Implementation and performance comparison of indirect Kalman filtering approaches for AUV integrated navigation system using low cost IMU

Abstract

Strapdown Inertial Navigation System (SINS) estimates position, velocity, and attitude of vehicle using the signals measured by accelerometer and gyroscope and is based on dead-reckoning principle. Due to different imperfections in measurements, and the consecutive integration of the acceleration signals, estimation error increases with time and it is acceptable only for short times in the low cost SINS. In order to reduce the error, auxiliary sensors together with inertial sensors are utilized and to combine the data estimated by SINS with the signals measured by auxiliary sensors, data fusion methods based on direct and indirect Kalman filtering is used. The feedforward and feedback structures are two common approaches of the indirect filtering. In this paper, an underwater integrated navigation system has been designed using indirect filtering approaches due to lower computational load and higher reliability with respect to the direct approach. The auxiliary sensors used consist of DVL, Gyrocompass, and depthmeter. The performance of the designed system has been studied using real measurements. The experimental results showed that the root mean square error in the estimated position for feedforward structure is reduced from 3.2 to 0.2 percent of the travelled distance when using feedback structure.

DGNSS Cooperative Positioning in Mobile Smart Devices: A Proof of Concept

“…While, if they become larger, the errors estimated by the EKF will drift with time. For this reason in navigation applications that integrate INS, a feedback loop is added to mitigate this drawback [65]. In the implementation of the proposed PoC, inertial sensors are not considered, therefore the error is estimated as the difference between the current measurements exposed by the receiver's post-correlation stage and the nominal ones, which are evaluated by applying the corrections found at the previous step, according to Figure 5.…”

DGNSS Cooperative Positioning in Mobile Smart Devices: a Proof of Concept

“…While, if they become larger, the errors estimated by the EKF will drift with time. For this reason in navigation applications that integrate INS, a feedback loop is added to mitigate this drawback [52]. In the implementation of the PoC, inertial sensors are not considered, therefore the error is estimated as the difference between the current measurements exposed by the receiver's post-correlation stage and the nominal ones, which are evaluated by applying the corrections found in the previous step, according to Figure 5.…”

A novel INS/ADS integrated navigation method based on INS error model-aided unbiased converted measurement

“…The INS error evolution equations are linear, and its computational cost is much smaller than that of direct filter mode [21]. Therefore, the indirect filter mode is more widely used in integrated navigation [21,22]. In [23,24], the TAS of ADS is converted to navigation frame using INS attitude directly, and then a standard KF can be used in INS/ADS integrated navigation.…”