The safety monitoring and tracking of aircraft is becoming more and more important. Under aerodynamic loading, the aircraft wing will produce large bending and torsional deformation, which seriously ...affects the safety of aircraft. The variation of load on the aircraft wing directly affects the ground observation performance of the aircraft baseline. To compensate for baseline deformations caused by wing deformations, it is necessary to accurately obtain the deformation of the wing shape. The traditional aircraft wing shape measurement methods cannot meet the requirements of small size, light weight, low cost, anti-electromagnetic interference, and adapting to complex environment at the same time, the fiber optic sensing technology for aircraft wing shape measurement has been gradually proved to be a real time and online dynamic measurement method with many excellent characteristics. The principle technical characteristics and bonding technology of fiber Bragg grating sensors (FBGs) are reviewed in this paper. The advantages and disadvantages of other measurement methods are compared and analyzed and the application status of FBG sensing technology for aircraft wing shape measurement is emphatically analyzed. Finally, comprehensive suggestions for improving the accuracy of aircraft wing shape measurement based on FBG sensing technology is put forward.
Tightly coupled GNSS/INS has been widely approved as a promising substitute for standalone GNSS in urban areas navigation. However, due to the frequent GNSS signal outages, the filter used in ...GNSS/INS should be insensitive to the less informative observations. In this paper, a novel sigma-points update method is proposed to enhance the robustness of cubature Kalman filter (CKF) under the circumstance of unavailable observations. First, the problems of existing sampling-based filters are analyzed. Then, by transforming the posterior sigma-points error matrix from prediction phase of filtering to the posterior domain of update, the updated sigma-points are expected to capture the covariance more precisely than traditional sigma-points. Finally, an improved CKF (ICKF) is developed by embedding these points into the Bayesian estimation framework, and the upper bounds of error covariance matrices are analyzed theoretically. Signal outages with different durations are simulated and results demonstrate that ICKF outperforms state-of-the-art methods.
Accurate monitoring of air quality is of great importance to our daily life. By predicting the air quality in advance, we can make timely warnings and defenses to minimize the threat to life. With a ...large number of environmental data, the air quality prediction based on deep learning technology is studied in depth. Based on long short-term memory (LSTM), a comprehensive prediction model with multi-output and multi-index of supervised learning (MMSL) was proposed. The particle concentration data (mainly PM2.5, means particles with aerodynamic diameter ≤ 2.5 mm) of the present monitoring station, as well as that of the nearest neighbor stations, the meteorological data, and the gaseous pollutant data in the air (mainly CO, NO2, O3, SO2) of the same period were integrated. All data were converted into the supervised learning format and normalized. The LSTM was used for training to obtain the predicted values of air quality pollution indicators (PM2.5, CO, NO2, O3, SO2). In the present study, the representative stations of the 35 monitoring stations in Beijing were selected, and input the air quality sequences of the representative stations with different data characteristics into the model to obtain the predicted concentration values of the air quality indicators of the representative stations, then calculated the average value as the overall air quality prediction result of Beijing. The air quality time series datasets collected from 35 air quality monitoring stations in Beijing from January 1, 2016, to December 31, 2017, were used to validate the performance of the model compared with other baseline models and the two most advanced models. Experimental results show that, overall, the performance of the present model is superior to other baseline models.
In view of the fact the accuracy of the third-degree Cubature Kalman Filter (CKF) used for initial alignment under large misalignment angle conditions is insufficient, an improved fifth-degree CKF ...algorithm is proposed in this paper. In order to make full use of the innovation on filtering, the innovation covariance matrix is calculated recursively by an innovative sequence with an exponent fading factor. Then a new adaptive error covariance matrix scaling algorithm is proposed. The Singular Value Decomposition (SVD) method is used for improving the numerical stability of the fifth-degree CKF in this paper. In order to avoid the overshoot caused by excessive scaling of error covariance matrix during the convergence stage, the scaling scheme is terminated when the gradient of azimuth reaches the maximum. The experimental results show that the improved algorithm has better alignment accuracy with large misalignment angles than the traditional algorithm.
In harsh sea conditions, a shipborne stable platform is required for smooth offshore operations due to the serious sway of the ship. Aiming at this requirement, a new active heave compensation system ...consisting of an Inertial Measurement Unit (IMU) and a Stewart platform is designed to compensate for the roll, pitch and heave motions of the ship. Firstly, the system is modeled in detail. Then, the task space controller is designed based on forward kinematics for the Stewart to eliminate the coupling errors of six links. To obtain accurate and real-time solutions of forward kinematics, a novel method based on Beetle Antennae Search (BAS) algorithm is proposed. In order to compensate for external disturbances, an improved adaptive control strategy based on Radial Basis Function Neural Network (RBFNN) with fading factors is designed. Finally, the effectiveness and rationality of the proposed method are validated by simulation and physical experiment. The maximum compensation errors of the proposed method are reduced by 70% in row/pitch and 40% in heave as compared to the traditional method.
In challenging circumstances, the estimation performance of integrated navigation parameters for tightly coupled GNSS/SINS is impacted by outlier measurements. An effective solution that employs a ...novel iterative sigma-point structure with a modified robustness optimization approach for enhancing the error compensation effectiveness and robustness of filters utilized in GNSS challenge conditions is proposed in this paper. The proposed method modifies the CKF scheme by incorporating nonlinear regression and numerous iteration processes for ameliorating error compensation. Subsequently, a loss function and penalty mechanism are implemented to enhance the filter's robustness to outlier measurements. Furthermore, to fully incorporate valid information of the innovation and speed up the operation of the proposed method, the outlier measurement detection criteria are established to bypass the penalty mechanism against measurement weights in the absence of outliers in GNSS measurements. Field experiments demonstrate that the proposed method outperforms traditional methods in mitigating navigation errors, particularly when multipath errors and non-line-of-sight (NLOS) reception are increased.
The conventional SINS/CNS integrated navigation system equipped on ballistic missiles is typically equipped with attitude measurement, which can only estimate the gyro drift and has no effect on ...accelerometer bias. To address the issue, an improved multi-source information fusion method containing a new nonlinear framework called SINS/RKCNS with the indirect horizon reference and kinematic constraint is proposed, and a MAP-based modified iterated CKF is involved to increase positioning accuracy and system robustness. Furthermore, to reduce the influence of correlated noise, state augmentation is employed in the iterative process. Eventually, experiments are conducted, and the results confirm the effectiveness of the proposed approach.
The traditional receiver employs scalar tracking loops, resulting in degraded navigation performance in weak signal and high dynamic scenarios. An innovative design of a vector tracking receiver ...based on nonlinear Kalman filter (KF) tracking loops is proposed in this paper, which combines the strengths of both vector tracking and KF-based tracking loops. First, a comprehensive description of the vector tracking receiver model is presented, and unscented Kalman filter (UKF) is applied to nonlinear tracking loop. Second, to enhance the stability and robustness of the KF tracking loop, we introduce square root filtering and an adaptive mechanism. The tracking loop based on square root UKF (SRUKF) can dynamically adjust its filtering parameters based on signal noise and feedback Doppler error. Finally, the proposed method is implemented on a software-defined receiver (SDR), and the field vehicle experiment demonstrates the superiority of this method over other tracking methods in complex dynamic environments.
The superiority of a global navigation satellite system (GNSS)/inertial navigation system (INS) ultra-tight integration navigation system has been widely verified. For those systems with centralized ...structure based on coherent-accumulation measurements (I/Q), the conversion from I/Q signals to navigation information is implemented by an observation equation. As a result, the model is highly complex and nonlinear, exerting essential influence on system performance. Based on the analysis of previous studies, a novel model and its linearization method are proposed, aiming at the integrity, stability and implicit nonlinear factors. Unlike the one-order precision in the common Jacobian matrix, two-order components are partly reserved in this model, which makes it possible for higher positioning accuracy and better convergence. For the positioning errors caused by ignoring code-loop deviation, a method to approximate code-phase is proposed without introducing new measurements. Consequently, the effect of code error can be significantly reduced, especially when the tracking loops are unstable. In the end, using real-sampled satellite signals, semi-physical experiments are carried out and the effectiveness and superiority of new methods are proved.
The resonance characteristics of the micro-coil resonator(MCR) with different turns and coupling conditions are presented, which show that the effective length of the MCR with small coupling ...coefficient at primary resonant is independent of the turns, and the resonance dips always appear in pairs corresponding to multiple critical-coupling because of the periodicity of coupling strength. However, complicated resonance is observed in an MCR with larger coupling coefficient and turns. The superluminal or subluminal propagation regime can be obtained respectively by adjusting the coupling condition, and the effect of loss on them in different coupling conditions is investigated. There is such a loss threshold in the critical-coupling and over-coupling conditions, near which the group refractive index abrupt changes between positive and negative extremum, and the threshold is increased by 10 5 orders of magnitude under over-coupling condition compared with critical-coupling. In view of the transmission near a resonant evolve not only with the detuning of transmission phase, but also drastically with the adjustment of coupling condition which will be modulated by the cladding refractive index and coupling length. Hence a potential sensitivity enhancement for biochemical and inertial sensing based on MCR is also investigated briefly.
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