A novel hybrid approach based-SRG model for vehicle position prediction in multi-GPS outage conditions

Havyarimana, Vincent; Hanyurwimfura, Damien; Nsengiyumva, Philibert; Xiao, Zhu

doi:10.1016/j.inffus.2017.07.002

Cited by 44 publications

(13 citation statements)

References 17 publications

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“…Set d 1 = 10 m, d 2 = 5 m, d x = 7 m, d y = 3 m, the error variance of the original baseline phase interferometer is σ ϕ = 0.0175 rad, and the measurement accuracy of the additional phase interferometer on fuselage axis and wing axis satisfies σ x ≤ 0.0776 rad and σ y ≤ 0.1567 rad, respectively –. For convenience, we choose σ x = 0.0776 rad and σ y = 0.1567 rad, the position of the fixed target is (107.15, 399.88, 0) km, the starting position of the observation platform is (0, 0, 10) km, the moving velocity of the observation platform is (300, 0, 0) m/s, the frequency of the target radiation signal is f T = 3 × 10 9 Hz, and signal observation interval is T = 1 second.…”

Section: Algorithm To Resolve the Phase Difference Ambiguitymentioning

confidence: 99%

Research on high‐precision passive localization based on phase difference changing rate

Zhou

Cheng

Lian

et al. 2018

Concurrency and Computation

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Summary Passive localization method has a wider applicable prospect as a rising localization resort with well flexibility. The acquisition of phase difference changing rate is a very practical problem in passive localization for the way how to get the phase difference, and a better error precision directly affects the accuracy of the localization. Firstly, based on the analysis of the positioning principle of phase difference changing rate, it studies the characteristics of phase difference changing rate and proposes a novel resolving phase ambiguity algorithm based on a double baselines system cosine theorem (DBSCT), which can eliminate the phase difference ambiguity efficiently. Secondly, it studies two typical methods to extract the high‐precision phase difference changing rate from the unambiguity phase difference, namely, difference algorithm and Kalman algorithm. Finally, it compares the Kalman algorithm with the difference algorithm through computer simulation. Simulation results show that the Kalman algorithm can efficiently smoothen the phase difference data and suppress the measurement noise to achieve a high positioning accuracy. Simulation results also show that with a certain phase difference measurement accuracy, the phase difference rate accuracy extracted by the Kalman algorithm is higher than that we require. Therefore, we can appropriately relax the phase difference measurement accuracy requirements to achieve the same positioning accuracy. So, it has a great application value.

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Section: Algorithm To Resolve the Phase Difference Ambiguitymentioning

confidence: 99%

Research on high‐precision passive localization based on phase difference changing rate

Zhou

Cheng

Lian

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…However, GNSS alone cannot give reliable positions all of the time, as the satellite signal may be blocked or corrupted as a result of high buildings, viaducts, tunnels, mountains, multi-path reflections, and bad weather conditions [3][4][5]. Because of their complementary properties, INS and GNSS are commonly integrated by a Kalman filter (KF) for providing continuous and high precision navigation [3][4][5][6]. In an INS/GNSS system, GNSS aids INS by estimating its errors in KF [7,8].…”

Section: Introductionmentioning

confidence: 99%

A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages

Fang

Jiang

et al. 2020

Remote Sensing

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Aiming to improve the navigation accuracy during global navigation satellite system (GNSS) outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding inertial navigation system (INS). The LSTM algorithm is investigated to generate the pseudo GNSS position increment substituting the GNSS signal. Almost all existing INS aiding algorithms, like the multilayer perceptron neural network (MLP), are based on modeling INS errors and INS outputs ignoring the dependence of the past vehicle dynamic information resulting in poor navigation accuracy. Whereas LSTM is a kind of dynamic neural network constructing a relationship among the present and past information. Therefore, the LSTM algorithm is adopted to attain a more stable and reliable navigation solution during a period of GNSS outages. A set of actual vehicle data was used to verify the navigation accuracy of the proposed algorithm. During 180 s GNSS outages, the test results represent that the LSTM algorithm can enhance the navigation accuracy 95% compared with pure INS algorithm, and 50% of the MLP algorithm.

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“…Because of the advantages in nonlinear mapping between inputs and outputs without the pre-defined mathematical model [10][11][12], the artificial neural networks (ANN) were proposed to reduce the INS navigation errors during GNSS outages. A brief summary of related research is introduced as follows.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Fusion Strategy for the Land Vehicle Navigation Using MEMS INS, Odometer and GNSS

Zhang

Gan

2020

IEEE Access

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How to limit the drifts of the navigation errors in an inertial navigation system (INS) with low-cost sensors is one of the main challenges for the land vehicle navigations. In this paper, we present a novel hybrid navigation strategy to integrate the Micro-Electric-Mechanic-systems (MEMS) INS, odometer (OD) and global navigation satellite systems (GNSS), with aim to enhance the positioning accuracy of the inertial system during GNSS outages. To accurately estimate the INS error states, the neural network (NN) is proposed to mimic the velocity of the navigation frame with the data from the MEMS INS, odometer, as well as the non-holonomic constraints (NHC). The long short-term memory (LSTM) NN is adopted in our approach due to its ability to adaptively use the data in the past. The road tests are conducted with two different MEMS IMUs to verify the proposed navigation strategy. Comparing to the traditional integrated MEMS INS/OD/GNSS system based on the extended Kalman filtering (EKF), our hybrid approach provides over 60% improvements in terms of the root mean square (RMS) and maximum horizontal position errors during GNSS outages.

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A novel hybrid approach based-SRG model for vehicle position prediction in multi-GPS outage conditions

Cited by 44 publications

References 17 publications

Research on high‐precision passive localization based on phase difference changing rate

Research on high‐precision passive localization based on phase difference changing rate

A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages

A Hybrid Fusion Strategy for the Land Vehicle Navigation Using MEMS INS, Odometer and GNSS

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