Target Motion Research Articles

Reflexive eye saccadic parameters in Parkinson’s disease

IntroductionAbnormal reactive saccade, with reduced saccadic gain, impaired smooth pursuit, and unwarranted reactions are clinically used to assess people with Parkinson’s disease (PwPD). However, there are inconsistent findings related to other saccade parameters such as latency and transition times. This study aimed to identify differences in the reflexive saccade parameters of early stage PwPD and aged-matched control (AMC).MethodsIn this observational study, the reactive eye-gaze was recorded for 70 participants (42 PwPD) and parameters of reflexive saccades and eye-gaze fluctuations were extracted. These parameters were then statistically analyzed using the Mann–Whitney U-test.ResultsResults showed that PwPD had significantly shorter latency than AMC for reflexive saccadic movement away from the center of the screen. The overshoot as a fraction of the screen width, a measure of the inaccuracy in reaching the target, was also significantly higher for PwPD. PwPD had greater horizontal and vertical eye gaze fluctuation with a steady target. The numbers of invalid saccades, i.e., when the gaze goes in the opposite direction from the target movement or is considered anticipatory, were similar for both groups; PwPD with 33.43% and AMC with 25.71%.DiscussionThis study shows that there are significant differences in the reflexive saccade of PwPD and AMC measured using an inexpensive eye-tracking device. The presence of invalid saccade trials, and differences between towards and away from screen center, both of which were not considered in earlier studies, may explain the discrepancies between earlier studies. The outcome of this study has the potential to be made a device that would assist neurologists in the assessment of PwPD.

UAV Visual Object Tracking Based on Spatio-Temporal Context

To balance the real-time and robustness of UAV visual tracking on a single CPU, this paper proposes an object tracker based on spatio-temporal context (STCT). STCT integrates the correlation filter and Siamese network into a unified framework and introduces the target’s motion model, enabling the tracker to adapt to target scale variations and effectively address challenges posed by rapid target motion, etc. Furthermore, a spatio-temporal regularization term based on the dynamic attention mechanism is proposed, and it is introduced into the correlation filter to suppress the aberrance of the response map. The filter solution is provided through the alternating direction method of multipliers (ADMM). In addition, to ensure efficiency, this paper proposes the average maximum response value-related energy (AMRE) for adaptive tracking state evaluation, which considers the time context of the tracking process in STCT. Experimental results show that the proposed STCT tracker can achieve a favorable balance between tracking robustness and real-time performance for UAV object tracking while running at ∼38 frames/s on a low-cost CPU.

Motion in the depth direction appears faster when the target is closer to the observer

The target velocity at the retina and the initial phase of target motion are known to affect the perceived velocity of a target in planar motion. For depth motion, however, the role of this information in velocity perception remains unclear. Therefore, the purpose of this study was to reveal the role of the angular velocity derived from the vergence angle and the initial phase of target motion on the perceived velocity for depth motion. We devised two experimental tasks with five stimuli and used a two-alternative forced-choice paradigm to investigate velocity perception. In the tasks, a target moving toward or away from the observer was used. The five stimuli in each task moved between 40 and 240 cm (standard stimulus), 20 and 240 cm, 20 and 220 cm, 40 and 260 cm, and 60 and 260 cm from the participants. In the comparison of the standard stimulus with other stimuli, the stimuli approaching or receding from a distance of 20 cm were perceived as faster than the standard stimulus approaching or receding from a distance of 40 cm. We also showed that the stimuli that receded starting from a distance of 60 cm were perceived as moving slower than the standard stimulus. Our results suggest that larger changes in angular velocity affect velocity perception for depth motion; thus, observers perceive the target velocity as faster when the target is closer to the observer.

Radar Moving Target Detection Based on Small-Sample Transfer Learning and Attention Mechanism

Moving target detection is one of the most important tasks of radar systems. The clutter echo received by radar is usually strong and heterogeneous when the radar works in a complex terrain environment, resulting in performance degradation in moving target detection. Utilizing prior knowledge of the clutter distribution in the space–time domain, this paper proposes a novel moving target detection network based on small-sample transfer learning and attention mechanism. The proposed network first utilizes offline data to train the feature extraction network and reduce the online training time. Meanwhile, the attention mechanism used for feature extraction is applied in the beam-Doppler domain to improve classification accuracy of targets. Then, a small amount of real-time data are applied to a small-sample transfer network to fine-tune the feature extraction network. Finally, the target detection can be realized by the fine-tuned network. Simulation experiments show that the proposed network can eliminate the influence of heterogeneous clutter on moving target detection, and the attention mechanism can improve clutter suppression under a low signal-to-noise ratio regime. The proposed network has a lower computational load compared to conventional neural networks, enabling its use in real-time applications on space-borne/airborne radars.

Mathematical model based on nonlinear differential equations and its control algorithm

Abstract A practical examination of the traditional robotic arm (RA) in operation revealed a significant limitation in its ability to control the position of motion. This underscores the urgent need to enhance the current RA’s position control capabilities. Therefore, this study proposes the use of nonlinear differential equations (NDEs) to establish a mathematical model and the design of NDE-based RA motion control algorithms in conjunction with a central pattern generator neural network. A comparison of the control effects showed that the proposed method was highly fitted to the target trajectory. The joint node (JN) motion tracking trajectories of the three RAs were similar, up to 90–85% to the target trajectories of the JNs. In addition, the control of the motion position was similar up to 95–98% to the target motion position trajectories. The motion control algorithm based on NDEs was effective in improving the average execution time of the Pareto optimal frontier of the RA by 58.29%. The joint velocity and angle changes of the three types of RAs under the NDE control algorithm exhibited a high degree of similarity to the fluctuations observed in the expected and predicted curves. These observations contribute to an understanding of the effectiveness of the system observer in observing the joint angle changes. This indicates that the motion control based on NDEs can effectively enhance the tracking effectiveness of the JN positions of the RA, improve the control ability of the RA motion, and increase the joint stability of the RA.

Lightweight Neural Network for Centroid Detection of Weak, Small Infrared Targets via Background Matching in Complex Scenes

In applications such as aerial object interception and ballistic estimation, it is crucial to precisely detect the centroid position of the target rather than to merely identify the position of the target bounding box or segment all pixels belonging to the target. Due to the typically long distances between targets and imaging devices in such scenarios, targets often exhibit a low contrast and appear as dim, obscure shapes in infrared images, which represents a challenge for human observation. To rapidly and accurately detect small targets, this paper proposes a lightweight, end-to-end detection network for small infrared targets. Unlike existing methods, the input of this network is five consecutive images after background matching. This design significantly improves the network’s ability to extract target motion features and effectively reduces the interference of static backgrounds. The network mainly consists of a local feature aggregation module (LFAM), which uses multiple-sized convolution kernels to capture multi-scale features in parallel and integrates multiple spatial attention mechanisms to achieve accurate feature fusion and effective background suppression, thereby enhancing the ability to detect small targets. To improve the accuracy of predicted target centroids, a centroid correction algorithm is designed. In summary, this paper presents a lightweight centroid detection network based on background matching for weak, small infrared targets. The experimental results show that, compared to directly inputting a sequence of images into the neural network, inputting a sequence of images processed by background matching can increase the detection rate by 9.88%. Using the centroid correction algorithm proposed in this paper can therefore improve the centroid localization accuracy by 0.0134.

How a co-actor’s (Un-) reliability modulates goal selection in a novel joint goal-setting paradigm

Sociomotor theory – an extension of ideomotor theory – suggests that actions can also be represented in terms of the effects they elicit from others. But what if those others violate one’s action effect anticipations? Here, we introduce a novel joint goal-setting paradigm to investigate effects of co-actors’ occasional and overall unreliability on an individual’s goal selection. In a first step, the participant moved a target halfway from the bottom center to the top left or right corner of the computer screen. In the second step, the co-actor moved the target to its final left or right position. In a learning block, the co-actor always continued the participant’s target movements. In the test block(s), the co-actor produced congruent action effects in 50% (unreliable) vs. 80% (reliable co-actor) of the trials. Experiment 1 consisted of one (between-participants), Experiment 2 and 3 of two (within-participants) test blocks; in Experiment 3, the co-actor changed between blocks. Results of Experiments 1 and 3 reveal that participants repeated their corner choice more often after incongruent trials, but only when the co-actor was generally reliable. Implications in terms of sociomotor action control and joint action are discussed.

Evaluation of dose distributions and respiratory motion tolerance for layer-stacking conformal carbon-ion radiotherapy.

While layer-stacking irradiation provides a conformal dose distribution, it is vulnerable to respiratory motion. Considering that the motion tolerance has not yet been demonstrated, this study aimed to determine the tolerance level for the amount of target motion. Dose distributions considering motion were simulated for a numerical water phantom using in-house software. Comparisons with measured and simulated physical dose distributions confirmed the validity of the simulation, with gamma analysis showing almost 90% or greater agreement under all conditions with a criterion of 3%/3mm. The variation in physical dose from static conditions followed a similar trend. Based on the evaluation of the simulated clinical dose uniformity, motion tolerance was derived. The acceptable motion amounts in the lateral direction were 11mm in respiratory-ungated condition and at least 20mm with 30% lateral gating at 4Gy (RBE). In the longitudinal (beam upstream) direction, the acceptable target motion amounts were 3mm without gating and 6mm with gating. These results employed worst-case scenarios considering multiple respiratory cycles. In both lateral and longitudinal directions, the motion amounts of 3mm for non-gating and 5mm for gating were acceptable. The acceptable target motion amounts improved by 1-9mm with gating and increased prescribed doses. The dose uniformity and motion tolerance under multiple conditions, although based on a simple system, may be useful for treatment involving target motion in layer-stacking irradiation.

An Enhanced Feature Learning CNN Using Auto-encoder for Partially Occluded Target Recognition in SAR Imagery

Deep learning algorithms have found extensive utility in Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR). Despite this, conventional deep learning-based ATR models often lack in their performance when subjected to Extended Operating Conditions (EOCs). Significant variations in depression, noise interference, limited training data, and partial occlusion are primary factors in EOCs. In this paper, we proposed a deep neural network-based technique to recognize SAR targets for Standard Operating Conditions (SOC) and Extended Operating Conditions (EOC). The proposed approach comprises an encoder and decoder network. The encoder network integrates multiple dilated convolutions to address noise-induced recognition challenges for extracting multi-scale features. Furthermore, a feature refinement module is integrated within the multi-scale channels to enhance the extraction of discriminative and resilient features. Feature refinement selectively emphasizes informative features while suppressing irrelevant ones. Additionally, a salient attention block and a spatial feature learning module are introduced for feature selection. Selected features are then utilized in the top-level network layer of the encoder network to preserve spatial relationships among diverse features. This spatial feature learning module aids in recognition performance, particularly when training data is scarce. The decoder network consists of stacked transposed convolution layers, facilitating the encoder network's acquisition of discriminative image features. Experimental evaluations were conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset to validate the efficacy of the proposed methodology.

Three-Dimensional CKANs: UUV Noncooperative Target State Estimation Approach Based on 3D Convolutional Kolmogorov–Arnold Networks

Accurate and stable estimation of the position and trajectory of noncooperative targets is crucial for the safe navigation and operation of sonar-equipped underwater unmanned vehicles (UUVs). However, the uncertainty associated with sonar observations and the unpredictability of noncooperative target movements often undermine the stability of traditional Bayesian methods. This paper presents an innovative approach for noncooperative target state estimation utilizing 3D Convolutional Kolmogorov–Arnold Networks (3DCKANs). By establishing a non-Markovian model that characterizes state estimation of UUV noncooperative targets under uncertain observations, we leverage historical data to construct 3D Convolutional Kolmogorov–Arnold Networks. This network learns the patterns of sonar observations and target state transitions from a substantial offline dataset, allowing it to approximate the posterior probability distribution derived from past observations effectively. Additionally, a sliding window technique is integrated into the convolutional neural network to enhance the estimator’s fault tolerance with respect to observation data in both temporal and spatial dimensions, particularly when posterior probabilities are unknown. The incorporation of the Kolmogorov–Arnold representation within the convolutional layers enhances the network’s capacity for nonlinear expression and adaptability in processing spatial information. Finally, we present statistical experiments and simulation cases to validate the accuracy and stability of the proposed method.

Rigid propagation of visual motion in the insect’s neural system

In the pursuit of developing an efficient artificial visual system for visual motion detection, researchers find inspiration from the visual motion-sensitive neural pathways in the insect’s neural system. Although multiple proposed neural computational models exhibit significant performance aligned with those observed from insects, the mathematical basis for how these models characterize the sensitivity of visual neurons to corresponding motion patterns remains to be elucidated. To fill this research gap, this study originally proposed that the rigid propagation of visual motion is an essential mathematical property of the models for the insect’s visual neural system, meaning that the dynamics of the model output remain consistent with the visual motion dynamics reflected in the input. To verify this property, this study uses the small target motion detector (STMD) neural pathway — one of the visual motion-sensitive pathways in the insect’s neural system — as an exemplar, rigorously demonstrating that the dynamics of translational visual motion are rigidly propagated through the encoding of retinal measurements in STMD computational models. Numerical experiment results further substantiate the proposed property of STMD models. This study offers a novel theoretical framework for exploring the nature of the visual motion perception underlying the insect’s visual neural system and brings an innovative perspective to the broader research field of insect visual motion perception and artificial visual systems.

Improved analytical solution with optimization constraints using TDOA and FDOA measurements for USV/AUV collaborative localization

The paper introduces an enhanced constrained two-step weighted least squares (WLS) analytic localization algorithm designed to determine the AUV’s position and velocity within the collaborative localization system, utilizing time difference of arrival and frequency difference of arrival measurements. This algorithm integrates optimization constraints between intermediate variables and target motion parameters, producing precise analytical solution based on WLS minimization criterion. The proposed process not only reduces computational burden by providing an analytical solution but also enhances estimation accuracy through the incorporation of optimization constraints. Analytical assessments and statistical simulations highlight the superior estimation accuracy of the proposed algorithm, and demonstrate that the position and velocity estimation accuracy align with the Cramér-Rao lower bound under appropriate measurement noise conditions.

Moving Target Detection in Clutter Environment Based on Track Posture Hypothesis Testing

ABSTRACTMoving target detection in a heavy clutter area is a challenging problem in the field of radar automatic target detection. Within the framework of track posture hypothesis testing, the radar‐blips‐oriented target detection algorithms named ErgSad and RanSaD are proposed for single target and formation targets detection, respectively in this paper. With the assumption that target motion is a linear Gaussian process in a short time, the algorithms use dual blips in different scans to generate the track hypothesis and test the hypothesis with residual blips in other scans to determine the existence of moving targets. To reduce time consumption and minimize the error of model estimation, the sampling rules and the supporting domain related to timestamps of track hypothesis models were designed. Simulation data experiments show that the proposed algorithms have more superior performance to detect targets than the state‐of‐the‐art algorithms in the serious clutter environment.

A Novel Method for the Estimation of Sea Surface Wind Speed from SAR Imagery

Wind is one of the important environmental factors influencing marine target detection as it is the source of sea clutter and also affects target motion and drift. The accurate estimation of wind speed is crucial for developing an efficient machine learning (ML) model for target detection. For example, high wind speeds make it more likely to mistakenly detect clutter as a marine target. This paper presents a novel approach for the estimation of sea surface wind speed (SSWS) and direction utilizing satellite imagery through innovative ML algorithms. Unlike existing methods, our proposed technique does not require wind direction information and normalized radar cross-section (NRCS) values and therefore can be used for a wide range of satellite images when the initial calibrated data are not available. In the proposed method, we extract features from co-polarized (HH) and cross-polarized (HV) satellite images and then fuse advanced regression techniques with SSWS estimation. The comparison between the proposed model and three well-known C-band models (CMODs)—CMOD-IFR2, CMOD5N, and CMOD7—further indicates the superior performance of the proposed model. The proposed model achieved the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), with values of 0.97 m/s and 0.62 m/s for calibrated images, and 1.37 and 0.97 for uncalibrated images, respectively, on the RCM dataset.

Search region updating with hierarchical feature fusion for accurate thermal infrared tracking

Due to their resilience against lighting variations, thermal infrared (TIR) images demonstrate robust adaptability in diverse environments, enabling effective object tracking even in intricate scenarios. Nevertheless, TIR target tracking encounters challenges such as fast target motion and interference from visually similar objects, substantially compromising the tracking precision of TIR trackers. To surmount these challenges, we propose a method grounded in the strategy of search region updating and hierarchical feature fusion, tailored for the precise TIR target-tracking task. Specifically, to address the issue of fast motion causing the target to depart from the search region, we propose to update the current search region by leveraging historical frame information. Additionally, we employ a hierarchical feature fusion strategy to contend with interference from visually similar objects in the tracking scenario. This strategy enhances the ability to model and represent the target more accurately, thereby elevating the tracker’s capacity to discriminate between the target and similar objects. Furthermore, to tackle the challenge of inaccurate estimation of target bounding boxes, we introduce an enhanced Intersection over Union (IoU) loss function, which improvement facilitates a more precise prediction of target bounding boxes, resulting in superior target localization. Extensive experiments substantiate that our tracker exhibits a commendable level of competitiveness when compared to other trackers.

Enhancing underwater target detection: Fusion of spatio‐temporal incompletely‐aligned AIS and sonar information via DTW and multi‐head attention mechanism

AbstractIn the field of underwater target detection, the passive sonar is an important means of long‐distance target detection. The sonar detection information typically includes both surface and underwater targets, whereas it is a great challenge on effectively distinguishing between surface and underwater targets solely based on sonar information. Effective fusion of sonar and AIS (Automatic Identification System) data can leverage their complementary nature to compensate for the limitation of sonar information. However, the sonar information and AIS information are acquired based on different detection principles and systems, which are essentially multi‐source heterogeneous information with obvious spatio‐temporal misalignment in nature. Existing fusion methods normally struggle to effectively align sonar and AIS data in both time and space subject to the complexity of the problem. In this study, the Dynamic Time Warping (DTW) algorithm is applied to align sonar and AIS data in the time domain. In addition, a deep learning algorithm with multi‐head attention mechanism is proposed to achieve the spatial alignment of sonar and AIS data, where the matching between the surface targets in AIS data and the same surface targets in sonar data can also be successfully achieved. It provides a priori knowledge to enhance the underwater target detection of the passive sonar by eliminating the interference of the surface targets. Based on the attention mechanism, the abstract features extracted from the intermediate‐layer of the neural networks are found to be effective to represent the typical features of the target motion trajectories, which also demonstrates the effectiveness of the attention mechanism. The experiment results show that the proposed method can successfully achieve a MatchingSucccessRate of over 95% between the AIS targets and sonar detection targets.

Target detection and obstacle avoidance for UAV based on dynamic vision sensor

Aiming at the problem of UAV sensing dynamic targets and avoiding high-speed dynamic obstacles in dynamic environments, a target detection and obstacle avoidance algorithm based on dynamic vision sensor is designed. A filtering method and motion compensation algorithm are designed to filter out background noise and hot spot noise in the event stream as well as redundant events caused by the camera's own motion. A dynamic target fusion detection algorithm that fuses event images and RGB images is designed to ensure the reliability of detection. The target motion trajectory is estimated based on the detection results, and the speed obstacle method is improved to avoid dynamic obstacles by combining the obstacle motion characteristics and UAV dynamic constraints. A large number of simulation tests, handheld tests and flight tests verify the feasibility of the proposed algorithm.

Occluded vehicle tracking by integrating full-scale features and trajectory correction

In order to improve the tracking drift and identity switching (IDs) problems caused by occlusion in vehicle tracking, a method for tracking occluded vehicles that integrates full-scale features and trajectory correction is proposed based on the Deep SORT algorithm. Firstly, a full-scale feature extraction network is introduced to extract the features of different scales of the target and realize adaptive dynamic fusion to enhance the target appearance characteristics. Then, a trajectory correction algorithm is proposed to repair the tracking trajectory during occlusion, and the Kalman filter parameters are updated to reduce the accumulated linear error during occlusion and optimize the target motion characteristics. Finally, the occluded vehicle is tracked by combining appearance features and motion features. The feasibility of the proposed method is verified by ablation experiments and visualization analysis. The experimental results on the KITTI dataset show that compared with the existing typical methods, the proposed method achieves the highest comprehensive score of 78.13 and the lowest number of IDs of 192, which effectively improves the IDs problem in occluded vehicle tracking and improves the robustness of vehicle tracking.

EKF-based parameter estimation method for radar maneuvering target with unknown time information

Moving target detection (MTD) is a research hotspot in radar signal processing. Generally, the time information of non-cooperative moving targets entering and leaving a radar coverage area is unknown, which would lead to severe performance loss for target parameter estimation, detection, and imaging. Unlike our previous research work, this paper addresses the motion parameters estimation and refocusing problem for a radar maneuvering target with unknown entry and departure time. A computationally efficient method that utilizes extended Kalman filtering (EKF) for phase tracking is proposed to estimate the entry and departure times. The proposed method first performs range cell migration correction (RCMC) on the pulse compression echo signal. Then, the maneuvering target signal is modeled as a polynomial phase signal (PPS) and utilizes the EKF to construct a binary state-space equation for polynomial phase tracking. Finally, by comparing the phase tracking results of the noise cell and the signal cell, one can derive estimates for the entry/departure time and motion parameters. Compared with existing methods, the proposed method avoids multi-dimension searching on the parameter space, so it has a prominent advantage in computational complexity. Moreover, the core of the proposed method lies in tracking the polynomial phase, which is not constrained by the order of target motion, and has wider applicability in practice. Both simulated and public radar data are used to validate the effectiveness of the proposed method.

Clinical implementation and patient-specific quality assurance solutions for real-time target tracking and dynamic delivery in Radixact synchrony.

The installation and testing of the first Radixact with Synchrony system in Colombia marked a significant milestone in Latin America's medical landscape. There was a need to devise a robust quality assurance protocol to comprehensively evaluate both dose delivery and motion tracking accuracy. However, testing experiences under clinical conditions have not been extensively reported. Additionally, there are limited recommended measuring devices for Synchrony evaluation. To validate and implement an alternative setup for dynamic-PSQA while testing Synchrony's functionality under clinical scenarios, including real-patient motion traces, and to provide guidance to new centers undergoing clinical implementation of Helical Synchrony. This approach involves using the Iba miniPhantomR with strategically placed fiducial markers for configuring Gafchromic-films and array-based setups. When paired with the CIRS Dynamic Platform, this enables an innovative dynamic setup with trackable features for Synchrony delivery testing. Assessment scenarios, including compensation (M1S1) and no-motion compensation (M1S0), were evaluated using 2D-gamma pass rate analysis with multiple clinical gamma criteria. The Synchrony-Simulation feature was used to assess pre-treatment performance and capture the patient's target motion pattern. Synchrony for common clinical cases with patient's motion-traces was validated. The results for M1S0 and M1S1 demonstrated consistency with previous studies evaluating Synchrony functionality. Analysis using different gamma criteria unveiled dosimetric differences and impacts across various motion ranges. The application of effective kV-dose subtraction for array-based methods is of upmost importance when evaluating dynamic-PSQA with stringent gamma-criteria. However, no significant kV-dose impact on EBT3-Film was detectable. Two implemented configurations for dynamic-PSQA setups were validated and successfully integrated into our clinic. We addressed both the benefits and limitations of array-based and film-based methods. The functionality and limitations of Synchrony were evaluated using the proposed setups. The potential utility of Synchrony-Simulation, along with the proposed patient-case classification table, can offer valuable support for new users during the clinical implementation of Synchrony treatments.