Search

To accurately track body attitude under high dynamic environments, a new attitude updating algorithm for the strapdown inertial navigation system is proposed after further applying higher degree polynomial to the quaternion Picard iteration (QPI) algorithm. With QPI, calculation error introduced by Picard approximation can be eliminated, but the angular rate fitting error introduced by substituting polynomial for angular rate of body will still affect the accuracy of the attitude updating algorithms which are designed based on polynomial model. Hence, a five- rather three-degree polynomial constructing method using four samples of gyro outputs with coning motion constrain is designed and tested. Simulation results indicate the proposed method owns more accuracy than QPI, optimal coning algorithm, and Fourth4Rot under both low and high dynamic environments.

To effectively compensate the non-commutativity error existing in attitude computation, a new rotation vector algorithm is presented in this study. In order to calculate the rotation vector, a high-order polynomial which accurately describes the angular motion of carrier is introduced. By this algorithm, it is easy to increase the attitude computation accuracy without the need for increasing the output frequency of the gyro. The algorithm's performance is compared with classical multi-sample algorithm and optimal coning algorithm in the context of coning motion. Simulation results demonstrate that the new algorithm outperforms optimal coning algorithm in high dynamic environment and classical multi-sample algorithm in low dynamic environment (by 2–3 and 1–2 orders, respectively).

To effectively improve the accuracy of attitude reconstruction under highly dynamic environments, a new numerical attitude updating algorithm is designed in this paper based on the high-order polynomial iteration according to the differential equation for quaternion. In this algorithm, a high-order polynomial is introduced to fit the angular rate accurately without increasing the number of gyro outputs during per attitude updating interval. This algorithm can provide an exact high-order polynomial solution for quaternion and the process of attitude reconstruction can be implemented efficiently. The algorithm's performance is evaluated as compared with optimal coning algorithm, attitude quaternion updating algorithm based on Picard iteration (QPI), and higher-order rotation vector attitude updating algorithm (Fourth4Rot) under coning motion. The simulation results show that this algorithm can improve the accuracy of attitude computation and clearly outperform the optimal coning algorithm, QPI, and Fourth4Rot in high dynamic environment.

Accelerometer bias in an inertial navigation system (INS) is the key factor determining the navigation accuracy. Two methods, named separated calibration and system calibration are always used for acceleration bias calibration. The former one needs a high precision turntable to provide reference and the latter cannot estimate accelerometer bias along horizontal direction because of coupling effects between horizontal misalignment angles and horizontal accelerometer bias. In this paper, a new estimation method is designed to estimate accelerometer biases and acquire high horizontal alignment accuracy without a turntable. In the proposed method, different expressions about gravity and accelerometer biases in an inertial frame are analyzed and coupling models about gravity and biases are constructed. Then, measurements from gyroscopes and accelerometers are used to construct gravitational apparent acceleration and Kalman filter used to estimate parameters describing apparent acceleration and estimate accelerometer bias. The simulation and turntable tests show that when the carrier is without translational but with swinging motion, the proposed method can finish gravitational apparent acceleration identification and accelerometer bias estimation effectively at the same time, and when the reconstructed gravitational apparent acceleration used for alignment, horizontal alignment errors can nearly approach zeros.

Field calibration is an important method to guarantee the accuracy of a strapdown inertial navigation system. Zero velocity update based on the zero-velocity constraint when the carrier is without translational motion is a typical system-level calibration method. In zero velocity update, there is a coupling between biases and horizontal misalignment angles. The accuracy of horizontal misalignment angles is determined by the equivalent accelerometer biases in horizontal directions, which means that improving the accuracy of horizontal angles needs accurate calibration of accelerometer biases. Meanwhile, alignment with gravitational apparent motion is widely used taking advantages of its alignment ability in a swinging condition. But it is an analytical method and cannot calibrate sensor biases and is always dealt as a coarse alignment method. In order to calibrate accelerometer biases and utilize advantages of the alignment method with gravitational motion, a method to estimate accelerometer biases based on an iterative optimization method and gravitational apparent motion is presented in this paper. First, accelerometer biases are introduced to calculate apparent acceleration and an objective function is constructed. Then, Newton's iteration is applied to iteratively optimize the parameters describing gravitational apparent motion and accelerometer biases. As revealed by the theoretical analysis and experimental results, different patterns of gravity and accelerometer biases will be generated when the carrier exhibits a swinging motion; thus, the convergence of the proposed algorithm will be ensured. After accelerometer biases are removed, initial alignment performed with the gravitational apparent motion reconstructed by the estimated parameters gives nearly zero horizontal misalignment angles.

The design, simulation, fabrication, and experiments of a novel flow sensor based on resonant sensing with a two-stage microleverage mechanism are presented in this paper. Different from the conventional detection methods for flow sensors, two differential resonators are adopted to implement air flow rate transformation through two-stage leverage magnification. The proposed flow sensor has a high sensitivity since the adopted two-stage microleverage mechanism possesses a higher amplification factor than a single-stage microleverage mechanism. The modal distribution and geometric dimension of the two-stage leverage mechanism and hair are analyzed and optimized by Ansys simulation. A digital closed-loop driving technique with a phase frequency detector-based coordinate rotation digital computer algorithm is implemented for the detection and locking of resonance frequency. The sensor fabricated by the standard deep dry silicon on a glass process has a device dimension of 5100 μm (length) × 5100 μm (width) × 100 μm (height) with a hair diameter of 1000 μm. The preliminary experimental results demonstrate that the maximal mechanical sensitivity of the flow sensor is approximately 7.41 Hz/(m/s)2 at a resonant frequency of 22 kHz for the hair height of 9 mm and increases by 2.42 times as hair height extends from 3 mm to 9 mm. Simultaneously, a detection-limit of 3.23 mm/s air flow amplitude at 60 Hz is confirmed. The proposed flow sensor has great application prospects in the micro-autonomous system and technology, self-stabilizing micro-air vehicles, and environmental monitoring.

Flow sensors inspired by the natural hair sensing mechanism have great prospect in the research of micro-autonomous system and technology (MAST) for the three-dimensional structure characteristics with high spatial and quality utilization. A novel bio-inspired hair flow sensor (BHFS) based on resonant sensing with a unique asymmetric design is presented in this paper. A hair transducer and a signal detector which is constituted of a two-stage micro-leverage mechanism and two symmetrical resonators (double ended tuning fork, DETF) are adopted to realize the high sensitivity to air flow. The sensitivity of the proposed BHFS is improved significantly than the published ones due to the high sensitivity of resonators and the higher amplification factor possessed by the two-stage micro-leverage mechanism. The standard deep dry silicon on glass (DDSOG) process is chosen to fabricate the proposed BHFS. The experiment result demonstrates that the fabricated BHFS has a mechanical sensitivity of 5.26 Hz/(m/s)2 at a resonant frequency of 22 kHz with the hair height of 6 mm.

The wideband source localization is analysed widely in shallow water. It is pointed out that its performance is poor when the number of array elements is few or the ocean environment is uncertain. A method of estimating the range and depth is studied by using a single hydrophone based on the relationship of the horizontal wavenumber difference between two modes with the waveguide invariant for low frequency underwater acoustic pulse signals in a range-independent shallow water waveguide. This localization method estimates the source range by using the rangedispersion two-dimensional（2D) plane focus phenomenon and also the source depth by matching the modal energy. So it can separately estimate the source range and source depth by single hydrophone. First, the signal received on a single hydrophone can be decomposed into a series of modes within the framework of normal mode theory. In order to obtain a better localization performance, the first few order modal dispersion parameters and waveguide invariant are regarded as the unknown parameters. And then the first few order modal dispersion parameters and waveguide invariant can be estimated by comparing the differences between the modal phase velocity calculated by Eq.(8) and that calculated by the Kraken model. Second, using the estimated dispersion parameters and waveguide invariant for dedispersion transform, the amplitudes of each normal mode can achieve maximum values but only when the range of the received signal after dedispersion transform is equal to the range of source. On range-dispersion 2D plane, there appears the sound pressure focus phenomenon, and this phenomenon can be used to estimate the source range. Simulation results from a shallow water Pekeris waveguide show that the time-frequency distribution represents well the dispersion characteristics of the underwater acoustic pulse signal and the dedisperision transform can eliminate this dispersion at the range of source, so that the source range can be estimated. Besides, the first few order modal signals received are clearly separated in time domain after dedispersion transform, and the first few order modal energy can be calculated accurately. So the source depth can be estimated by matching the modal energy. The errors in range estimation and depth estimation are little in simulation. Finally, the data collected from airgun sources during an experiment in the shallow water are used to verify the presented method, and the experimental results obtained using airgun sources on a straight line are shown. The presented method is very significant for estimating the range and depth in shallow water.

Acoustic propagation in shallow water is greatly influenced by the properties of the sea bottom. The dispersion characteristics of modes are relatively sensitive to the bottom parameters and have been used to invert the bottom parameters. Since the inversion error using a single wideband sound source increases with increasing range, a far distance inversion method based on the modal dispersion curve using a single hydrophone with two wideband sound sources is presented in this paper, in which a warping transform is applied so that it can accurately extract the modal dispersion curve from the warped signal spectrum. Experimental data used for the inversion are acquired using a hydrophone of vertical array in the South Sea of China during the Autumn in 2012. The transmitted signals are explosive signals, and the bottom sound speed and density are inverted by matching the theoretical arrival time differences of various modes and frequencies with those calculated using the experimental data. The attenuation coefficient is deduced using the transmission loss data recorded in the experiment. A genetic algorithm (GA) is used for optimization search for the parameter bounds. Inversion results demonstrate that the arrival time difference when using the bottom sound speed and density show a high consistency with those obtained using the experimental data. Moreover, the attenuation coefficient is nonlinear over the frequency band from 100 to 315 Hz. The validity of inverted parameters is evaluated by the posteriori probability distributions, and the numerical results of arrival time differences calculated using the inverted sound speed and density are in good agreement with those extracted from the other two wideband explosive signals at different distances. In addition, the theoretical transmission loss calculated using the inverted attenuation coefficient matches the experiment data very well. It is shown that the inversion scheme can provide a valid and stable environmental estimation.