Neumann-Hoffman Code Evasion and Stripping Method For BeiDou Software-defined Receiver

Meng, Qian; Liu, Jianye; Zeng, Qinghua; Feng, Shaojun; Chen, Rui-zhi

doi:10.1017/s0373463316000515

Cited by 15 publications

(9 citation statements)

References 19 publications

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“…The code and navigation data of the B1 signal are multiplied by the carrier of the channel I (or Q) directly instead of going through a multiplexer (Xie et al, 2016), and the modulation process is illustrated in Figure 3. Different from the modulation method of GPS L1 navigation data, the BeiDou D1 navigation message is modulated with a 1 kbps Neumann-Hoffman (NH) code (Meng et al, 2017). Therefore, in order to eliminate the effect of NH code jump on the correlation peak value, 2 ms data are usually sampled for BeiDou satellite signal acquisition while 1 ms data is needed for GPS signal acquisition.…”

Section: Signal Model Of the B1 Signalmentioning

confidence: 99%

Parallel Frequency Acquisition Algorithm for BeiDou Software Receiver Based on Coherent Downsampling

et al. 2019

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The time it takes to acquire a satellite signal is one of the most important parameters for a Global Navigation Satellite System (GNSS) receiver. The Parallel Frequency space search acquisition Algorithm (PFA) runs faster than the Parallel Code phase search acquisition Algorithm (PCA) when the approximate phase of Pseudo-Random Noise (PRN) code and the approximate value of a Doppler shift are known. However, a large amount of data is needed to be dealt with by the Fast Fourier Transform (FFT) in a traditional PFA algorithm because it processes a narrow-band signal with the initial sampling frequency after the PRN code is stripped. In order to reduce the computational complexity of the traditional PFA algorithm, a down-conversion module and a downsampling module were added to the traditional PFA in the work reported here. Experiments demonstrated that this method not only succeeded in acquiring BeiDou B1I signals, but also the time for acquirement was reduced by at least 80% with the modified PFA algorithm compared with the traditional PFA algorithm. The loss in Signal-to-Noise Ratio (SNR) did not exceed 0·5 dB when the number of coherent points was less than 500.

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Section: Signal Model Of the B1 Signalmentioning

confidence: 99%

Parallel Frequency Acquisition Algorithm for BeiDou Software Receiver Based on Coherent Downsampling

et al. 2019

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“…As autonomous vehicles require decimetre-level even centimetre-level positioning accuracy, most of the current researches focus on the robust and reliable navigation solution based on multi-sensor. 5,6 It results that the performance assessment system is got less attentions in above safety-critical application. To some extent, the navigation safety requirements are much more important as they determine the status of safety and define the performance of sensor solutions at scale.…”

Section: Introductionmentioning

confidence: 99%

Integrity for autonomous vehicles and towards a novel alert limit determination method

Meng

Hsu

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Integrity is one critical performance indicator for navigation in safety-critical applications such as autonomous vehicles. Alert limit is one of the representative parameters in integrity monitoring which defines the maximum tolerable positioning error for an operation to safely proceed. But the integrity requirements for global navigation satellite system (GNSS) assessment are quite different from those for other applications. For autonomous vehicles, a reasonable alert limit needs to ensure the vehicle security and take full advantage of the space between vehicle and lane as much as possible. Based on the analysis of integrity application differences from civil aviation to autonomous vehicles, an improved alert limit determination method is proposed in this paper. The kinematic model is firstly introduced into the online determination of alert limit. The integrity risk on two sides are allocated optimally respect to the road geometry and kinematic model. The fixed cuboid bounding box is replaced by a subversive fan-shaped bounding box which is more reasonable to cover the safety-critical areas. The discussion compared with the Ford model also verified the superiority of the proposed method. Finally the paper also gives the alert limits calculated based on the Chinese standards and hopefully it could provide some references for the navigation integrity assessment for autonomous vehicles.

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“…Although an IMU is able to track sudden motions both in angular velocity and linear acceleration, it is subject to a growing unbounded error caused by the biases of the sensors. The absolute velocity and position of the vehicle can be measured by GNSS directly, which is very helpful in decreasing the accumulated errors of an IMU (Wang et al, 2013; Meng et al, 2017). In a GNSS denied environment (indoor environments for example), other sensors such as pseudolites (Gioia and Borio, 2014) and visual sensors are applied to supply the navigation information for MAVs.…”

Section: Introductionmentioning

confidence: 99%

Micro Aerial Vehicle Navigation with Visual-Inertial Integration Aided by Structured Light

et al. 2019

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Considering that traditional visual navigation cannot be utilised in low illumination and sparse feature environments, a novel visual-inertial integrated navigation method using a Structured Light Visual (SLV) sensor for Micro Aerial Vehicles (MAVs) is proposed in this paper. First, the measurement model based on an SLV sensor is studied and built. Then, using the state model based on error equations of an Inertial Navigation System (INS), the measurement model based on the error of the relative motion measured by INS and SLV is built. Considering that the measurements in this paper are mainly related to the position and attitude information of the present moment, the state error accumulation in traditional visual-inertial navigation can be avoided. An Adaptive Sage-Husa Kalman Filter (ASHKF) based on multiple weighting factors is proposed and designed to make full use of the SLV measurements. The results of the simulation and the experiment based on real flight data indicate that high accuracy position and attitude estimations can be obtained with the help of the algorithm proposed in this paper.

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Neumann-Hoffman Code Evasion and Stripping Method For BeiDou Software-defined Receiver

Cited by 15 publications

References 19 publications

Parallel Frequency Acquisition Algorithm for BeiDou Software Receiver Based on Coherent Downsampling

Parallel Frequency Acquisition Algorithm for BeiDou Software Receiver Based on Coherent Downsampling

Integrity for autonomous vehicles and towards a novel alert limit determination method

Micro Aerial Vehicle Navigation with Visual-Inertial Integration Aided by Structured Light

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