Target State Estimation Research Articles

A novel adaptive δ‐generalised labelled multi‐Bernoulli filter for multi‐target tracking with heavy‐tailed noise

AbstractConventional δ‐generalised labelled multi‐Bernoulli filter (δ‐GLMB) cannot deal with the problem of the heavy‐tailed process noise and measurement noise. In order to solve this problem, an adaptive δ‐GLMB approach based on minimising Kullback‐Leibler Divergence (KLD) is proposed in this study. The inverse wishart and Student‐t mixture is used to approximate the joint posterior distribution of process noise covariance and measurement noise covariance together with multi‐target state, and the multi‐target state and noise parameters are jointly estimated by minimising the KLD. The simulation results show that the proposed approach is robust to multi‐target tracking for condition with heavy‐tailed process noise covariance and measurement noise covariance. Accurate target number and target state estimation are obtained by effectively estimating the process noise covariance and measurement noise covariance.

Research on Area of Uncertainty of Underwater Moving Target Based on Stochastic Maneuvering Motion Model.

Considering the influence of measurement error on target state estimation, there is an uncertain dispersion region for target position estimate, that is, the area of uncertainty (AOU, area of uncertainty). In underwater target tracking, the state estimation is point estimation without AOU estimation and its accuracy is poor in the early stage because of large measurement errors. Fast tracking with higher accuracy and AOU estimation are of great significance to time-sensitive target tracking. To improve the state estimation accuracy in the early stage, and estimate the AOU, a method of AOU estimation of underwater moving target is presented based on a stochastic maneuvering motion (SMM, stochastic maneuvering motion) model. The stochastic maneuvering motion model is established based on the Langevin equation to reflect the movement characteristics of an underwater moving target. Then, the target state is estimated with a noise adaptive Kalman filter by constructing the measurement equation and state equation according to measurement error characteristic and stochastic maneuvering model. Based on the physical significance of the error covariance matrix from the Kalman filter, the parameters of AOU are deduced. Simulation results of underwater target tracking and AOU estimation are presented to demonstrate the relative performance of the proposed algorithm compared with the adaptive Kalman filter. It is clearly shown from the results that SMM tracking algorithm achieves higher accuracy of state estimation in the initial stage of tracking, and the predicted AOU is consistent with the actual distribution of underwater moving targets while yielding more concentrated distribution, which reveals that estimated AOU can be precisely represented by the confidence ellipses. The presented approach and obtained results may be useful in time-sensitive target threat analysis and weapon strike applications.

Auto-Learning Correlation-Filter-Based Target State Estimation for Real-Time UAV Tracking

Most existing tracking methods based on discriminative correlation filters (DCFs) update the tracker every frame with a fixed learning rate. However, constantly adjusting the tracker can hardly handle the fickle target appearance in UAV tracking (e.g., undergoing partial occlusion, illumination variation, or deformation). To mitigate this, we propose a novel auto-learning correlation filter for UAV tracking, which fully exploits valuable information behind response maps for adaptive feedback updating. Concretely, we first introduce a principled target state estimation (TSE) criterion to reveal the confidence level of the tracking results. We suggest an auto-learning strategy with the TSE metric to update the tracker with adaptive learning rates. Based on the target state estimation, we further developed an innovative lost-and-found strategy to recognize and handle temporal target missing. Finally, we incorporated the TSE regularization term into the DCF objective function, which by alternating optimization iterations can efficiently solve without much computational cost. Extensive experiments on four widely-used UAV benchmarks have demonstrated the superiority of the proposed method compared to both DCF and deep-based trackers. Notably, ALCF achieved state-of-the-art performance on several benchmarks while running over 50 FPS on a single CPU. Code will be released soon.

Coordinated target tracking in sensor networks by maximizing mutual information

Abstract This paper addresses the problem of coordinated target tracking in sensor networks. For a typical target tracking scene with nonlinear bearing-only measurements, we first investigate the mutual information between multiple sensors and the target state. To improve the performance of target tracking, we analyzed the relative positions between sensor agents and the target to be tracked and derived the optimal positions for sensors in the network by mutual information maximization. Simulation results are presented and discussed to demonstrate that the performance of estimated target states could be improved by the proposed method.

Control Optimization Design of Radio Frequency Identification Technology in IoT Express Logistics Distribution System

In order to solve the problems of high consumption cost and long transportation and distribution cycle of cross-border logistics, a control optimization design method of radio frequency identification technology in the express logistics distribution system of the Internet of Things is proposed. The details of the method are RFID positioning technology, laser ranging technology, RFID and laser fusion positioning feasibility analysis, and moving target state estimation. The experimental results show that when the phase shift threshold φ and the included angle of the antenna are 90°, the error can be reduced to 0.36 m, and the recognition rate can be increased to 94.8%. The simulation results show that, on the premise of meeting the customer’s expectation of timeliness, the actual logistics distribution cost is significantly lower than the customer’s expected logistics distribution cost, which verifies the effectiveness of the proposed method.

Maneuvering target state estimation based on separate modeling of target trajectory shape and dynamic characteristics

The state estimation of a maneuvering target, of which the trajectory shape is independent on dynamic characteristics, is studied. The conventional motion models in Cartesian coordinates imply that the trajectory of a target is completely determined by its dynamic characteristics. However, this is not true in the applications of road-target, sea-route-target or flight route-target tracking, where target trajectory shape is uncoupled with target velocity properties. In this paper, a new estimation algorithm based on separate modeling of target trajectory shape and dynamic characteristics is proposed. The trajectory of a target over a sliding window is described by a linear function of the arc length. To determine the unknown target trajectory, an augmented system is derived by denoting the unknown coefficients of the function as states in mileage coordinates. At every estimation cycle except the first one, the interaction (mixing) stage of the proposed algorithm starts from the latest estimated base state and a recalculated parameter vector, which is determined by the least squares (LS). Numerical experiments are conducted to assess the performance of the proposed algorithm. Simulation results show that the proposed algorithm can achieve better performance than the conventional coupled model-based algorithms in the presence of target maneuvers.

Equal probability embedded cubature particle PHD filter algorithm in multi-target tracking

In target tracking, multi-target tracking is the focus of research. It is primarily concerned with the issue of collaboratively estimating the number, state, or trajectory of targets based on sensor measurements in the presence of data association uncertainty, detection uncertainty, false observation, and noise. The current research hotspot in multi-target tracking is nonlinear multi-target tracking based on the probability hypothesis density algorithm. Furthermore, there are several considerations with this algorithm’s multi-target tracking, such as low estimate accuracy, filter divergence, and poor real-time performance. Based on equivalent probability sampling and the embedded volume criterion, this article demonstrates an equal probability embedded cubature particle probability hypothesis density filter algorithm. In the sampling stage, the algorithm implements the equal probability sampling method, divides the entire sampling area into several areas with equal probability, extracts particles from each region using the established criteria, generates limited integral points using the third-order embedded volume criterion, filters each sampling particle, fits the important density function, and predicts and updates the probability hypothesis density of multi-target state. The simulation results demonstrate that the equal probability sampling strategy outperforms other multi-target position and number estimation methods. Simultaneously, it demonstrates that the equal probability embedded cubature particle probability hypothesis density filter algorithm can effectively track multiple targets. The equal probability embedded cubature particle probability hypothesis density filter algorithm performs better in real-time and has a more accurate target number and state estimate than other algorithms.

UAV Standoff Tracking for Narrow-Area Target in Complex Environment

This article studies the path planning for an unmanned aerial vehicle (UAV) standoff tracking a narrow-area target in complex environment. Since the target state estimation considerably influences the target tracking performance, the constrained interacting multiple model, which uses the geographic information of narrow-areas to enhance the estimation accuracy, is proposed. Then, based on the obtained estimation results, the dynamic Lyapunov guidance vector field (DLGVF) is proposed, which can guide UAVs to converge to not only the expected standoff distance in the horizontal plane but also the desired height in the vertical plane. Moreover, the DLGVF renders the UAV trajectories flexible and shortens the convergence time by skillfully introducing adjustment parameters. Finally, to simultaneously complete the missions of target tracking and obstacle avoidance, an integrated method based on the DLGVF and the interfered fluid dynamical system is presented. Simulations are implemented to demonstrate that the proposed framework is effective.

A Hybrid SVSF Algorithm for Automotive Radar Tracking

This paper concerns the robust state estimation of automotive radar targets in presence of model uncertainty. Smooth variable structure filter (SVSF) achieves error-bounded estimation for target state, even with an inaccurate description of target kinematic model. However, it suffers the undesired chattering phenomenon especially in case of a high model uncertainty level, and its performance is sensitive to a preset smoothing boundary layer parameter. In this paper, we propose a novel hybrid SVSF algorithm to handle these two problems simultaneously. First, we derive a nonlinear generalized variable smoothing boundary layer (NGVBL) parameter based on the conventional Tanh-SVSF method by minimizing the pseudo posterior estimation error covariance. Then this NGVBL is employed to realize an adaptive two-module switching strategy with respect to the uncertainty level to calculate the correction gain. If the uncertainty level is high, the undesired chattering is effectively suppressed by the standard Tanh-SVSF gain. In case of a low uncertainty level, the NGVBL is utilized to replace the preset smoothing boundary layer parameter and reformulate the correction gain. Furthermore, it is demonstrated that the NGVBL-based gain is quasi-optimal in the mean square error (MSE) sense. Accordingly, this novel NGVBL-based hybrid SVSF (NGVBL-SVSF) algorithm improves the estimation performance by avoiding parameter sensitivity in a low uncertainty level case, and maintains effective chattering suppression and robustness to increasing uncertainties. Simulation and real-world automotive radar data experiment results show that, the proposed NGVBL-SVSF outperforms existing SVSFs and the classical Kalman filter in terms of tracking accuracy and track continuity.

Auto-Perceiving Correlation Filter for UAV Tracking

Discriminative correlation filter (DCF)-based methods have demonstrated superior performance in UAV tracking via fusing multiple types of features and updating models online. However, most DCF-based trackers simply cascade different features, failing to fully take advantage of their complementary strength. In addition, online update strategies are limited to using a single and fixed learning rate, which often leads to model degradation when suffering tracking challenges. In this paper, we present an Auto-Perceiving Correlation Filter (APCF) which explicitly models the target and context with a novel Target State and Background Perception (TSBP) feature. Concretely, we first propose a simple yet effective State Evaluation Metric (SEM) to estimate target states by analyzing the spatial distribution of responses. Based on SEM, we extract TSBP features by adaptively selecting effective features depending on the current target state. Accordingly, a new online model update strategy is also introduced to avoid model degradation. Moreover, we further introduce a perception regularization term to make the extracted feature emphasis more on the target rather than background. Extensive experiments on four widely-used UAV benchmarks have well demonstrated the superiority of the proposed method compared with both DCF and deep learning based trackers while running at a high speed of 76.7 FPS on a single CPU. In addition, APCF with deep features also performs favorably against state-of-the-art trackers.

An Improved Kalman Filter Based on Long Short-Memory Recurrent Neural Network for Nonlinear Radar Target Tracking

The target tracking of nonlinear maneuvering radar in dense clutter environments is still an important but difficult problem to be solved effectively. Traditional solutions often rely on motion models and prior distributions. This paper presents a novel improved architecture of Kalman filter based on a recursive neural network, which combines the sequence learning of recurrent neural networks with the precise prediction of Kalman filter in an end-to-end manner. We employ three LSTM networks to model nonlinear motion equation, motion noise, and measurement noise, respectively, and learn their long-term dependence from a large amount of training data. They are then applied to the prediction and update process of Kalman filter to calculate the estimated target state. Our approach is able to address the tracking problem of nonlinear maneuvering radar target online end-to-end and does not require the motion models and prior distributions. Experimental results show that our method is more effective and faster than the traditional methods and more accurate than the method with LSTM network alone.

Transformer Feature Enhancement Network with Template Update for Object Tracking.

This paper proposes a tracking method combining feature enhancement and template update, aiming to solve the problems of existing trackers lacking global information attention, weak feature characterization ability, and not being well adapted to the changing appearance of the target. Pre-extracted features are enhanced in context and on channels through a feature enhancement network consisting of channel attention and transformer architectures. The enhanced feature information is input into classification and regression networks to achieve the final target state estimation. At the same time, the template update strategy is introduced to update the sample template judiciously. Experimental results show that the proposed tracking method exhibits good tracking performance on the OTB100, LaSOT, and GOT-10k benchmark datasets.

Radar sensor network resource allocation for fused target tracking: A brief review

Traditional networked radar fusion technology adopts an open-loop signal processing manner, in which multiple nodes independently detect or track target first, and then send their raw data or processed measurements to the fusion center for target state estimation. In such a working manner, each radar sensor operates within predetermined rules, adopts fixed transmit and receiving mode for changeable target tracking environment, and thus will definitely cause an insufficient use of its limited resource. Resource allocation (RA) has emerged as a promising technology to exploit the limited radar resource in a more efficient way by allocating the available resource parameters w.r.t. multiple nodes collaboratively in view of the fused target tracking performance. The goal of this paper is to provide a brief overview of the RA methods for fused target tracking in radar sensor network. We mainly investigate the existing RA works and divide them into two types, namely tracking quality constrained RA type and performance driven RA type. Solution techniques for both types appeared in the literatures are also summarized. Finally, some potential future research directions in this field are discussed.

Moving Target Tracking Algorithm Based on Improved Resampling Particle Filter in UWB Environment

In this paper, a moving target tracking (MTT) algorithm based on the improved resampling particle filter (IRPF) was put forward for the reduced accuracy of particle filter (PF) due to the lack of particle diversity resulting from traditional resampling methods. In this algorithm, the influences of the likelihood probability distribution of particles on the PF accuracy were firstly analyzed to stratify the adaptive regions of particles, and a particle diversity measurement index based on stratification was proposed. After that, a threshold was set for the particle diversity after resampling. If the particle diversity failed to reach the set threshold, all new particles would be subjected to a Gaussian random walk in a preset variance matrix to improve the particle diversity. Finally, the performance of related algorithms was tested in both simulation environment and actual indoor ultrawideband (UWB) nonline-of-sight (NLOS) environment. The experimental results revealed that the nonlinear target state estimation accuracy was maximally and minimally improved by 12.83% and 1.97%, respectively, in the simulation environment, and the root mean square error (RMSE) of MTT was reduced from 17.131 cm to 10.471 cm in actual UWB NLOS environment, indicating that the IRPF algorithm can enhance the target estimation accuracy and state tracking capability, manifesting better filter performance.

Recursive Set-Membership State Estimation Over a FlexRay Network

In this article, we investigate the set-membership state estimation problem for a class of time-varying systems with non-Gaussian noises over a FlexRay network. To mitigate the communication load and improve the flexibility of the data scheduling, the FlexRay protocol (FRP) governed by both the time-triggered and event-triggered rules is exploited to regulate the signal transmission in a cyclic fashion. A new expression of the input signal to the state estimator is formulated with intention to account for the effect of the FRP. Accordingly, a multirate model (orchestrating the sampling/updating rates of the target plant, sensors, and state estimator) is proposed and then transformed into a single-rate one with the help of the lifting technique and the vector augmentation method. Subsequently, sufficient conditions are provided for the true states to always reside in an ellipsoid at each time instant in the presence of the non-Gaussian noises, and such an ellipsoid is then minimized in the matrix-trace sense. An online optimization algorithm is developed to parameterize the estimator gains by means of the solution to certain recursive matrix inequalities. Numerical results demonstrate the validity of the proposed protocol-based set-membership state estimator design scheme.

Dynamic Particle Filter Framework for Robust Object Tracking

Most of siamese network and correlation filter (CF) based trackers usually employ the context regression scheme to achieve appealing performance in both accuracy and efficiency. However, they are prone to drifting in challenging situations exhibiting occlusion, out-of-view and large-scale variations due to the lack of failure correction ability in these regressors. Particle filter based trackers can help to recover from tracking failures since several particles of high confidence about the target can be remained for the probability estimation of next frames, but need the large numbers of particles for each frame. In this paper, we propose a generic dynamic particle filter framework, which can reasonably control the number of particles in different scenarios, to improve the robustness of siamese and CF trackers by jointing the target classifier to relieve drifting. Our fundamental insight is that general scenarios are processed efficiently by the context regressor with few particles, while special scenarios are handled effectively by the target classifier with many particles. We propose a novel measure to determine whether to adopt the regressor and few particles to estimate target states with high measure scores, or increase the number of particles to prevent drifting with the proposed multi-template matching strategy in the classifier. In extensive experiments on eight large-scale benchmarks including OTB, UAV, TC128, VOT2017, VOT2019, LaSOT, TrackingNet and Got-10k, the proposed framework enables many basic siamese and CF trackers to operate at least over 24 frames per second and achieve superior tracking performance than themself, as well as the comparable accuracy with the state-of-the-art trackers. Furthermore, our framework is flexible and still has great potential for improvement and generalization.

Bearings-only Tracking Based on Distributed Multisensor Pseudolinear Kalman Filter

For bearings-only tracking (BOT), there are mainly two problems of nonlinear filtering and poor range observability. In the paper, a new distributed multisensor pseudolinear Kalman filter (PLKF) algorithm is proposed. The sensors use an instrumental vector PLKF (IV-PLKF) to process the measurements of the target independently, which can tackle the bias arising from the correlation between the measurement vector and pseudolinear noise by the bias compensation PLKF (BC-PLKF). The IV-PLKF embeds the recursive instrumental vector estimation method into the BC-PLKF, uses it to construct the instrumental vector, and applies the method of selective angle measurement to modify the local target state estimation and covariance. In the fusion center, the target state can be estimated by using the multisensor optimal information fusion criterion. Then the Cramer-Rao lower bound (CRLB) of multisensor BOT is derived. Simulation results show the effectiveness of the algorithm.

Autonomous Navigation and Control System for Capturing A Moving Drone

This paper describes an autonomous navigation and control system for capturing the maneuvering drones. A vision-based navigation method seeks and detects the intruding drone, then, the target trajectory is predicted by fusing onboard vision and inertial-measurement resources. The target’s relative position, velocity and acceleration are also obtained at the same time. Then, we present a modified proportional-derivative (PD) algorithm based on the estimated target states. In addition, the boundary constraints of the protected area are considered to avoid a collision. The proposed capture navigation and control system has demonstrated its efficiency both in simulation, flight experiments, and MBZIRC 2020, where our team won the Challenge I competition.

Local to global Tracker: A Siamese Network for Long-term Tracking

Visual object tracking is a basic task in computer vision, which can be applied to different applications. Former methods of single object tracking problem demands to perform target state estimation according to object position of previous frame, but these methods are poorly capable of handling the fast movement of the target. In this paper, we introduce a Siamese network for single object tracking task. The network consists of two branches, one of which is the classification branch used to predict positive or negative samples, and the other is the regression branch used to predict the specific location of the object in sequence. In addition, we propose a local to global algorithm for long-term tracking to expand the search region when the target is out-of-view or full occlusion. Experiments show that our tracker achieves very promising results on VOT2018LT datasets while running at over 89 FPS.

Evolutionary Optimization Based Set Joint Integrated Probabilistic Data Association Filter

The joint integrated probabilistic data association (JIPDA) algorithm is widely used for the automatic tracking of multiple targets, but it has the well-known problem of track coalescence. By optimizing the posterior density, the accuracy of the target state estimation can be improved. Motivated by this idea, we developed a novel evolutionary optimization based joint integrated probabilistic data association (EOJIPDA) filter to overcome the coalescence problem of the JIPDA filter. The trace for the covariance matrix of the posterior density is used as the objective function for the above optimization problem. It is shown that the accuracy of the target state estimation can be improved by reducing the trace. Evolutionary optimization was employed to minimize the trace and optimize the posterior density. More specifically, we enumerated all the possible permutations of the targets and assign a unique index to each permutation. The resulting indices were randomly assigned to all possible association hypothesis events. Each assignment indicated one possible gene in the evolutionary algorithm. This process was repeated several times to arrive at the initial population. An illustrative example shows that the EOJIPDA filter can effectively improve the accuracy of state estimation. Numerical studies are presented for two challenging multi-target tracking scenarios with clutter and missed detections. The experimental results demonstrate that the EOJIPDA filter provides better tracking accuracy than traditional coalescence-avoiding methods.