Target Motion Research Articles

A Camera Calibration Method for Limited Motion of Target in Space

In modern industrial production, vision measurement systems are widely used for product quality inspection. Camera calibration is a crucial step in vision measurement systems that directly affects the accuracy of measurement results. However, in practical applications, the limited space for measurement and restricted movement of calibration targets present new challenges for camera calibration. In this paper, we propose a camera calibration method that is suitable for calibration targets with limited movement space. This method only requires one image of the calibration target to complete system calibration, greatly simplifying the on-site calibration work. Experimental results show that our method has a re-projection error of 0.0187 pixels, indicating high reliability and applicability. The application of this method in the inspection of seamless steel pipes demonstrates its potential for practical industrial production.

Detecting Correlated Target Motion in Moving and Static Dot Arrays

Detecting Correlated Target Motion in Moving and Static Dot Arrays

Comparison of different pseudo-linear estimators for vision-based target motion estimation

Comparison of different pseudo-linear estimators for vision-based target motion estimation

Characterization of deorbiting satellites and space debris with radar

During deorbiting, be it natural orbital decay or otherwise (e.g. self-disposal or active debris removal), precise knowledge of the condition of the target is of critical importance. The Tracking and Imaging Radar (TIRA) is an instrument that possesses the ability to accurately determine satellite and space debris orbits while also generating detailed radar images of them.We present recent results from the characterization of deorbiting satellites and space debris employing TIRA. Drawing examples from orbital decay events (Tiangong-1, Soyuz 2.1v R/B), deorbiting (Tiangong-2) and self-disposal operations (Carbonite-1, MicroSCOPE), we demonstrate the potential of TIRA. Furthermore, we focus on the emerging field of active debris removal (ADR), where reliable information of the on-orbit status and attitude motion of potential targets can “make or break” an ADR mission. In this context, the on-orbit status of a JAXA R/B (43682) and a VESPA adapter (39162) are investigated.We employ inverse synthetic aperture radar (ISAR) imaging and 3D modelling of ISAR images, obtained with TIRA, to characterize the status and attitude motion of satellites and space debris. When certain conditions render direct imaging unsuitable, the more powerful L-band tracking radar of TIRA is used to obtain and analyze the temporal evolution of their radar cross-section (RCS). In this work, we employ for the first time, a wavelet time–frequency analysis of such RCS signatures, specifically of NABEO-1 (43692) in LEO and an Ariane 5 rocket body (28904) in geostationary transfer orbit (GTO), to characterize their on-orbit status. The wavelet analysis delivers results in good agreement with more traditional tools, with added information on time localization of periodicities. We are thus able to confirm the tumbling motion of NABEO-1 and constrain the rotational velocity of Ariane 5 upper stage (28904).

Long‐time coherent integration for radar detection of manoeuvring targets based on accurate range evolution model

AbstractLong‐time coherent integration (LTCI) is one of the most effective approaches to enhance radar detection ability of weak targets. The existing LTCI methods generally assume that the target motion satisfies polynomial models in range coordinates. However, it may be hard to provide precise description of common target motions, especially manoeuvring motions, using a polynomial with finite terms. In this study, LTCI for radar detection of weak manoeuvring targets is investigated. The range evolution rules over time are derived and employed to formulate the target signal for two common manoeuvring motions, constant acceleration (CA) and constant turn (CT) motions in Cartesian coordinates. Aiming at realising accurate energy accumulation and improving detection performance of CA and CT targets, two LTCI algorithms based on the derived range evolution model are proposed. By extracting target signal and compensating for phase fluctuation between samples in accordance with the accurate range evolution model, the coherent accumulation of target signal can be effectively accomplished, eliminating the complex range and Doppler frequency migration effects simultaneously. The use of accurate range evolution model guarantees effective energy integration regardless of target manoeuvrability and integration time as long as the target performs CA or CT motion. Moreover, more information of target kinematic characteristics such as target acceleration and turn rate, additional to range and Doppler, can be observed. Simulations are conducted to validate the effectiveness of the proposed algorithms.The cover image is based on the Research Article Long‐time coherent integration for radar detection of manoeuvring targets based on accurate range evolution model by Zeyu Xu et al., https://doi.org/10.1049/rsn2.12445.

A Computationally Efficient Estimator for the Target Angle, Delay, and Doppler Parameters in Passive Radar

The azimuth, elevation, time delay (TD), and Doppler shift (DS) of the target echo signal are important parameters for target localization in passive radar. In this paper, for the problem of joint estimation of target azimuth, elevation, TD, and DS parameters in passive radar, a computationally efficient estimator is proposed based on segmented coherent integration and uniform circular array (UCA) interferometer direction finding. First, according to the parameters of the target motion and illuminators of opportunity, the reference and surveillance signals are divided into segments, with fast time within a segment and slow time across the segments; then, matching filtering along the fast time and fast Fourier transform (FFT) along the slow time are performed to accumulate the target echo energy to the same delay-Doppler cell; finally, the UCA interferometer two-dimensional direction finding is performed at the delay-Doppler cell where the target echo is located, to achieve the target azimuth and elevation estimate. Simulation results demonstrate that the proposed algorithm has low computational complexity, high real-time processing capability, and can achieve efficient and real-time estimation of the azimuth, elevation, TD, and DS parameters of weak target echoes in passive radar.

Accuracy of on-site teleoperated milling with haptic assistance

PurposeIn bone surgery specialties, like orthopedics, neurosurgery, and oral and maxillofacial surgery patient safety and treatment success depends on the accurate implementation of computer-based surgical plans. Unintentional plan deviations can result in long-term functional damage to the patient. With on-site teleoperation, the surgeon operates a slave robot with a physically-decoupled master device, while being directly present at the operation site. This allows the surgeon to perform surgical tasks with robotic accuracy, while always remaining in the control loop.MethodsIn this study the master- and slave-side accuracy of an on-site teleoperated miniature cooperative robot (minaroHD) is evaluated. Master-side accuracy is investigated in a user study regarding scale factor, target feed rate, movement direction and haptic guidance stiffness. Scale factors are chosen to correspond to primarily finger, hand, and arm movements. Slave-side accuracy is investigated in autonomous milling trials regarding stepover, feed rate, movement direction, and material density.ResultsMaster-side user input errors increase with increasing target feed rate and scale factor, and decrease with increasing haptic guidance stiffness. Resulting slave-side errors decrease with increasing scale factor and are < 0.07 mm for optimal guidance parameters. Slave-side robot position errors correlate with the feed rate but show little correlation with stepover distance. For optimal milling parameters, the 95th percentile of tracked slave-side position error is 0.086 mm with a maximal error of 0.16 mm.ConclusionFor optimal guidance and milling parameters, the combined error of 0.23 mm is in the range of the dura mater thickness (< 0.27 mm) or mandibular canal wall (~ 0.85 mm). This corresponds to safety margins in high-demand surgical procedures like craniotomies, laminectomies, or decortication of the jaw. However, for further clinical translation, the performance and usability of on-site teleoperated milling must be further evaluated for real-life clinical application examples with consideration of all error sources in a computer-assisted surgery workflow.

MMTD: Multistage Moving Target Defense for Security-Enhanced D-FACTS Operation

In recent studies, moving target defense (MTD) has been applied to detect false data injection (FDI) attacks using distributed flexible AC transmission system (D-FACTS) devices. However, the inherent conflict between the security goals of MTD (i.e., detecting FDI attacks) and the economic goals of D-FACTS devices (i.e., reducing power losses) would impede the application of MTD in real systems. Moreover, the detection capabilities of existing MTDs are often insufficient. This paper proposes a multi-stage MTD (MMTD) approach to resolve these two issues by adding a group of designed security-oriented schemes before D-FACTS’ economy-oriented scheme to detect FDI attacks. We keep these security-oriented schemes for a very short time interval and then revert to the economy-oriented scheme for the remaining time to ensure the economic requirements. We prove that a designed MMTD can significantly improve the detection capability compared to existing one-stage MTDs. We find the supremum of MMTD’s detection capability and study its relationship with system topology and D-FACTS deployment. Meanwhile, a greedy algorithm is proposed to search the MMTD strategy to reach this supremum. Simulation results show that the proposed MMTD can achieve the supremum against FDI attacks while outperforming current MTD strategies on economic indicators.

Hybrid UWB-Inertial TDoA-Based Target Tracking With Concentrated Anchors

In this paper, hybrid radio/inertial mobile target tracking for accurate and smooth path estimation is considered. The proposed tracking approach builds upon an Ultra WideBand (UWB)-based positioning algorithm, based on the Linear Hyperbolic Positioning System (LinHPS), with Time Difference of Arrival (TDoA) processing and anchors concentrated on a single hotspot at the center of the environment where the target moves. First, we design an Adaptive Radio-based Extended Kalman Filter (AREKF), which does not require a priori statistical knowledge of the noise in the target movement model and estimates the measurement noise covariance, at each sampling time, according to a proper LookUp Table (LUT). In order to improve the performance of AREKF, we incorporate inertial data collected from the target and propose three “hybrid” radio/inertial algorithms, denoted as Hybrid Inertial Measurement Unit (IMU)-aided Radio-based EKF (HIREKF), Hybrid Noisy Control EKF (HNCEKF), and Hybrid Control EKF (HCEKF). Our results on experimentally acquired paths show that the proposed algorithms achieve an average instantaneous position estimation error on the order of a few centimeters. Moreover, the minimum target path length estimation error, obtained with HCEKF, is on the order of 6% and 1% for two paths with lengths equal to approximately 17 m and 46 m, respectively.

METHOD OF DATA IDENTIFICATION IN THE SYSTEM OF INFORMATION SUPPORT OF ANTI-AIRCRAFT MISSILE UNITS BASED ON THE INTERPOLATION METHOD

The issue of making timely decisions in the control system of anti-aircraft missile units in order to ensure the rapid detection of air targets and their timely firing at long distances requires constant attention from military specialists of the leading countries of the world. The article presents the results of developing a methodology for identifying data in the information support system of anti-aircraft missile units based on the interpolation method. The object of research is the process of data identification in the information support system of anti-aircraft missile units. The subject of research is data interpolation methods. The purpose of the research is to develop a methodology for identifying data in the information support system of anti-aircraft missile units based on the interpolation method. Conclusions. An algorithm has been developed that implements a methodology for identifying the targeting data of the air defense missile control center, obtained from data from radio engineering troops, with the data of the unit's own or attached reconnaissance and targeting station and allows to increase the accuracy of combat information. To determine that the marks from the target received from the reconnaissance and targeting station and from the control center of the air defense missile unit are marks of the same target, an interpolation procedure is performed, provided that the data received from both sources is secondary information of the target observation vector. The interpolation procedure itself involves the formation of a spatial identification strobe and a comparison of the target trajectory obtained from the trace data of the own (supporting) reconnaissance and targeting station and the interpolated data of the target's movement relative to the latest external targeting data in the opposite direction. This algorithm can be used to implement the relevant tasks in the complex of automation tools for the control center of military units of air defense missile forces, automated workstations of commanders of air defense missile forces, which will reduce the time for searching and detecting air targets by the missile guidance station of the air defense missile system and directly increase the firepower of these units.

Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios.

Moving target detection (MTD) is a crucial task in computer vision applications. In this paper, we investigate the problem of detecting moving targets in infrared (IR) surveillance video sequences captured using a steady camera in a maritime setting. For this purpose, we employ robust principal component analysis (RPCA), which is an improvement of principal component analysis (PCA) that separates an input matrix into the following two matrices: a low-rank matrix that is representative, in our case study, of the slowly changing background, and a sparse matrix that is representative of the foreground. RPCA is usually implemented in a non-causal batch form. To pursue a real-time application, we tested an online implementation, which, unfortunately, was affected by the presence of the target in the scene during the initialization phase. Therefore, we improved the robustness by implementing a saliency-based strategy. The advantages offered by the resulting technique, which we called "saliency-aided online moving window RPCA" (S-OMW-RPCA) are the following: RPCA is implemented online; along with the temporal features exploited by RPCA, the spatial features are also taken into consideration by using a saliency filter; the results are robust against the condition of the scene during the initialization. Finally, we compare the performance of the proposed technique in terms of precision, recall, and execution time with that of an online RPCA, thus, showing the effectiveness of the saliency-based approach.

Target Detection for Synthetic Aperture Radiometer Based on Satellite Formation Flight.

Synthetic aperture interferometers formed by satellite formations have been adopted to improve spatial resolution. Due to the limited number of satellites and limited integrated time, the use of sparse baselines can result in distorted reconstructed images, which will generate false targets or miss true targets. When detecting a target on the Earth from a geostationary orbit, the target usually occupies only one pixel, and it is almost submerged by noise. Considering the slow-varying characteristics of the observation area, combined with historical observation data and the motion characteristics of the target itself, a target detection method based on multi-frame snapshot images is proposed. Firstly, the observation background is estimated using multi-frame historical data, and background elimination is used to suppress the background noise. Then, potential targets are selected using the local brightness temperature characteristics of the targets. Lastly, the target motion tracks are applied to erase false targets and correct the positions of missed targets. Simulation experiments have been conducted, and the false alarm rate and the missing alarm rate are counted for randomly distributed targets.

Obstacle avoidance in aerial pursuit

Pursuing prey through clutter is a complex and risky activity requiring integration of guidance subsystems for obstacle avoidance and target pursuit. The unobstructed pursuit trajectories of Harris' hawks Parabuteo unicinctus are well modeled by a mixed guidance law feeding back target deviation angle and line-of-sight rate. Here we ask how their pursuit behavior is modified in response to obstacles, using high-speed motion capture to reconstruct flight trajectories recorded during obstructed pursuit of maneuvering targets.We find that Harris' hawks use the same mixed guidance law during obstructed pursuit but appear to superpose a discrete bias command that resets their flight direction to aim at a clearance of approximately one wing length from an upcoming obstacle as they reach some threshold distance from it. Combining a feedback command in response to target motion with a feedforward command in response to upcoming obstacles provides an effective means of prioritizing obstacle avoidance while remaining locked-on to a target. We therefore anticipate that a similar mechanism may be used in terrestrial and aquatic pursuit. The same biased guidance law could also be used for obstacle avoidance in dronesdesigned to intercept other drones in clutter, or to navigate between fixed waypoints in urban environments.

Enhancing the target visibility with synthetic target specific digitally reconstructed radiograph for intrafraction motion monitoring: A proof-of-concept study.

Intrafraction motion monitoring in External Beam Radiation Therapy (EBRT) is usually accomplished by establishing a correlation between the tumor and the surrogates such as an external infrared reflector, implanted fiducial markers, or patient skin surface. These techniques either have unstable surrogate-tumor correlation or are invasive. Markerless real-time onboard imaging is a noninvasive alternative that directly images the target motion. However, the low target visibility due to overlapping tissues along the X-ray projection path makes tumor tracking challenging. To enhance the target visibility in projection images, a patient-specific model was trained to synthesize the Target Specific Digitally Reconstructed Radiograph (TS-DRR). Patient-specific models were built using a conditional Generative Adversarial Network (cGAN) to map the onboard projection images to TS-DRR. The standard Pix2Pix network was adopted as our cGAN model. We synthesized the TS-DRR based on the onboard projection images using phantom and patient studies for spine tumors and lung tumors. Using previously acquired CT images, we generated DRR and its corresponding TS-DRR to train the network. For data augmentation, random translations were applied to the CT volume when generating the training images. For the spine, separate models were trained for an anthropomorphic phantom and a patient treated with paraspinal stereotactic body radiation therapy (SBRT). For lung, separate models were trained for a phantom with a spherical tumor insert and a patient treated with free-breathing SBRT. The models were tested using Intrafraction Review Images (IMR) for the spine and CBCT projection images for the lung. The performance of the models was validated using phantom studies with known couch shifts for the spine and known tumor deformation for the lung. Both the patient and phantom studies showed that the proposed method can effectively enhance the target visibility of the projection images by mapping them into synthetic TS-DRR (sTS-DRR). For the spine phantom with known shifts of 1 mm, 2 mm, 3 mm, and 4 mm, the absolute mean errors for tumor tracking were 0.11 ± 0.05 mm in the x direction and 0.25 ± 0.08 mm in the y direction. For the lung phantom with known tumor motion of 1.8 mm, 5.8 mm, and 9 mm superiorly, the absolute mean errors for the registration between the sTS-DRR and ground truth are 0.1 ± 0.3 mm in both the x and y directions. Compared to the projection images, the sTS-DRR has increased the image correlation with the ground truth by around 83% and increased the structural similarity index measure with the ground truth by around 75% for the lung phantom. The sTS-DRR can greatly enhance the target visibility in the onboard projection images for both the spine and lung tumors. The proposed method could be used to improve the markerless tumor tracking accuracy for EBRT.

Trajectory Guided Robust Visual Object Tracking With Selective Remedy

Siamese trackers have received a lot of attentions due to their promising performance and real-time high speed. However, the robustness is generally limited especially in challenging conditions, such as occlusion. Motivated by that tracking failure does not always occur and the target movement tends to follow certain patterns, in the paper, we propose a generic, fast and flexible approach to improve the robustness of Siamese trackers with two light-load novel modules: Trajectory Guidance Module (TGM) and Selective Refinement Module (SRM). Specifically, TGM encourages to pay a soft attention on possible target location based on short-term historical trajectory. SRM selectively remedies the tracking results at the risk of failure with little impact on the speed. The proposed algorithm can be easily establish upon state-of-the-art Siamese trackers and obtains better performance on seven benchmarks with high real-time tracking speed. The code is available at https://github.com/TJUMMG/TGSR.

Dual Receptive Fields Underlying Target and Wide-Field Motion Sensitivity in Looming-Sensitive Descending Neurons.

Responding rapidly to visual stimuli is fundamental for many animals. For example, predatory birds and insects alike have amazing target detection abilities, with incredibly short neural and behavioral delays, enabling efficient prey capture. Similarly, looming objects need to be rapidly avoided to ensure immediate survival, as these could represent approaching predators. Male Eristalis tenax hoverflies are nonpredatory, highly territorial insects that perform high-speed pursuits of conspecifics and other territorial intruders. During the initial stages of the pursuit, the retinal projection of the target is very small, but this grows to a larger object before physical interaction. Supporting such behaviors, E. tenax and other insects have both target-tuned and loom-sensitive neurons in the optic lobes and the descending pathways. We here show that these visual stimuli are not necessarily encoded in parallel. Indeed, we describe a class of descending neurons that respond to small targets, to looming and to wide-field stimuli. We show that these descending neurons have two distinct receptive fields where the dorsal receptive field is sensitive to the motion of small targets and the ventral receptive field responds to larger objects or wide-field stimuli. Our data suggest that the two receptive fields have different presynaptic input, where the inputs are not linearly summed. This novel and unique arrangement could support different behaviors, including obstacle avoidance, flower landing, and target pursuit or capture.

High-Speed Maneuvering Target Inverse Synthetic Aperture Radar Imaging and Motion Parameter Estimation Based on Fast Spare Bayesian Learning and Minimum Entropy

High-speed maneuvering target inverse synthetic aperture radar (ISAR) imaging is always a hot topic in signal processing. High-speed maneuvering targets often have high-order maneuvering characteristics, such as translational and rotational characteristics, which destroy the signal structure of stationary targets and make basic imaging processing methods such as range-Doppler (RD) algorithm no longer suitable. In this paper, a high-resolution imaging method for high-speed maneuvering targets is proposed, which uses the fast sparse Bayesian learning (SBL) algorithm and the minimum entropy algorithm for ISAR high-resolution imaging and motion parameter estimation, respectively. Because SBL makes full use of the characteristics of the target and the environment, it can obtain an ISAR high-resolution image of the maneuvering target. However, the high computational complexity caused by matrix inversion and some matrix operations in SBL iteration limit the practical application of SBL in ISAR imaging. In view of the special structure of the matrix required to be inverted, we propose a fast SBL algorithm, which uses a new decomposition method to obtain the decomposition formula of the inverse matrix. Based on the decomposition factors, the multiplication operation involving the inverse matrix can be quickly calculated using fast Fourier transform (FFT), which greatly improves the computational efficiency. Image entropy represents the sharpness and focusing degree of an image, and so the minimum entropy algorithm can estimate the motion parameters of maneuvering targets more accurately. We combine the minimum entropy algorithm with the fast SBL algorithm to realize phase error correction and high-resolution imaging, which has better noise sensitivity and can obtain the best focusing degree image. Finally, simulation results prove the effectiveness of this algorithm.

Phasor-Like Interpretation of the Angular Velocity of the Wheels of Omnidirectional Mobile Robots

Omnidirectionality is a feature that allows motion in any direction without orientation maneuvers. Omnidirectional mobile robots are usually based on omni or mecanum wheels. The motion of an omnidirectional mobile robot is defined by a target motion command M=v,α,ω, where v is the module of the translational velocity; α is the angular orientation of the translational velocity, and ω is the angular velocity of the mobile robot. The motion is achieved by converting the target motion command into the target angular velocities that must be applied to the active wheels of the robot. This work proposes a simplified phasor-like interpretation of the relationship between the parameters of a specific motion command and the angular velocities of the wheels. The concept of phasor-like notation is validated from the analysis of the kinematics of omnidirectional mobile robots using omni wheels and mecanum wheels. This simplified phasor-like notation fosters unconstrained conceptual design of single-type and hybrid multi-wheeled omnidirectional mobile robots without the distribution or type of wheels being a design constraint.

Integration of Zero Crossing Method in a Nonuniform Sampling System Using Optical Feedback Interferometry

In this paper, a zero-crossings based method is proposed to improve the displacement sensing performance of non-uniform sampling (NUS) theory based use of optical feedback interferometry (OFI) under moderate optical feedback regime. The incorporation of zero-crossings as phase quantization levels allows doubling the samples for subsequent interpolation, without using any dithering scheme. When compared to the simple dither-free NUS method, the proposed method yields an overall improvement in RMS error over a wide range of target motion amplitudes and frequencies, with maximum improvement at low amplitudes. Experimentally, an improvement of up to 87% is obtained in precision as compared to simple NUS system for an OFI sensor with laser wavelength of 785 nm.

Study on Multi-Heterogeneous Sensor Data Fusion Method Based on Millimeter-Wave Radar and Camera.

This study presents a novel multimodal heterogeneous perception cross-fusion framework for intelligent vehicles that combines data from millimeter-wave radar and camera to enhance target tracking accuracy and handle system uncertainties. The framework employs a multimodal interaction strategy to predict target motion more accurately and an improved joint probability data association method to match measurement data with targets. An adaptive root-mean-square cubature Kalman filter is used to estimate the statistical characteristics of noise under complex traffic scenarios with varying process and measurement noise. Experiments conducted on a real vehicle platform demonstrate that the proposed framework improves reliability and robustness in challenging environments. It overcomes the challenges of insufficient data fusion utilization, frequent leakage, and misjudgment of dangerous obstructions around vehicles, and inaccurate prediction of collision risks. The proposed framework has the potential to advance the state of the art in target tracking and perception for intelligent vehicles.