New time-differenced carrier phase approach to GNSS/INS integration

Mao, Yi; Sun, Rui; Wang, Junhui; Cheng, Qi; Loo, Chu Kiong; Ochieng, Washington Yotto

doi:10.1007/s10291-022-01314-3

Cited by 70 publications

(23 citation statements)

References 24 publications

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“…For physical recognition, we processed data from three sensors: magnetometer, gyroscope, and accelerometer [58][59][60][61]. Various statistical features were extracted.…”

Section: Feature Extraction For Physical Activitymentioning

confidence: 99%

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Almujally,

Khan,

Al Mudawi

et al. 2024

Sensors

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The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo–Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

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“…For physical recognition, we processed data from three sensors: magnetometer, gyroscope, and accelerometer [58][59][60][61]. Various statistical features were extracted.…”

Section: Feature Extraction For Physical Activitymentioning

confidence: 99%

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Almujally,

Khan,

Al Mudawi

et al. 2024

Sensors

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“…It correlates with the previous findings of correlation of satellite data (Tian et al, 2019;Zhou et al, 2021;Tian et al, 2021;Zhou et al, 2021b;Jianbo et al, 2022). The correlation of several factors can provide useful information (Zhang et al, 2019;Sun et al, 2021;Wang et al, 2021;Mao et al, 2022;Zhong et al, Frontiers in Astronomy and Space Sciences frontiersin.org 2022). Moreover, the anomalous pattern from Swarm satellites within a 5-10-day window before the main shock also confirms the existence of ionospheric anomalies.…”

Section: Figurementioning

confidence: 99%

Possible seismo-ionospheric anomalies associated with the 2016 Mw 6.5 Indonesia earthquake from GPS TEC and Swarm satellites

Khan

Ghaffar

Shah

et al. 2022

Front. Astron. Space Sci.

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Ionospheric anomalies through satellites can provide useful information about forthcoming earthquakes (EQs) over the epicentral regions. In this paper, we investigated seismo-ionospheric anomalies associated with the Mw 6.5 Sumatra earthquake that occurred in Indonesia on 06 December 2016 at 22:03 UT. We analyzed the total electron content (TEC) from Global Positioning System (GPS) signals received at the nearby stations around the epicenter. Furthermore, we also studied the TEC in local daytime and nighttime from Swarm satellites to confirm the EQ-induced ionospheric perturbations. The TEC showed significant perturbation within 5–10 days before the main shock in the form of positive anomalies beyond the upper bound. Similarly, Swarm satellites also validated the anomalies observed in the GPS TEC from nearby operating stations within 5–10 days over the epicentral region. The geomagnetic indices were quiet for the observed TEC anomalies within 5–10 days before the main shock, having Dst ≤ −40 nt and Kp ≤ 3. This study reveals the legitimate anomalies mainly associated with the EQ and suggests using the TEC from GPS and other satellites to look for possible future precursors with a more equipped satellite cluster.

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“…In the GNSS/INS integrated system, Soon et al linearized the TDCP model and introduced it into the GNSS/INS compact combination to achieve sub-meter positioning accuracy [19]; Chen et al applied TDCP to the GNSS/INS integrated system to suppress the error divergence when the satellite loses lock [20]; Chen et al used Kalman filtering to deal with the small cycle slip problem of TDCP, so as to improve the position of the entire GNSS/INS integrated system performance [21]; Wang et al introduced the ionosphere-free wide-lane carrier phase based on the TDCP/INS compact combination model to achieve higher-precision combined positioning [22]; Mao et al proposed a TDCP derivation controlled GNSS/IMU integration scheme [23]; Based on TDCP-GPS/IMU, Lyu et al designed an optimal time difference strategy for formulating no integer constraints across multiple epochs in the time window [24]; Zhao introduced the improved TDCP into the Kalman filter as an observation value, which greatly improved the accuracy of estimating velocity and position compared with the traditional TDCP model [25].…”

Section: Introductionmentioning

confidence: 99%

Integrated navigation model based on TDCP constrained algorithm

Liu,

Li,

Ning

2023

Meas. Sci. Technol.

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The velocity information estimated by the GNSS receiver is an important element for the dynamic alignment of the inertial navigation system (INS), and it is of great significance to analyze it deeply and meticulously. A variety of GNSS velocity measurement models show different characteristics in a changeable environment, and this status quo is bound to break the monotonous situation in which the Doppler model is widely used. In this regard, this paper applies different GNSS velocity measurement models to SINS dynamic alignment and continuous observation. In addition, aiming at the shortcomings of the traditional time-differenced carrier phase (TDCP) algorithm, an optimization method is deduced from the formula level, and two effective constraint algorithms are given. Then, according to the vehicle test results, comprehensively compare the integrated navigation performance of various velocity measurement models and analyze the improvement effect of the proposed TDCP algorithm. This paper provides a summary for the comprehensive study of GNSS velocity measurement model and the application of optimized carrier phase to integrated navigation, which has certain practical value.

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New time-differenced carrier phase approach to GNSS/INS integration

Cited by 70 publications

References 24 publications

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Possible seismo-ionospheric anomalies associated with the 2016 Mw 6.5 Indonesia earthquake from GPS TEC and Swarm satellites

Integrated navigation model based on TDCP constrained algorithm

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