Target Tracking Problem Research Articles

Target Tracking and Circumnavigation Control for Multi-Unmanned Aerial Vehicle Systems Using Bearing Measurements

This paper addresses the problem of target tracking and circumnavigation control for a bearing-only multi-Unmanned Aerial Vehicle (UAV) system. First, using the bearing measurements, an adaptive algorithm in the form of a Proportional Integral (PI) controller is developed to estimate the target state. Subsequently, a distributed circumnavigation control protocol is established to evenly encircle the target. Then, we use the local information from each UAV in the network to calculate the relative position of the target, and further enhance the accuracy of estimation and circumnavigation algorithms by employing a Kalman filter. Finally, numerical simulation experiments are conducted to validate the effectiveness of the proposed tracking control algorithm.

A Hybrid Integration Method Based on SMC-PHD-TBD for Multiple High-Speed and Highly Maneuverable Targets in Ubiquitous Radar

Based on the characteristic of ubiquitous radar emitting low-gain wide beam, a method of long-time coherent integration (LTCI) is required to enhance target detection capability. However, high-speed and highly maneuverable targets can cause Doppler frequency migration (DFM), range migration (RM), and velocity ambiguity (VA), severely degrading the performance of LTCI. Additionally, the number of targets is unknown and variable, and the presence of clutter further complicates the target tracking problem. To address these challenges, we propose a hybrid integration method to achieve joint detection and estimation of multiple high-speed, and highly maneuverable targets. Firstly, we compensate for first-order RM using the keystone transform (KT) and generate corresponding sub-range-Doppler (SRD) planes with different folding factors to achieve VA compensation. These SRD planes are then stitched together to form an extended range-Doppler (ERD) plane, which covers a broader velocity range. Secondly, during the track-before-detect (TBD) process, tracking is performed directly on the ERD plane. We use the sequential Monte Carlo (SMC) approximation of the probability hypothesis density (PHD) to propagate multi-target states. Additionally, we propose an amplitude-based adaptive prior distribution method and a line spread model (LSM) observation model to compensate for DFM. Since the acceleration of the target is included in the particle state, using particles to search for DFM does not increase the computational load. To address the issue of misclassifying mirror targets as real targets in the SRD plane, we propose a particle space projection method. By stacking the SRD planes to create a folding range-Doppler (FRD) space, particles are projected along the folding factor dimension, and then, the particles are clustered to eliminate the influence of the mirror targets. Finally, through simulation experiments, the superiority of the LSM for targets with acceleration was demonstrated. In comparative experiments, the proposed method showed superior performance and robustness compared to traditional methods, achieving a balance between performance and computational efficiency. Furthermore, the proposed method’s capability to detect and track multiple high-speed and highly maneuverable targets was validated using actual data from a ubiquitous radar system.

UAV Path Optimization for Angle-Only Self-Localization and Target Tracking Based on the Bayesian Fisher Information Matrix.

In this paper, new path optimization algorithms are developed for uncrewed aerial vehicle (UAV) self-localization and target tracking, exploiting beacon (landmark) bearings and angle-of-arrival (AOA) measurements from a manoeuvring target. To account for time-varying rotations in the local UAV coordinates with respect to the global Cartesian coordinate system, the unknown orientation angle of the UAV is also estimated jointly with its location from the beacon bearings. This is critically important, as orientation errors can significantly degrade the self-localization performance. The joint self-localization and target tracking problem is formulated as a Kalman filtering problem with an augmented state vector that includes all the unknown parameters and a measurement vector of beacon bearings and target AOA measurements. This formulation encompasses applications where Global Navigation Satellite System (GNSS)-based self-localization is not available or reliable, and only beacons or landmarks can be utilized for UAV self-localization. An optimal UAV path is determined from the optimization of the Bayesian Fisher information matrix by means of A- and D-optimality criteria. The performance of this approach at different measurement noise levels is investigated. A modified closed-form projection algorithm based on a previous work is also proposed to achieve optimal UAV paths. The performance of the developed UAV path optimization algorithms is demonstrated with extensive simulation examples.

Target Tracking Systems on a Sphere With Topographic Information.

We consider the target tracking problem on a sphere with topographic structure. For a given moving target on the unit sphere, we suggest a double-integrator autonomous system of multiple agents that track the given target under the topographic influence. Through this dynamic system, we can obtain a control design for target tracking on the sphere and the adapted topographic data provides an efficient agent trajectory. The topographic information, described as a form of friction in the double-integrator system, affects the velocity and acceleration of the target and agents. The target information required by the tracking agents consists of position, velocity, and acceleration. We can obtain practical rendezvous results when agents utilize only target position and velocity information. If the acceleration data of the target is accessible, we can get the complete rendezvous result using an additional control term in the form of the Coriolis force. We provide mathematically rigorous proofs for these results and present numerical experiments that can be visually confirmed.

Multi‐target detection and tracking of shallow marine organisms based on improved YOLO v5 and DeepSORT

AbstractIn order to solve the related problems of detection and tracking of shallow marine organisms, this paper designs a YOLO v5 multi‐target detection and tracking algorithm with attention mechanism. When working underwater, the authors usually encounter many difficulties. Different luminosity and complex coral background usually affect the detection of marine organisms. At the same time, the unrestricted movement of marine organisms, the ability to hide behind rocks and algae, and their mutual occlusion while swimming pose additional challenges to this task. Considering the characteristics of shallow marine organisms activity environment, the attention mechanism is added to the feature extraction network of YOLO v5 to reduce redundant information and improve the detection accuracy of shallow marine organism targets in complex environment. The improved algorithm improves the average detection accuracy of marine organisms target detection by 3.2%. Aiming at the problem of shallow marine organisms target tracking, a shallow marine organisms multi‐target tracking algorithm based on improved Deep Simple Online And Realtime Tracking (SORT) is designed. The improved YOLO v5 algorithm is used to replace Faster R‐CNN (Faster Region‐Convolutional Neural Networks) as the detector of DeepSORT tracking algorithm, and the cascade matching strategy is adopted to solve the problem that the target cannot be tracked continuously when it is occluded for a long time. The experimental results show that the shallow marine organisms multi‐target tracking algorithm based on improved DeepSORT reduces the number of id transformation of marine biological target tracking in shallow sea environment, and improves the accuracy of shallow marine organisms multi‐target tracking.

Online Predictive Visual Servo Control for Constrained Target Tracking of Fixed-Wing Unmanned Aerial Vehicles

This paper proposes an online predictive control method for fixed-wing unmanned aerial vehicles (UAVs) with a pan-tilt camera in target tracking. It aims to achieve long-term tracking while concurrently maintaining the target near the image center. Particularly, this work takes the UAV and pan-tilt camera as an overall system and deals with the target tracking problem via joint optimization, so that the tracking ability of the UAV can be improved. The image captured by the pan-tilt camera is the unique input associated with the target, and model predictive control (MPC) is used to solve the optimization problem with constraints that cannot be performed by the classic image-based visual servoing (IBVS). In addition to the dynamic constraint of the UAV, the perception constraint of the camera is also taken into consideration, which is described by the maximum distance between the target and the camera. The accurate detection of the target depends on the amount of its feature information contained in the image, which is highly related to the relative distance between the target and the camera. Moreover, considering the real-time requirements of practical applications, an MPC strategy based on soft constraints and a warm start is presented. Furthermore, a switching-based approach is proposed to return the target back to the perception range quickly once it exceeds the range, and the exponential asymptotic stability of the switched controller is proven as well. Both numerical and hardware-in-the-loop (HITL) simulations are conducted to verify the effectiveness and superiority of the proposed method compared with the existing method.

Analysis of Various Machine Learning Algorithms for Using Drone Images in Livestock Farms

With the development of artificial intelligence, the intelligence of agriculture has become a trend. Intelligent monitoring of agricultural activities is an important part of it. However, due to difficulties in achieving a balance between quality and cost, the goal of improving the economic benefits of agricultural activities has not reached the expected level. Farm supervision requires intensive human effort and may not produce satisfactory results. In order to achieve intelligent monitoring of agricultural activities and improve economic benefits, this paper proposes a solution that combines unmanned aerial vehicles (UAVs) with deep learning models. The proposed solution aims to detect and classify objects using UAVs in the agricultural industry, thereby achieving independent agriculture without human intervention. To achieve this, a highly reliable target detection and tracking system is developed using Unmanned Aerial Vehicles. The use of deep learning methods allows the system to effectively solve the target detection and tracking problem. The model utilizes data collected from DJI Mirage 4 unmanned aerial vehicles to detect, track, and classify different types of targets. The performance evaluation of the proposed method shows promising results. By combining UAV technology and deep learning models, this paper provides a cost-effective solution for intelligent monitoring of agricultural activities. The proposed method offers the potential to improve the economic benefits of farming while reducing the need for intensive hum.

Using Control Barrier Functions to Incorporate Observability: Application to Range-Based Target Tracking

Abstract In many nonlinear systems, the observability of the system is dependent on its state and control input. Thus, incorporating observability into a control scheme can enhance an observer's ability to recover accurate estimates of unmeasured states, minimize estimation error, and ultimately, allow the original control objective to be achieved. The accommodation of observability, however, may conflict with the original control goal at times. In this paper, we propose the use of control barrier functions (CBFs) to enforce observability and thereby facilitate the convergence of the state estimate to the true state while accommodating the original control objectives. Motivated by practical applications for autonomous robots operating in global positioning system-denied environments, we focus on the problem of target tracking for a unicycle model when only the distance to the target is measured. The proposed approach is compared in simulation with a model predictive control (MPC) approach that treats an observability-related metric as part of the cost function, where several different options for the observability metric are explored. It is found that the CBF-based approach achieves control and estimation performance that is comparable to that of the MPC approach, but with significantly less computational complexity. These findings are further experimentally verified in range-based target tracking with a swimming robotic fish.

Space dynamic target tracking method based on five-frame difference and Deepsort

For the problem of space dynamic target tracking with occlusion, this paper proposes an online tracking method based on the combination between the five-frame difference and Deepsort (Simple Online and Realtime Tracking with a Deep Association Metric), which is to achieve the identification first and then tracking of the dynamic target. First of all, according to three-frame difference, the five-frame difference is improved, and through the integration with ViBe (Visual Background Extraction), the accuracy and anti-interference ability are enhanced; Secondly, the YOLOv5s (You Look Only Once) is improved using preprocessing of DWT (Discrete Wavelet Transformation) and injecting GAM (Global Attention Module), which is considered as the detector for Deepsort to solve the missing in occlusion, and the real-time and accuracy can be strengthened; Lastly, simulation results show that the proposed space dynamic target tracking can keep stable to track all dynamic targets under the background interference and occlusion, the tracking precision is improved to 93.88%. Furthermore, there is a combination with the physical depth camera D435i, experiments on target dynamics show the effectiveness and superiority of the proposed recognition and tracking algorithm in the face of strong light and occlusion.

Robust visual tracking via modified Harris hawks optimization

Due to its outstanding efficiency and high precision, Harris hawks optimization (HHO for short) is suitable for solving the problem of visual target tracking under conditions of occlusion, deformation, rotation and in other complicated tracking scenes. A visual target tracker based on HHO is proposed in this study. To further promote the efficiency and stability of the standard HHO method and reduce the probability of the iteration falling into local optima and algorithm prematurity, in this study we propose an improved method called Super-HHO and apply it to visual target tracking. Compared with standard HHO, Super-HHO is superior due to its parameter optimization and updating strategy. We first optimize the random parameters of HHO via chaos theory to avoid frequent repeated exploration of the feasible region. Next, we design a nonlinear renewal strategy for the escape energy, which solves the problem in traditional HHO in which the fixed escape energy cannot accurately reflect the real hunting process of Harris hawks. Mutation strategies are also designed for the locations of the prey and the hunters to improve the optimization ability and eliminate the risk of falling into local extremes. In addition, a frame scale adjustment method model is developed to address the the issue in which the use of a size-fixed tracking frame makes it easy to include too many invalid features, which reduces the efficiency. Finally, we use the OTB2015, and VOT2018 tracking evaluation datasets, which contain hundreds of visual sequences and more than 10 complex interference scenes to conduct a qualitative analysis, a quantitative analysis and a statistical analysis of ours and other classic trackers, and to effectively test and compare the success ratio, precision and stability of each tracker. The proposed method was also compared with other classic trackers using classic large-scale benchmarks such as LaSOT and TrackingNet. Experimental data prove that ours performs well in terms of robustness, precision and efficiency.

VB-Based Gaussian Sum Cubature Kalman Filter for Adaptive Estimation of Unknown Delay and Loss Probability

The traditional Kalman filter assumes that all measurements can be obtained in real time, which is invalid in practical engineering. Therefore, a variational Bayesian- (VB-) based Gaussian sum cubature Kalman filter is proposed to solve the nonlinear tracking problem of multistep random measurement delay and loss (MRMDL) with unknown probability. First, the measurement model with MRMDL is modified by Bernoulli random variables. Then, the expression of the likelihood function is reformulated as a mixture of multiple Gaussian distributions, and the cubature rule is used to improve the estimation accuracy under the framework of Gaussian sum filter in the process of time update. Finally, by constructing a hierarchical Gaussian model, the unknown and time-varying measurement delay and loss probability are estimated in real time with the state jointly using the VB method in the measurement update stage. The algorithm does not need to calculate the equivalent noise covariance matrix so as to avoid the possible division by zero operation, which improves the stability of the algorithm. Simulation results for a target tracking problem show that the proposed algorithm has a better performance in the presence of MRMDL and can estimate the unknown measurement delay and loss probability accurately.

Sequential centralized fusion of multiple passive acoustic sensors with unknown propagation delays

In this paper, we address the problem of target tracking using multiple acoustic sensors to observe the target state with unknown propagation delays. This problem occurs because the measurements received by multiple acoustic sensors located at different positions are from different unknown emission times of the acoustic signal even if the sensors receive measurements simultaneously; thus, they cannot be stacked up directly for centralized fusion as usual. However, the target states at different unknown emission times can be aligned to a common measurement received time by the retrodiction of state prediction. On this basis, herein we propose the centralized fusion of multiple acoustic sensors via sequential processing, namely, sequential centralized fusion (SCF). First, the measurement received time is chosen as the target state time, and the target state is predicted to this time for tracking. Second, state prediction is retrodicted to the signal emission times by solving augmented implicit nonlinear equations through Wegstein's method. Third, the state prediction is updated with acoustic measurements sequentially at measurement received time. Compared with the existing distributed fusion methods, our proposed SCF method has smaller computational complexity and better tracking performance. Illustrative examples demonstrate that SCF outperforms covariance intersection and the largest ellipsoid approximation.

A Comparative Study of Control Laws for a Maritime Surface Vessel Tracking an Underwater Vehicle

This paper compares the tradeoffs between performance and implementation efforts of three pure pursuit control laws; a proportional-integral-derivative (PID), nonlinear model predictive (NMPC) and a deep reinforcement learning (DRL) trained neural network (NN). The control laws are employed on a target tracking problem where the issue is to keep an unmanned surface vessel (USV) within proximity to a submerged autonomous underwater vessel (AUV) for optical communication to operate. The results from a simulation case study, show that the PID settled with a steady error of 2.0 m from the target, the NMPC at 2.4 m and the NN at 2.3 m. The relative control effort is highest for the PID at 100%, the NMPC settled at 98% and the NN at 96%. The PID represents a methodology that has transparent and time-efficient tuning rules together with a high update rate of 11,5 kHz. The NMPC provides a flexible tuning method but requires an accurate dynamic model and has the slowest update rate of 5.2 Hz. The DRL stand as a viable solution to solve the control problem and has an update rate of 4,2 kHz, but it bears the most time-consuming implementation and tuning of the three alternatives presented here.

Multiple maneuvering target tracking based on hierarchical Dirichlet process and hidden Markov model

This paper is concerned with multiple maneuvering target tracking problems. Although the interacting multiple model (IMM) algorithm can be used for maneuvering targets tracking, the performance is limited as the motion mode set of the target is fixed in advance. In this paper, we address the problem of multiple maneuvering targets with unknown maneuvering models in cluttered environments. A finite hidden Markov model (HMM) is developed with truncated Hierarchical Dirichlet process (HDP) to recognize the unknown and changing motion modes. Further, the HDP-HMM is introduced into multiple hypothesis tracking (MHT) and probabilistic multiple hypothesis tracking (PMHT) approaches for multiple maneuvering targets tracking with data association uncertainty. To joint estimate the HDP-HMM parameters and target states, the particle learning and variational inference approaches are used, and the solutions of HDP-HMM-MHT and HDP-HMM-PMHT algorithms are obtained based on Rao-Blackwellized particle filter and variational Bayesian methods, respectively. As the changes of maneuver models can be learned using HDP, the multi-target tracking accuracy of the proposed algorithm is improved. Simulation results show that the proposed algorithms outperform the traditional IMM-MHT method with fixed model sets.

A Modified δ-Generalized Labeled Multi-Bernoulli Filtering for Multi-Source DOA Tracking With Coprime Array

For the target tracking problem where the number of targets fluctuates with time and the measurement is a point measurement, the random finite set (RFS) class filtering is an available solution. However, in direction of arrival (DOA) tracking, the array observation is a super-positional value, and the tracking performance can be severely impaired if the RFS-based filter approach is applied. As a result, a novel measurement association mapping (NMAP) approach has been presented to cope with the mapping problem between the array observations and sources. Nevertheless, the tracking performance is poor when the number of particles is small. In this paper, a modified delta-Generalized Labeled Multi-Bernoulli (δ-GLMB) DOA tracking particle filter is proposed in combination with the NMAP strategy, which can achieve the same tracking performance with a smaller number of particles by modifying the particles in the δ-GLMB prediction step. Furthermore, the approach is extended to a coprime array and can achieve better DOA tracking performance than a uniform linear array. Simulation experiments validate the effectiveness of the proposed algorithm.

Kalman filter based on intermediate transition state for radar target tracking with nonlinear measurements

Aiming at the nonlinear measurements problem of target tracking, a Kalman filter algorithm based on intermediate transition state (ITS-KF) is proposed in this paper. The new intermediate transition states are introduced in two- and three-dimension scenarios, and the derivations of state transition between the state in Cartesian coordinate and intermediate transition state in measurement coordinate are given. Based on the intermediate transition state, a new ITS-KF algorithm is proposed and the filtering process is given. For maneuvering target tracking case, ITS-KF is combined with the improved IMM (IIMM) and the ITS-KF-IIMM is given. The simulation results show that the tracking accuracy and consistency of the proposed algorithm is obviously improved compared with existing algorithms especially in the case of large angle measurement errors both in non-maneuvering and maneuvering case.

Comparing Multiple Extended Object Tracking with Point Based Multi Object Tracking for LiDAR in a Maritime Context

In this paper, we compare a recently developed multiple extended object tracking method with a point-based tracker for maritime applications by evaluating the methods on both real and simulated LiDAR data. Being able to track other vessels is a key part of maritime situational awareness, and multi-object tracking is well suited to this task. Traditionally, multi-object tracking uses the point approximation to simplify the target tracking problem, meaning that an object is assumed to only generate a single measurement. With recent advances in sensor technology, this approximation is no longer valid. Multiple extended object tracking instead makes use of methods that estimates the extent of a target using all available measurements. However, the target models used in extended object tracking are by necessity more complex. We compare these two approaches on real and simulated LiDAR data, which was gathered by tracking smaller vessels. We find that the extended object method has greater performance on simulated data and experimental data with comparatively little wake clutter. However, on experimental data with more wake clutter, the point-based method outperforms the extended object method since it is less sensitive to the disturbance caused by wake clutter. We can conclude that multiple extended object tracking methods have the potential to be more accurate for LiDAR data, but since the models are more complex, measurement sources need to be modeled correctly.

Memory‐based deep reinforcement learning for cognitive radar target tracking waveform resource management

AbstractA cognitive radar (CR) system can offer enhanced target tracking performance due to its intelligence on the perception‐action cycle, wherein a CR adaptively allocates the limited transmitting resources based on its perception of surrounding environments. To effectively manage the transmit waveform resource for the target tracking task, CR resource management problem is formulated under the partially observable Markov decision process framework. The sequential decision‐making and the inherent partial observability for target tracking problem are considered. In the proposed method, a long short‐term memory (LSTM)‐based twin delayed deep deterministic policy gradient (TD3) algorithm is developed to effectively solve the problem. A reward function is designed considering Haykin's cognitive executive attention mechanism for radar systems such that the CR resource management policy has stability in the decision of transmit waveform, which follows the principle of minimum disturbance. Simulation results demonstrate the superiority of the proposed LSTM memory‐based TD3 with improved target tracking performance and increased mean rewards for CR.

Kalman Filter and Its Application in Data Assimilation

In 1960, R.E. Kalman published his famous paper describing a recursive solution, the Kalman filter, to the discrete-data linear filtering problem. In the following decades, thanks to the continuous progress of numerical computing, as well as the increasing demand for weather prediction, target tracking, and many other problems, the Kalman filter has gradually become one of the most important tools in science and engineering. With the continuous improvement of its theory, the Kalman filter and its derivative algorithms have become one of the core algorithms in optimal estimation. This paper attempts to systematically collect and sort out the basic principles of the Kalman filter and some of its important derivative algorithms (mainly including the Extended Kalman filter (EKF), the Unscented Kalman filter (UKF), the Ensemble Kalman filter (EnKF)), as well as the scope of their application, and also to compare their advantages and limitations. In addition, because there are a large number of applications based on the Kalman filter in data assimilation, this paper also provides examples and classifies the applications of both the Kalman filter and its derivative algorithms in the field of data assimilation.

PMBM Filter for Multiple Extended Targets With Unknown Clutter Rate and Detection Probability

In a multi-target tracking (MTT) scenario, the prior knowledge of parameters such as clutter rate and detection probability are usually uncertain or estimated offline from training data. However, significant parameters mismatch in the clutter and detection model will result in biased estimates. In such cases, the ability to adaptively online estimate clutter rate and detection probability is critical in MTT under the random finite sets (RFS) framework. In this paper, we propose a robust Poisson multi-Bernoulli mixture (PMBM) filter that can accommodate model mismatch in clutter rate and detection probability for multiple extended target tracking problems. Moreover, the closed-form solution to the proposed method is derived by the use of Beta and Gamma Gaussian inverse-Wishart (GGIW) distribution, where Beta is used to describe unknown detection probability and GGIW is used to model extended target extension as an ellipse. Simulation results are presented to verify the effectiveness of the proposed method.