An LSTM-based aerodynamic parameter recognition method for skip gliding targets

This paper puts forward an aerodynamic parameter estimation method which combines the Wavelet Neural Network(WNN) with Modified Particle Swarm Optimization(MPSO) technique. This method directly and accurately constructs the relationship among flight parameters and aircraft aerodynamic parameters. Preliminary aerodynamic parameters and derivatives are derived from wind tunnel data. With strong nonlinear mapping ability, WNN is used to construct the relationship among Mach number, angle of attack, and rudder with longitudinal aerodynamic parameters. Then, the MPSO is used to estimate aerodynamic parameters based on the test-flight data. And WNN is retrained to amend the relationship among Mach number, angle of attack, and rudder with longitudinal aerodynamic parameters. Simulation verification indicates that MPSO has better estimation accuracy than Maximum Likelihood(ML) method. Comparison results of simulation experiments and flight-test data of a tactical missile show that simulated data based on estimated parameters matches with the flight-test data, which prove the effectiveness and validity of the trained WNN.

The path travel time between trips are proved to be strongly linear correlated, and the regression parameters are gradually converging to a steady value with buses travel on the route. A regression-based method for bus trajectory estimation at downstream stops was proposed, and its performance on estimation accuracy was compared with the historical-based model. The research results show that the proposed regression-based model shows a conditional higher accuracy than the historical-based model on trajectory estimation, and a two-stage model for trajectory estimation was suggested.

Multi-object tracking (MOT) in cluttered and dynamic environments remains challenging, especially for maneuvering objects. While trajectory-based random finite set (RFS) filters provide principled solutions for trajectory estimation, they typically rely on a single motion model, limiting their adaptability. To address this gap, we propose the Multiple-Model Trajectory Poisson Multi-Bernoulli Mixture (MM-TPMBM) filter, which integrates jump Markov system (JMS) dynamics within the trajectory RFS framework. The filter enables closed-form Bayesian recursion for joint trajectory estimation and motion model switching. We derive its prediction and update equations, implement it using Gaussian mixtures for computational efficiency, and evaluate performance against benchmark filters (MM-PMBM, TPMBM, δ-GLMB) via Monte Carlo simulations. Results demonstrate that the MM-TPMBM filter achieves superior accuracy in trajectory estimation, localization, and cardinality, reducing the generalized optimal sub-pattern assignment (GOSPA) error by up to 24% and cardinality error by up to 52% compared to state-of-the-art methods, validating its robustness in complex tracking scenarios.

Worst-case, typical, and best-case scenarios for survival, based on simple multiples of an individual's expected survival time (EST), estimated by their oncologist, are a useful way of formulating and explaining prognosis. We aimed to determine the accuracy and prognostic significance of oncologists' estimates of EST, and the accuracy of the resulting scenarios for survival time, in advanced gastric cancer. Sixty-six oncologists estimated the EST at baseline for each of the 152 participants they enrolled in the INTEGRATE trial. We hypothesized that oncologists' estimates of EST would be unbiased (∼50% would be longer or shorter than the observed survival time [OST]); imprecise (<33% within 0.67-1.33 times the OST); independently predictive of overall survival (OS); and accurate at deriving scenarios for survival time with approximately 10% of patients dying within a quarter of their EST (worst-case scenario), 50% living within half to double their EST (typical scenario), and 10% living three or more times their EST (best-case scenario). Oncologists' estimates of EST were unbiased (45% were shorter than the OST, 55% were longer); imprecise (29% were within 0.67-1.33 times observed); moderately discriminative (Harrell's C-statistic 0.62, p = .001); and an independently significant predictor of OS (hazard ratio, 0.89; 95% confidence interval, 0.83-0.95; p = .001) in a Cox model including performance status, number of metastatic sites, neutrophil-to-lymphocyte ratio ≥3, treatment group, age, and health-related quality of life (EORTC-QLQC30 physical function score). Scenarios for survival time derived from oncologists' estimates were remarkably accurate: 9% of patients died within a quarter of their EST, 57% lived within half to double their EST, and 12% lived three times their EST or longer. Oncologists' estimates of EST were unbiased, imprecise, moderately discriminative, and independently significant predictors of OS. Simple multiples of the EST accurately estimated worst-case, typical, and best-case scenarios for survival time in advanced gastric cancer. Results of this study demonstrate that oncologists' estimates of expected survival time for their patients with advanced gastric cancer were unbiased, imprecise, moderately discriminative, and independently significant predictors of overall survival. Simple multiples of the expected survival time accurately estimated worst-case, typical, and best-case scenarios for survival time in advanced gastric cancer.

Both kinematics and measurement model help process the trajectory data and estimate aerodynamic parameters of vehicle. In this paper, three dynamics are proposed, which are aerodynamics parameter's model, acceleration's model and α - β - γ model. The measurement model is TDRS (Tracking and Data Relay Satellite), which produces a complicated trajectory in reentering purpose. A fuzzy IMM algorithm is proposed in this paper, which through a fuzzy inference to get the matching degree of every UKF filtering model, instead of calculating model transition probabilities in IMM. With the matched degrees, the estimation from each filtering is weighted to get maneuvering target overall estimation and its covariance. Simulation results show that the FIMM algorithm performs well in tracking re-entering vehicle and estimating the aerodynamics parameters as well.

Aerodynamic parameter identification method based on physics-informed radial basis function-deep neural networks.

The importance of accurate soil moisture data for the development of modern closed-loop irrigation systems cannot be overstated. Due to the diversity of soil, it is difficult to obtain an accurate model for agro-hydrological system. In this study, soil moisture estimation in 1D agro-hydrological systems with model mismatch is the focus. To address the problem of model mismatch, a nonlinear state-space model derived from the Richards equation is utilized, along with additive unknown inputs. The determination of the number of sensors required is achieved through sensitivity analysis and the orthogonalization projection method. To estimate states and unknown inputs in real-time, a recursive expectation maximization (EM) algorithm derived from the conventional EM algorithm is employed. During the E-step, the extended Kalman filter (EKF) is used to compute states and covariance in the recursive Q-function, while in the M-step, unknown inputs are updated by locally maximizing the recursive Q-function. The estimation performance is evaluated using comprehensive simulations. Through this method, accurate soil moisture estimation can be obtained, even in the presence of model mismatch.

Detection and tracking of marine weak targets can be effectively solved by track-before-detect (TBD) algorithms based on particle filtering. However, these algorithms are susceptible to influence from neighboring targets, leading to potential issues like misassociation and tracking failure. In this paper, an auxiliary particle flow track-before-detect algorithm designed for marine neighboring weak targets is proposed which can effectively track marine neighboring weak targets under long-tail sea clutter. Firstly, marine neighboring targets are modeled by the generalized Pareto model, and an offline lookup table is utilized to obtain a non-closed solution, decreasing calculation cost. Subsequently, prediction is employed to classify targets, and measurement information is iteratively used to determine the sequence of target updates, effectively suppressing influence from neighboring targets. Finally, particles with higher measurement energy are chosen, and the Geodesic particle flow is employed to guide the particles toward better importance distribution, which enhances the accuracy of target trajectory estimation. Simulation experiments indicate that compared with track-before-detect algorithms based on parallel partition (PP) and auxiliary parallel partition (APP), the proposed algorithm shows an increase of 43.1% and 25.8% in detection probability at 6 dB, and a reduction of 76.6% and 66.2% in Root Mean Square Error (RMSE). Detection ability and trajectory estimation performance are effectively improved in the simulation, and excellent tracking performance is also confirmed in real clutter experiments.

This paper focuses on the aircraft inflight icing detection and protection problem. A fault-tolerant flight control with the time-varying aerodynamic parameter online estimation is prweoposed to stabilize the aircraft attitude and maintain the flight height during the wing ice accretion process. The eigenstructure assignment and the proportional-integral- derivative control methods are utilized to design the flight control law. An aerodynamic parameter estimation method is adopted for the online ice detection and the control law reconfiguration. Then the accuracy of the parameter estimator and the performance of the closed control loop are evaluated by a steady-level cruise scenario of an ice research aircraft. The results indicate the feasibility of realizing the ice detection and control protection simultaneously in the fault-tolerant flight control loop.

Different techniques have been advocated for estimating single molecule locations from microscopy images. The question arises as to which technique produces the most accurate results. Various factors, e.g. the stochastic nature of the photon emission/detection process, extraneous additive noise, pixelation, etc., result in the estimated single molecule location deviating from its true location. Here, we review the results presented by [Abraham et. al, Optics Express, 2009, 23352-23373], where the performance of the maximum likelihood and nonlinear least squares estimators for estimating single molecule locations are compared. Our results show that on average both estimators recover the true single molecule location in all scenarios. Comparing the standard deviations of the estimates, we find that in the absence of noise and modeling inaccuracies, the maximum likelihood estimator is more accurate than the non-linear least squares estimator, and attains the best achievable accuracy for the sets of experimental and imaging conditions tested. In the presence of noise and modeling inaccuracies, the maximum likelihood estimator produces results with consistent accuracy across various model mismatches and misspecifications. At high noise levels, neither estimator has an accuracy advantage over the other. We also present new results regarding the performance of the maximum likelihood estimator with respect to the objective function used to fit data containing both additive Gaussian and Poisson noise. Comparisons were also carried out between two localization accuracy measures derived previously. User-friendly software packages were developed for single molecule location estimation (EstimationTool) and localization accuracy calculations (FandPLimitTool).

The results of verification of the aerodynamic method for calculating turbulent heat fluxes between the glacier and the atmosphere using the eddy covariance method are presented. The experiment was conducted on Aldegondabreen, Svalbard, in August 2022. Comparison of the methods showed a high agreement between the fluxes estimated by the two methods (R2 = 0.7), but the aerodynamic method underestimated the flux (with a mean error of 14%). The eddy covariance method allowed us to estimate the aerodynamic roughness length parameter for Aldegondabreen z0m = 0.8 mm, and the thermal roughness length z0h = 0.08 mm. The uncertainty analysis of the aerodynamic method revealed systematic errors related to the wind flow direction. The coefficient of proportionality was 0.67–0.70 for the wind direction along the glacier slope and 0.98 for the wind direction perpendicular to the glacier slope. Mean values of heat fluxes during the ablation season for the period 1991–2020 were calculated for Aldegondabreen: the shortwave balance is 72.6–113.8 W m–2; the longwave balance is 14.5 W m–2; the turbulent fluxes of sensible and latent heat determined by the aerodynamic method are 20.5 and about 1 W m–2, respectively. Thus, even allowing for a systematic method error of 14% (~3 W m–2), the total turbulent heat exchange of 24.5 W m–2 was lower than the mean estimates for the shortwave balance.

Conventional pedestrian dead-reckoning systems that use shoe-mounted inertial sensors are not suitable for use in daily life. For example, such systems are not convenient because they compel individuals to wear such pairs of shoes; moreover, these systems are not suitable for use in barefoot conditions. A novel gait logging system is proposed to estimate foot steps and walking trajectories using a combination of high frequency vibration and inertial measurements from the upper part of the ankle. First, a three-step method is proposed to estimate Heel Strike and Toe Off. It is verified that the present method is suitable for estimating Midstance (MSt) time. Then, a trajectory and position estimation procedure is proposed that considers the MSt ankle velocity. A model for MSt ankle velocity is derived. The performance of the trajectory estimation is evaluated with various types of footwear. It is verified that the proposed method is suitable for estimating step lengths of each stride and total distance regardless of the footwear. Furthermore, the trajectories estimated using the proposed method approximated the actual walking path with high accuracy.

During the initial stages of missile design, finding an efficient method for analyzing aerodynamic characteristics is crucial. This paper proposes a novel aerodynamic modeling method based on a limited computational fluid dynamics (CFD) dataset, combining transfer learning (TL) with Dendritic Net (DD). Initially, we employ CFD-calculated aerodynamic data to establish a pretrained model using DD. Subsequently, the model is adapted to the target domain by TL, predicting aerodynamic parameters under specific conditions. The overall aerodynamic parameters are utilized to generate a relational spectrum through DD’s white-box features, from which primary features are extracted to establish an aerodynamic polynomial model. Finally, the model’s practicality is validated by ballistic flight simulations. The innovation lies in leveraging DD to generate a relational spectrum of aerodynamic parameters, leading to a high-precision polynomial model. Research shows DD outperforms traditional cell-based networks in predicting aerodynamic parameters, and TL reduces the CFD computation workload in the target domain by 3/4 while maintaining prediction accuracy. The polynomial model exhibits superior accuracy compared to the empirical fitting formulas. The method reduces the computational workload for aerodynamic data collection, and through system identification, a high-precision polynomial model is obtained, which provides a reliable basis for missile controller design.

&lt;div class="htmlview paragraph"&gt;In this paper, the problem of estimation and prediction of the projectile trajectory, MET data, and the impact point is formulated within the extended Kalman filtering framework. Factorized implementations of extended Kalman filtering, smoothing, and prediction algorithms are presented. Different wind models are discussed. The algorithms are demonstrated through simulation.&lt;/div&gt;

In this paper, single-target tracking using radar measurements is addressed. Recently, algorithms based on Bernoulli random finite sets have proved efficient in a cluttered environment. However, in Bayesian approaches, the choice of the motion model impacts the trajectory estimation accuracy. To select an appropriate set of motion models, a joint tracking and classification (JTC) algorithm can be used. The principle is to consider different target classes depending on their maneuvrability, each of them being associated to a set of motion models. In this context, additional information such as a target length extent measurement can improve both classification and trajectory estimation. Therefore, we propose a multiple-model Bernoulli filter to perform JTC. To jointly estimate the trajectory and the target length which is constant, a Rao-Blackwellized approach is considered. Another contribution is that a bank of probabilistic data association filters is run instead of Kalman filters to account for false detections.