The navigation accuracy of the inertial navigation system (INS) can be greatly improved when the inertial measurement unit (IMU) is effectively calibrated and compensated, such as gyro drifts and accelerometer biases. To reduce the requirement for turntable precision in the classical calibration method, a continuous dynamic self-calibration method based on a three-axis rotating frame for the hybrid inertial navigation system is presented. First, by selecting a suitable IMU frame, the error models of accelerometers and gyros are established. Then, by taking the navigation errors during rolling as the observations, the overall twenty-one error parameters of hybrid inertial navigation system (HINS) are identified based on the calculation of the intermediate parameter. The actual experiment verifies that the method can identify all error parameters of HINS and this method has equivalent accuracy to the classical calibration on a high-precision turntable. In addition, this method is rapid, simple and feasible.

/pdf/an-improved-fast-self-calibration-method-for-hybrid-inertial-22odr0skns.pdf

An Improved Fast Self-Calibration Method for Hybrid Inertial Navigation System under Stationary Condition

The periodic-calibration of missile borne strap-down inertial navigation system(SINS) is the key to realize missile precision attack, especially for strategic missile. However, the traditional periodic-calibration method can only be realized on the three-axis turntable by disassembly, which affects the fast launch ability. It is urgent to propose a periodic free disassembly self-calibration method for strap-down inertial navigation. The assembly strap-down inertial navigation system can only be sensitive to one-degree-of-freedom rotation in the storage period with the help of the rolling maneuver of the missile. It has so far been thought to be impossible in the absence of bi-axial rotation to realize the self-calibration of missile-borne SINS. In this paper, the skewed six axis redundancy configuration mode for strap-down inertial navigation system and observability optimal allocation method have been proposed to solve the self-calibration problem of strap-down inertial navigation. Multiple calculations of variety configurations are conduct to accomplish the calibration, instead of single calculation in traditional method. Eventually, the hardware-in-the-loop simulations results, including the quantities of calibrated sensors and observability degree of main errors, were superior to traditional calibration method reported so far. The method presented here provides a new perspective to realize self-calibration of missile-borne INS during the storage period in pure strap-down way. 

A Novel Storage-Period Self-Calibration Method of Missile-Borne SINS With Redundant Configuration

The periodic-calibration of missile borne strap-down inertial navigation system(SINS) is the key to realize missile precision attack, especially for strategic missile. However, the traditional periodic-calibration method can only be realized on the three-axis turntable by disassembly, which affects the fast launch ability. It is urgent to propose a periodic free disassembly self-calibration method for strap-down inertial navigation. The assembly strap-down inertial navigation system can only be sensitive to one-degree-of-freedom rotation in the storage period with the help of the rolling maneuver of the missile. It has so far been thought to be impossible in the absence of bi-axial rotation to realize the self-calibration of missile-borne SINS. In this paper, the skewed six axis redundancy configuration mode for strap-down inertial navigation system and observability optimal allocation method have been proposed to solve the self-calibration problem of strap-down inertial navigation. Multiple calculations of variety configurations are conduct to accomplish the calibration, instead of single calculation in traditional method. Eventually, the hardware-in-the-loop simulations results, including the quantities of calibrated sensors and observability degree of main errors, were superior to traditional calibration method reported so far. The method presented here provides a new perspective to realize self-calibration of missile-borne INS during the storage period in pure strap-down way.

In order to improve the hitting accuracy of inertial-guided launch vehicle, the outliers in the output Euler angles and accelerations of Inertial Navigation System (INS) must be detected before launching. Current outlier detection methods in INS are mainly supervised approaches and may result in too many false positives. In this paper, we investigate and introduce the Long Short-Term Memory (LSTM) recurrent neural networks to forecast time sequences in INS. Due to its gate mechanism and capability to maintain internal state, our method could get relatively low prediction errors. Once the prediction model is trained, we propose a method to set a threshold to detect the outliers. We introduce the Exponentially Weighted Moving Average (EWMA) method to dampen spikes in prediction errors due to abrupt changes in the outputs of INS. Finally, we carried out experiments on the real outputs of INS using our method to detect outliers. The results demonstrate the suitability of LSTM networks for outlier detection.

Detecting Inertial Navigation System Outliers Using Long Short-Term Memory Recurrent Neural Networks

The measuring error of Inertial Navigation System (INS) is the main fact that affects the hitting accuracy of inertial-guided missile. Error parameters compensation could get high-precision. First of all, this article builds an error model of gyro and accelerometer, and then in order to achieve these error parameters' value as closely as possible to their true value in application, a quick self-calibration method based on one-time electrification before launching for inertial platform is proposed. Via this method, INS rotates to 9 particular positions, outputs of inertial sensors are collected in drift state and each error parameter can be separated. The self-calibration method requires no additional devices, improving the maneuverability of the whole system. Finally, experiments are carried out using both the self-calibration method and unit calibration method, the two methods' results show that the largest compared accuracy is 9.40%, which demonstrates the practicability and validity of the suggested method.

Ran Duan

Papers

A self-calibration method based on one-time electrification before launching for Inertial Navigation System