Maneuver Detection Research Articles

Space-Based Passive Orbital Maneuver Detection Algorithm for High-Altitude Situational Awareness

Orbital maneuver detection for non-cooperative targets in space is a key task in space situational awareness. This study develops a passive maneuver detection algorithm using line-of-sight angles measured by a space-based optical sensor, especially for targets in high-altitude orbit. Emphasis is placed on constructing a new characterization for maneuvers as well as the corresponding detection method. First, the concept of relative angular momentum is introduced to characterize the orbital maneuver of the target quantitatively, and the sensitivity of the proposed characterization is analyzed mathematically. Second, a maneuver detection algorithm based on the new characterization is designed in which sliding windows and correlations are utilized to determine the mutation of the maneuver characterization. Subsequently, a numerical simulation system composed of error models, reference missions and trajectories, and computation models for estimating errors is established. Then, the proposed algorithm is verified through numerical simulations for both long-range and close-range targets. The results indicate that the proposed algorithm is effective. Additionally, the sensitivity of the proposed algorithm to the width of the sliding window, accuracy of the optical sensor, magnitude and number of maneuvers, and different relative orbit types is analyzed, and the sensitivity of the new characterization is verified using simulations.

Maneuver strategies of Starlink satellite based on SpaceX-released ephemeris

In the face of huge collision risks posed by the Starlink mega-constellation, the detection of unpredictable orbital maneuvers of Starlink satellites emerges as an important topic of Space Situational Awareness (SSA). The ephemerides released by the SpaceX company on the Space-track website provide a timely and valuable opportunity for addressing this problem. Based on the mean orbital elements extracted from Starlink ephemerides released at the end of 2023, the maneuver strategies, including maneuver frequency and characteristics of Starlink satellites in different orbital phases, are discussed, and the start time and end time with error ranges of confirmed maneuvers are acquired. It is found that operational satellites in Shell 1 perform an orbit maintenance maneuver approximately every 2 days, while those in Shells 2–5 execute a maneuver roughly once a day. Moreover, the parking satellites maneuver approximately every 0.25 days on average. As for satellites in the climbing orbit and the deorbiting orbit, they maneuver around every 0.1 days. The maneuver strategies of Starlink summarized in this paper can contribute to optimizing the observation plan of the Starlink constellation.

Real-time hybrid method for maneuver detection and estimation of non-cooperative space targets

Real-time hybrid method for maneuver detection and estimation of non-cooperative space targets

Symbolic Dynamics for Radar Target Maneuver Detection With High Data Rates

Symbolic Dynamics for Radar Target Maneuver Detection With High Data Rates

Driving Maneuver Detection at Intersections for Connected Vehicles: A Micro-Cluster-Based Online Adaptable Approach

Driving Maneuver Detection at Intersections for Connected Vehicles: A Micro-Cluster-Based Online Adaptable Approach

Maneuver Detection Method based on Doppler Residual for Maneuvering Target Tracking

Maneuver Detection Method based on Doppler Residual for Maneuvering Target Tracking

Influence of sport expertise in facilitating and inhibiting the recognition of the opponent's intentions in sailing.

Starting from the proposed role of the mirror neuron system in the recognition of the intention underlying the actions of others, an experimental paradigm was implemented to test the role of sailing motor expertise in predicting the outcome of a competitor's action. It was hypothesized that subjects with experience in sailing would correctly interpret the maneuver performed due to the activation of domain specific motor representations of the same movements and that subjects who practiced a sport different from sailing would perform worse because of the activation of irrelevant motor patterns. For doing so, a series of video clips, in which a professional sailor performed a tack or a feint, have been manipulated so that the video clips would stop at the moment of the dunkin, namely, when the boat acquires speed to tack or continue straight ahead. The task consisted in predicting whether the action following the dunkin was an actual tack or a feint. The performance of 87 subjects, divided into three subgroups (sailors, tennis players, sedentary), was evaluated in terms of accuracy in identifying the sailor's intentions and correlated to age, gender, manual dominance, education, job, hours spent weekly playing videogames, and experience in playing sports. Results showed that the percentage of correct identifications of the intention to do a tack or feint was the highest in the group of sailors and the lowest in tennis players. An inverse relation between tennis experience and ability in recognizing the sailor's intention was found in the group of tennis players. Gender, age, manual dominance, education, job, and experience with videogames were not found to be correlated with performance. Findings support the possible implication of the mirror neuron system in maneuver detection in sailing and may be a starting point for the development of psychological training in this sport.

An Urban Traffic Dataset Composed of Visible Images and Their Semantic Segmentation Generated by the CARLA Simulator

A dataset of aerial urban traffic images and their semantic segmentation is presented to be used to train computer vision algorithms, among which those based on convolutional neural networks stand out. This article explains the process of creating the complete dataset, which includes the acquisition of the images, the labeling of vehicles, pedestrians, and pedestrian crossings as well as a description of the structure and content of the dataset (which amounts to 8694 images including visible images and those corresponding to the semantic segmentation). The images were generated using the CARLA simulator (but were like those that could be obtained with fixed aerial cameras or by using multi-copter drones) in the field of intelligent transportation management. The presented dataset is available and accessible to improve the performance of vision and road traffic management systems, especially for the detection of incorrect or dangerous maneuvers.

Detection and estimation of spacecraft maneuvers for catalog maintenance

Building and maintaining a catalog of resident space objects involves several tasks, ranging from observations to data analysis. Once acquired, the knowledge of a space object needs to be updated following a dedicated observing schedule. Dynamics mismodeling and unknown maneuvers can alter the catalog’s accuracy, resulting in uncorrelated observations originating from the same object. Starting from two independent orbits, this work presents a convex-optimization based approach to find admissible maneuvers to correlate the two orbits. In case of a feasible maneuver detection, the time profile of the maneuver is estimated, exploiting the conjugate unscented transform to map the orbits’ covariance into the maneuver uncertainty. It is also shown that a careful choice of the coordinate system is pivotal to obtain the solution in one iteration, without successive convexification.

Chimpanzees make tactical use of high elevation in territorial contexts.

Tactical warfare is considered a driver of the evolution of human cognition. One such tactic, considered unique to humans, is collective use of high elevation in territorial conflicts. This enables early detection of rivals and low-risk maneuvers, based on information gathered. Whether other animals use such tactics is unknown. With a unique dataset of 3 years of simultaneous behavioral and ranging data on 2 neighboring groups of western chimpanzees, from the Taï National Park, Côte d'Ivoire, we tested whether chimpanzees make decisions consistent with tactical use of topography to gain an advantage over rivals. We show that chimpanzees are more likely to use high hills when traveling to, rather than away from, the border where conflict typically takes place. Once on border hills, chimpanzees favor activities that facilitate information gathering about rivals. Upon leaving hills, movement decisions conformed with lowest risk engagement, indicating that higher elevation facilitates the detection of rivals presence or absence. Our results support the idea that elevation use facilitated rival information gathering and appropriate tactical maneuvers. Landscape use during territorial maneuvers in natural contexts suggests chimpanzees seek otherwise inaccessible information to adjust their behavior and points to the use of sophisticated cognitive abilities, commensurate with selection for cognition in species where individuals gain benefits from coordinated territorial defense. We advocate territorial contexts as a key paradigm for unpicking complex animal cognition.

Comparison of satellite manoeuvre detection methods based on timeline of orbit elements

This paper proposes two innovative methods for detecting satellite manoeuvres by analysing a timeline of orbit elements. The first one makes use of a Linear Kalman Filter (LKF) to track the evolution of orbit elements and identify changes, while the second one refers to a particular manoeuvre strategy of geostationary satellites and detects in-plane orbit corrections by evaluating differences in mean longitude. Their detection performances are compared to those of already-existing techniques and tested, when applicable, for both Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) regimes. An optimization process is put in place to fairly confront different detection algorithms with distinct tuning parameters in order to ensure that every method is always providing its best detection performance. The defined loss function can also be used for a relative comparison of results, and this proves a better performance of the newly developed manoeuvre detection techniques.

A station-keeping maneuver detection method of non-cooperative geosynchronous satellites

Orbit maneuver detection is one of the major concerns for the work of Space Situation Awareness of GEO protected region. In this study, we propose a non-cooperative GEO satellites station-keeping maneuver detection approach based on the two-dimensional Convolutional Neural Network (CNN) and the public Two-Line Element (TLE). The proposed method focuses on identifying “station-keeping categories” using the time series segments of TLE data. The time series TLE of the selected objects are converted into several equal-sized segments using a window slicing method. These segments are then used to train a two-dimensional CNN classification model that can divide the input data into three categories of maneuver. The applicability of the proposed method on satellites in the same orbital region were further tested using other objects that had not been included during the modeling process. Experimental results demonstrated the feasibility and robustness of the proposed method, with a classification macro F1-score of 94.45%, macro precision of 94.48%, and macro recall of 94.46%. The results suggest that the proposed method provides a promising solution for detecting GEO station-keeping maneuvers.

Satellite Manoeuvre Detection with Multistatic Radar

Traditional radar sensors used for surveillance rely on monostatic radar principles. However, recently the use of remote radio frequency telescopes as bistatic receivers represents an interesting way to reuse existing facilities while providing additional information to improve tracking accuracy. In this paper we study the benefits of using such a system for the task of manoeuvre detection in satellites in LEO and MEO. We investigate the conditions in which a multistatic radar is advantageous for this purpose, and show concrete results based on simulated data. Moreover, we propose novel manoeuvre detection methods, and compare their accuracy to methods found in the literature. A more general way of assessing the accuracy of these manoeuvre detection methods is also proposed, with the aim of taking into account that the parameters of the manoeuvre that actually takes place also have an effect on the accuracy. These can be split into optimal control based methods, and statistical methods. We found the addition of multistatic radar to allow considerable improvement in the accuracy of the manoeuvre detection process, an improvement that is shown to be greater the greater the baseline, i.e., the distance of the receiver to the transmitter. Furthermore, the manoeuvre detection methods that accurately model the uncertainty in the measurements were found to be the most accurate.

Bowel preparation in children and adolescents undergoing ileo-colonoscopy: what is new?

Ileo-colonoscopy is a vital diagnostic and therapeutic tool undertaken for evaluating and treating gastrointestinal tract pathologies in children and adolescents as well as adults. Proper visualization of the lower intestinal mucosa, completion of the procedure including terminal ileum/cecal intubation, detection of pathological lesions and therapeutic maneuvers are hinged on how adequate the bowel preparation is. About 25% percent of pediatric patients have inadequate bowel preparations, which can lead to prolonged colonoscopy procedure time, missed pathology, difficult ileal intubation/incomplete examination, and requiring cancellation or repeat procedure with attendant anesthetic risks. An ideal bowel preparation regimen should clear the colon of fecal material with no alteration of the colonic mucosa that could affect the histological findings. There is no acceptable universal regimen for bowel preparation in children; however wide variability of practices exists globally. Hence, the current review is aimed at analyzing recent published literature and personal practical experiences as well as developing a standard bowel preparation guideline that will improve outcome of the ileo- colonoscopy procedure in children particularly in resource limited settings.

A satellite orbit maneuver detection and robust multipath mitigation method for GPS coordinate time series

Multipath effect is the main factor limiting the high-precision positioning of Global Navigation Satellite System (GNSS) because it cannot be eliminated by double-difference observations or existing empirical models. Although the receiver antenna technology can reduce the multipath effect, it also brings the cost burden and cannot effectively solve the short-delay multipath error of carrier phase observation. Sidereal filtering (SF) is a common method used to mitigate multipath effect in static positioning mode. However, the coordinate time series may be contaminated by outliers, and satellite orbits may be affected by maneuvers. These will lead to inaccurate estimation of the Multipath Repetition Period (MRP), and even deteriorate the multipath mitigation effect of the SF. To solve these problems, a Sidereal Filtering method with the Satellite Maneuver Detection and Robustness (SFSMDR) is proposed. In this method, based on the Orbital Repeat Time Method (ORTM), the Multipath Repetition Period with the Satellite Maneuvers Detection (MRPSMD) is determined. Furthermore, considering the robustness of median filtering to outliers, the coordinate time series of the first day were preprocessed by the median filtering. After applying the proposed SFSMDR method, experimental results show that the proposed SFSMDR can effectively detect satellite maneuvers. In the absence of satellite maneuvers, there is no need to re-estimate MRP, which can save computational cost to a certain extent. When the coordinate time series on the first day is contaminated by outliers, the positioning accuracy of the MRPSMD for the east and north directions, is decreased by 2.26% and 95.73%, respectively. In contrast, the positioning accuracy of the SFSMDR is increased by 46.21% and 52.71%, respectively.

GEO spacecraft maneuver detection based on causal inference

Geosynchronous (GEO) spacecraft maneuver detection is deemed crucial for space domain awareness (SDA) as it helps to maintain catalogs of high value resident space objects (RSOs). However, several difficulties in resolving maneuver time and requirements for historical data quality still exist for the current research. This paper proposes a method based on causal inference for accurate and timely detection of the maneuver using real-time observational data from optical sensors. Firstly, the residual information of time series observations is obtained through counterfactual reasoning. Secondly, according to the Gaussianity of the optical sensor observation noise, a structural causal equation (SCE) of the residuals of time series observations is derived. Lastly, the maneuver is abstracted as a soft intervention, and the minimum average treatment effect (MATE) is proposed to estimate the effect of the maneuver and used as a feature parameter to identify the GEO spacecraft maneuver. After experimental analysis, the detection rate and detection timeliness of the proposed method reach more than 90%, a significant improvement compared with the conventional χ2, skewness, and kurtosis test methods.

BDS Orbit Maneuver Detection Based on Epoch-Updated Orbits Estimated by SRIF

The orbit maneuver detection is crucial in Global Navigation Satellite System (GNSS) precise orbit determination, which is necessary for adjusting data processing strategies. The frequency of orbit maneuvers for the BeiDou Navigation System is significantly higher than that of other navigation systems, especially for geosynchronous orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites. We propose a novel real-time and postprocessing method for detecting orbit maneuvers for BeiDou satellites based on the orbit differences between the epoch-updated orbit estimated using square root information (SRIF) and the predicted orbit according to the precise orbit estimated during non-maneuver period, as well as the orbital state difference during maneuver and non-maneuver periods. This method has significant advantages over using observation residuals and it is not affected by observation outliers, thus improving the accuracy and timeliness of orbit maneuver detection. We demonstrated that 32 orbit maneuver events of BeiDou satellites were successfully detected in 2022, of which 1 was for medium Earth orbit (MEO), 7 were for IGSO with an average detected maneuvering time of 7–8 min, and 24 were for GEO satellites with an average detected time of 4–5 min. Moreover, our method can be easily integrated into current real-time filter-based precise orbit determination (POD) processing without any extra task line, which simplifies the overall data processing. The data used in this method can be accessed easily, including GNSS observation data, broadcast ephemeris, and other open-source information files.

Sensitivity and Consistency Analysis of BDS-3 GEO Satellite Orbit Maneuver Detection

In this study, 22 maneuvering events of C59 and C60 geostationary equatorial orbit (GEO) satellites of the BeiDou global navigation satellite system (BDS-3) in 2021 were detected and analyzed based on a time-differenced carrier phase velocity measurement algorithm combined with a broadcast ephemeris and station data. We set different empirical thresholds to analyze the effect of threshold selection on the maneuvering detection sensitivity. The sensitivity and consistency of this algorithm for GEO satellite maneuvering detection were analyzed by combining BDS-3 with other Global navigation satellite systems (GNSS) and selecting different geographic stations. The results demonstrate that changing the threshold significantly affects the maneuvering detection of C60, with the optimal maneuvering thresholds of C59 and C60 being 0.045 and 0.02, respectively. A combination of BDS-3 and GPS with equal weights was identified to be most suitable for maneuvering detection of GEO satellites. For any specific maneuver, the difference between the maneuvering time detection of C59 by stations at different geographical locations was no more than 1 min. During the maneuvering detection of C60, the start time of maneuvering detected by African stations was 4.26–8.61 min earlier than that detected by the Chinese and Australian stations, and the end time of maneuvering detected by the African stations was consistent with that detected by the Chinese and Australian stations.

A two-step method to detect broadcast ephemeris unavailable periods caused by BeiDou satellite orbital maneuvers

The space constellation of the BeiDou navigation satellite system (BDS) is a hybrid constellation containing medium earth orbit (MEO) satellites, geostationary earth orbit (GEO) satellites, and inclined geosynchronous orbit (IGSO) satellites. Due to the geosynchronous characteristics of GEO and IGSO, GEO satellites and IGSO satellites often need to perform orbital maneuvers, which can affect the signal-in-space (SIS) availability performance of BeiDou satellites. A two-step detection method for BeiDou satellite orbital maneuvers has been proposed in this paper. The first step is to identify orbital maneuvers based on time series analysis of broadcast ephemeris, and the second step is to verify orbital maneuvers based on bidirectional orbit prediction. The two-step detection method was used to detect the orbital maneuvers of BeiDou satellites in 2019. Through the double guarantees of identification and verification, the detection accuracy of BeiDou satellite orbital maneuvers has been effectively improved. And the orbital maneuver detection results are continued to be used to assess the SIS availability of BeiDou satellites. The results show that the availability loss of GEO satellite orbital maneuvers is about 0.45%–1.07%, and the availability loss of IGSO satellite orbital maneuvers is about 0.12%–0.19%.

Driver Maneuver Detection and Analysis Using Time Series Segmentation and Classification.

The current paper implements a methodology for automatically detecting vehicle maneuvers from vehicle telemetry data under naturalistic driving settings. Previous approaches have treated vehicle maneuver detection as a classification problem, although both time series segmentation and classification are required since input telemetry data are continuous. Our objective is to develop an end-to-end pipeline for the frame-by-frame annotation of naturalistic driving studies videos into various driving events including stop and lane-keeping events, lane changes, left-right turning movements, and horizontal curve maneuvers. To address the time series segmentation problem, the study developed an energy-maximization algorithm (EMA) capable of extracting driving events of varying durations and frequencies from continuous signal data. To reduce overfitting and false alarm rates, heuristic algorithms were used to classify events with highly variable patterns such as stops and lane-keeping. To classify segmented driving events, four machine-learning models were implemented, and their accuracy and transferability were assessed over multiple data sources. The duration of events extracted by EMA was comparable to actual events, with accuracies ranging from 59.30% (left lane change) to 85.60% (lane-keeping). Additionally, the overall accuracy of the 1D-convolutional neural network model was 98.99%, followed by the long-short-term-memory model at 97.75%, then the random forest model at 97.71%, and the support vector machine model at 97.65%. These model accuracies were consistent across different data sources. The study concludes that implementing a segmentation-classification pipeline significantly improves both the accuracy of driver maneuver detection and the transferability of shallow and deep ML models across diverse datasets.