Target Motion Research Articles

Single institution experience of MRI-guided radiotherapy for thoracic tumors and clinical characteristics impacting treatment duty cycle.

MRI-guided radiotherapy (MRgRT) allows for direct motion management and real-time radiation treatment plan adaptation. We report our institutional experience using low strength 0.35T MRgRT for thoracic malignancies, and evaluate changes in treatment duty cycle between first and final MRgRT fractions. All patients with intrathoracic tumors treated with MRgRT were included. The primary reason for MRgRT (adjacent organ at risk [OAR] vs. motion management [MM] vs. other) was recorded. Tumor location was classified as central (within 2cm of tracheobronchial tree) vs. non-central, and further classified by the Expanded HILUS grouping. Gross tumor volume (GTV) motion, planning target volume expansions, dose/fractionation, treatment plan time, and total delivery time were extracted from the treatment planning system. Treatment plan time was defined as the time for beam delivery, including multileaf collimator (MLC) motion, and gantry rotation. Treatment delivery time was defined as the time from beam on to completion of treatment, including treatment plan time and patient respiratory breath holds. Duty cycle was calculated as treatment plan time/treatment delivery time. Duty cycles were compared between first and final fraction using a two-sample t-test. Twenty-seven patients with thoracic tumors (16 non-small cell lung cancer and 11 thoracic metastases) were treated with MRgRT between 12/2021 and 06/2023. Fifteen patients received MRgRT due to OAR and 11 patients received MRgRT for motion management. 11 patients had central tumors and all were treated with MRgRT due to OAR risk. The median dose/fractionation was 50 Gy/5 fractions. For patients treated due to OAR (n=15), 80% had at least 1 adapted fraction during their course of radiotherapy. There was no plan adaptation for patients treated due to motion management (n=11). Mean GTV motion was significantly higher for patients treated due to motion management compared to OAR (16.1mm vs. 6.5mm, p=0.011). Mean duty cycle for fraction 1 was 54.2% compared to 62.1% with final fraction (p=0.004). Mean fraction 1 duty cycle was higher for patients treated due to OAR compared to patients treated for MM (61% vs. 45.0%, p=0.012). Duty cycle improved from first fraction to final fraction possibly due to patient familiarity with treatment. Duty cycle was improved for patients treated due to OAR risk, likely due to more central location and thus decreased target motion.

An Interactive Transient Model Correction Active Sonar Target Tracking Method

Active sonar can usually only directly measure the distance and bearing information of underwater targets, and cannot directly obtain target velocity, acceleration and other information. Therefore, the amount of information is relatively small, making it difficult to support the construction of complex motion models. At the same time, the motion state of underwater maneuvering targets is changeable. In response to the problem of detecting and tracking underwater moving targets by active sonar, this paper proposes a target transient model correction (TMC) filtering tracking method. Based on the conventional Kalman filter (KF) estimation, residual covariance is used as a signal quantity. When there is a large change in it, a transient filter with constant gain is adopted to filter the measurement value. The filtered output is used to correct the KF gain matrix and the target motion state model, to avoid the problem of increasing or even diverging KF estimation errors caused by changes in process noise. Using this method can solve the problem of maintaining stability and filtering estimation accuracy of active sonar tracking of underwater maneuvering targets with less computational and engineering costs.

Parameter Adaptive Non-Model-Based State Estimation Combining Attention Mechanism and LSTM

Nowadays, state estimation is widely used in fields such as autonomous driving and drone navigation. However, in practical applications, it is difficult to obtain accurate target motion models and noise covariance.This leads to a decrease in the estimation accuracy of traditional Kalman filters. To address this issue, this paper proposes an adaptive model free state estimation method based on attention parameter learning module. This method combines Transformer's encoder with Long Short Term Memory Network (LSTM), and obtains the system's operational characteristics through offline learning of measurement data without modeling the system dynamics and measurement characteristics. In addition, based on the output of the attention learning module, the expectation maximization (EM) algorithm is used to estimate the system model parameters online, and a Kalman filter is used to obtain state estimation. This paper was validated using the GPS trajectory path dataset, and the experimental results showed that the proposed parameter adaptive model free state estimation method has better estimation accuracy than other models, providing an effective method for using deep learning networks for state estimation.

The clinical practice and dosimetric outcome of the manual adaptive planning during definitive radiotherapy for cervical cancer

ProposeTo evaluate the advantage of the manual adaptive plans comparing to the scheduled plans, and explored clinical factors predicting patients suitable for adaptive strategy.Methods and materialsEighty two patients with weekly online cone-beam computed tomography (CBCT) were enrolled. The re-CT simulation was performed after 15 fractions and a manual adaptive plan was developed if a significant deviation of the planning target volume (PTV) was found. To evaluate the dosimetric benefit, D98, homogeneity index (HI) and conformity index (CI) for the planning target volume (PTV), as well as D2cc of the bowel, bladder, sigmoid and rectum were compared between manual adaptive plans and scheduled ones. The clinical factors influencing target motion during radiotherapy were analyzed by chi-square test and logistic regression analysis.ResultsThe CI and HI of the manual adaptive plans were significantly superior to the scheduled ones (P = 0.0002, 0.003, respectively), demonstrating a better dose coverage of the target volume. Compared to the scheduled plans, D98 of the manual adaptive plans increased by 3.3% (P = 0.0002), the average of D2cc to the rectum, bladder decreased 0.358 Gy (P = 0.000034) and 0.240 Gy (P = 0.03), respectively. In addition, the chi-square test demonstrated that age, primary tumor volume, and parametrial infiltration were the clinical factors influencing target motion during radiotherapy. Multivariate analysis further identified the large tumor volume (≥ 50cm3, OR = 3.254, P = 0.039) and parametrial infiltration (OR = 3.376, P = 0.018) as the independent risk factors.ConclusionWe found the most significant organ motion happened after 15 fractions during treatment. The manual adaptive plans improved the dose coverage and decreased the OAR doses. Patients with bulky mass or with parametrial infiltration were highly suggested to adaptive strategy during definitive radiotherapy due to the significant organ motion.

Continuous-discrete extended Kalman filtering based on the neural ordinary differential equations method

In bearings-only tracking (BOT), the accuracy of state estimation significantly diminishes when confronted with an unknown target motion model. Therefore, this study proposed a novel continuous-discrete filtering algorithm, the neural ordinary differential equations-based continuous-discrete extended Kalman filtering (NODE-CD-EKF). The approach utilizes the neural ordinary differential equation (NODE) to build the target's system model, which is then used in the time update to precisely predict the target state at the next time point within the continuous-discrete Kalman framework. The NODE concisely and accurately describes the motion model in the form of ordinary differential equations in a data-driven way. This approach addresses the limitations of traditional Kalman filters, which suffer from degraded performance due to a lack of a precise mathematical model of target states. Our method is evaluated and compared to existing multiple-model and other data-driven algorithms in simulations of BOT and another scenario using the Accumulated Root Mean Square Error (ARMSE) of position. The ARMSE of our method is only 16.30 % of the multiple-model methods at least. Overall, this approach outperforms other multiple-model and data-driven algorithms and possesses the potential for solving other nonlinear tasks in real-world applications.

Joint track initiation of resolvable group target

This paper studies the problem of resolvable group target track initiation (RGTI) and proposes a novel method to obtain the discrete association vectors and continuous trajectory parameters of individual targets within the group, which benefits from the fitting representations of target tracks and the utilization of group target motion constraint. Specifically, every target track within the group is modeled as a weighted sum of a same set of time basis functions with different weights, and the aim is to solve multiple pairs of association vectors and trajectory parameters, i.e., unknown weights, according to the measurement equation and group target motion constraint. Next, a joint RGTI framework, which includes track extraction, track purification, trajectory parameter and association vector correction, is proposed to transform the original complex constraint set solving problem into multiple solvable sub-problems. And the convergence of RGTI is ensured through iterative optimization of track purification, trajectory parameter correction and association vector correction. Finally, simulation example demonstrates the superiority of the proposed method compared with existing multi-target track initiation methods.

A novel bionic mantis shrimp robot for tracking underwater moving objects

ABSTRACT Inspired by the agility of mantis shrimp, this paper presents the development of a novel bionic mantis shrimp robot with multiple pleopods coupled motion for underwater target recognition and tracking. Firstly, the novel bionic robot is constructed based on the inspiration from biological mantis shrimp. Secondly, a Kalman filter-based algorithm, namely MobileNet-YOLO (KMY), is created to collect datasets and train neural networks. Thirdly, a moving target following system is developed for the bionic mantis shrimp robot, in which a dual PID controller is used to track underwater moving targets. The real robot testing results show that the proposed control system can enable our bionic robot to follow a specific moving target in a narrow pool (2m × 1m × 1m), and the minimum turning radius can be up to 0.55m when the angle between the robot's initial motion and the motion of target is 90°.

Augmenting complex and dynamic performance through mindfulness-based cognitive training: An evaluation of training adherence, trait mindfulness, personality and resting-state EEG.

Human performance applications of mindfulness-based training have demonstrated its utility in enhancing cognitive functioning. Previous studies have illustrated how these interventions can improve performance on traditional cognitive tests, however, little investigation has explored the extent to which mindfulness-based training can optimise performance in more dynamic and complex contexts. Further, from a neuroscientific perspective, the underlying mechanisms responsible for performance enhancements remain largely undescribed. With this in mind, the following study aimed to investigate how a short-term mindfulness intervention (one week) augments performance on a dynamic and complex task (target motion analyst task; TMA) in young, healthy adults (n = 40, age range = 18-38). Linear mixed effect modelling revealed that increased adherence to the web-based mindfulness-based training regime (ranging from 0-21 sessions) was associated with improved performance in the second testing session of the TMA task, controlling for baseline performance. Analyses of resting-state electroencephalographic (EEG) metrics demonstrated no change across testing sessions. Investigations of additional individual factors demonstrated that enhancements associated with training adherence remained relatively consistent across varying levels of participants' resting-state EEG metrics, personality measures (i.e., trait mindfulness, neuroticism, conscientiousness), self-reported enjoyment and timing of intervention adherence. Our results thus indicate that mindfulness-based cognitive training leads to performance enhancements in distantly related tasks, irrespective of several individual differences. We also revealed nuances in the magnitude of cognitive enhancements contingent on the timing of adherence, regardless of total volume of training. Overall, our findings suggest that mindfulness-based training could be used in a myriad of settings to elicit transferable performance enhancements.

Optimizing Camera Motion with MCTS and Target Motion Modeling in Multi-Target Active Object Tracking

In this work, we are dedicated to multi-target active object tracking (AOT), where the goal is to achieve continuous tracking of targets through real-time control of camera. This form of active camera control can be applied to unmanned aerial vehicles (UAV), intelligent robots, and sports events. Our work is conducted in an environment featuring multiple cameras and targets, where our goal is to maximize target coverage. Contrasting with previous research, our work introduces additional degrees of freedom for the cameras, allowing them not only to rotate but also to move along boundary lines. In addition, we model the motion of target to predict the future position of the target in environment. With target’s future position, we use Monte Carlo Tree Search (MCTS) method to find the optimal action of camera. Since the action space is large, we propose to leverage the action selection from multi-agent reinforcement learning (MARL) network to prune the search tree of Monte Carlo Tree Search method, so as to find the optimal action more efficiently. We establish a multi-target 2D environment to simulate several sports games, and experimental results demonstrate that our method can effectively improve the target coverage. The code is available at: http://github.com/HopeChanger/ActiveObjectTracking .

Research on key technology of cooled infrared bionic compound eye camera based on small lens array

Traditional 2D imaging technologies are limited by the need for a large field of view and their sensitivity to small target motion. Inspired by the characteristics of insect compound eye structure, we propose an infrared bionic compound eye camera based on a small lens array. The camera is composed of 61 small lens arrays mounted on a curved spherical shell and a relay optical system. The imaging device is a high-performance cooled mid-wave infrared detector. This is an innovative design for an infrared biomimetic compound eye camera system that provides a wide field of view and all-day detection capability. Aimed to meet the specified requirements. The optical system achieves a 100% cold-membrane match between the infrared optical system and the cooled detector, and the relay optical system optimizes the large-field aberration by introducing a higher-order aspheric surface and modifying the geometric surface of the lenses. Our entire system enables an observation field angle of 108∘×108∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$108^\\circ \ imes 108^\\circ$$\\end{document}. The experiments showed that the image quality of the system is high, each ommatidium was effective within the imaging range of the compound eye camera, resulting in an improved signal-to-noise ratio in various scenes.

A Co-Localization Algorithm for Underwater Moving Targets with an Unknown Constant Signal Propagation Speed and Platform Errors.

Underwater mobile acoustic source target localization encounters several challenges, including the unknown propagation speed of the source signal, uncertainty in the observation platform's position and velocity (i.e., platform systematic errors), and economic costs. This paper proposes a new two-step closed-form localization algorithm that jointly estimates the angle of arrival (AOA), time difference of arrival (TDOA), and frequency difference of arrival (FDOA) to address these challenges. The algorithm initially introduces auxiliary variables to construct pseudo-linear equations to obtain the initial solution. It then exploits the relationship between the unknown and auxiliary variables to derive the exact solution comprising solely the unknown variables. Both theoretical analyses and simulation experiments demonstrate that the proposed method accurately estimates the position, velocity, and speed of the sound source even with an unknown sound speed and platform systematic errors. It achieves asymptotic optimality within a reasonable error range to approach the Cramér-Rao lower bound (CRLB). Furthermore, the algorithm exhibits low complexity, reduces the number of required localization platforms, and decreases the economic costs. Additionally, the simulation experiments validate the effectiveness of the proposed localization method across various scenarios, outperforming other comparative algorithms.

SAR Target Recognition Method based on Adaptive Weighted Decision Fusion of Deep Features

Background: This paper proposes a synthetic aperture radar (SAR) target recognition method based on adaptive weighted decision fusion of multi-level deep features. Methods: The trained ResNet-18 is employed to extract multi-level deep features from SAR images. Afterwards, based on the joint sparse representation (JSR) model, the multi-level deep features are represented to obtain the corresponding reconstruction error vectors. Considering the differences in the abilities of different levels of features to distinguish the target, the reconstruction error vectors are analyzed based on entropy theory, and their corresponding weights are adaptively obtained. Finally, the fused reconstruction error result is obtained through adaptively weighted fusion, and the target label is determined accordingly. Results: Experiments are conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under different conditions, and the proposed method is compared with published methods, including multi-feature decision fusion, JSR-based decision fusion and other types of ResNets. Conclusion: The experimental results under standard operating condition (SOC) and extended operating conditions (EOCs) including depression angle variance and noise corruption validate the advantages of the proposed method.

Motion-guided large aperture ULA to enhance DOA estimation: An inverse synthetic aperture perspective

Direction-of-arrival (DOA) estimation is a critical issue in array signal processing, especially in radar applications. However, the array aperture of high-frequency radars is constrained by the site and platform, which in turn leads to limited DOA estimation performance. In this paper, inspired by the inverse synthetic aperture radar, we design a novel DOA estimation method based on motion-guided large-aperture uniform linear array (ULA). Referring to the concept of inverse synthetic aperture, the novel method utilizes the relative motion between the radar and the target to construct a larger ULA. We demonstrate that the key condition for reconstructing ULAs is suitable data segmentation and reshaping within the coherent integration time. However, the correct segmentation position is related to the target's motion trajectory. For non-cooperative targets, the uncertain segmentation position is regarded as a mismatch problem between the steering and weighting vectors, such that the spatial spectrum becomes a product of two sinc-like functions. With the analysis of the main lobe and grating lobes, the maximum peak-to-grating-lobe ratio criterion is introduced to find the ambiguity-free DOA. The experimental results demonstrate the performance advantages of the proposed DOA estimation algorithm in terms of noise robustness and resolution.

Real-time and Intelligent Moving Targets Tracking based on UAV Remote Sensing Video Camera and Brain-like Computing Chips

Abstract. Moving target tracking technology based on Unmanned Aerial Vehicles (UAV) is widely used in many fields such as automatic inspection and emergency response. The existing moving target tracking methods usually have the problems of large computation and low tracking efficiency. Limited by the computing power of the UAV platform, real-time tracking and analysis of multiple targets based on the video data collected by UAV platform is a difficult task. In this paper, we proposed a novel Target Specific Filtering Tracking with Memory (TSFMTrack) method designed for UAV-based real-time tracking tasks, which involves a Tracklet Filtering Module (TFM) for capturing object appearance features and a Tracklet Matching Module (TMM) for bounding box association in each frame. By experimental comparison with other State-Of-The-Art (SOTA) methods on popular MOT and UAV tracking datasets, the TSFMTrack have shown obvious advantages in accuracy, computational efficiency and reliability. Furthermore, we deployed the TSFMTrack on the brain-inspired chip Lynchip KA200, the experimental results have shown that the TSFMTrack is effective on edge computational platform and suitable for UAV real-time tracking tasks.

Continuous psychophysics shows millisecond-scale visual processing delays are faithfully preserved in movement dynamics.

Image differences between the eyes can cause interocular discrepancies in the speed of visual processing. Millisecond-scale differences in visual processing speed can cause dramatic misperceptions of the depth and three-dimensional direction of moving objects. Here, we develop a monocular and binocular continuous target-tracking psychophysics paradigm that can quantify such tiny differences in visual processing speed. Human observers continuously tracked a target undergoing Brownian motion with a range of luminance levels in each eye. Suitable analyses recover the time course of the visuomotor response in each condition, the dependence of visual processing speed on luminance level, and the temporal evolution of processing differences between the eyes. Importantly, using a direct within-observer comparison, we show that continuous target-tracking and traditional forced-choice psychophysical methods provide estimates of interocular delays that agree on average to within a fraction of a millisecond. Thus, visual processing delays are preserved in the movement dynamics of the hand. Finally, we show analytically, and partially confirm experimentally, that differences between the temporal impulse response functions in the two eyes predict how lateral target motion causes misperceptions of motion in depth and associated tracking responses. Because continuous target tracking can accurately recover millisecond-scale differences in visual processing speed and has multiple advantages over traditional psychophysics, it should facilitate the study of temporal processing in the future.

Mobility target tracking with meta‐heuristic aided target movement prediction scheme in WSN using adaptive distributed extended Kalman filtering

SummaryIn wireless sensor networks (WSNs), target tracking has been prominently raised in recent days. Because of the frequent utilization of WSN, the attention on target tracking is greatly increased. The estimated optimal value derived from the earlier moment is rarely taken into consideration in traditional target‐tracking algorithms. One of the most crucial uses of WSNs is mobile target tracking, and it is especially used for spying. Precision surveillance is heavily dependent on localization or distance estimation, and extensive study has been done in this area. This research aims to develop a new target‐tracking network‐assisted target movement prediction model in WSN with reduced energy consumption. The major phases involved in the proposed target movement prediction in WSN are (a) mobility target tracking and (b) target movement prediction. Initially, the mobility target tracking is done with the help of adaptive distributed extended Kalman filtering (ADEKF). The mobility target tracking performance is improved by optimally tuning the parameters from ADEKF with the support of the improved squid game optimizer (ISGO). Then, the target movement prediction phase is executed with the help of input parameters like “Angle of Arrival (AoA) and Received Signal Strength (RSS),” and the optimal progress of the mobile node is predicted. The implementation outcome of the proposed tracking network‐assisted target movement prediction model is validated concerning various performance metrics. Overall performance analysis shows that the developed model offers 2.5% in terms of RMSE measures. The developed model shows better analysis while validating with existing approaches.

Dose delivery error from respiratory motion in free breathing left breast irradiation

PurposeMany institutions worldwide currently deliver left breast radiotherapy in free breathing mode, mostly due to the unavailability of a Deep Inspiration Breath Hold technique (DIBH). This study aims at quantifying the error in dose delivery (compared to treatment plan) due to respiratory motion in free breathing irradiation of left breast or chest wall. Since subfields often consist in small, fine-tuned, highly targeted fields, slight intrafractional target motion may compromise their subtle benefit. Thus we analyzed the respiratory motion effect on target dose coverage, dose homogeneity and left lung dose. MethodsTreatment plans for twenty left breast or chest wall cancer patients previously treated at our center were retrieved and retrospectively planned with the introduction of an appropriate shift in isocenter location to simulate free breathing target motion. ResultsNo clinically significant dosimetric changes were found in all twenty cases when breathing motion was accounted for. Changes in target dose coverage (V95%), in target maximum dose (D2%) and in V20Gy lung dose were respectively less than 1.5%, 0.3% and 2.6%. ConclusionThe findings suggest that breast irradiation in free breathing mode does not undermine the dosimetric merits of the field-in-field technique and does not produce clinically significant dosimetric differences in dose delivery for target and lung compared to plan.

Vision-Guided Tracking and Emergency Landing for UAVs on Moving Targets

This paper presents a vision-based adaptive tracking and landing method for multirotor Unmanned Aerial Vehicles (UAVs), designed for safe recovery amid propulsion system failures that reduce maneuverability and responsiveness. The method addresses challenges posed by external disturbances such as wind and agile target movements, specifically, by considering maneuverability and control limitations caused by propulsion system failures. Building on our previous research in actuator fault detection and tolerance, our approach employs a modified adaptive pure pursuit guidance technique with an extra adaptation parameter to account for reduced maneuverability, thus ensuring safe tracking of moving objects. Additionally, we present an adaptive landing strategy that adapts to tracking deviations and minimizes off-target landings caused by lateral tracking errors and delayed responses, using a lateral offset-dependent vertical velocity control. Our system employs vision-based tag detection to ascertain the position of the Unmanned Ground Vehicle (UGV) in relation to the UAV. We implemented this system in a mid-mission emergency landing scenario, which includes actuator health monitoring of emergency landings. Extensive testing and simulations demonstrate the effectiveness of our approach, significantly advancing the development of safe tracking and emergency landing methods for UAVs with compromised control authority due to actuator failures.

Research on prediction of water target motion status of unmanned surface vehicle based on lidar

To improve the perception ability of unmanned boats on water targets and improve the autonomous collision avoidance function of unmanned boats, the problem of target motion state prediction is studied and a method for predicting the motion state of unmanned water targets based on lidar is proposed. Firstly, a three-dimensional point cloud space model is established and the down-sampling and clutter filtering are completed through various filters. Secondly, based on European clustering target segmentation, a target tracking algorithm combined with 3D-IOU metric is proposed to achieve target tracking. Finally, the Kalman filter is introduced for denoising, combined with the iterative closest point algorithm (Iterative Closest Point, ICP) and the centroid calculation algorithm to realize the prediction of the target speed and course. After the test on the lake, the method can meet the real-time requirements, complete the accurate prediction of the target motion state and control the error at a low level. At the same time, this method can also lay the foundation for the development of subsequent autonomous navigation tasks of unmanned vehicles.

A coherent integration method of an active sonar for maneuvering turning target detection.

The detection probability of underwater weak targets using active sonar is low, and inter-pulse coherent integration can improve the signal-to-noise ratio of echoes. When a target executes a maneuvering turn, complex range and Doppler frequency migrations occur during the coherent integration time that decrease the coherent integration gain. Most existing integration methods simplify the target motion to a finite-order polynomial model but fail to integrate a maneuvering turning target (MTT) due to model mismatch. Hence, this study proposes an underwater MTT integration method based on the modified Radon-Fourier transform. The proposed method constructs a theoretically accurate motion model for the MTT and a phase compensation function to compensate for the Doppler frequency migration. Furthermore, it yields a well-focused integration peak in the range-velocity and offset angle-turn rate dimensions and accurately estimates the target motion parameters. Moreover, the proposed method is suitable for targets with radial and oblique uniform motions. The effectiveness of the proposed method is demonstrated through simulations and a lake test. The proposed method demonstrates good integration performance, with an integration gain approximately 4-7 dB higher than that of traditional methods when using 30 integration pulses.