scholarly journals C/N0 Estimator Based on the Adaptive Strong Tracking Kalman Filter for GNSS Vector Receivers

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 739 ◽  
Author(s):  
Shiming Liu ◽  
Sihai Li ◽  
Jiangtao Zheng ◽  
Qiangwen Fu ◽  
Yanhua Yuan
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Signal Amplitude ◽  
Satellite System ◽  
Static Field ◽  
Weak Signal ◽  
Important Indicator ◽  
Vector Receiver ◽  
Averaging Time ◽  
Global Navigation Satellite

The carrier-to-noise ratio (C/N0) is an important indicator of the signal quality of global navigation satellite system receivers. In a vector receiver, estimating C/N0 using a signal amplitude Kalman filter is a typical method. However, the classical Kalman filter (CKF) has a significant estimation delay if the signal power levels change suddenly. In a weak signal environment, it is difficult to estimate the measurement noise for CKF correctly. This article proposes the use of the adaptive strong tracking Kalman filter (ASTKF) to estimate C/N0. The estimator was evaluated via simulation experiments and a static field test. The results demonstrate that the ASTKF C/N0 estimator can track abrupt variations in C/N0 and the method can estimate the weak signal C/N0 correctly. When C/N0 jumps, the ASTKF estimation method shows a significant advantage over the adaptive Kalman filter (AKF) method in terms of the time delay. Compared with the popular C/N0 algorithms, the narrow-to-wideband power ratio (NWPR) method, and the variance summing method (VSM), the ASTKF C/N0 estimator can adopt a shorter averaging time, which reduces the hysteresis of the estimation results.

scholarly journals Estimation of fractional cycle bias for GPS/BDS-2/Galileo based on international GNSS monitoring and assessment system observations using the uncombined PPP model

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Jin Wang ◽  
Qin Zhang ◽  
Guanwen Huang
Keyword(s):  
Estimation Method ◽  
Satellite Orbit ◽  
Satellite System ◽  
Assessment System ◽  
Navigation Satellite ◽  
Dual Frequency ◽  
Fractional Cycle Bias ◽  
The Stability ◽  
High Consistency ◽  
Global Navigation Satellite

AbstractThe Fractional Cycle Bias (FCB) product is crucial for the Ambiguity Resolution (AR) in Precise Point Positioning (PPP). Different from the traditional method using the ionospheric-free ambiguity which is formed by the Wide Lane (WL) and Narrow Lane (NL) combinations, the uncombined PPP model is flexible and effective to generate the FCB products. This study presents the FCB estimation method based on the multi-Global Navigation Satellite System (GNSS) precise satellite orbit and clock corrections from the international GNSS Monitoring and Assessment System (iGMAS) observations using the uncombined PPP model. The dual-frequency raw ambiguities are combined by the integer coefficients (4,− 3) and (1,− 1) to directly estimate the FCBs. The details of FCB estimation are described with the Global Positioning System (GPS), BeiDou-2 Navigation Satellite System (BDS-2) and Galileo Navigation Satellite System (Galileo). For the estimated FCBs, the Root Mean Squares (RMSs) of the posterior residuals are smaller than 0.1 cycles, which indicates a high consistency for the float ambiguities. The stability of the WL FCBs series is better than 0.02 cycles for the three GNSS systems, while the STandard Deviation (STD) of the NL FCBs for BDS-2 is larger than 0.139 cycles. The combined FCBs have better stability than the raw series. With the multi-GNSS FCB products, the PPP AR for GPS/BDS-2/Galileo is demonstrated using the raw observations. For hourly static positioning results, the performance of the PPP AR with the three-system observations is improved by 42.6%, but only 13.1% for kinematic positioning results. The results indicate that precise and reliable positioning can be achieved with the PPP AR of GPS/BDS-2/Galileo, supported by multi-GNSS satellite orbit, clock, and FCB products based on iGMAS.

scholarly journals Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform

Aerospace ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 280
Author(s):  
Farzan Farhangian ◽  
Hamza Benzerrouk ◽  
Rene Landry
Keyword(s):  
Kalman Filter ◽  
State Space Model ◽  
Flight Test ◽  
Satellite System ◽  
Low Earth Orbit ◽  
Measurement Unit ◽  
Starting Point ◽  
Leo Satellites ◽  
Positioning Method ◽  
Global Navigation Satellite

With the emergence of numerous low Earth orbit (LEO) satellite constellations such as Iridium-Next, Globalstar, Orbcomm, Starlink, and OneWeb, the idea of considering their downlink signals as a source of pseudorange and pseudorange rate measurements has become incredibly attractive to the community. LEO satellites could be a reliable alternative for environments or situations in which the global navigation satellite system (GNSS) is blocked or inaccessible. In this article, we present a novel in-flight alignment method for a strapdown inertial navigation system (SINS) using Doppler shift measurements obtained from single or multi-constellation LEO satellites and a rotation technique applied on the inertial measurement unit (IMU). Firstly, a regular Doppler positioning algorithm based on the extended Kalman filter (EKF) calculates states of the receiver. This system is considered as a slave block. In parallel, a master INS estimates the position, velocity, and attitude of the system. Secondly, the linearized state space model of the INS errors is formulated. The alignment model accounts for obtaining the errors of the INS by a Kalman filter. The measurements of this system are the difference in the outputs from the master and slave systems. Thirdly, as the observability rank of the system is not sufficient for estimating all the parameters, a discrete dual-axis IMU rotation sequence was simulated. By increasing the observability rank of the system, all the states were estimated. Two experiments were performed with different overhead satellites and numbers of constellations: one for a ground vehicle and another for a small flight vehicle. Finally, the results showed a significant improvement compared to stand-alone INS and the regular Doppler positioning method. The error of the ground test reached around 26 m. This error for the flight test was demonstrated in different time intervals from the starting point of the trajectory. The proposed method showed a 180% accuracy improvement compared to the Doppler positioning method for up to 4.5 min after blocking the GNSS.

scholarly journals Distributed Orbit Determination for Global Navigation Satellite System with Inter-Satellite Link

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1031 ◽  
Author(s):  
Yuanlan Wen ◽  
Jun Zhu ◽  
Youxing Gong ◽  
Qian Wang ◽  
Xiufeng He
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Global Navigation Satellite System ◽  
Orbit Determination ◽  
Measurement Data ◽  
Satellite System ◽  
Navigation Satellite ◽  
Satellite Link ◽  
Global Navigation ◽  
Global Navigation Satellite

To keep the global navigation satellite system functional during extreme conditions, it is a trend to employ autonomous navigation technology with inter-satellite link. As in the newly built BeiDou system (BDS-3) equipped with Ka-band inter-satellite links, every individual satellite has the ability of communicating and measuring distances among each other. The system also has less dependence on the ground stations and improved navigation performance. Because of the huge amount of measurement data, the centralized data processing algorithm for orbit determination is suggested to be replaced by a distributed one in which each satellite in the constellation is required to finish a partial computation task. In the present paper, the balanced extended Kalman filter algorithm for distributed orbit determination is proposed and compared with the whole-constellation centralized extended Kalman filter, the iterative cascade extended Kalman filter, and the increasing measurement covariance extended Kalman filter. The proposed method demands a lower computation power; however, it yields results with a relatively good accuracy.

A Precise Weighting Approach with Application to Combined L1/B1 GPS/BeiDou Positioning

2014 ◽  
Vol 67 (5) ◽  
pp. 911-925 ◽  
Author(s):  
Changsheng Cai ◽  
Lin Pan ◽  
Yang Gao
Keyword(s):  
Time Window ◽  
A Priori ◽  
Estimation Method ◽  
Satellite System ◽  
Positioning Accuracy ◽  
Satellite Systems ◽  
Beidou System ◽  
Component Estimation ◽  
Navigation Service ◽  
Global Navigation Satellite

The BeiDou system has been providing a regional navigation service since 27 December 2012. The Global Navigation Satellite System (GNSS) user community will benefit from combined Global Positioning System (GPS)/BeiDou positioning due to improved positioning accuracy, reliability and availability. But to achieve the best positioning solutions, precise weights of the GPS and BeiDou observations are important since this involves the processing of measurements from two different satellite systems with different quality. Currently, a priori variances are typically used to determine the weights of different types of observations. However, such an approach may not be precise since many un-modelled errors are not accounted for. The Helmert variance component estimation method is more appropriate in this case to determine the weights of GPS and BeiDou observations. This requires high redundant observations in order to obtain reliable solutions, which will be a concern in the case of insufficient numbers of visible satellites. To address this issue, a weighting approach is proposed by a combination of the Helmert method and a moving-window average filter. In this approach, the filter is applied to combine all epoch-by-epoch weight estimates within a time window. As a result, more precise and reliable weights for GPS and BeiDou observations can be obtained at every epoch. Both static and kinematic tests in open sky and under tree environments are conducted to assess the performance of the new weighting approach. The results indicate significantly improved positioning accuracy.

scholarly journals Moving Horizon Estimation for GNSS-IMU sensor fusion

2020 ◽  
pp. 112-122
Author(s):  
Guido Sánchez ◽  
Marina Murillo ◽  
Lucas Genzelis ◽  
Nahuel Deniz ◽  
Leonardo Giovanini
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Satellite System ◽  
Measurement Unit ◽  
Moving Horizon Estimation ◽  
Ground Vehicle ◽  
Global Navigation Satellite ◽  
Time Systems ◽  
True Values

The aim of this work is to develop a Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensor fusion system. To achieve this objective, we introduce a Moving Horizon Estimation (MHE) algorithm to estimate the position, velocity orientation and also the accelerometer and gyroscope bias of a simulated unmanned ground vehicle. The obtained results are compared with the true values of the system and with an Extended Kalman filter (EKF). The use of CasADi and Ipopt provide efficient numerical solvers that can obtain fast solutions. The quality of MHE estimated values enable us to consider MHE as a viable replacement for the popular Kalman Filter, even on real time systems.

scholarly journals Moving Target Detection in Multi-Static GNSS-Based Passive Radar Based on Multi-Bernoulli Filter

2020 ◽  
Vol 12 (21) ◽  
pp. 3495
Author(s):  
HongCheng Zeng ◽  
Jie Chen ◽  
PengBo Wang ◽  
Wei Liu ◽  
XinKai Zhou ◽  
...  
Keyword(s):  
Target Detection ◽  
Sequential Monte Carlo ◽  
Signal To Noise Ratio ◽  
Power Level ◽  
Estimation Method ◽  
Satellite System ◽  
Moving Target ◽  
Passive Radar ◽  
Moving Target Detection ◽  
Global Navigation Satellite

Over the past few years, the global navigation satellite system (GNSS)-based passive radar (GBPR) has attracted more and more attention and has developed very quickly. However, the low power level of GNSS signal limits its application. To enhance the ability of moving target detection, a multi-static GBPR (MsGBPR) system is considered in this paper, and a modified iterated-corrector multi-Bernoulli (ICMB) filter is also proposed. The likelihood ratio model of the MsGBPR with range-Doppler map is first presented. Then, a signal-to-noise ratio (SNR) online estimation method is proposed, which can estimate the fluctuating and unknown map SNR effectively. After that, a modified ICMB filter and its sequential Monte Carlo (SMC) implementation are proposed, which can update all measurements from multi-transmitters in the optimum order (ascending order). Moreover, based on the proposed method, a moving target detecting framework using MsGBPR data is also presented. Finally, performance of the proposed method is demonstrated by numerical simulations and preliminary experimental results, and it is shown that the position and velocity of the moving target can be estimated accurately.

scholarly journals Correlation between Ionospheric TEC and the DCB Stability of GNSS Receivers from 2014 to 2016

2019 ◽  
Vol 11 (22) ◽  
pp. 2657 ◽  
Author(s):  
Choi ◽  
Sohn ◽  
Lee
Keyword(s):  
Estimation Method ◽  
Satellite System ◽  
Electron Content ◽  
Strong Positive Correlation ◽  
Low Latitude ◽  
Total Electron ◽  
Gnss Receiver ◽  
Gnss Receivers ◽  
Global Navigation Satellite ◽  
The Relationship

The Global Navigation Satellite System (GNSS) differential code biases (DCBs) are a major obstacle in estimating the ionospheric total electron content (TEC). The DCBs of the GNSS receiver (rDCBs) are affected by various factors such as data quality, estimation method, receiver type, hardware temperature, and antenna characteristics. This study investigates the relationship between TEC and rDCB, and TEC and rDCB stability during a three-year period from 2014 to 2016. Linear correlations between pairs of variables, measured with Pearson’s coefficient (), are considered. It is shown that the correlation between TEC and rDCB is the smallest in low-latitude regions. The mid-latitude regions exhibit the maximum value of. In contrast, the correlation between TEC and rDCB root mean square (RMS, stability) was greater in low-latitude regions. A strong positive correlation (R≥0.90) on average between TEC and rDCB RMS was also revealed at two additional GNSS stations in low-latitude regions, where the correlation shows clear latitudinal dependency. We found that the correlation between TEC and rDCB stability is still very strong even after replacing a GNSS receiver.

scholarly journals Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; GNSS/IMU Case Study

2018 ◽  
Author(s):  
Siavash Hosseinyalamdary
Keyword(s):  
Kalman Filter ◽  
Satellite System ◽  
Measurement Unit ◽  
Sources Of Information ◽  
Error Sources ◽  
Multiple Sensors ◽  
Efficient Integration ◽  
Global Navigation Satellite ◽  
Gnss Observations

The Bayes filters, such as Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of the unknowns. Efficient integration of multiple sensors requires deep knowledge of their error sources and it is not trivial for complicated sensors, such as Inertial Measurement Unit (IMU). Therefore, IMU error modelling and efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we develop deep Kalman filter to model and remove IMU errors and consequently, improve the accuracy of IMU positioning. In other words, we add modelling step to the prediction and update steps of Kalman filter and the IMU error model is learned during integration. Therefore, our deep Kalman filter outperforms Kalman filter and reaches higher accuracy.

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