An Improved Innovation Adaptive Kalman Filter for Integrated INS/GPS Navigation

Sun, Bo; Zhang, Zhenwei; Qiao, Dianju; Mu, Xiaotong; Hu, Xiaochen

doi:10.3390/su141811230

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Sun et al proposed an improved innovation adaptive Kalman filter (IAKF) is proposed [32]. The algorithm utilizes the innovation to construct a chi-squared test, enabling the identification of abnormal measurement noise.…”

Section: State and Covariance Updatementioning

confidence: 99%

Optimize Kalman Filter For Smart Phone Location Tracking Using GNSS Measurements

Yokanathan

2023

Int J on Adv. in ICT for Emerging Countries

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Point precise positioning (PPP) method allows to locate user with Global Navigation Satellite System under good condition. But this method is quite challenging to locate user when user is in urban areas with obstacles. Another alternative solutions such as Real Time Kinematic solve these problems with differential measurements. In real world scenario, raw GNSS measurements have usually noisy and uncertain data. Kalman filter is successful method to optimally update variables with indirect measurements. This research proposes the method to optimize Kalman filter based PPP to improve smart phone location tracking. Evaluation method showed that proposed method worked well with approximately 3.0 meter accuracy and outperformed base line model which is calculated using Weighted-Least-Square (WLS) solver.

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Section: State and Covariance Updatementioning

confidence: 99%

Optimize Kalman Filter For Smart Phone Location Tracking Using GNSS Measurements

Yokanathan

2023

Int J on Adv. in ICT for Emerging Countries

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“…An improved innovation adaptive Kalman filter (IAKF) was proposed by B. Sun et al [41] in 2022 to solve the vulnerability of KF in challenging environments. Compared with ordinary KF, this method improves the adaptability.…”

Section: Introductionmentioning

confidence: 99%

An GNSS/INS Integrated Navigation Algorithm Based on PSO-LSTM in Satellite Rejection

et al. 2023

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When the satellite signal is lost or interfered with, the traditional GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) integrated navigation will degenerate into INS, which results in the decrease in navigation accuracy. To solve these problems, this paper mainly established the PSO (particle swarm optimization) -LSTM (Long Short-Term Memory) neural network model to predict the increment of GNSS position under the condition of satellite rejection and accumulation to obtain the pseudo-GNSS signal. The signal is used to compensate for the observed value in the integrated system. The model takes the advantages of LSTM, which is good at processing time series, and uses PSO to obtain the optimal value of important hyperparameters efficiently. Meanwhile, the improved threshold function is used to denoise the IMU (inertial measurement unit) data, which improves the SNR (signal-to-noise ratio) of IMU outputs effectively. Finally, the performance of the algorithm is proved by actual road test. Compared with INS, the method can reduce the maximum errors of latitude and longitude by at least 98.78% and 99.10% while the satellite is lost for 60 s, effectively improving the accuracy of the GNSS/INS system in satellite rejection.

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