Search

In-motion alignment of Strapdown Inertial Navigation Systems (SINS) without any geodetic-frame observations is one of the toughest challenges for odometer aided navigation system. It is difficult to obtain an accurate attitude within a short time using coarse alignment under intense maneuvering condition, so one of the main obstacles during in-motion alignment for SINS/OD is to estimate an accurate attitude during fine alignment process under large azimuth misalignment angle. However, most current in-motion alignment methods were based on linear error model. In this paper, an OD aided in-motion alignment method based on backtracking scheme for large misalignment angles is proposed. Three improvements were made as the following: (1) A continuous nonlinear 16-dimensional (16D) SINS/OD error model is derived. (2) We proposed a backtracking-based Extended Kalman filter (EKF) and (3) a strict reverse navigation method is utilized in the backward process. Experimental results illustrated that the proposed method can autonomously improve the estimated attitude accuracy under the condition of large misalignment angle.

In-motion alignment of navigation-grade strapdown inertial navigation system (SINS) combined with odometer (OD) is one of the challenging issues for the land vehicle navigation, while most current in-motion alignment methods still rely on the measurements of the global positioning system or only aim at improving the attitude alignment, thus neglecting the position alignment. Thus, one of the main obstacles during the in-motion alignment for SINS/OD is to autonomously and simultaneously obtain the high-precision attitude and position within specified time. In this paper, an in-motion alignment scheme based on the backtracking scheme is presented to solve this problem. The proposed method consists of two steps. First, an improved optimization-based coarse alignment (IOBCA) is employed to obtain the attitude and position with reasonable precision, and the gravity deflexion error projection in inertial frame caused by the positioning error is diminished, which contributes to better alignment results for the subsequent process. Second, initialized with the alignment results provided by IOBCA, a backtracking-scheme-based fine alignment combined with the Kalman filter is investigated to make a further improvement in the precision of attitude and position, during which the known initial binding position and zero velocity have been utilized again. The car-mounted field experimental results illustrate that the proposed method can not only autonomously improve the precision of attitude within a short time but also simultaneously achieve the position with high precision.

Pedestrian Navigation System (PNS) is one of the research focuses of indoor positioning in GNSS-denied environments based on the MEMS Inertial Measurement Unit (MIMU). However, in the foot-mounted pedestrian navigation system with MIMU or mobile phone as the main carrier, it is difficult to make the sampling time of gyros and accelerometers completely synchronous. The gyro-accelerometer asynchronous time affects the positioning of PNS. To solve this problem, a new error model of gyro-accelerometer asynchronous time is built. The effect of gyro-accelerometer asynchronous time on pedestrian navigation is analyzed. A filtering model is designed to calibrate the gyro-accelerometer asynchronous time, and a zero-velocity detection method based on the rate of attitude change is proposed. The indoor experiment shows that the gyro-accelerometer asynchronous time is estimated effectively, and the positioning accuracy of PNS is improved by the proposed method after compensating for the errors caused by gyro-accelerometer asynchronous time.

In the field of high accuracy strapdown inertial navigation system (SINS), the inertial measurement unit (IMU) biases can severely affect the navigation accuracy. Traditionally we use Kalman filter (KF) to estimate those biases. However, KF is an unbiased estimation method based on the assumption of Gaussian white noise (GWN) while IMU sensors noise is irregular. Kalman filtering will no longer be accurate when the sensor’s noise is irregular. In order to obtain the optimal solution of the IMU biases, this paper proposes a novel method for the calibration of IMU biases utilizing the KF-based AdaGrad algorithm to solve this problem. Three improvements were made as the following: (1) The adaptive subgradient method (AdaGrad) is proposed to overcome the difficulty of setting step size. (2) A KF-based AdaGrad numerical function is derived and (3) a KF-based AdaGrad calibration algorithm is proposed in this paper. Experimental results show that the method proposed in this paper can effectively improve the accuracy of IMU biases in both static tests and car-mounted field tests.

Dry-aging is the aerobic aging process where meat carcasses or primal cuts are hanged and aged for a period of time (28 to 55 days, or even longer) under specific environmental conditions of temperature (0–4 ℃), relative humidity (61%–85%), and air flow (0.5–2.0 m/s). Dry-aging in a breathable bag, dry-aging combined with ultraviolet irradiation treatment, and stepwise dry/wet-aging have been successively developed. Due to dehydration during dry-aging, the surface of meat shrinks to produce a hard ‘crust’, resulting in significant mass loss and trim loss. However, compared with wet-aging, dry-aging can significantly enhance meat flavor characteristics, imparting a strong ‘dry-aged flavor’ and unique roasted beef-like, roasted nutty and buttery aromas to aged meat. Dry-aging can be used effectively to improve the eating quality and economic value of low-marbled beef. In this paper, the key parameters of dry-aging are reviewed, the potential mechanism by which dry-aging improves meat quality, especially flavor, is discussed. Future prospects for the application and development of dry-aging are discussed as well. This review will provide theoretical support for the development of the meat industry and for exploring the market for high-quality meat.