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An innovative electro-optical navigation system called Optical Navigation System (OptoNav) for Autonomous Landing consisting of a machine vision camera, and a set of co-registered structured light beacons is proposed. OptoNav is used to directly sense the altitude and two orientation states of an air vehicle with respect to the flight path direction. Motivated by autonomous landing applications involving fixed-wing aircraft, the machine vision camera is used to process the images of the runway so as to establish a moving coordinate system affixed to the runway, making use of the perspective geometry of the camera system. Three or more structured light emitters are used to make direct measurements of the range using the geometry of the perspective projection model by making use of the co-registration of the camera with respect to the structured light emitting sources. A unique formulation of the minimal relative motion states is used to directly estimate the three states of altitude, pitch and roll angle of the airframe with respect to the moving coordinates affixed to the runway. Laboratory experiments are used to validate the utility of the OptoNav sensor system. [ABSTRACT FROM AUTHOR]

For INS/CNS integrated navigation system, the performance of data fusion algorithms based on the Kalman filter is seriously degraded when the subsystems have discrepant sampling-frequency. Therefore, a fusion algorithm based on locally weighted linear regression (LWLR), long short-term memory (LSTM), and cubature Kalman filter (CKF) is proposed. First, we construct a deeper coupled INS/CNS system model based on the low-frequency error of the star sensor. Then, during the CNS data sampling interval, LWLR is employed to fit the CNS data, and CKF is used to fuse the fitted CNS data with INS data. And the estimated error calculated using CKF is optimized by LSTM. Finally, the experimental results show that the proposed algorithm suppresses the divergence problem caused by different sampling-frequency and improves the attitude estimation accuracy of the navigation system. [ABSTRACT FROM AUTHOR]

The noise and drift requirements for a navigation-grade gyroscope are widely known, yet there is no simple analytic model of how the noise and drift of a gyroscope influence the fix displacement error (FDE) of an inertial navigation system (INS). This work derives simple analytical expressions for the cross-track and along-track errors of an aircraft whose INS consists solely of a three-axis gyroscope system with perfect knowledge of the vertical direction. The error signal of each gyroscope is Gaussian white noise and drift modeled as a first-order Markov random walk. These expressions provide a straightforward mean of calculating the FDE of an aircraft as a function of the flight duration, velocity, noise amplitude, drift amplitude, and drift’s time constant. These expressions are validated with Monte-Carlo simulations of long flights. This model quantifies the noise-versus-drift trade-off for a gyroscope in an inertial navigation system. It can save time when estimating the noise and drift that a gyroscope must exhibit to satisfy a given position-error requirement, or vice versa. They are used in particular to confirm the values, often cited without demonstration, of the noise and drift required to meet the maximum position error of an aircraft imposed by the Federal Aviation Administration’s required navigation performance 10 specification. Finally, it demonstrates that using the minimum in the measured Allan deviation of a gyroscope as a metric of the drift is incorrect, because it fails to capture the drift’s time constant. The proper metric is the maximum in the Allan deviation. [ABSTRACT FROM AUTHOR]

The rotational inertial navigation system (RINS) could greatly improve navigation accuracy by rotating the inertial measurement unit with gimbals. But it will also excite and amplify the corresponding error parameters, so it is more necessary to calibrate the typical error parameters accurately. Fortunately, the self-calibration strategy could be achieved conveniently in RINS. In this paper, an innovative self-calibration strategy for error parameters of dual-axis is proposed. The calibration strategy is designed that the inner and outer gimbals rotate at different angular speeds at the same time, and the whole calibration process lasts for 12 min. The relationship between the error parameters to be calibrated and the navigation error is derived, and the principle of error separation is analyzed, which proves the rationality of the strategy. The calibration strategy is verified by simulation and experiment, and the 21 error parameters of the system can be well estimated. The 3h static navigation experiment shows that the position error is less than 0.15nmile/h(CEP) after compensating the error parameters calibrated by the self-calibration strategy, and achieved the expected accuracy of the dual-axis RINS. Finally, it is proved that the calibration strategy has the obvious advantages of short calibration time and simple process, which is of great significance to improve the navigation accuracy of dual-axis RINS. [ABSTRACT FROM AUTHOR]

The use of the automatic dependent surveillance-broadcast (ADS-B) as a source of alternative positioning, navigation, and timing is attractive as the system has been or soon will be fielded. In this study, a wide-area multilateration (WAM) testbed was developed that implements a time difference of arrival (TDOA) positioning algorithm using the 1090 MHz ADS-B Mode S ES signal in the Taiwan area. The positioning performance of the TDOA-based WAM is influenced by the geometric distribution of ground stations. Common requirements for terrestrial navigation systems target nominal horizontal dilution of precision (HDOP) of 2.828 or lower to support aviation navigation and surveillance –. Meeting such a requirement would necessitate a significant and geometrically diverse number of stations which limits the WAM implementation in countries with limited real estate. It also makes implementation expensive in general. Provided the measurements have adequate accuracy, it is still possible to meet position accuracy targets without such a geometry requirement. However, reducing geometry requirements presents issues with regards to developing a reliable and accurate position solution. This paper proposes a reasonable means of providing an accurate solution given limited station availability and demonstrates the concept in a testbed system implemented in Taiwan. In order to provide more reliable solution, a positioning algorithm is proposed that combines iterative and non-iterative methods to provide reliable convergence to the near optimal solution given the available measurements and the addition of barometric altitude from ADS-B to improve geometry. Importantly, the paper demonstrates positioning performance that meets the requirements for aviation navigation and surveillance services collected with actual flight data and the developed WAM. [ABSTRACT FROM AUTHOR]

A novel particle filter (PF) based terrain referenced navigation (TRN) is presented for computational efficiency in this paper. TRN is a navigation method using terrain elevation information, mainly using a Bayesian filter such as a PF.PF based TRN has a disadvantage in that it requires a large number of particles to achieve a good estimation performance, which increases the computationalburden.To solve this disadvantage, two stage particle filter (TSPF) is proposed. TSPF consists of two stages and estimates the nonlinear state variables and the linear state variables separately. In the first stage, the nonlinear state variables are estimated by PF. Then, the linear state variables are estimated by a Kalman filter in the second stage considering a correlation between nonlinear and linear state variables. Since TSPF estimates only nonlinear state variables using PF and estimates linear state variables with only one Kalman filter, the computational efficiency can be greatly improved although the estimation performance is degraded a bit.In order to verify the proposed method, simulationsare performed to compare TSPF with PF andRao Blackwellized PF (RBPF)which has the same purpose as TSPF. The simulation results show similar estimation performance with fewer particles than with all state variables estimated using PF, resulting in less computational burden. [ABSTRACT FROM AUTHOR]

This paper proposes a new aviation unusual-weather detection system constructed to augment existing aviation unusual-weather alert systems. Due to a lack of ground meteorological stations with sufficient ground altitude near airports, existing aviation unusual-weather alert systems can only detect unusual-weather conditions near the ground surface. Thus, this paper uses the automatic dependent surveillance—broadcast (ADS-B) signal transmitted by commercial aircraft to acquire vertical weather information for low-level weather conditions. Specifically, we propose an aviation unusual-weather detection model to establish the system using both the aircraft irregular-movement detection algorithm and the machine learning method. The performance of the proposed unusual-weather detection model is validated with actual ADS-B signals from several flights collected at the airport. The experiment results show the accuracy rates of aviation normal/unusual-weather classification above 96% and false positive rates below 1% for decent flight phases. [ABSTRACT FROM AUTHOR]

The strap-down inertial navigation system/celestial navigation system (SINS/CNS)-integrated system can be divided into the loosely coupled integrated mode and the tightly coupled integrated mode. Because the loosely coupled integrated mode requires the star sensor to observe at least three stars at the same time to obtain the attitude matrix, the tightly coupled integrated mode, which can still work with only one star, is more practical in reality, especially for the aircraft that need to work in the daytime. The star sensor is a vital part of the CNS, while its installation error impacts the navigation accuracy seriously in both the modes. Therefore, the star sensor installation error of the SINS/CNS-integrated navigation system must be corrected. The installation error calibration has been settled for the loosely coupled SINS/CNS-integrated navigation system when the attitude matrix is available. However, it is still a problem for the tightly coupled mode. This paper proposes a fast calibration method of the star sensor installation error for the tightly coupled SINS/CNS-integrated navigation system based on maneuvers and observability analysis. Simulations indicate that the proposed method is feasible and effective when only one star is visible at a time. The mean position error of the tightly coupled SINS/CNS-integrated navigation decreases by about 63.23% after compensation for the star sensor installation error. [ABSTRACT FROM AUTHOR]

Navigation accuracy of the inertial navigation system (INS) could be greatly improved by rotating the inertial measurement unit around gimbals. However, the attitude output accuracy of rotational INS (RINS) would be affected by the non-orthogonal angles of gimbals, which should be accurately calibrated and compensated. In this paper, a novel self-calibration method for non-orthogonal angles of gimbals is proposed in tri-axis RINS. The three non-orthogonal angles could be calibrated using attitude errors and velocity errors as measurements without external equipment. The self-calibration scheme in this method could reduce the coupling of non-orthogonal angles with other errors, and the observability is clear to demonstrate during calibration. The proposed method is verified by both simulations and experiments. The experimental results show that the calibration accuracy of non-orthogonal angles could be less than 2”, and after compensation, the attitude output accuracy is improved from 200” to less than 10”. Therefore, the proposed calibration method could greatly improve the attitude output accuracy and make RINS more effective in some task systems where high attitude output accuracy is urgently required. [ABSTRACT FROM PUBLISHER]

Many social insects, such as desert ant and honeybees, are able to utilize the natural skylight polarization information for navigation, which has advantages in immunity to the interference of external environment and thus shows higher stability and accuracy. Inspired by it, we attempt to explore a new bionic method for attitude determination by use of polarization pattern. In practice, we first capture the polarization data and based on it, to figure out the location of the sun through clustering. Then, with respect to the relatively consistent spatio-temporal relation between the sun and the zenith, the location of the zenith is accordingly determined. The coordinate of the zenith is adopted to establish the attitude rotation matrix and by which to work out the vehicle’s attitude. Finally, we set up a simulation vehicle platform to test the effectiveness of the approach with the theoretical polarization pattern derived from the Rayleigh single-scattering theory and actually detected pattern by the polarization analyzer. [ABSTRACT FROM PUBLISHER]

Polarized skylight pattern is one of the earth’s natural traits, which can provide compass cues for some insects’ navigation. Decades of behavioral and anatomic studies of the mechanisms that insects employed have found that some insects can home unfailingly through detecting the polarized skylight with their sophisticated compound eyes. Largely counting on the natural skylight, their navigation is immune to the interference of external environment and shows advantages in stability and accuracy over the conventional navigating devices, which sheds new light on the detection of skylight polarization pattern for attitude determination since it is the first concern for aerial vehicles to achieve accurate navigation. In this paper, we take the biological research as the guidance to simulate and detect the real-time polarized skylight pattern. In addition, with the polarization data, we are trying to explore a new algorithm to solve the three attitude angles, and test the validity of the algorithm with actual simulation experiment, just aiming at the purpose of finding an unambiguous way of attitude determination. [ABSTRACT FROM PUBLISHER]

In order to overcome the shortcomings of the Beihang University of Aeronautical and Astronautics inertial terrain-aided navigation II (BITAN II) algorithm, which is based on extended Kalman filtering, a novel algorithm combined with the terrain contour matching (TERCOM) algorithm and particle filter is proposed. Compared with BITAN II, the proposed algorithm involves a new algorithm framework, acquisition mode, and mode control logic. In particular, the acquisition mode employs TERCOM to implement a consistency voting algorithm, which can quickly and accurately obtain an initial position given in a large initial position error; the mode control logic is designed using the instantaneous information of strapdown inertial navigation system, such that false fix can be effectively detected, and security and accuracy can be guaranteed as well. Experiments are carried out using the digital elevation model and real flight data, and show that, in comparison with BITAN II, the algorithm presented in this paper is reliable and achieves relatively high positioning accuracy in the case of large initial position errors and large altimeter measurement noises. [ABSTRACT FROM PUBLISHER]