Maneuver Detection Research Articles

Extended input estimation method for tracking non‐linear manoeuvring targets with multiplicative noises

In this study, a new input estimation method is proposed for targets with non-linear dynamics and unknown inputs (manoeuvres), where measurement model is corrupted with both additive and multiplicative noises. First, the authors proposed an innovative model by adding unknown inputs as a new state to the original state vector and constructed an augmented state vector. Therefore, manoeuvring model turns into a non-manoeuvring model. The proposed model does not need any manoeuvre detection stage procedure but it causes a correlation between the process and measurement noises. Subsequently, the authors modify the augmented model to deal with the cross-correlation problem. Finally an augmented extended Kalman filter scheme is proposed, which unlike the most existing literature, it can estimate unknown inputs as quickly as possible and copes with the multiplicative noises and the cross-correlation problem. The efficiency of the proposed method is demonstrated in computer simulations for a manoeuvring target with non-linear dynamics through Monte-Carlo simulation.

Telehealth monitor to measure physical activity and pressure relief maneuver performance in wheelchair users

ABSTRACTThis study demonstrated the feasibility of a device for monitoring pressure relief maneuvers and physical activity for wheelchair users. The device counts the number of wheel pushes based on wheelchair acceleration and measures pressure relief maneuvers using a seat sensor consisting of three force sensing resistors (FSRs). To establish the feasibility of the seat sensor for the detection of pressure relief maneuvers, 10 wheelchair users and 10 non-disabled controls completed a series of wheelchair depression raises, forward trunk leans, and lateral trunk leans. The seat sensor was placed underneath the user’s seat cushion. To establish the feasibility of wheel push counting, 10 full-time wheelchair users navigated a flat 50-m outdoor track and a 100-m outdoor obstacle course during self-propulsion (e.g., wheel pushes) and during assisted-propulsion (e.g., no wheel pushes). Of the 240 performed pressure relief, 225 were properly classified by the seat sensor (accuracy: 94%, sensitivity: 96%, specificity: 80%). Sensitivity was highest for depression raises (98%) and lowest for front lean maneuvers (80%). The wheelchair activity monitor measured 2,112 pushes during the self-propulsion trials compared to 2,162 pushes measured with the instrumented push-rim (97.7%). During assisted-propulsion trials, there were 477 incorrectly identified pushes (8.0 per trial).

Vehicle Maneuver Detection with Accelerometer-Based Classification.

In the mobile computing era, smartphones have become instrumental tools to develop innovative mobile context-aware systems. In that sense, their usage in the vehicular domain eases the development of novel and personal transportation solutions. In this frame, the present work introduces an innovative mechanism to perceive the current kinematic state of a vehicle on the basis of the accelerometer data from a smartphone mounted in the vehicle. Unlike previous proposals, the introduced architecture targets the computational limitations of such devices to carry out the detection process following an incremental approach. For its realization, we have evaluated different classification algorithms to act as agents within the architecture. Finally, our approach has been tested with a real-world dataset collected by means of the ad hoc mobile application developed.

Control Cost and Mahalanobis Distance Binary Hypothesis Testing for Spacecraft Maneuver Detection

An anomaly hypothesis testing technique using the minimum-fuel control distance metric is extended to incorporate non-Gaussian boundary-condition uncertainties and employ binary hypothesis testing. The adjusted control distance metric uses Gaussian-mixture models to model non-Gaussian boundary conditions, and binary hypothesis testing allows inclusion of anomaly detection thresholds and allowable error rates. An analogous framework accommodating Gaussian-mixture models and binary hypothesis testing is developed using Mahalanobis distance. Both algorithms are compared using simulated and empirical satellite maneuver data. The north–south station-keeping scenario shows control distance to be less sensitive with increased uncertainty than Mahalanobis distance but more consistent with respect to observation gap duration, a trend which is corroborated using available real-world data. The same consistency with respect to observation gap is observed in East–west station-keeping while also showing the control distance metric to be more sensitive for shorter observation gaps. In the non-Gaussian boundary-condition case, control distance outperforms Mahalanobis distance in both detection and computational complexity. A synergistic operational application of these methods is presented.

Effect of intelligent transport systems on traffic safety

One of the biggest problems of constantly growing transport sector is traffic safety. Innovative technologies are an effective tool to deal with the following issue. In order to ensure efficient and coordinated development of intelligent transport systems, it is essential to know their individual performance and impact on traffic safety. This article gives the summary of the effect of intelligent transport systems on road traffic safety. The aim of the research is to highlight intelligent transport systems and intelligent transport systems applications which have the greatest positive impact on traffic safety. To achieve this aim, meta-analysis of previous studies and analysis of experts interviews was carried out. The research consider the following thirteen driver support and control systems regarding their potentials to reduce the number of fatalities: average speed control system, stationary speed cameras, “Alcolock” ‒ application of drunk driver detection, “eCall” system, variable speed limit sign, red light violation detection cameras, forbidden maneuver detection system, vehicle identification system, variable message boards, intelligent road studs, incident detection system, weighin- motion system and seat belt control system.

Application of Bar-Shalom and Fortmann’s Input Estimation for Underwater Target Tracking

Background/Objectives: The Extended Kalman Filter (EKF) using range and bearing measurements is analyzed for undersea target tracking. The Input estimation technique, developed by Bar-Shalom and Fortmann for radar applications is implemented for sonar applications. Methods/Statistical Analysis: Input estimation is used to estimate the target acceleration whenever the target makes a maneuver. Findings: The algorithm estimates target kinematics using zero mean chi-square distributed random sequence residual. Upon detection of target maneuver, this algorithm corrects the velocity and position components using acceleration components. Application/Improvements: Finally, the performance of this algorithm is evaluated in Monte-Carlo simulations and results conform the effectiveness of input estimation technique.

Maneuver Detection with Event Representation Using Thrust Fourier Coefficients

A systematic way of detecting unknown maneuvers is developed by representing an unknown acceleration tied to an event with thrust Fourier coefficients. Event representation using thrust Fourier coefficients can rigorously represent an unknown maneuver by generating an equivalent maneuver with the same secular behavior. By appending 14 thrust Fourier coefficients as solve-for states, the modified sequential filter processes observation data both forward and backward in time to detect maneuver onset and termination time, respectively. Along with the represented perturbing acceleration, the detection algorithm provides more accurate postmaneuver orbit solutions. A case study of detecting unknown maneuvers with different types of simulated measurement data verifies the presented approach.

A modified variable rate particle filter for maneuvering target tracking

To address the problem of maneuvering target tracking, where the target trajectory has prolonged smooth regions and abrupt maneuvering regions, a modified variable rate particle filter (MVRPF) is proposed. First, a Cartesian-coordinate based variable rate model is presented. Compared with conventional variable rate models, the proposed model does not need any prior knowledge of target mass or external forces. Consequently, it is more convenient in practical tracking applications. Second, a maneuvering detection strategy is adopted to adaptively adjust the parameters in MVRPF, which helps allocate more state points at high maneuver regions and fewer at smooth regions. Third, in the presence of small measurement errors, the unscented particle filter, which is embedded in MVRPF, can move more particles into regions of high likelihood and hence can improve the tracking performance. Simulation results illustrate the effectiveness of the proposed method.

Position Prediction Based Frequency Control of Beacons in Vehicular Ad Hoc Networks

In VANETs, frequent beacon broadcasting can lead to high bandwidth consumption and channel congestion. In this paper, a position prediction based beacon approach is proposed to reduce beacon frequency and decrease bandwidth consumption. Vehicles track their neighbors using the predicted position instead of using periodic beacon broadcasting. Only when the prediction error is higher than a predefined tolerance will a beacon broadcasting be triggered. For improving the prediction accuracy, we classify the motion of vehicles into two typical patterns: a constant speed pattern and a maneuvering pattern. A maneuver detection module is responsible for recognizing current motion patterns, and a variable dimension filter that can switch dynamically between the two patterns is employed to generate high accurate position prediction. The simulation results show the proposed scheme can reduce significantly the number of beacons than three existing beacon approaches.

Implicit-Runge–Kutta-based methods for fast, precise, and scalable uncertainty propagation

A large class of methods for propagating the state of an object and its associated uncertainty require the propagation of an ensemble of particles or states through nonlinear dynamics. Existing sigma point- or particle-based methods for uncertainty propagation solve the ensemble of initial value problems one-by-one without exploiting the proximity of the initial conditions. In this paper, we demonstrate how implicit Runge–Kutta methods can be modified to solve initial-value-problem ensembles collectively in order to significantly reduce the computational cost of uncertainty propagation. Examples and variations of this approach are given in the context of the perturbed two-body problem of orbital mechanics that arises in space-object tracking, conjunction analysis, maneuver detection, and other Astrodynamics functions. Particular attention is given to the accuracy of the solution to the initial-value-problem ensemble when the particles are propagated collectively.

Maneuver Detection and Reconstruction of Stationkeeping Spacecraft at GEO using the Optimal Control-Based Estimator

Abstract In this paper, we develop an algorithm called the Optimal Control-Based Estimator, which is an optimal state estimator that provides information on mismodeled dynamics including natural dynamics and actuated maneuvers. We then apply this algorithm to a tracking problem where satellites in GEO undergo unknown autonomous stationkeeping maneuvers. Through these simulations we demonstrate this algorithm's ability to detect and reconstruct maneuvers given no a priori information about these maneuvers. The specific simulations in this paper demonstrate these properties for two different maneuvers models: (1) continuous smooth thrusting and (2) an impulsive control law.

Airborne Behaviour Monitoring Using Regular-Expressions Based Pattern Matching

Abstract This paper addresses the problem of target manoeuvre detection and behaviour recognition using string-matching techniques which could bring a great impact to the multiple UAV operations. The overall proposed solution consists of a trajectory analysis tool, which classifies the target position in time into a sequence of driving modes, and a string-matching method. For the latter, two techniques are described and compared: a classical approach based on simple strings and a newly proposed regular-expressions-based method. The advantages obtained from the use of regular expressions are extensively analysed, and results from numerical simulations supporting our conclusions are provided.

Three-dimensional analysis of relationship between relative orientation and motion modes

Abstract Target motion modes have a close relationship with the relative orientation of missile-to-target in three-dimensional highly maneuvering target interception. From the perspective of relationship between the sensor coordinate system and the target body coordinate system, a basic model of sensor is stated and the definition of relative angular velocity between the two coordinate systems is introduced firstly. Then, the three-dimensional analytic expressions of relative angular velocity for different motion modes are derived and simplified by analyzing the influences of target centroid motion, rotation around centroid and relative motion. Finally, the relationships of the relative angular velocity directions and values with motion modes are discussed. Simulation results validate the rationality of the theoretical analysis. It is demonstrated that there are significant differences of the relative orientation in different motion modes which include luxuriant information about motion modes. The conclusions are significant for the research of motion mode identification, maneuver detection, maneuvering target tracking and interception using target signatures.

Bearings-only constant velocity target maneuver detection via expected likelihood

This work addresses the problem of target maneuver detection using passive bearing measurements when the target motion dynamics can be described by a discrete constant velocity (CV) model. The proposed expected likelihood (EL) maneuver detector (ELMD) extends the EL approach from a direction of arrival estimation framework to maneuver detection of targets governed by a CV model. As a result of its array signal processing nature, and in contrast to measurement residual-based maneuver detectors, ELMD does not require statistical assumptions of the bearing measurements, and its maneuver detection performance improves with increasing signal-to-noise ratio and sample support. Maneuver detection performance of ELMD and two conventional maneuver detectors for the passive bearings-only target tracking problem are evaluated in this work via simulations using mean detection delay, detection delay standard deviation, and probability of false alarm criteria. ELMD is shown to outperform the other two evaluated algorithms in all tested practical scenarios.

Driver Behavior Detection Based on Multi-GNSS Precise Point Positioning Technology

Driver behavior is the key to safe mobility. In general, vehicle maneuvers can be determined from acceleration of the vehicle. Physically, the acceleration and brake can be detected with longitudinal acceleration while turning and lane change can be detected with lateral acceleration. Nowadays, navigation system technologies have been much improved both on availability and accuracy with combination of multiple navigation satellite systems. Normally, it’s called Multi-GNSS (multiple global navigation satellite system). With decimeter precision and the update rate scale up to 10-Hz, the GNSS could be an alternative solution for driver behavior detection. In this paper, advance Multi-GNSS with precise point position (PPP) technique was presented with a simple maneuver detection algorithm. The advantage of PPP over conventional navigation is decimeter accuracy without direct connection to any reference base station. The experimental Multi-GNSS receiver was JAVAD Delta G3T that installed on a utility vehicle. This high performance multi-GNSS navigation system was investigated with the driving behavior detection algorithm. The precise point positioning (PPP) technique in combination with multiple satellite navigation system (GPS+GLONASS+GALOLEO+QZSS) were applied in this study. The PPP technique improved the output of detection algorithm in acceleration limit from 260% error in conventional navigation system (GPS) to 20% of incidents with PPP.

Identifying and Estimating Mismodeled Dynamics via Optimal Control Policies and Distance Metrics

This paper presents a new method for estimating the mismodeled dynamics parameters of natural forces through the analysis of optimal control policies and control distance metrics. Using similarities between natural dynamics and optimal control policies, this algorithm is able to accurately estimate mismodeling in atmospheric drag and solar radiation pressure perturbations through a batch process in the presence of boundary state uncertainty. An accompanying maneuver detection algorithm determines when the system’s dynamics are mismodeled and when a single actuated maneuver occurs. By providing a more accurate dynamical model, estimation methods such as this are important to predicting and avoiding collisions: a growing concern as the population of this environment rapidly increases. This algorithm is also specifically geared toward data-sparse applications, as it becomes more accurate the longer the gap between observations. This makes it a good fit for noncooperative orbit tracking applications. Derivations of the estimation method and the maneuver detection method are provided along with numerical simulations, demonstrating the performance of this new algorithm in tracking scenarios that span from low Earth orbit to geosynchronous orbit.

Maneuvering target tracking using fuzzy logic-based recursive least squares filter

In this paper, a fuzzy logic-based recursive least squares filter (FLRLSF) is presented for maneuvering target tracking (MTT) in situations of observations with unknown random characteristics. In the proposed filter, fuzzy logic is applied in the standard recursive least squares filter (RLSF) by the design of a set of fuzzy if-then rules. Given the observation residual and the heading change in the current prediction, these rules are used to determine the magnitude of the fading factor of RLSF. The proposed filter has an advantage in which the restrictive assumptions of statistical models for process noise, measurement noise, and motion models are relaxed. Moreover, it does not need a maneuver detector when tracking a maneuvering target. The performance of FLRLSF is evaluated by using a simulation and real test experiment, and it is found to be better than those of the traditional RLSF, the fuzzy adaptive α-β filter (FAα-βF), and the hybrid Kalman filter in tracking accuracy.

Simple Kalman filter alternative: the multi‐fractional order estimator

This study is an extension of a presentation given at the 2012 Radar Conference in Glasgow, UK entitled: Simple Disambiguation of Orthogonal Projection in Kalman's Filter Derivation . It is shown therein that Kalman misunderstood and consequently misapplied orthogonal projection in his derivation. Shocking analysis shows the Kalman filter (KF) to be truly optimum only in the absence of specific additive statistical measurement noise it is explicitly designed to filter out, rendering it a deterministic curve-fitting algorithm. Also included are two examples demonstrating that the KF is not the universally asserted minimum variance linear unbiased estimator. These consequences are based on the newly introduced multi-fractional order estimator (MFOE). Included herein are an additional background derivation and supplementary material as well as several new examples to better illustrate internal MFOE mechanics. Examples also show major problems in fine tuning the KF with a trade-off between process and measurement noises. Finally, an example shows a very simple, automatic, seamless, smooth, optimal, non-recursive second to third-order estimator transition which successfully overcomes inadequate and complicated KF adaptivity, including thresholding, feedback, manoeuvre detection, deweighting matrices, manoeuvre probability, dual filter weighting, variable dimension models, state augmentation, multiple models, interactive multiple models, interactive acceleration compensation and so on.

Stochastic Differential Equations for Modeling of High Maneuvering Target Tracking

In this paper, we propose a new adaptive single model to track a maneuvering target with abrupt accelerations. We utilize the stochastic differential equation to model acceleration of a maneuvering target with stochastic volatility (SV). We assume the generalized autoregressive conditional heteroscedasticity (GARCH) process as the model for the tracking procedure of the SV. In the proposed scheme, to track a high maneuvering target, we modify the Kalman filtering by introducing a new GARCH model for estimating SV. The proposed tracking algorithm operates in both the non-maneuvering and maneuvering modes, and, unlike the traditional decision-based model, the maneuver detection procedure is eliminated. Furthermore, we stress that the improved performance using the GARCH acceleration model is due to properties inherent in GARCH modeling itself that comply with maneuvering target trajectory. Moreover, the computational complexity of this model is more efficient than that of traditional methods. Finally, the effectiveness and capabilities of our proposed strategy are demonstrated and validated through Monte Carlo simulation studies.

A Switched IMM Estimator Based on the Model Probability Cumulant

Taking into account the disadvantage of the potential loss of accuracy due to the overmodeling at these nonmaneuvering times in the interacting multiple model (IMM) method, an improved IMM algorithm based on the Viterbi technique and the model probability cumulant is proposed. First, select one or two optimal models from the model set to enhance the model utilization rate. Then, judge the variation of the target motion state according to the maneuvering detection mechanism based on the model probability cumulant, and finally switch the different IMM methods. Simulation results show that the proposed method has an enhanced performance in tracking a maneuvering target. DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.308 9