Ground Moving Target Tracking Research Articles

Domain-knowledge-aided airborne ground moving targets tracking

This paper investigates the problem of traffic surveillance using an unmanned aerial vehicle (UAV) and proposes a domain-knowledge-aided airborne ground moving target tracking algorithm. To improve the accuracy of multiple target tracking, the proposed algorithm incorporates domain knowledge into the joint probabilistic data association (JPDA) filter as state constraints. The domain knowledge considered in this paper includes both road information extracted from a given map and local traffic regulations. Conventional track update method is modified to enhance the capability of recognition of temporarily track loss. A variable structure multiple model (VS-MM) method is developed to assign the road segment to a given target. The main contribution of this paper is that we establish a comprehensive framework for target tracking with the aid of domain knowledge and analyze the effect of diverse constraints on target tracking performance, while the limitations involve only 2-D target behaviors are considered, and the sophisticated interactions between targets are neglected. The effectiveness of proposed algorithm is demonstrated through extensive numerical simulations.

A Distributed Control for Ground Target Tracking of Nonlinear Unmanned Aerial Vehicles Formation

This paper investigates a circular formation control strategy for multiple unmanned aerial vehicles (multi-UAV) formation for ground moving target tracking. Each UAV obtains the observation value of target information through sensors and exchanges data with its neighbors. Since the formation cannot get the parameter of the target directly, we proposed a distributed error observer. A new guidance law based on the error value obtained has been designed, and it was proved through the interconnection system that it was consistent with Lyapunov stability. Finally, a simulation example is used to illustrate the effectiveness of the proposed guidance law.

Multisensor Management Method for Ground Moving Target Tracking Based on Doppler Blind Zone Information

Tracking ground moving target with sensors proves to be a challenge due to the uncertainty of target motion area, the existence of Doppler blind zone (DBZ), and the complex terrain. In this paper, a multisensor management method based on DBZ information is presented, in which the available sensors are selected to obtain the best operational revenues for ground moving target tracking. First, the ground target motion model is established considering the off-road/on-road state based on road topology information. Second, the sensor measurement model is given combined with the DBZ information, and a decorrelation method of measurement noise is proposed. Third, a target state estimation algorithm is derived using particle filter, in which the DBZ information is regarded as prior information. Then, combined with the variable structure interacting multiple model method, an estimation algorithm for tracking maneuvering target is proposed. Furthermore, an optimization model of nonmyopic sensor management is constructed to obtain the best sensor management scheme. Finally, the advancement and effectiveness of the proposed management method are verified in the simulations.

Video SAR Moving Target Tracking Using Joint Kernelized Correlation Filter

Video synthetic aperture radar (ViSAR) has been found very useful for the surveillance of ground moving targets. The target energy can be utilized for ground moving target tracking, while the dynamic shadows of moving targets enable an alternative tracking approach. However, neither of these two approaches can stand alone to provide reliable target tracking. The smeared shadow and energy both degrade the tracking performance when the target is maneuvering. A moving target tracking framework based on the joint kernelized correlation filter (JKCF) has been developed. Based on the feature training of JKCF, the target is tracked by combining its shadow in the sequential SAR imagery and the corresponding energy in the range-Doppler (RD) spectra. Aiming at the problems of tracking drift and collapse, interactive processing is adopted to enhance the target positioning and feature update based on the confidence assessment. By cooperating with the initialization and feature update strategy, the tracking success rate and precision can be improved significantly.

Ground Moving Target Tracking Filter Considering Terrain and Kinematics.

This paper addresses ground target tracking (GTT) for airborne radar. Digital terrain elevation data (DTED) are widely used for GTT as prior information under the premise that ground targets are constrained on terrain. Existing works fuse DTED to a tracking filter in a way that adopts only the assumption that the position of the target is constrained on the terrain. However, by kinematics, it is natural that the velocity of the moving ground target is constrained as well. Furthermore, DTED provides neither continuous nor accurate measurement of terrain elevation. To overcome such limitations, we propose a novel soft terrain constraint and a constraint-aided particle filter. To resolve the difficulties in applying the DTED to the GTT, first, we reconstruct the ground-truth terrain elevation using a Gaussian process and treat DTED as a noisy observation of it. Then, terrain constraint is formulated as joint soft constraints of position and velocity. Finally, we derive a Soft Terrain Constrained Particle Filter (STC-PF) that propagates particles while approximately satisfying the terrain constraint in the prediction step. In the numerical simulations, STC-PF outperforms the Smoothly Constrained Kalman Filter (SCKF) in terms of tracking performance because SCKF can only incorporate hard constraints.

Three-dimensional guidance and control for ground moving target tracking by a quadrotor

Abstract This paper develops a three-dimensional guidance and control algorithm to ensure that a manoeuverable target is preserved by a quadrotor in a long-term tracking scenario. The proposed guidance approach determines the desired altitude of the quadrotor to adjust the field of view (FOV) to the union of two desired trusted and critical regions. The dimensions of the desired trusted region depend on the controller performance that is evaluated by the distance of the target from the center of the FOV. The critical region is a predefined margin around the trusted region that is defined by the operator based on the upper bounds of the quadrotor and target localisation errors. It also depends on the duration and magnitude of the temporal increase in the target velocity compared to the quadrotor velocity. A sufficient condition is provided for the minimum desired altitude of the quadrotor to ensure that the target is maintained in the FOV. Furthermore, a model predictive control (MPC) is employed to preserve the target at the center of the aerial image and the desired altitude determined by the guidance law. Also, the integrals of the position errors are used to achieve null steady-state errors in the presence of wind disturbances. The simulation results show the effectiveness of the proposed approach in preserving the manoeuverable target in the FOV in the presence of the wind, the uncertainty of the target and quadrotor localisation, accelerations estimation errors, and terrain altitude variation.

Designing a predictive guidance and control system for maneuverable ground moving target tracking in 3D space using a Hexarotor

Designing a predictive guidance and control system for maneuverable ground moving target tracking in 3D space using a Hexarotor

Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR

Stable and efficient ground moving target tracking and refocusing is a hard task in synthetic aperture radar (SAR) data processing. Since shadows in video-SAR indicate the actual positions of moving targets at different moments without any displacement, shadow-based methods provide a new approach for ground moving target processing. This paper constructs a novel framework to refocus ground moving targets by using shadows in video-SAR. To this end, an automatic-registered SAR video is first obtained using the video-SAR back-projection (v-BP) algorithm. The shadows of multiple moving targets are then tracked using a learning-based tracker, and the moving targets are ultimately refocused via a proposed moving target back-projection (m-BP) algorithm. With this framework, we can perform detecting, tracking, imaging for multiple moving targets integratedly, which significantly improves the ability of moving-target surveillance for SAR systems. Furthermore, a detailed explanation of the shadow of a moving target is presented herein. We find that the shadow of ground moving targets is affected by a target’s size, radar pitch angle, carrier frequency, synthetic aperture time, etc. With an elaborate system design, we can obtain a clear shadow of moving targets even in X or C band. By numerical experiments, we find that a deep network, such as SiamFc, can easily track shadows and precisely estimate the trajectories that meet the accuracy requirement of the trajectories for m-BP.

The Technology of Moving Target Detection and Tracking Based on Sequential SAR Image

Although the multi-channel SAR-GMTI technology can be used in ground moving target detection, but only can get trace point of the ground moving target, the ground moving target tracking and track reconstruction can’t be achieved. This article presents a new method of ground moving target detection and tracking which is based on sequence SAR images. Firstly, the moving target detection and velocity estimation can be realized using the method of multi-channel processing. Secondly, the location of moving target can be estimated accurately using a high accuracy target location method based on knowledge-aid. The method uses the matching and target tracking technique based on sequential SAR images to realize the tracking and track reconstruction of moving target. Next, the image reference locking technology is used to achieve accurate image registration for a series of sequential SAR images obtained. Finally, the moving target can be tracked. The results of measured data shows the validity of the presented method.

Quantized genetic resampling particle filtering for vision-based ground moving target tracking

This paper concentrates on the quantized measurements fusion problem. Because of the non-Gaussian property of quantized measurements, classical nonlinear filters become not applicable, especially when the quantization noise is large. Motivated by the above problem, we propose a novel particle-based filtering named quantized genetic resampling particle filtering (QGRPF) in order to fuse quantized measurements optimally and improve estimation accuracy. Unlike the Gaussian assumption in traditional filers, the probability density function (PDF) of sensor measurement is modeled here as a discrete PDF that consists of a series of Dirac impulses. The particle filtering is employed to estimate the state, where the posterior of the state based on quantized measurements is approximated and updated by a set of weighted particles. As the likelihood function is a multi-dimensional integral of Gaussian density, which has no analytical solution, Genz's transformation and quasi-Monte Carlo method are combined to calculate the integral numerically. By integrating genetic resampling method into the particle filtering method, the proposed method can avoid particle degeneracy, in the meantime, guarantee particle diversity. The proposed QGRPF is demonstrated with an illustrative vision-based ground moving target (GMT) tracking example. Simulation results show the proposed approach is effective in fusing quantized measurements.

Information geometry-based action decision-making for target tracking by fixed-wing unmanned aerial vehicle

In this article, we study the ground moving target tracking problem for a fixed-wing unmanned aerial vehicle equipped with a radar. This problem is formulated in a partially observable Markov process framework, which contains the following two parts: in the first part, the unmanned aerial vehicle utilizes the measurements from its radar and employs a Kalman filter to estimate the target’s real-time location; in the second part, the unmanned aerial vehicle optimizes its trajectory in a real-time manner so that the radar’s measurements can include more useful information. To solve the trajectory optimization problem, we proposed an information geometry-based partially observable Markov decision process method. Specifically, the cumulative amount of information in the observation is represented by Fisher information of information geometry, and acts as the criterion of the partially observable Markov decision process problem. Furthermore, to guarantee the real-time performance, an important trade-off between the optimality and computation cost is made by an approximate receding horizon approach. Finally, simulation results corroborate the accuracy and time-efficiency of our proposed method and also show our advantage in computation time compared to existing methods.

Unknown ground moving target tracking using multiple unmanned aerial vehicles

This paper investigates the problem of robust guidance law design for multiple unmanned aerial vehicles to achieve desired formation pattern for standoff tracking of an unknown ground moving target. The proposed guidance law consists of two main parts: relative range regulation and space angle control. For the first mission, a novel control law is proposed to regulate the relative distance between the unmanned aerial vehicle and the ground moving target to zero asymptotically based on adaptive sliding mode control approach. Considering the discontinuous property of the sign function, which is often used in traditional sliding mode control and will result in high-frequency chattering in the control channel, the proposed controller adopts the continuous saturation function for chattering elimination. Besides the continuous property, convergence to the origin asymptotically can be guaranteed theoretically with the proposed controller, which is quite different from traditional boundary layer technique, where only bounded motion around the sliding manifold can be ensured. For asymptotic stability, it is only required that the lumped uncertainty is bounded, but the upper bound may be unknown by virtue of the designed adaptive methodology. For space angle control, a new multiple leader–follower information architecture is introduced and an acceleration command is then derived for each unmanned aerial vehicle to space them about the loiter circle defined by the first controller. Simulation results with different conditions clearly demonstrate the superiority of the proposed formulation.

Target Tracking Based on a Nonsingular Fast Terminal Sliding Mode Guidance Law by Fixed-Wing UAV

This paper proposes a modified nonsingular fast terminal sliding mode (NFTSM) guidance law to solve the problem of ground moving target tracking for fixed-wing unmanned aerial vehicle (UAV) in a planar environment. Firstly, the loitering algorithm is analysed, which can steer the UAV to follow and circle around a ground moving target with the desired distance by heading angle control. Secondly, the effects of different parameters on the convergence time of sliding manifold is presented which is helpful for the designing of sliding manifold. Singularity can be avoided by using a modified saturation function which is obtained through a small range around the null point. Moreover, the NFTSM sliding manifold is used in the loitering algorithm. By using the Lyapunov theory, the finite-time convergence of the proposed method has been proved in the the reaching phase and the sliding phase. In order to verify the approach’s feasibility and benefits, numerical simulations are performed by using a moving target with three different motion states in comparison with the conventional sliding model control method. Simulation results indicate that, under the proposed NFTSM guidance law, the UAV can reach the desired distance in a short time.

New Multiple-Target Tracking Strategy Using Domain Knowledge and Optimization

This paper proposes an environment-dependent vehicle dynamic modeling approach considering interactions between the noisy control input of a dynamic model and the environment in order to make best use of domain knowledge. Based on this modeling, a new domain knowledge-aided moving horizon estimation (DMHE) method is proposed for ground moving target tracking. The proposed method incorporates different types of domain knowledge in the estimation process considering both environmental physical constraints and interaction behaviors between targets and the environment. Furthermore, in order to deal with a data association ambiguity problem of multiple-target tracking in a cluttered environment, the DMHE is combined with a multiple-hypothesis tracking structure. Numerical simulation results show that the proposed DMHE-based method and its extension could achieve better performance than traditional tracking methods which utilize no domain knowledge or simple physical constraint information only.

A modified guidance law for ground moving target tracking with a class of the fast adaptive second-order sliding mode

In this paper, a solution for the problem of uncertainty presence in the estimated position of a target is indicated. The dynamic error of ground moving target tracking with special unknown bounded uncertainty has been described. To solve this problem, a class of the second-order sliding mode has been proposed. In order to increase the robustness of the proposed controller against unknown uncertainty, a set of adaptive rules has been added to the guidance law. New adaptive terms have been used to estimate the higher unknown bound of the uncertainty. The offered method, based on the sliding mode strategy, has reduced the chattering phenomena, which is harmful for the production of the command states. Finally, a fast adaptive guidance law has been added and its stability has been proved by the Lyapunov criterion. The designed adaptive fast guidance law improves the classical sliding mode control guidance law. Various simulations and comparison studies have been applied to verify the efficiency of the modified guidance law despite the disturbance and uncertainty. The results show that the uncertain terms have been compensated and the tracking position has been satisfied accurately.

Ground Target Tracking and Collision Avoidance for UAV Based Guidance Vector Field

This paper focuses on ground-moving target tracking of an unmanned aerial vehicle (UAV) in the presence of static obstacles and moving threat sources. Due to a UAV is restricted by airspace restrictions and measurement limitations during flight, we derive a dynamic path planning strategy by generating guidance vector filed combined Lyapunov vector field with collision avoidance potential function to track target in standoff distance loitering pattern, and resolved collision avoidance, simultaneously. This method relies only on the current information of the UAV and target, and generates a single-step route plan in realtime. Its performance is simple, efficient, and fast and have low computational complexity. The results of numerical simulation verify the effectiveness of the tracking and collision avoidance process of the UAV.

Method of multi-target tracking in wide area surveillance airborne radar system basing on clustering analysis

This paper proposed a robust method based on the definition of Mahalanobis distance to track ground moving target. The feature and the geometry of airborne ground moving target tracking systems are studied at first. Based on this feature, the assignment relation of time-nearby target is calculated via Mahalanobis distance, and then the corresponding transformation formula is deduced. The simulation results show the correctness and effectiveness of the proposed method.

Exploiting Doppler Blind Zone Information for Ground Moving Target Tracking with Bistatic Airborne Radar

One of the main challenges in the domain of ground target tracking based on measurements from airborne ground moving target indication (GMTI) radar is the masking of targets due to the Doppler blind zone of the sensor. This blind zone arises from the clutter cancellation by space-time adaptive processing (STAP) to separate moving target returns from the general clutter background. Low-Doppler targets can easily be masked by such a blind zone, leading to sequences of missed detections that strongly deteriorate the performance of a standard tracking filter. This work investigates the Doppler blind zone in the case of bistatic transmitter and receiver configurations and its impact on the tracking of ground targets. The knowledge on the Doppler blind zone is incorporated into a Bayesian unscented Kalman filter multiple hypothesis tracker (UKF-MHT) tracking filter that is also capable of using road-map information. Based on different simulation scenarios, it is demonstrated that in contrast to a monostatic configuration with sideways-looking radar, the blind zone width determined by the minimum detectable velocity is not constant in general. The obtained track results reveal the benefit of exploiting this additional knowledge, especially in combination complementary with road-map information if available.

Factor graph aided multiple hypothesis tracking

Since closely moving targets exist extensively in the ground moving target tracking, the uncertainty of data association greatly increases making the measurement-to-track association more difficult. Especially, traditional multiple hypothesis tracking (MHT) has high false tracking rate and track swap. This paper first investigates the measurement based factor graph in data association, and gives the corresponding message passing algorithm. Then, a factor graph aided multiple hypothesis tracking (FGA-MHT) method is proposed, which introduces factor graph based m-best hypothesis producing technique and exploits factor graph based probability refinement algorithm to reduce the uncertainty of measurement-to-track association. Experiment results demonstrate that FGA-MHT reduces times of track swap and increases the correct data association rate in closely moving target tracking.

Ground Moving Target Tracking with VS-IMM Using Mean Shift Unscented Particle Filter

In order to track ground moving target, a variable structure interacting multiple model (VS-IMM) using mean shift unscented particle filter (MS-UPF) is proposed in this paper. In model-conditioned filtering, sample particles obtained from the unscented particle filter are moved towards the maximal posterior density estimation of the target state through mean shift. On the basis of stop model in VS-IMM, hide model is proposed. Once the target is obscured by terrain, the prediction at prior time is used instead of the measurement at posterior time; in addition, the road model set used is not changed. A ground moving target indication (GMTI) radar is employed in three common simulation scenarios of ground target: entering or leaving a road, crossing a junction and no measurement. Two evaluation indexes, root mean square error (RMSE) and average normalized estimation error squared (ANEES), are used. The results indicate that when the road on which the target moving changes, the tracking accuracy is effectively improved in the proposed algorithm. Moreover, track interruption could be avoided if the target is moving too slowly or masked by terrain.