Extended Target Tracking Research Articles

Maneuvering extended target tracking method based on transformer network

This research introduces a new approach to address the challenges related to inaccuracies in shape estimation and motion state tracking during the tracking of maneuvering extended targets. The method combines a novel neural network model with a cubature Kalman filter. Initially, the target’s shape variation is represented using a second-order autoregressive model, and Bayesian filtering is employed for target contour estimation in static environments. Subsequently, the kinematic state tracking of maneuvering targets is enhanced by modifying a neural network model based on Transformer architecture. Through the effective integration of these techniques, precise tracking of maneuvering extended targets is achieved, facilitating accurate estimation of irregular contour information while tracking the targets’ motion state in real-time. The efficacy of the proposed approach is further confirmed through various simulation scenarios, underscoring its suitability for tracking maneuvering extended targets.

Irregular extended target tracking with unknown measurement noise covariance

To address the challenge of tracking irregular extended target while adapting to unknown measurement noise, this paper presents a novel tracking algorithm for irregular extended target. Considering the flexibility of the Gaussian process (GP) model for representing shape, in the proposed algorithm, we utilize the GP model to delineate the radial function of the tracked extended target’s contour. Then, we incorporate the GP model into the variational Bayesian technique to formulate the posterior distribution for both the unknown measurement noise and the shape state of the tracked target. Subsequently, the square-root cubature Kalman filter is employed to tackle the nonlinearity filtering in the problem of irregular extended target tracking (ETT), enabling simultaneous estimation of intricate shape details and the unknown measurement noise covariance. Through extensive experiments using both simulated and real-time lidar data, the proposed algorithm demonstrates superior performance compared to the existing ETT algorithms for extended target tracking in both accuracy and robustness.

Trajectory planning for airborne radar in extended target tracking based on deep reinforcement learning

In this article, an effective algorithm is proposed for trajectory optimization in airborne radar for extended target tracking (ETT). The key idea of the proposed airborne radar trajectory planning (ARTP) scheme is to model this problem as a partially observable Markov decision process (POMDP) and plan the waypoints of airborne radar using deep reinforcement learning (DRL) in order to improve the target's kinematic state and shape tracking accuracy, subject to the airborne radar maneuverability limitation. Specifically, at each tracking interval, the predicted Gaussian Wasserstein Distance (GWD) is analytically calculated and subsequently adopted as a reward function to evaluate the performance of extended target tracking. Then the DRL agent autonomously learns trajectory planning strategies to maximize ETT performance. Simulation results demonstrate that the proposed ARTP algorithm can deliver superior performance in terms of maximizing the overall ETT performance compared with other benchmarks.

Multiple Extended Target Tracking Algorithm Based on Spatio-Temporal Correlation

In the clutter environment, the measurement of a set of multiple extended targets, with an unknown number of targets, poses challenges in partitioning, and the computational cost is high. In particular, the multiple extended target tracking method, based on distance partition, has obvious potential estimation errors when the extended targets intersect. This paper proposes a partition algorithm, based on spatio-temporal correlation, which considers the correlation between adjacent moments of the extended target and uses this prior information to divide the measurement set into a survival target measurement set and a born target measurement set for the first time. Then, the survival target measurement set is clustered by the K-means++ algorithm, and the extended target tracking is transformed into point target tracking. The born target measurement undergoes preprocessing by the DBSCAN clustering algorithm, and then uses the directed graph with shared nearest neighbors (SNN) dividing the measurement set. The method proposed in this paper significantly reduces the number of partitions and the computational time. The effectiveness of the algorithm is demonstrated through experimental simulations.

An Iteratively Extended Target Tracking by Using Decorrelated Unbiased Conversion of Nonlinear Measurements.

Extended target tracking (ETT) based on random matrices typically assumes that the measurement model is linear. However, nonlinear measurements (such as range and azimuth) depending on locations of a series of unknown scattering centers always exist in many practical tracking applications. To address this issue, this paper proposes an iteratively extended target tracking based on random matrices by using decorrelated unbiased conversion of nonlinear measurements (ETT-IDUCM). First, we utilize a decorrelated unbiased converted measurement (DUCM) method to convert nonlinear measurements depending on unknown scatters of target extent in polar coordinates into the ones in Cartesian coordinates with equivalent measurement noise covariances. Subsequently, a novel method, combining iteratively extended Kalman filter (IEKF) updates with variational Bayesian (VB) cycles is developed for precise estimation of the target's kinematic state and extension. This method leverages the synergy between external IEKF iterations, which use the estimated state as a new prediction and input for DUCM, and internal VB iterations, which realize a closed-form approximation of the joint posterior probability. This approach progressively enhances estimation accuracy. Simulation results demonstrate the ETT-IDUCM algorithm's superior precision in estimating the target's kinematic state and extension compared to existing methods.

Generalised covariance intersection‐Gamma Gaussian Inverse Wishart‐Poisson multi‐Bernoulli Mixture: An intelligent multiple extended target tracking scheme for mobile aquaculture sensor networks

AbstractPoisson multi‐Bernoulli Mixture (PMBM) filter has been known as an available or practical point and multiple extended target tracking (METT) method. The authors present an improved PMBM filter with adaptive detection probability and adaptive newborn distributions, accompanying with an associated distributed fusion strategy for the tracking extended multiple targets. First, the augmented state of unknown and changing target detection probability is assumed as Gamma (GAM) distribution. Second, extended states are described by Inverse Wishart (IW) distribution based on this augmented state, accompanying with dynamic states presented by Gaussian distribution. And then, an adaptive newborn distribution is adopted to describe the newborn targets appearing arbitrarily. Consequently, the closed‐form solutions of the proposed filter can be derived by approximating the intensity of newborn and potential targets to the Gamma Gaussian Inverse Wishart (GGIW) form. Moreover, the fused means that Generalised Covariance Intersection (GCI) is performed in such a large‐scale aquaculture sensor network. Experiments are presented to verify the availability of the GCI‐GGIW‐PMBM method, and comparisons with other METT filters also demonstrate that tracking behaviours are improved largely.

Extended Target Tracking Utilizing Machine-Learning Software–With Applications to Animal Classification

Extended Target Tracking Utilizing Machine-Learning Software–With Applications to Animal Classification

An Assignment Method for Multiple Extended Target Tracking With Azimuth Ambiguity Based on Pseudo Measurement Set

An Assignment Method for Multiple Extended Target Tracking With Azimuth Ambiguity Based on Pseudo Measurement Set

A Maneuvering Extended Target Tracking IMM Algorithm Based on Second-Order EKF

A Maneuvering Extended Target Tracking IMM Algorithm Based on Second-Order EKF

Cost-Effective Gaussian Processes Based Extended Target Tracking

Cost-Effective Gaussian Processes Based Extended Target Tracking

Bernoulli Filter for Extended Target Tracking in the Framework of Possibility Theory

Extended objects give rise to a varying number of noisy measurements from its reflection (scattering or feature) points. Due to imperfect detection, only some of the feature points are detected in each scan of input data, while false alarms can also be present. The optimal sequential Bayesian state estimator in the framework of random set theory is the Bernoulli filter for an extended target (BF-X). In this paper we formulate and derive the analog of the BF-X in the framework of possibility theory, where uncertainty is represented using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">possibility</i> functions, rather than <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">probability</i> distributions. Possibility functions have the capacity to model partial (imprecise) probabilistic specifications and thus the main advantage of the proposed possibilistic BF-X, is enhanced robustness in the absence of precise measurements or dynamic models.

A Bi-Direction People Counting Method Based on Multiscale Clustering and Multiple Extended Target Tracking Using MIMO Radar

A Bi-Direction People Counting Method Based on Multiscale Clustering and Multiple Extended Target Tracking Using MIMO Radar

Extended target tracking under multitarget tracking framework for convex polytope shapes

This paper discusses the problem of extended target tracking for a single 2D extended target with a convex polytopic shape and known dynamics. Extended targets are those that produce multiple measurements for a single frame. One of the major challenges in extended target tracking is the joint uncertainty in the shape and the kinematics of the target. Another challenge is the lack of visibility due to self-occlusion in targets with a finite extent (as opposed to zero extent for point targets). To address these challenges, we develop a framework for tracking single (or widely separated) extended targets. This framework is based on the existing point multitarget tracking framework by modeling different parts of an extended target as separate targets. An algorithm is developed using the proposed framework for tracking convex polytope-shaped targets. The proposed shape function consists only of the boundary of the target since the center may not be observable. The algorithm is capable of dynamically changing the number of parameters used to describe the shape as more parts of the target become visible over time. The performance of the algorithm is evaluated for various scenarios using root mean square error (RMSE) of velocity, center, and intersection over union (IoU) metrics. It is seen that the algorithm is able to handle the self-occlusion problem and estimate the whole target shape even when different parts of the target are visible at different frames, for various shapes, and for various conditions of measurement noise covariance and number of measurements. New faces are added to the shape estimate as more parts of the target become visible. The algorithm is able to conserve the parts of the target that were visible in the previous frames but are no longer visible.

Tracking of oil spill extended targets based on random point pattern and GLMB

In the marine environment, oil spill surfaces exhibit complex drifting and spreading behavior, which makes it difficult to obtain the number of oil spill sources, as well as important parameters such as the location, shape, drifting speed and expansion rate of oil spill surfaces. Moreover, due to external factors such as sea winds, tides, and currents, multiple oil slicks may overlap and merge, making the accurate tracking of multiple oil spill surfaces even more difficult. To address the aforementioned issues, the proposed an oil spill extended target tracking method based on random point pattern and the Generalized Label Multi-Bernoulli (GLMB) filter. Firstly, to address the challenge of solving the parameters for the mixture distribution, the Expectation-Maximum (EM) algorithm of point pattern is introduced to obtain two types of parameters: cardinality distribution and feature distribution; Secondly, the GLMB filtering method is utilized to track the centroid drift trajectory of the oil spill while simultaneously recording information about the shape and concentration of the oil spill surface. Theoretical and simulation experiments demonstrate that the algorithm is capable of accurately estimating the parameters and state of the oil spill, with a tracking error precision of 0.001°(equivalent to 3.6′′ of Earth’s longitude and latitude). The final tests in the real water environment in the laboratory show that the tracking error of the oil spill surface is 0.4 cm, which proves the effectiveness and practicality of the algorithm.

Variational Approximation for Adaptive Extended Target Tracking in Clutter With Random Matrix

As a computationally efficient framework, the random matrix approach can simultaneously estimate the kinematic state and extent of the extended target. For the extended target tracking in clutter, the measurement origin uncertainty, the unknown detection probability and measurement rate challenge the existing methods. In this paper, we propose the Beta Gamma Gaussian inverse Wishart filter based on the variational approximation. The proposed method takes the association event as an unknown parameter with a prior distribution. Following a more rigorous path, we derive an approximate posterior distribution of the unknowns using the analytical techniques of variational Bayesian inference. The joint estimations of the kinematic state, extent, detection probability, measurement rate and association events are obtained in this work. The simulation results illustrate the effectiveness and robustness of the proposed approach.

RFS-Based Multiple Extended Target Tracking With Resolved Multipath Detections in Clutter

RFS-Based Multiple Extended Target Tracking With Resolved Multipath Detections in Clutter

Adaptive tracking of non-ellipsoidal extended target with varying number of sub-objects based on variational Bayesian

High-resolution sensors make it less difficult to detect the true shape of an extended target, and the method of using a single ellipsoid to approximate the extended state of the target is no longer applicable. We can use multiple ellipsoids to approximate the shape of non-ellipsoidal extended targets, but the method of fixing number of sub-objects again fails due to changes in the target shape. In this work, we propose a novel variational Bayesian adaptive filter for non-ellipsoidal extended target tracking (NETT) with a varying number of sub-objects called VN-NETT-VBAF. The proposed filter is used to estimate the motion, extended states, and mixture probabilities of sub-objects describing a non-ellipsoidal extended target. First, the framework of VN-NETT-VBAF is established based on the spawning and combination of sub-objects. Second, the inverse Gamma (IG) distribution is used to model the unknown measurement noise covariance. Based on the variational Bayesian theory, the joint posterior probability density function (PDF) of the system parameters is iteratively updated to achieve the joint estimation of the extended target state and the measurement noise covariance. Finally, an adaptive forgetting factor is designed to update the noise covariance to further improve the robustness of the algorithm to abrupt noise. The simulation results show that the algorithm in this paper can reasonably change the number of sub-objects to adapt to the change of the target extended state, and effectively deal with the abrupt noise to obtain higher filtering accuracy.

B-spline based Extended target tracking technology in cluttered environments

In order to solve the problem of tracking single extended targets in a cluttered environment, this thesis proposes a B-spline based probabilistic data association extended target tracking method (ET-BS-PDA). Firstly, the state of the extended target is modelled using B-sample strips, secondly, all events associated with the extended target will be counted based on valid measurements, and the probability of the associated events will be computed using the full probability principle. Finally, we use probabilistic data association algorithms to update state and covariance of extended targets and verify effectiveness of extended target tracking algorithms in cluttered environments through simulations.

Extended Target Tracking in Human–Robot Coexisting Environments via Multisensor Information Fusion: A Heteroscedastic Gaussian Process Regression-Based Approach

In this article, a new systematic approach to sensor fusion and state estimation is proposed for extended target tracking in human–robot coexisting environments. The developed method, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">human feature-based extended target tracking via multisensor information fusion</i> (HFBETT-MSIF), can assimilate information from the onboard camera and sonar sensor of a mobile robot in a unified way, during tracking of a pair of human shoes. A novel generalized measurement model containing the complete information of the human target is formulated for both sensors, thus rendering the tracking system potentially robust to the failure of any one sensor. The study illustrates how <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heteroscedastic Gaussian process</i> (HGP) regression can be used to derive the measurement model. It also develops an advanced HGP model, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bias-minimized most likely HGP</i> , to interpret the real-world shoe-contour data subjected to heteroscedastic noise. Performance evaluations conducted for real-life shoe tracking demonstrate the supremacy of the HFBETT-MSIF.

Track-Oriented Marginal Poisson Multi-Bernoulli Mixture Filter for Extended Target Tracking

Track-Oriented Marginal Poisson Multi-Bernoulli Mixture Filter for Extended Target Tracking