results confirm the superiority of the proposed method in improving the MTT performance in C-MIMO radar system.

Intelligent querying for target tracking in camera networks using deep Q-learning with n-step bootstrapping

Surveillance camera networks are a useful infrastructure for various visual analytics applications, where high-level inferences and predictions could be made based on target tracking across the network. Most multi-camera tracking works focus on target re-identification and trajectory association problems to track the target. However, since camera networks can generate enormous amount of video data, inefficient schemes for making re-identification or trajectory association queries can incur prohibitively large computational requirements. In this paper, we address the problem of intelligent scheduling of re-identification queries in a multi-camera tracking setting. To this end, we formulate the target tracking problem in a camera network as an MDP and learn a reinforcement learning based policy that selects a camera for making a re-identification query. The proposed approach to camera selection does not assume the knowledge of the camera network topology but the resulting policy implicitly learns it. We have also shown that such a policy can be learnt directly from data. Using the NLPR MCT and the Duke MTMC multi-camera multi-target tracking benchmarks, we empirically show that the proposed approach substantially reduces the number of frames queried.

Fully Distributed Joint Localization and Target Tracking With Mobile Robot Networks

In this article, we study the problem of joint localization and target tracking using a mobile robot network. Here, a team of mobile robots equipped with onboard sensors simultaneously localize themselves and track multiple targets. We introduce a fully distributed algorithm that is applicable to generic robot motion, target process, and measurement models and is robust to time-varying sensing and communication topologies and changing blind robots (the robots not directly sensing the targets). Instead of treating localization and target tracking as two separate problems, we explicitly account for the influence of one to the other and exploit it to improve performance in a fully distributed context. Two novel kinds of distributed estimates are derived. By employing them, each robot can estimate the pose (position and orientation) of itself (localization) and the states of targets (tracking) using only its local information and information from its one-hop communicating neighbors while preserving consistency. Furthermore, it is proven that, in the case of linear time-varying models, the estimation errors are bounded in the mean-square sense under very mild conditions on the sensing and communication graph and system observability. The effectiveness of our approach is demonstrated extensively through Monte Carlo simulations, and experiments carried out using a real-world data set. It is also shown better performance in the pose estimates of the robots is achieved when jointly estimating the robots' poses and targets' states.

A Visual Tracking Method Based on an Adaptive Overlapping Correlation Filter for Robotic Real-Time Cognitive Imaging

Computer vision is a very important research direction in the cognitive computing field. Robots encounter various target-tracking problems with computer vision systems. Robust scale estimation is an important issue in tracking algorithms. Most of the available methods have difficulty addressing even reasonable changes of scale in complex videos. In this paper, we propose a visual tracking method based on robust scale estimation, which uses a discriminant correlation filter based on a time-dependent scale-space filter and an adaptive cross-correlation filter. The tracker uses separate essential filters for sample migration and scale estimation. Furthermore, the built-in scale estimation method can be introduced into other tracking algorithms. We validate the proposed method on the UAV123 dataset. The results of comparison experiments with the traditional correlation filter tracking method demonstrate that the proposed method improves the success rate and tracking accuracy while controlling the computational complexity; its success rate measured by the area under the curve is 0.638, while at a location error precision of 20%, it is 0.649.

A consensus-based square-root cubature Kalman filter for manoeuvring target tracking in sensor networks

In this paper, a novel distributed tracking method is proposed for the problem of manoeuvring target tracking in sensor networks. Firstly, an adaptive adjustment tracking model is established by extended state observer (ESO) theory. Then, the consensus-based square-root cubature Kalman filter (SCKF) algorithm is proposed in order to improve the global accuracy and stability. In addition, the integrated model could reduce the influence of measurement noise. Finally, simulation is performed to verify the effectiveness of the scheme, whereby comparison results show that the estimation accuracy of the method proposed is higher than that of the traditional ESO and SCKF.