Two New Classes of Strapdown Navigation Algorithms

Litmanovich, Yu. A.; Lesyuchevsky, Vladimir M.; Gusinsky, Valery Z.

doi:10.2514/2.4517

Cited by 29 publications

(11 citation statements)

References 5 publications

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“…Based on the analytical expression of the sculling error term, it is known that when high-frequency angular and linear vibrations occur in two or more axes simultaneously, the sculling error in velocity will be generated in the inertial sensor [ 10 , 12 , 17 ]. To improve the velocity accuracy, a series of high-rate algorithms for sculling integral compensation using angle and velocity increments as algorithm inputs have been developed [ 25 , 26 ]. However, they are not suitable for modern inertial sensors whose outputs are the angular rate and specific force.…”

Section: Sculling Error Formula In Velocity Updatementioning

confidence: 99%

Sculling Compensation Algorithm for SINS Based on Two-Time Scale Perturbation Model of Inertial Measurements

Wang

Xin

2018

Sensors

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In order to decrease the velocity sculling error under vibration environments, a new sculling error compensation algorithm for strapdown inertial navigation system (SINS) using angular rate and specific force measurements as inputs is proposed in this paper. First, the sculling error formula in incremental velocity update is analytically derived in terms of the angular rate and specific force. Next, two-time scale perturbation models of the angular rate and specific force are constructed. The new sculling correction term is derived and a gravitational search optimization method is used to determine the parameters in the two-time scale perturbation models. Finally, the performance of the proposed algorithm is evaluated in a stochastic real sculling environment, which is different from the conventional algorithms simulated in a pure sculling circumstance. A series of test results demonstrate that the new sculling compensation algorithm can achieve balanced real/pseudo sculling correction performance during velocity update with the advantage of less computation load compared with conventional algorithms.

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Section: Sculling Error Formula In Velocity Updatementioning

confidence: 99%

Sculling Compensation Algorithm for SINS Based on Two-Time Scale Perturbation Model of Inertial Measurements

Wang

Xin

2018

Sensors

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“…In order to improve the accuracy of the MEMS-SINS, two methods are usually used: design a more accurate SINS or use the algorithmic method to correct the SINS errors [ 10 ]. The first method involves the availability of new technology, which will take a long time and require serious financial costs, while the second method can achieve the desired accuracy in a short period of time with minimal financial costs.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Estimation Algorithm for Correcting Low-Cost MEMS-SINS Errors of Unmanned Vehicles under the Conditions of Abnormal Measurements

Zhang

Viktorovich

Selezneva

et al. 2021

Sensors

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In this paper, a low-cost small-sized strap-down inertial navigation system (SINS)—Gyrolab GL-VG 109—is studied. When the system is installed on an unmanned vehicle and works in autonomous mode, it is difficult to determine the navigation parameters of the unmanned vehicle. Correcting the SINS information from the Global Navigation Satellite System (GNSS) can significantly increase the determination accuracy of the navigation parameters. However, this is only available when the GNSS signals are stable. A new adaptive estimation algorithm that can automatically detect, evaluate, and process the abnormal measurements is proposed in the present work. The determination of the navigation parameters can reach the third accuracy class using the proposed method. The effectiveness of the algorithm is verified by the mathematical simulation and the experimental tests (with a real SINS GL-VG 109), which are conducted in urban environments with a GNSS signal containing 15% and 40% abnormal measurements. The results show that the proposed method can significantly reduce the impact of abnormal measurements and improve the estimation accuracy.

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“…The equivalency allows a coning algorithm based on incremental angle input to be converted to its corresponding sculling algorithm using a simple mathematical formula [5]. In [6] two alternative approaches were developed for deriving strapdown navigation sculling algorithms. A key point of the two approaches is the use of additional gyro/accelerometer output signals which are the increments of the angular rate/specific force multiple integrals over the iteration interval.…”

Section: Introductionmentioning

confidence: 99%

Strapdown Sculling Velocity Algorithms Using Novel Input Combinations

Huang

Xie

et al. 2018

Mathematical Problems in Engineering

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Sculling motion is a standard input to evaluate the performance of the velocity algorithm in a highly dynamic environment. Conventional sculling algorithms usually adopt incremental angle/specific force increments or angular rate/specific force as algorithm inputs. However modern inertial sensors have different output types now, which do not correspond to the inputs of those traditional algorithms. For example, some inertial sensors have the integrated angular rate (incremental angle)/specific force outputs or angular rate/specific force increments outputs. Hence the conventional sculling algorithms cannot be easily applied to these situations. A novel sculling algorithm using incremental angle/specific force inputs or angular rate/specific force increments inputs is developed in this paper. The advantage of the novel algorithm is that it can calculate the carrier velocity directly without converting the dimension of inertial sensor outputs values. Theoretical analysis, digital simulations, and a trial study are carried out to verify our algorithm. The results demonstrate that for corresponding types of strapdown inertial navigation systems (SINS) the novel sculling algorithm exhibits better performance than the conventional sculling velocity algorithms.

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Two New Classes of Strapdown Navigation Algorithms

Cited by 29 publications

References 5 publications

Sculling Compensation Algorithm for SINS Based on Two-Time Scale Perturbation Model of Inertial Measurements

Sculling Compensation Algorithm for SINS Based on Two-Time Scale Perturbation Model of Inertial Measurements

Adaptive Estimation Algorithm for Correcting Low-Cost MEMS-SINS Errors of Unmanned Vehicles under the Conditions of Abnormal Measurements

Strapdown Sculling Velocity Algorithms Using Novel Input Combinations

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