Calibration of inertial measurement units (IMU) is carried out to estimate the coefficients which transform the raw outputs of inertial sensors to meaningful quantities of interest. Based on the fact that the norms of the measured outputs of the accelerometer and gyroscope cluster are equal to the magnitudes of specific force and rotational velocity inputs, respectively, an improved multi-position calibration approach is proposed. Specifically, two open but important issues are addressed for the multi-position calibration: (1) calibration of inter-triad misalignment between the gyroscope and accelerometer triads and (2) the optimal calibration scheme design. A new approach to calibrate the inter-triad misalignment is devised using the rotational axis direction measurements separately derived from the gyroscope and accelerometer triads. By maximizing the sensitivity of the norm of the IMU measurement with respect to the calibration parameters, we propose an approximately optimal calibration scheme. Simulations and real tests show that the improved multi-position approach outperforms the traditional laboratory calibration method, meanwhile relaxing the requirement of precise orientation control.

In-motion alignment of Strapdown Inertial Navigation Systems (SINS) without any geodetic-frame observations is one of the toughest challenges for Autonomous Underwater Vehicles (AUV). This paper presents a novel scheme for Doppler Velocity Log (DVL) aided SINS alignment using Unscented Kalman Filter (UKF) which allows large initial misalignments. With the proposed mechanism, a nonlinear SINS error model is presented and the measurement model is derived under the assumption that large misalignments may exist. Since a priori knowledge of the measurement noise covariance is of great importance to robustness of the UKF, the covariance-matching methods widely used in the Adaptive KF (AKF) are extended for use in Adaptive UKF (AUKF). Experimental results show that the proposed DVL-aided alignment model is effective with any initial heading errors. The performances of the adaptive filtering methods are evaluated with regards to their parameter estimation stability. Furthermore, it is clearly shown that the measurement noise covariance can be estimated reliably by the adaptive UKF methods and hence improve the performance of the alignment.

A novel class of multi-D-shaped optical fiber suited for refractive index measurements is presented. The multi-D-shaped optical fiber was constructed by forming several D-sections in a multimode optical fiber at localized regions with femtosecond laser pulses. The total number of D-shaped zones fabricated could range from three to seven. Each D-shaped zone covered a sensor volume of 100 μm depth, 250 μm width, and 1 mm length. The mean roughness of the core surface obtained by the AFM images was 231.7 nm, which is relatively smooth. Results of the tensile test indicated that the fibers have sufficient mechanical strength to resist damage from further processing. The multi-D-shaped optical fiber as a high sensitive refractive-index sensor to detect changes in the surrounding refractive index was studied. The results for different concentrations of sucrose solution show that a resolution of 1.27 × 10−3–3.13 × 10−4 RIU is achieved for refractive indices in the range of 1.333 to 1.403, suggesting that the multi-D-shaped fibers are attractive for chemical, biological, and biochemical sensing with aqueous solutions.

To achieve high Strapdown Inertial Navigation System (SINS) alignment accuracy within a short period of time is still a challenging issue for underwater vehicles. In this paper, a new SINS initial alignment scheme aided by the velocity derived from Doppler Velocity Log (DVL) is proposed to solve this problem. In the stage of the coarse alignment, the velocity of DVL is employed to reduce the impact of the linear motion. With a backtracking framework, the fine alignment runs with the data recorded during the process of the coarse alignment and thus will speed up the overall alignment process. In addition, by using this new scheme, it is equivalent to length the alignment time for both coarse and fine alignments, so the accuracy of the alignments will be improved. In order to reduce the volume of the data that has to be recorded, a new model for SINS fine alignment is derived in the inertial reference frame which makes it feasible for real time applications. The experimental results are presented for both unaided static and in-motion alignment using DVL aiding. It is clearly shown that the proposed method meets the requirement of SINS alignment for underwater vehicles.K E Y WO R D S 1. Inertial Alignment.

Nomenclature C s = coefficient of compressed coning correction algorithm c k r = combination, k elements selected from a set of r elements without regard to the order of selection f 3 ; f 4 ; : : : = coefficients in the derivatives of δφ unc t m = computer interval index, as subscript indicates parameter value at computer cycle m N = total number of samples used in the coning calculation n = number of samples in current iteration time interval o = an equivalent infinitesimal to ( ) r = order of derivatives s = index of coefficients for compressed coning algorithm T k = sampling time interval t = time α, α = integral of ω over t m−1 ; t time interval and its magnitude β = normalized coning frequency Δα N1−i , Δα N1−j = gyro data samples spaced backward in time from time t δϕ c t = coning integral over the time interval from t m−1 to t δφ cmp t = compressed coning correction algorithm δφ cmpf t = compressed frequency-series coning algorithm δφ unc t = uncompressed coning correction algorithm δφ uncE t = uncompressed Explicit coning algorithm δφ uncE3 t; δφ uncE4 t; : : := uncompressed Explicit coning algorithms taking three, four, and five samples of gyro data per update δφ uncf t = uncompressed frequency-seriesbased coning algorithm δφ uncf3 t; δφ uncf4 t; : : := uncompressed frequency-series coning correction algorithms taking three, four, and five samples of gyro data per update ς ij = coefficients of uncompressed frequency-series coning algorithms _ ΦΩ, _ Φ Eval Ω = coning rate amplitude Ω = coning frequency ω = angular rate vector _ ω, ω, ω first-, second-, third-, fourth-, fifth-, and sixth-order derivatives of ω × = skew symmetric cross-product matrix form of vector ( ) that indicates × ×