Target Motion Research Articles

Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle (UUV) in underwater combat, this paper proposes a battlefield ambush scene with UUV considering ocean current. Firstly, by establishing these mathematical models of ocean current environment, target movement, and sonar detection, the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively. Then, based on the Hybrid Quantum-behaved Particle Swarm Optimization (HQPSO) algorithm, the path with the highest target search probability is found. Finally, through simulation calculations, the influence of different UUV parameters and target parameters on the target search probability is analyzed, and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated, and the optimal search path scheme is obtained. The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.

Steering-by-leaning: Feasibility of Utilising Dynamic Backrests to Control Steering in Manual Wheelchairs

Steering-by-leaning is a promising innovation for manual wheelchairs. It may enable improved energy efficiency, one-handed manoeuvrability, and increased trunk activity during wheelchair use in daily life. To explore the feasibility of this concept, the lateral trunk function of active wheelchair users was assessed before comparing 3 preliminary dynamic backrest designs in a virtual steering exercise. Repeated measures, cross-over study. A convenience sample of 15 individuals who had been full-time users of manual wheelchair for at least 1 year. Active core strength and lateral leaning range of motion were captured while sitting freely. Participants subsequently tested 3 dynamic wheelchair backrest designs on an individually adjusted laboratory wheelchair prototype by performing a virtual steering exercise. Deviations from a target movement path were analysed using repeated measures analysis of variance and Pearson correlation coefficients. Functional leaning range of motion ranged from below 10° to almost 70°, but increased significantly with use of the simplest backrest design based on a 2-dimensional hinge joint. No correlation was found between functional levels and performance parameters in the virtual steering exercise. Using an individually fitted and calibrated design, upper body-actuated wheelchair steering using a laterally tilting backrest is accessible to wheelchair users across a wide spectrum of physical abilities.

Location and tracking of environmental pollution sources under multi-UAV vision based on target motion model

Location and tracking of environmental pollution sources under multi-UAV vision based on target motion model

Film measurement and analytical approach for assessing treatment accuracy and latency in a magnetic resonance-guided radiotherapy system.

We measure the dose distribution of gated delivery for different target motions and estimate the gating latency in a magnetic resonance-guided radiotherapy (MRgRT) system. The dose distribution accuracy of the gated MRgRT system (MRIdian, Viewray) was investigated using an in-house-developed phantom that was compatible with the magnetic field and gating method. This phantom contains a simulated tumor and a radiochromic film (EBT3, Ashland, Inc.). To investigate the effect of the number of beam switching and target velocity on the dose distribution, two types of target motions were applied. One is that the target was periodically moved at a constant velocity of 5mm/s with different pause times (0, 1, 3, 10, and 20 s) between the motions. During different pause times, different numbers of beams were switched on/off. The other one is that the target was moved at velocities of 3, 5, 8, and 10mm/s without any pause (i.e., continuous motion). The gated method was applied to these motions at MRIdian, and the dose distributions in each condition were measured using films. To investigate the relation between target motion and dose distribution in the gating method, we compared the results of the gamma analysis of the calculated and measured dose distributions. Moreover, we analytically estimated the gating latencies from the dose distributions measured using films and the gamma analysis results. The gamma pass rate linearly decreased with increasing beam switching and target velocity. The overall gating latencies of beam-hold and beam-on were 0.51±0.17 and 0.35±0.05 s, respectively. Film measurements highlighted the factors affecting the treatment accuracy of the gated MRgRT system. Our analytical approach, employing gamma analysis on films, can be used to estimate the overall latency of the gated MRgRT system.

Real-time motion monitoring using orthogonal cine MRI during MR-guided adaptive radiation therapy for abdominal tumors on 1.5T MR-Linac.

Real-time motion monitoring (RTMM) is necessary for accurate motion management of intrafraction motions during radiation therapy (RT). Building upon a previous study, this work develops and tests an improved RTMM technique based on real-time orthogonal cine magnetic resonance imaging (MRI) acquired during magnetic resonance-guided adaptive RT (MRgART) for abdominal tumors on MR-Linac. A motion monitoring research package (MMRP) was developed and tested for RTMM based on template rigid registration between beam-on real-time orthogonal cine MRI and pre-beam daily reference 3D-MRI (baseline). The MRI data acquired under free-breathing during the routine MRgART on a 1.5T MR-Linac for 18 patients with abdominal malignancies of 8 liver, 4 adrenal glands (renal fossa) and 6 pancreas cases were used to evaluate the MMRP package. For each patient, a 3D mid-position image derived from an in-house daily 4D-MRI was used to define a target mask or a surrogate sub-region encompassing the target. Additionally, an exploratory case reviewed for an MRI dataset of a healthy volunteer acquired under both free-breathing and deep inspiration breath-hold (DIBH) was used to test how effectively the RTMM using the MMRP can address through-plane motion (TPM). For all cases, the 2D T2/T1-weighted cine MRIs were captured with a temporal resolution of 200 ms interleaved between coronal and sagittal orientations. Manually delineated contours on the cine frames were used as the ground-truth motion. Common visible vessels and segments of target boundaries in proximity to the target were used as anatomical landmarks for reproducible delineations on both the 3D and the cine MRI images. Standard deviation of the error (SDE) between the ground-truth and the measured target motion from the MMRP package were analyzed to evaluate the RTMM accuracy. The maximum target motion (MTM) was measured on the 4D-MRI for all cases during free-breathing. The mean (range) centroid motions for the 13 abdominal tumor cases were 7.69 (4.71-11.15), 1.73 (0.81-3.05), and 2.71 (1.45-3.93) mm with an overall accuracy of <2 mm in the superior-inferior (SI), the left-right (LR), and the anterior-posterior (AP) directions, respectively. The mean (range) of the MTM from the 4D-MRI was 7.38 (2-11) mm in the SI direction, smaller than the monitored motion of centroid, demonstrating the importance of the real-time motion capture. For the remaining patient cases, the ground-truth delineation was challenging under free-breathing due to the target deformation and the large TPM in the AP direction, the implant-induced image artifacts, and/or the suboptimal image plane selection. These cases were evaluated based on visual assessment. For the healthy volunteer, the TPM of the target was significant under free-breathing which degraded the RTMM accuracy. RTMM accuracy of <2 mm was achieved under DIBH, indicating DIBH is an effective method to address large TPM. We have successfully developed and tested the use of a template-based registration method for an accurate RTMM of abdominal targets during MRgART on a 1.5T MR-Linac without using injected contrast agents or radio-opaque implants. DIBH may be used to effectively reduce or eliminate TPM of abdominal targets during RTMM. This article is protected by copyright. All rights reserved.

Human Arm Workout Classification by Arm Sleeve Device Based on Machine Learning Algorithms.

Wearables have been applied in the field of fitness in recent years to monitor human muscles by recording electromyographic (EMG) signals. Understanding muscle activation during exercise routines allows strength athletes to achieve the best results. Hydrogels, which are widely used as wet electrodes in the fitness field, are not an option for wearable devices due to their characteristics of being disposable and skin-adhesion. Therefore, a lot of research has been conducted on the development of dry electrodes that can replace hydrogels. In this study, to make it wearable, neoprene was impregnated with high-purity SWCNTs to develop a dry electrode with less noise than hydrogel. Due to the impact of COVID-19, the demand for workouts to improve muscle strength, such as home gyms and personal trainers (PT), has increased. Although there are many studies related to aerobic exercise, there is a lack of wearable devices that can assist in improving muscle strength. This pilot study proposed the development of a wearable device in the form of an arm sleeve that can monitor muscle activity by recording EMG signals of the arm using nine textile-based sensors. In addition, some machine learning models were used to classify three arm target movements such as wrist curl, biceps curl, and dumbbell kickback from the EMG signals recorded by fiber-based sensors. The results obtained show that the EMG signal recorded by the proposed electrode contains less noise compared to that collected by the wet electrode. This was also evidenced by the high accuracy of the classification model used to classify the three arms workouts. This work classification device is an essential step towards wearable devices that can replace next-generation PT.

MATHEMATICAL ASPECTS OF DETERMINING THE MOTION PARAMETERS OF A TARGET BY UAV

Research relevance When performing reconnaissance missions, a UAV system primarily conducts surveillance of the target area, locates stationary or moving enemy targets, determines their coordinates or transmits their images directly to the command post. Directing artillery fire as well as guiding missiles or carrying out target designation (illumination of targets) for high-precision weapons, automatic determination of target coordinates and their prompt transmission to the appropriate ground stations is essential. This article provides a mathematical solution to the issue of determining the motion parameters of a target spotted during surveillance of an area by a reconnaissance drone (UAV). The subject of the study in the article is UAV- rocket-artillery systems. The purpose of the work is to develop a mathematical model for determining the parameters of the target's movement using a UAV. The following tasks are solved in the article: to develop target positioning diagram; to develop a mathematical model for determining the parameters of the target's movement. The methods used in the article are the methods of mathematical modelling. The following results were obtained: а target positioning scheme has been compiled to determine the coordinates and movement parameters; a mathematical dependence has been developed to determine the coordinates of a fixed target; a mathematical model has been developed to determine the parameters of the target's movement. Conclusions: by calculating the current coordinates of a moving target using the method proposed in this paper, it is possible to determine the coordinates of the target and parameters of the target's movement, including the direction of movement and the trajectory.

SiamST: Siamese network with spatio-temporal awareness for object tracking

Recently, Siamese trackers have achieved remarkable tracking performance. However, challenges such as accurate feature representation of targets with different spatial regions and the utilization of diverse target temporal states still need to be addressed. Here, we proposed a Siamese network with spatio-temporal awareness called SiamST. The standard square convolutional kernels and the single feature matching operation hardly represent the targets with different shapes accurately. Therefore, we designed a refined region fusion module that combines multiple convolutional kernels to fit targets with different aspect ratios. Furthermore, we proposed a multi-granularity matching module to obtain more robust feature matching results by combining fine-grained and coarse-grained matching results. However, most existing Siamese trackers do not adequately employ target temporal states. They usually only update the templates, which automatically causes motion information loss. Therefore, we built dynamic templates by screening high-quality samples to describe the target appearance changes accurately. In addition, we designed a trend guidance module to adjust the location prior constraint appropriately to match the tracking results to the target's motion trajectory. Extensive experimental results on eight tracking benchmarks demonstrate the competitive performance of SiamST compared to many advanced trackers.

4DCT is long overdue for improvement.

4DCT is long overdue for improvement.

Adaptive behavior in sonar prey tracking tasks: Empirical data and modeling

Echolocating bats can discriminate small differences in target range (Simmons, 1973) and show a clutter interference zone along the range axis of 8–9 cm (Simmons et al., 1988). Because bats use an active sensing system, they can employ various strategies to mitigate the effects of clutter. We studied clutter rejection behaviors of the big brown bat, Eptesicus fuscus, as it tracked a moving target under controlled conditions. We also created data-driven models using control theory and system identification techniques to evaluate oscillating target motion tracking strategies. We trained bats to rest on a stationary platform and track a moving tethered mealworm in two conditions (1) approaching and passing by stationary clutter objects at varying distances and angular offsets, and (2) moving in sinusoidal patterns in an open room. Data from the first experimental condition revealed adaptive echolocation call features and head movements that enabled target tracking in the presence of clutter. Under the oscillating target condition, we analyzed adaptive echolocation behavior to create a data-driven model that maps changes in target motion with sonar call rate. The results of this study demonstrate the bat’s use of active motor control that separates targets from clutter and to track complex motion trajectories.

Array invariant-based range-only target motion analysis for ship noise measured by two vertical line arrays in shallow water

A novel approach for underwater acoustic localization by using passive line array sonar will be proposed in this talk. In the case of the received signal having a finite pulse duration, the target location can be easily estimated using the time-difference-of-arrival (TDOA) method between sensors. However, it is difficult to use the TDOA method for signals that exist for a relatively long time, such as ship noise. An array invariant method has been proposed to measure the source-receiver distance, but it does not accurately estimate the target location. In this talk, target localization will be performed with the range-only target motion analysis method using non-linear target tracking after estimating the target range by the array invariant method. The proposed method is tested using the ship noise of the R/V Onnuri measured by two vertical line arrays during the Shallow-water Acoustic Variability Experiment (SAVEX-15), and the result will be discussed in this talk. [This research was supported by the Ministry of Oceans and Fisheries of Korea (PM63010, 2022).]

Research on the angular velocity estimation method based on the state augmentation method

In the maneuvering target tracking research due to the unpredictability of the target’s maneuvering, it makes it difficult to establish an accurate target motion model to reflect the real motion trajectory of the target. Based on the determination of the parametric maneuver model and the radar measurement conditions of the target, the joint estimation method of the motion state and the maneuver parameters is designed to obtain the optimal estimation of the motion state and the maneuver parameters based on the optimal evaluation criteria. Currently, in the descriptive category of the state space model, the state augmentation method is obtained to solve the joint estimation of the motion state and maneuver parameters based on the estimation criterion [1]. Therefore, the angular velocity estimation method of the state augmentation method is used to consider the estimated value of the target angular velocity and the estimated variance comprehensively to improve the convergence speed of target tracking, minimize the error between the filtered angular velocity and the real angular velocity of the maneuvering target, and finally achieve the purpose of improving the target tracking accuracy.

Line of Sight Tracking Method Based on Feedback Linearization and Command Planning

When the target just enters the field of view of the optoelectronic tracking system, there is a big pointing deviation. If the closed-loop control is directly performed at this time, overshoot or even oscillation will occur, which will easily cause the target to leave the field of view again. At present, most control algorithms are concerned about enhancing the steady-state tracking precision of the system, but seldom consider the transient performance of the system. In this paper, a new definition of pointing deviation is given, in addition, the model of the line-of-sight (LOS) tracking system is realized. On this basis, a method based on feedback linearization is raised, which can compensate measurable disturbance. For both transient response and steady-state performance, a new LOS planning method is presented, which accomplishes the planning and design of the target motion trajectory on the imaging plane. It can be able to avoid the target departing from the field of view and speed up the system response based on fixed control parameters. Through the analysis of experimental results, it can be indicated that the LOS tracking method proposed in this paper can availably promote the transient performance of the system and is existent of real value.

Fusion of Inverse Synthetic Aperture Radar and Camera Images for Automotive Target Tracking

Automotive targets undergoing turns in road junctions offer large synthetic apertures over short dwell times to automotive radars that can be exploited for obtaining fine cross-range resolution. Likewise, the wide bandwidths of the automotive radar signal yield high-range resolution profiles. Together, they are exploited for generating inverse synthetic aperture radar (ISAR) images that offer rich information regarding the target vehicle's size, shape, and trajectory which is useful for object recognition and classification. However, a key requirement for ISAR is translation motion compensation and estimation of the turning velocity of the target. State-of-the-art algorithms for motion compensation trade-off between computational complexity and accuracy. An alternative low complexity method is to use an additional sensor for tracking the target motion. In this work, we propose to exploit computer vision algorithms to identify the radar target object in the sensor field-of-view (FoV) with high accuracy. Further, we propose to track the target vehicle's motion through fusion of vision and radar data. Vision data facilitates the accurate estimation of the lateral position of the target and its lateral velocity which complements the radar's capability of accurate estimation of range and radial velocity. Through simulations and experimental evaluations with a monocular camera and Texas Instrument's millimeter wave radar, we demonstrate the effectiveness of sensor fusion for accurate target tracking for translational motion compensation and generation of high quality ISAR images.

On a Moving Target Selection Model in Virtual Reality Based on Decision Trees

On a Moving Target Selection Model in Virtual Reality Based on Decision Trees

A Moving Target Detection Model Inspired by Spatio-Temporal Information Accumulation of Avian Tectal Neurons

Moving target detection in cluttered backgrounds is always considered a challenging problem for artificial visual systems, but it is an innate instinct of many animal species, especially the avian. It has been reported that spatio-temporal information accumulation computation may contribute to the high efficiency and sensitivity of avian tectal neurons in detecting moving targets. However, its functional roles for moving target detection are not clear. Here we established a novel computational model for detecting moving targets. The proposed model mainly consists of three layers: retina layer, superficial layers of optic tectum, and intermediate-deep layers of optic tectum; in the last of which motion information would be enhanced by the accumulation process. The validity and reliability of this model were tested on synthetic videos and natural scenes. Compared to EMD, without the process of information accumulation, this model satisfactorily reproduces the characteristics of tectal response. Furthermore, experimental results showed the proposed model has significant improvements over existing models (EMD, DSTMD, and STMD plus) on STNS and RIST datasets. These findings do not only contribute to the understanding of the complicated processing of visual motion in avians, but also further provide a potential solution for detecting moving targets against cluttered environments.

Low-Density sEMG-Based Pattern Recognition of Unrelated Movements Rejection for Wrist Joint Rehabilitation.

sEMG-based pattern recognition commonly assumes a limited number of target categories, and the classifiers often predict each target category depending on probability. In wrist rehabilitation training, the patients may make movements that do not belong to the target category unconsciously. However, most pattern recognition methods can only identify limited patterns and are prone to be disturbed by abnormal movement, especially for wrist joint movements. To address the above the problem, a sEMG-based rejection method for unrelated movements is proposed to identify wrist joint unrelated movements using center loss. In this paper, the sEMG signal collected by the Myo armband is used as the input of the sEMG control method. First, the sEMG signal is processed by sliding signal window and image coding. Then, the CNN with center loss and softmax loss is used to describe the spatial information from the sEMG image to extract discriminative features and target movement recognition. Finally, the deep spatial information is used to train the AE to reject unrelated movements based on the reconstruction loss. The results show that the proposed method can realize the target movements recognition and reject unrelated movements with an F-score of 93.4% and a rejection accuracy of 95% when the recall is 0.9, which reveals the effectiveness of the proposed method.

Clinical 3D/4D cumulative proton dose assessment methods for thoracic tumours with large motion

PurposeDespite the anticipated clinical benefits of intensity-modulated proton therapy (IMPT), plan robustness may be compromised due to its sensitivity to patient treatment uncertainties, especially for tumours with large motion. In this study, we investigated treatment course-wise plan robustness for intra-thoracic tumours with large motion comparing a 4D pre-clinical evaluation method (4DREM) to our clinical 3D/4D dose reconstruction and accumulation methods. Materials and methodsTwenty patients with large target motion (>10 mm) were treated with five times layered rescanned IMPT. The 3D-robust optimised plans were generated on the averaged planning 4DCT. Using multiple 4DCTs, treatment plan robustness was assessed on a weekly and treatment course-wise basis through the 3D robustness evaluation method (3DREM, based on averaged 4DCTs), the 4D robustness evaluation method (4DREM, including the time structure of treatment delivery and 4DCT phases) and 4D dose reconstruction and accumulation (4DREAL, based on fraction-wise information). ResultsBaseline target motion for all patients ranged from 11–17 mm. For the offline adapted course-wise dose assessment, adequate target dose coverage was found for all patients. The target volume receiving 95% of the prescription dose was consistent between methods with 16/20 patients showing differences < 1%. 4DREAL showed the highest target coverage (99.8 ± 0.6%, p < 0.001), while no differences were observed between 3DREM and 4DREM (99.3 ± 1.3% and 99.4 ± 1.1%, respectively). ConclusionOur results show that intra-thoracic tumours can be adequately treated with IMPT in free breathing for target motion amplitudes up to 17 mm employing any of the accumulation methods. Anatomical changes, setup and range errors demonstrated a more severe impact on target coverage than motion in these patients treated with fractionated proton radiotherapy.

Individualized breathing trace quality assurance for lung radiotherapy patients undergoing 4DCT simulation.

4DCT simulation is a popular solution for radiotherapy simulation of lung cancer patients as it allows the clinician to gain an appreciation for target motion during the patient breathing cycle. Resultant binning of images and production of the 4DCT dataset relies heavily on the recorded breathing trace; but quality assurance is not routinely performed on these and there lacks any substantial recommendations thereof. An application was created for Windows in C# that was able to analyze the VXP breathing trace files from Varian RPM/RGSC and quantify various metrics associated with the patient breathing cycle. This data was then used to consider errors in voluming of targets for several example cases in order to justify recommendations on quality assurance. For 281 real patient breathing traces from 4DCT simulation of lung targets, notable differences were found between RGSC and application calculations of phase data. For any new patient without individualized QA, the average marked phase calculation (which is used for 4DCT reconstruction) is only accurate to within 19% of the actual phases. The error in BPM within the scan due to breathing rate variation is 37%. The uncertainty in amplitude due to breathing variation is 34% in the mean. Phase uncertainty leads to misbinning which we have shown can lead to missing 66% of the target for gated treatment. Variation in inhalation/exhalation level leads to voluming errors which, without individualized QA, can be assumed to be 11% (PTV is smaller than actual). Without individualized quality assurance of patient breathing traces, large uncertainties have to be assumed for metrics of both phase and amplitude, leading to clinically significant uncertainties in treatment. It is recommended to perform individualized quality assurance as this provides the clinician with an accurate quantification of uncertainty for their patient.

GraphSTGAN: Situation understanding network of slow-fast high maneuvering targets for maritime monitor services of IoT data

With the rapid growth of the maritime Internet of Things (IoT) devices for Maritime Monitor Services (MMS), maritime traffic controllers could not handle a massive amount of data in time. For unmanned MMS, one of the key technologies is situation understanding. However, the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult, and pose significant challenges to maritime situation understanding. In order to comprehend the situation accurately and thus offer unmanned MMS, it is crucial to model the complex dynamics of multi-agents using IoT big data. Nevertheless, previous methods typically rely on complex assumptions, are plagued by unstructured data, and disregard the interactions between multiple agents and the spatial-temporal correlations. A deep learning model, Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN), is proposed in this paper, which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions. Extensive experiments show the effectiveness and robustness of the proposed method.