The attitude determination (AD) based initial alignment for Doppler Velocity Log (DVL) aided Strapdown Inertial Navigation System (SINS) is investigated. Three issues involved in practical applications are pointed out and addressed, which have been not discussed in previous works. Firstly, the explicit AD based initial alignment procedure is presented for DVL aided SINS with consideration of the different sampling frequency of the two devices. Secondly, an outliers detection and isolation procedure for DVL is proposed based on the fact that the transverse or up speed is always near zero. Thirdly, it is pointed out that the constructed vector observations should be normalized when used in the AD. The validity and superiority of the proposed methods is demonstrated through experimental study.

The current research on integrated navigation is mainly focused on filtering or integrated navigation equipment. Studies systematically comparing and analyzing how to choose appropriate integrated filtering methods under different circumstances are lacking. This paper focuses on integrated navigation filters that are used by different filters and attitude parameters for inertial integrated navigation. We researched integrated navigation filters, established algorithms, and examined the relative merits for practical integrated navigation. Some suggestions for the use of filtering algorithms are provided. We completed simulations and car-mounted experiments for low-cost strapdown inertial navigation system (SINS) to assess the performance of the integrated navigation filtering algorithms.

The gravity gradient is the second derivative of gravity potential. A gravity gradiometer can measure the small change of gravity at two points, which contains more abundant navigation and positioning information than gravity. In order to solve the problem of passive autonomous, long-voyage, and high-precision navigation and positioning of submarines, an aided navigation method based on strapdown gravity gradiometer is proposed. The unscented Kalman filter framework is used to realize the fusion of inertial navigation and gravity gradient information. The performance of aided navigation is analyzed and evaluated from six aspects: long voyage, measurement update period, measurement noise, database noise, initial error, and inertial navigation system device level. When the parameters are set according to the benchmark parameters and after about 10 h of simulation, the results show that the attitude error, velocity error, and position error of the gravity gradiometer aided navigation system are less than 1 arcmin, 0.1 m/s, and 33 m, respectively.

High-precision filtering estimation is one of the key techniques for strapdown inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation system, and its estimation plays an important role in the performance evaluation of the navigation system. Traditional filter estimation methods usually assume that the measurement noise conforms to the Gaussian distribution, without considering the influence of the pollution introduced by the GNSS signal, which is susceptible to external interference. To address this problem, a high-precision filter estimation method using Gaussian process regression (GPR) is proposed to enhance the prediction and estimation capability of the unscented quaternion estimator (USQUE) to improve the navigation accuracy. Based on the advantage of the GPR machine learning function, the estimation performance of the sliding window for model training is measured. This method estimates the output of the observation information source through the measurement window and realizes the robust measurement update of the filter. The combination of GPR and the USQUE algorithm establishes a robust mechanism framework, which enhances the robustness and stability of traditional methods. The results of the trajectory simulation experiment and SINS/GNSS car-mounted tests indicate that the strategy has strong robustness and high estimation accuracy, which demonstrates the effectiveness of the proposed method.

We comment on a paper which describes a quaternion-based method for the strap-down inertial navigation systems/synthetic aperture radar (SINS/SAR) integrated navigation system. Although the paper proposes an interesting strategy, there exists a flaw in the algorithm regarding the quaternion averaging involved in the unscented particle filtering (UPF) algorithm. This letter suggests how to fix the problem.