Cooperative Target Tracking Research Articles

GNSS-Denied Joint Cooperative Terrain Navigation and Target Tracking Using Factor Graph Geometric Average Fusion

GNSS-Denied Joint Cooperative Terrain Navigation and Target Tracking Using Factor Graph Geometric Average Fusion

Cooperative Localization and Target Tracking With Data Exception Based on Classification

Cooperative Localization and Target Tracking With Data Exception Based on Classification

Reinforcement Learning-Based Cooperative Target Tracking Control of Unmanned Surface Vehicles Under Data Falsification Attacks

Reinforcement Learning-Based Cooperative Target Tracking Control of Unmanned Surface Vehicles Under Data Falsification Attacks

Cooperative Localization and Target Tracking in Mobile Sensor Networks With Uncertainties in Measurement Models

Cooperative Localization and Target Tracking in Mobile Sensor Networks With Uncertainties in Measurement Models

Coordinated target localization and tracking with node scheduling for a network of autonomous underwater vehicles

Underwater mobile sensor networks (UMSNs) have the advantages of large area coverage, flexible network structure, and good local resolution. Particularly, spatial diversity can be exploited through node mobility to get more target information. To take those advantages in the context of regional cooperative target localization and tracking, a joint node scheduling and acoustic model fusion method is proposed for an UMSN consisting of multiple autonomous underwater vehicles (AUVs). First, a target tracking algorithm framework based on the random finite set theory is established. To take the acoustic environment into account, the a priori acoustic field and target distribution information are combined to evaluate the detection probability. A path-planning scheme with node scheduling is then developed based on the information gain to optimize the detection performance. To achieve effective node scheduling, three reward functions with different information gains respectively derived from the Rényi divergence and Fisher information matrix are proposed and compared. The rationale and the performance of the developed method are verified via simulations. A simplified version of the approach is also tested in field experiments with three AUVs, verifying the effectiveness of node scheduling in a realistic scenario.

Research on the Cooperative Target State Estimation and Tracking Optimization Method of Multi-UUV.

This work studied two sub-problems of the cooperative state estimation and cooperative optimization of tracking paths in multiple unmanned underwater vehicle (multi-UUV) cooperative target tracking. The mathematical model of each component of the multi-UUV cooperative target tracking system was established. According to the target bearing-only information obtained by each unmanned underwater vehicle's (UUV) detection, the extended Kalman filter algorithm based on interacting with multiple model bearing-only data was used to estimate the target state in a distributed way, and the federal fusion algorithm was used to fuse the estimated results of each UUV. The fused target state was predicted, and, based on the predicted target state, to achieve the persistent tracking of the target, the particle swarm optimization algorithm was used for the online collaborative optimization of the UUV tracking path. The simulation results showed that the multi-UUV distributed fusion filtering algorithm could obtain a better target state estimation effect, and the online path collaborative optimization method based on the prediction of the target state could achieve persistent target tracking.

Integrated Design of Cooperative Area Coverage and Target Tracking with Multi-UAV System

Integrated Design of Cooperative Area Coverage and Target Tracking with Multi-UAV System

A multi-sensor cooperative detection target tracking method based on radar-optical linkage control

Radar is a common means of tracking a target, and with active enemy interference, it often causes the target to lose its track, thus causing the radar to lose continuous tracking of the target. To improve the tracking effect, a multi-sensor cooperative detection target tracking method based on radar photoelectric linkage control was established. The study is based on radar photoelectric linkage, constant velocity (CV), constant acceleration (CA) and current statistical model (CSM) as the mathematical model of moving targets for this study. Improved interactive multi-model (IMM) and standard IMM were compared for targets in different motion states, as well as single sensor electronic support measures (ESM) and multi-sensor electronic support measures (ESM), infrared search and track (IRST). The research results show that in variable speed motion, the improved IMM algorithm and multiple sensors are used for target tracking. The azimuth and elevation tracking errors of the target are low, which can effectively solve the problem of model mismatch during the conversion of motion modes such as CV and CA. The azimuth and elevation image curves fluctuate smoothly, and have high stability. This method can achieve better tracking results.

An adaptive dynamic programming in cooperative target tracking for energy acquisition in wireless sensor networks

In order to further study the application of energy acquisition model, based on the relevant theory of adaptive dynamic programming, the wireless sensor is tracked by modifying the energy acquisition model, and the relevant data and parameters are quantitatively analyzed according to the test results. Finally, the parameters of different samples are tracked and identified. The results show that the evaluation analysis curve with four different parameters can be divided into two stages according to the different variation trends: fluctuation stage and stable stage. It indicates that the increase of time can effectively improve the stability corresponding to the convergence of neural network. The trajectory under the three different parameter states shows a general trend of rapid decline at first and then tends to be stable. There is a certain disturbance in the rapid decline of the curve, but when the time is between 1 and 2, it corresponds to the drop of the curve, and the curve gradually flattens after rapid decline. Parameters in the adaptive dynamic programming model are shown as follows: parameter W decreases slowly to the lowest point and then rises slowly with the increase of iteration steps; The proportion of parameter W1 in the model shows a trend of slow increase. Relevant research can provide theoretical basis for the application of energy acquisition model based on adaptive dynamic programming and analysis in other fields.

Viewpoint-Driven Formation Control of Airships for Cooperative Target Tracking

For tracking and motion capture (MoCap) of animals in their natural habitat, a formation of safe and silent aerial platforms, such as airships with on-board cameras, is well suited. In our prior work we derived formation properties for optimal MoCap, which include maintaining constant angular separation between observers w.r.t. the subject, threshold distance to it and keeping it centered in the camera view. Unlike multi-rotors, airships have non-holonomic constrains and are affected by ambient wind. Their orientation and flight direction are also tightly coupled. Therefore a control scheme for multicopters that assumes independence of motion direction and orientation is not applicable. In this paper, we address this problem by first exploiting a periodic relationship between the airspeed of an airship and its distance to the subject. We use it to derive analytical and numeric solutions that satisfy the formation properties for optimal MoCap. Based on this, we develop an MPC-based formation controller. We perform theoretical analysis of our solution, boundary conditions of its applicability, extensive simulation experiments and a real world demonstration of our control method with an unmanned airship. Open source code <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://tinyurl.com/AsMPCCode</uri> and a video of our method is provided at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://tinyurl.com/AsMPCVid</uri>

Robust Cooperative Control of UAV Swarms for Dual-Camp Divergent Tracking of a Heterogeneous Target

Agents are used to exhibit swarm intelligence in the sense of convergence, while divergence is equivalently common in nature and useful in complex applications for multi-UAV systems. This paper proposes a robust target-tracking control algorithm, where UAV swarms are partitioned by a signed graph to perform opposite movements along or against the trajectory of the target. Uncertainties take place in both the fractional-order model of the target and the double-integrator dynamics of the UAVs. To tackle the challenge induced by the bipartite behavior and unknown components in the multi-UAV systems, the article comes up with a backstepping cascade controller and a new method for uncertainty estimation-compensation via a combined approach based on a neural network (NN) and an Uncertainty and Disturbance Estimator (UDE). Steered by the controller, UAVs in a structurally balanced network will display symmetry of their paths, pursuing or away from the target with respect to the origin. Theoretical derivation and numerical simulations have evidenced that the tracking errors converge to zero. Compared with the traditional NN method to solve such problems, this method is proposed for the first time, which can effectively improve the precision of cooperative target tracking and reduce the chattering phenomena of the controller.

Cooperative target search and tracking for multi-UAVs based on control barrier functions

In this paper, a multiple unmanned aerial vehicles (multi-UAVs) cooperative target search and tracking strategy with collision avoidance and flocking constraints are presented applying the control barrier functions (CBFs). First, the constraints of collision avoidance and flocking maintenance are designed based on CBF. Second, the information map and Lyapunov guidance vector field (LGVF) are used to design the corresponding nominal controllers in search mode and tracking mode, respectively. To minimize the difference between the actual controller and the nominal controller, the controller based on quadratic programming (QP) is proposed to achieve the target search and tracking on the premise of security. Finally, the simulation results verify the effectiveness of the proposed method.

Current-Aided Multiple-AUV Cooperative Localization and Target Tracking in Anchor-Free Environments

In anchor-free environments, where no devices with known positions are available, the error growth of autonomous underwater vehicle (AUV) localization and target tracking is unbounded due to the lack of references and the accumulated errors in inertial measurements. This paper aims to improve the localization and tracking accuracy by involving current information as extra references. We first integrate current measurements and maps with belief propagation and design a distributed current-aided message-passing scheme that theoretically solves the localization and tracking problems. Based on this scheme, we propose particle-based cooperative localization and target tracking algorithms, named CaCL and CaTT, respectively. In AUV localization, CaCL uses the current measurements to correct the predicted and transmitted position information and alleviates the impact of the accumulated errors in inertial measurements. With target tracking, the current maps are applied in CaTT to modify the position prediction of the target which is calculated through historical estimates. The effectiveness and robustness of the proposed methods are validated through various simulations by comparisons with alternative methods under different trajectories and current conditions.

Cooperative Target Tracking of Multiple Autonomous Surface Vehicles Under Switching Interaction Topologies

This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles (ASVs) under switching interaction topologies. For the target to be tracked, only its position can be measured/received by some of the ASVs, and its velocity is unavailable to all the ASVs. A distributed extended state observer taking into consideration switching topologies is designed to integrally estimate unknown target dynamics and neighboring ASVs' dynamics. Accordingly, a novel kinematic controller is designed, which takes full advantage of known information and avoids the approximation of some virtual control vectors. Moreover, a disturbance observer is presented to estimate unknown time-varying environmental disturbance. Furthermore, a distributed dynamic controller is designed to regulate the involved ASVs to cooperatively track the target. It enables each ASV to adjust its forces and moments according to the received information from its neighbors. The effectiveness of the derived results is demonstrated through cooperative target tracking performance analysis for a tracking system composed of five interacting ASVs.

Fixed-Time Resilient Edge-Triggered Estimation and Control of Surface Vehicles for Cooperative Target Tracking Under Attacks

This paper is concerned with the cooperative target tracking of under-actuated unmanned surface vehicles (USVs) with event-triggered communications subject to denial-of-service (DoS) attacks. The target position information can be sensed by a fraction of follower USVs only. A fixed-time resilient cooperative edge-triggered estimation and control architecture is presented for achieving cooperative target tracking under DoS attacks. Specifically, a distributed edge-triggered fixed-time extended state observer (ESO) is designed to recover the position and velocity of the target with a prescribed time regardless of the unreliable communication network subject to DoS attacks. Moreover, the communication burden of the network is reduced by the proposed edge-triggered mechanism. In the control law design, a fixed-time ESO is designed for estimating the model uncertainties and external disturbances in an earth-fixed reference frame. Then, a fixed-time target tracking control law is proposed for each follower USV based on the fixed-time ESO. It is proven that the error signals in the closed-loop control system of USVs are convergent to the origin in a fixed time. An example is provided to substantiate the effectiveness of the proposed fixed-time resilient cooperative edge-triggered estimation and control architecture for USVs.

Versatile formation patterns for cooperative target tracking using ground vehicles

Versatile formation patterns for cooperative target tracking using ground vehicles

Target tracking and enclosing via UAV/UGV cooperation using energy estimation pigeon-inspired optimization and switchable topology

PurposeThe research of unmanned air/ground vehicle (UAV/UGV) cooperation has attracted much attention due to its potential applications in disaster rescue and target surveillance. This paper aims to focus on the UAV/UGV cooperative target tracking and enclosing, considering the limits of detection and sensor failures.Design/methodology/approachThe UAV/UGV cooperation structure is designed, contributing to homogeneous consistency and heterogeneous communication. The target tracking of UAVs is converted into a constraint optimization problem involving tracking cost, and the target enclosing of UGVs is modeled as formation control.FindingsThe energy estimation pigeon-inspired optimization is developed to generate control inputs for UAVs. And the controller combined with switchable topology is proposed, where the switching rule is flexible in dealing with some emergencies.Practical implicationsThe proposed structure and algorithms can be easily applied to practice and help design the UAV/UGV control system.Originality/valueThe energy estimation mechanism is proposed for the target tracking of UAVs, and the rules of switching topologies ensure the target enclosing process of UGVs.

Uncertainty-Constrained Belief Propagation for Cooperative Target Tracking

Cooperative localization is essential for many Internet of Things (IoT)-related applications in harsh environments. Generally, the inertial navigation system is self-contained and adopted as the basis of a cooperative tracking system, but it still faces the problem of accumulated errors and cannot provide long-term, high-precision positioning. The particle filter (PF) is widely used to fuse multiple information to inhibit accumulative errors. However, particle degradation and impoverishment remain unsolved. This article proposed an IMU/time-of-arrival (TOA) fusion-based tracking method, namely, uncertainty-constrained belief propagation (UCBP). We address particle degradation and impoverishment by introducing uncertainty-constrained optimization into belief propagation (BP). An uncertainty-constrained resampling (UCR) method is applied to quantify the uncertainty in cooperative systems. Hierarchical resampling is realized to solve the particle impoverishment issue. Meanwhile, particle degradation is resolved through constrained resampling while ensuring the diversity of particles. Furthermore, we illustrated the factor graph (FG) structure of UCBP to mitigate the accumulation of errors through message fusion over the graph. Compared with the state-of-the-art methods, our proposed UCBP algorithm has better precision and robustness without introducing much time overhead.

Cooperative tracking in camera sensor networks with sliding mode control

This paper presents robust cooperative tracking control in camera sensor networks. We consider a camera sensor network with a Pan-Tilt (PT) mechanism whose dynamics is expressed as the Euler–Lagrange equation. We propose a distributed control law based on sliding mode control to achieve cooperative target tracking. The stability of the closed-loop system is conducted by Lyapunov's stability theory considering discontinuities of the control input. We also verify the effectiveness of the proposed method with a numerical example of synchronization of a PT camera sensor network under external disturbance.

Cooperative Target Tracking Algorithm Based on Massive Beacon Coordinates System in Directional Molecular Communication.

As nanotechnology advances, it is possible to build nanomachines to conduct tasks on the nano-scale. Since the EM wave, the traditional communication medium does not apply in nano-scale, Molecular Communication (MC) has been recently given increasing attention for its excellent biocompatibility and low energy consumption within the human body. Diffusion channel-based MC(DbMC) is mainly studied in the aquatic environment. However, due to the randomness of diffusion, DbMC suffers high losses, low propagation speed, and limited communication range. Directional Molecular Communication(DMC) is proposed by introducing chemotaxis. DMC can significantly improve the efficiency of molecular communication because it keeps molecules moving along a predetermined path. It is particularly suitable in the scenario of target tracking related to some applications such as drug delivery. In this paper, we have proposed a novel massive beacon coordinates system model to aid target tracking. Beacons in this system navigate nanomachines, and the beacon system can uniquely determine their position coordinates. Each nanomachine carries a lot of bacteria carrier (E.coli) to share information. Information is encoded in DNA molecules and transferred to other nanomachines by bacteria carriers. With the help of bacteria carriers, nanomachines can share their current position information with others to realize cooperated fast target tracking. We have evaluated its performance in target tracking through simulation by comparison with the diffusion-based model. Some key factors that may influence target tracking are also taken into consideration.