Implementation and performance evaluation of a fast relocation method in a GPS/SINS/CSAC integrated navigation system hardware prototype

2020

IEEE Access

Self Cite

Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) are the most commonly used navigation systems. They both have unique advantages and disadvantages. GNSS is capable of generating precise navigation solutions while enough satellites are in view. However, the GNSS signals are sensitive to the environment. While the signals is attenuated, the GNSS receiver will fail to provide reliable navigation solutions. INS is an advanced navigation system built based on Newton's law. Due to the random noises contained in the Inertial Measurement Unit (IMU), the INS navigation solutions errors diverge over time. Therefore, effective integration of the two common systems can obtain better navigation results than any individual system. In this paper, for enhancing the GNSS/INS tightly integration system with low cost, multiple receivers were employed in the tight integration. Pseudo-range and Pseudo-range rates from the multiple receivers were employed to compose the measurement vector of the integration filter. In order to reduce the computation load, a measurement difference method was proposed. The state vector dimension could be reduced with the measurement difference scheme. Both simulation and field test were carried out for evaluating the performance of the proposed method. Since it was hard to obtain GNSS raw measurements from commercial receivers, self-developed DSP+FPGA (Digital Signal Processor, DSP; Field Programmable Gate Array, FPGA) based GNSS receivers were employed in the field test. The statistical analysis of the results showed that the positioning errors decreased with the receiver's amount increasing. In addition, a measurement difference method was proposed to reduce the state vector dimension for saving the computation load with the identical navigation solutions accuracy. INDEX TERMS GNSS, INS, tight integration, multiple receivers. II. MULTIPLE RECEIVERS/MEMS-IMU TIGHT INTEGRATION MODEL

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Enhancement of GNSS/MEMS-IMU Tightly Integration Navigation System Using Multiple Receivers

Zhu

2020

IEEE Access

Self Cite

“…With continuously receiving the signal from the navigation satellites in orbit, the GNSS receiver is able to provide reliable and constant positioning, navigation and timing (PNT) information [ 6 , 7 , 8 , 9 , 10 ]. However, limited by the principle that at least four satellites are essential for computing positioning and velocity, the GNSS receiver will fail to work normally under challenging signal conditions [ 6 , 7 , 8 , 9 , 10 ]. The navigation signal transmits from the satellites a long way, and becomes too weak when reaching the ground, therefore, it can be easily blocked temporarily by the environment.…”

Section: Introductionmentioning

confidence: 99%

A MEMS IMU De-Noising Method Using Long Short Term Memory Recurrent Neural Networks (LSTM-RNN)

Shuai

Chen

et al. 2018

Sensors

Self Cite

Microelectromechanical Systems (MEMS) Inertial Measurement Unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in position and navigation, due to gradually improved accuracy and its small size and low cost. However, the errors of a MEMS IMU based standalone Inertial Navigation System (INS) will diverge over time dramatically, since there are various and nonlinear errors contained in the MEMS IMU measurements. Therefore, MEMS INS is usually integrated with a Global Positioning System (GPS) for providing reliable navigation solutions. The GPS receiver is able to generate stable and precise position and time information in open sky environment. However, under signal challenging conditions, for instance dense forests, city canyons, or mountain valleys, if the GPS signal is weak and even is blocked, the GPS receiver will fail to output reliable positioning information, and the integration system will fade to an INS standalone system. A number of effects have been devoted to improving the accuracy of INS, and de-nosing or modelling the random errors contained in the MEMS IMU have been demonstrated to be an effective way of improving MEMS INS performance. In this paper, an Artificial Intelligence (AI) method was proposed to de-noise the MEMS IMU output signals, specifically, a popular variant of Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) RNN was employed to filter the MEMS gyroscope outputs, in which the signals were treated as time series. A MEMS IMU (MSI3200, manufactured by MT Microsystems Company, Shijiazhuang, China) was employed to test the proposed method, a 2 min raw gyroscope data with 400 Hz sampling rate was collected and employed in this testing. The results show that the standard deviation (STD) of the gyroscope data decreased by 60.3%, 37%, and 44.6% respectively compared with raw signals, and on the other way, the three-axis attitude errors decreased by 15.8%, 18.3% and 51.3% individually. Further, compared with an Auto Regressive and Moving Average (ARMA) model with fixed parameters, the STD of the three-axis gyroscope outputs decreased by 42.4%, 21.4% and 21.4%, and the attitude errors decreased by 47.6%, 42.3% and 52.0%. The results indicated that the de-noising scheme was effective for improving MEMS INS accuracy, and the proposed LSTM-RNN method was more preferable in this application.

“…With the booming of the location-based service (LBS), the demand for position, velocity, and time (PVT) information has gained a significant increment [1][2][3][4][5]. Global Navigation Satellite System (GNSS) receiver has been the indispensable equipment for various vehicles, carriers, and smart devices, for instance, unmanned ground vehicles (UGV), unmanned aerial vehicle (UAV), smartphone, and so on [1][2][3][4][5]. With a GNSS receiver, these users are able to obtain accurate PVT information under an open-sky environment [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…However, while the signal reaches the earth, it is quite weak and easy to be disturbed or interfered [8][9][10]. As illustrated in past publications, the following two reasons account for this phenomena: (1) the transmitting signal power is limited by the energy of the navigation satellite, saving the energy and for keeping the satellite life span, it is hard to enlarge the navigation signal strength will consume more energy; (2) for saving the cost of constructing the GNSS, while meeting the demand of covering the earth with fewer satellites, the orbit is far away from the earth and the signal transmits a long distance before reaching the earth [8][9][10][11]. Due to the above drawbacks, a standalone GNSS is usually unable to output seamless and ubiquitous navigation solutions.…”

Section: Introductionmentioning

confidence: 99%

A MEMS Gyroscope Noise Suppressing Method Using Neural Architecture Search Neural Network

Zhu

Mathematical Problems in Engineering

2019

Self Cite

Inertial measurement unit (IMU) (an IMU usually contains three gyroscopes and accelerometers) is the key sensor to construct a selfcontained inertial navigation system (INS). IMU manufactured through the Micromechanics Electronics Manufacturing System (MEMS) technology becomes more popular, due to its smaller column, lower cost, and gradually improved accuracy. However, limited by the manufacturing technology, the MEMS IMU raw measurement signals experience complicated noises, which cause the INS navigation solution errors diverge dramatically over time. For addressing this problem, an advanced Neural Architecture Search Recurrent Neural Network (NAS-RNN) was employed in the MEMS gyroscope noise suppressing. NAS-RNN was the recently invented artificial intelligence method for time series problems in data science community. Different from conventional method, NAS-RNN was able to search a more feasible architecture for selected application. In this paper, a popular MEMS IMU STIM300 was employed in the testing experiment, and the sampling frequency was 125 Hz. e experiment results showed that the NAS-RNN was effective for MEMS gyroscope denoising; the standard deviation values of denoised three-axis gyroscope measurements decreased by 44.0%, 34.1%, and 39.3%, respectively. Compared with the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), the NAS-RNN obtained further decreases by 28.6%, 3.7%, and 8.8% in standard deviation (STD) values of the signals. In addition, the attitude errors decreased by 26.5%, 20.8%, and 16.4% while substituting the LSTM-RNN with the NAS-RNN.