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In this paper, a new in-flight alignment (IFA) method of the integrated inertial navigation system (INS) and global positioning system (GPS) is proposed for the aerial mapping applications. The integrated INS/GPS measurement system is used to provide attitude information and, based on this, the exterior orientation parameters can be derived for the purpose of direct georeference of the airborne imagery. The IFA plays an important role in achieving high accuracy of attitude estimation. However, the statistics of INS noise is usually time-varying and seriously degrades the estimation accuracy in practice. In order to solve the problem, two strategies are taken into account in the paper. First, an adaptive estimation algorithm is developed by adjusting the window size of data processing in IFA, so that the covariances of INS noise can be estimated and updated online to improve the state estimation performance. Second, a strong tracking filter is applied to guarantee the convergence of the IFA algorithm as well as its robustness against parameter perturbations and trajectory maneuvers. Finally, an aerial mapping experiment is implemented, and the results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Fault detection (FD) plays an important role in guaranteeing system safety and reliability for unmanned aerial vehicles (UAVs). This paper focuses on developing an alternative approach to FD for airspeed sensor in UAVs by using data from gyros, accelerometers, global positioning system, and wind vanes. Based on the kinematics model of the UAV, an estimator is proposed to provide analytical redundancy using information from the above-mentioned sensors, which are commonly implemented on UAVs. This filter process is independent of the airspeed measurement and the aircraft dynamics model. Furthermore, we employ the observability rank criterion based on Lie derivatives and prove that the nonlinear system describing the airspeed kinematics is observable. The \chi ^2 test and cumulative sum detector are employed to detect the occurrence of airspeed measurement faults together. Finally, the performance of the proposed methodology has been evaluated through flight experiments of UAVs. [ABSTRACT FROM AUTHOR]

This article is concerned with the estimation problem of Toolface for dynamic point-the-bit rotary steerable drilling tool systems. First, considering the unknown frequency and amplitude of vibration during the drilling process, the Toolface system is modeled as a time-varying stochastic system with unknown and time-varying noise covariance matrices. Under the assumption that the noises and their covariance matrices, respectively, obey the Gaussian distribution and the inverse Wishart distribution, the variational Bayesian-based moving horizon estimation algorithm is proposed. Then, the state and the noise covariance matrices are inferred by iteratively updating their approximate posterior probability distributions. Finally, simulations and experiments are provided to demonstrate the effectiveness and superiority of the developed estimation scheme. [ABSTRACT FROM AUTHOR]

In this article, the estimation problem of Toolface is investigated for the dynamic point-the-bit rotary steerable system (DPRSS), and it is essentially a nonlinear filtering problem under strong interference. First, the DPRSS is modeled by a continuous-time nonlinear system, which involves polynomial nonlinearities and strong interference whose amplitude is much larger than that of system states. By employing the Carleman approximation approach, a linear parameter varying system is derived from the polynomial nonlinear system, and a novel polynomial filter is constructed. Subsequently, several sufficient conditions in terms of parameter-dependent linear matrix inequalities (PDLMIs) are obtained to ensure the input-to-state stability of the filtering error system. Furthermore, the filter parameter can be calculated by solving PDLMIs through the sum of squares decomposition method. Finally, an experimental study on the DPRSS prototype is provided to show the effectiveness and applicability of the developed filtering scheme. [ABSTRACT FROM AUTHOR]

This paper is concerned with the moving horizon (MH) estimation issue for a type of networked nonlinear systems (NNSs) with the so-called random access (RA) protocol scheduling effects. To handle the signal transmissions between sensor nodes and the MH estimator, a constrained communication channel is employed whose channel constraints implies that at each time instant, only one sensor node is permitted to access the communication channel and then send its measurement data. The RA protocol, whose scheduling behavior is characterized by a discrete-time Markov chain (DTMC), is utilized to orchestrate the access sequence of sensor nodes. By extending the robust MH estimation method, a novel nonlinear MH estimation scheme and the corresponding approximate MH estimation scheme are developed to cope with the state estimation task. Subsequently, some sufficient conditions are established to guarantee that the estimation error is exponentially ultimately bounded in mean square. Based on that the main results are further specialized to linear systems with the RA protocol scheduling. Finally, two numerical examples and the corresponding figures are provided to verify the effectiveness/correctness of the developed MH estimation scheme and approximate MH estimation scheme. [ABSTRACT FROM AUTHOR]

This paper presents a probabilistic approach to fault detection (FD) for nonlinear systems subject to l2[0,N]-norm bounded unknown input. The major contribution is to design an evaluation function for robust FD in a unified framework of l2-norm estimation of unknown input and determine a threshold based on probabilistic analysis of FD performance. The problem of robust FD is first formulated as to find a minimal estimation of the l2[0,N]-norm of unknown input including unknown initial state. It is shown that such an estimation leads to a unified design of evaluation function for FD using extended Kalman filter or Hi/H\infty optimization-based FD filter. Based on this, a probabilistic approach to threshold determination and FD performance verification is proposed. In particular, if the l2[0,N]-norm boundedness of unknown input is not available, a choice of threshold can be made in the framework of probabilistic analysis for achieving a tradeoff between false alarm rate and FD rate. Finally, a nonlinear UAV control system model is given to demonstrate the effectiveness of the proposed method and show the feasibility of practical application. [ABSTRACT FROM AUTHOR]

This paper focuses on the fault-tolerant control problem for an Internet-based three-tank system in the presence of possible sensor bias faults. The Internet-based three-tank system is an experimental setup that can be regarded as a typical networked system for evaluating networked fault-diagnosis and fault-tolerant control methods. Packet dropout phenomenon in the sensor-to-controller link is considered in this paper, and the fault type we deal with is chosen as the sensor bias fault. Fault-diagnosis unit is designed toward an auxiliary system. Sensor bias faults can be detected by comparing the residual signal generated by the fault detection filter and a prescribed threshold. After that, the fault can be isolated by using the residual analysis approach. Once the fault is isolated, it can be estimated iteratively in the least-squares sense. A fault accommodation method is proposed, and a fault-tolerant control strategy is achieved based on the fault information provided by the fault-diagnosis unit. The approach brought forward in this paper is demonstrated via an experimental study on the practical Internet-based three-tank system. Results show the effectiveness and the applicability of the proposed techniques. [ABSTRACT FROM PUBLISHER]

The asynchronous estimation fusion problem is investigated for an arbitrary number of sensors with arbitrary sampling rates. By constructing an augmented measurement equation at the fusion time instant, a centralized asynchronous fusion algorithm is developed based on the Kalman filter first without ignoring the correlation between the process noise and the augmented measurement noise. It is optimal in the minimum mean-squared error (MMSE) sense. A distributed asynchronous fusion algorithm is then proposed by reconstructing the optimal centralized fusion result with asynchronous local estimates and their error covariance matrices. It is equivalent to the centralized fusion algorithm under the full-rate communication assumption and outperforms the latter when at least one sensor communicates with the fusion center at a lower rate than its sampling rate. Compared with the existing distributed fusion algorithms for asynchronous sensors, the proposed distributed fusion algorithm avoids the complicated calculation of cross-covariance matrices between each pair of asynchronous local estimates. The communication burden can also be reduced since neither sensor measurement matrices nor local filtering gains need to be transmitted to the fusion center. Moreover all available local estimates are utilized as well as the fused one-step prediction. Some practical considerations of the proposed distributed fusion algorithm are also discussed. Performance of the proposed centralized and distributed fusion algorithms are illustrated through numerical simulations. [ABSTRACT FROM PUBLISHER]