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

Hosseinyalamdary, Siavash

doi:10.3390/s18051316

Cited by 39 publications

(12 citation statements)

References 17 publications

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“…Recently, Deep Learning (DL) has gained a boom and performed excellently in various applications including image processing, Nature Language Processing (NLP) and sequential signal processing [30][31][32][33][34][35][36][37]. In aspects of time series processing, a recurrent neural network (RNN) was always the most feasible selection [30][31][32][33][34][35][36][37]. A common RNN was not sufficient, thus, variants of RNN were proposed for enhancing the performance.…”

Section: Introductionmentioning

confidence: 99%

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A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing

Jiang

Chen

et al. 2019

Electronics

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Currently, positioning, navigation, and timing information is becoming more and more vital for both civil and military applications. Integration of the global navigation satellite system and /inertial navigation system is the most popular solution for various carriers or vehicle positioning. As is well-known, the global navigation satellite system positioning accuracy will degrade in signal challenging environments. Under this condition, the integration system will fade to a standalone inertial navigation system outputting navigation solutions. However, without outer aiding, positioning errors of the inertial navigation system diverge quickly due to the noise contained in the raw data of the inertial measurement unit. In particular, the micromechanics system inertial measurement unit experiences more complex errors due to the manufacturing technology. To improve the navigation accuracy of inertial navigation systems, one effective approach is to model the raw signal noise and suppress it. Commonly, an inertial measurement unit is composed of three gyroscopes and three accelerometers, among them, the gyroscopes play an important role in the accuracy of the inertial navigation system’s navigation solutions. Motivated by this problem, in this paper, an advanced deep recurrent neural network was employed and evaluated in noise modeling of a micromechanics system gyroscope. Specifically, a deep long short term memory recurrent neural network and a deep gated recurrent unit–recurrent neural network were combined together to construct a two-layer recurrent neural network for noise modeling. In this method, the gyroscope data were treated as a time series, and a real dataset from a micromechanics system inertial measurement unit was employed in the experiments. The results showed that, compared to the two-layer long short term memory, the three-axis attitude errors of the mixed long short term memory–gated recurrent unit decreased by 7.8%, 20.0%, and 5.1%. When compared with the two-layer gated recurrent unit, the proposed method showed 15.9%, 14.3%, and 10.5% improvement. These results supported a positive conclusion on the performance of designed method, specifically, the mixed deep recurrent neural networks outperformed than the two-layer gated recurrent unit and the two-layer long short term memory recurrent neural networks.

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Section: Introductionmentioning

confidence: 99%

“…Among the variants, Long Short Term Memory (LSTM) and the Gated Recurrent Unit (GRU) were most popular. LSTM-RNN and GRU-RNN both obtained excellent performance in NLP [30][31][32][33][34][35][36][37]. In addition, in our previous paper, LSTM was employed and compared in MEMS gyroscope de-noising [8].…”

Section: Introductionmentioning

confidence: 99%

A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing

Jiang

Chen

et al. 2019

Electronics

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“…Generally, in the data science community, sequence prediction problems have been around for a long period of time in a wide range of applications, including stock price prediction and sales pattern finding, language translation and speech recognization [ 37 , 38 , 39 , 40 , 41 ]. Recently, a new breakthrough has happened in the data science community, and a Long Short Term Memory Recurrent Neutral Networks (LSTM-RNN) has been proposed and has been demonstrated more effective for almost all of these sequence prediction problems [ 37 , 38 , 39 , 40 , 41 ]. Compared with conventional RNN, LSTM-RNN introduces the “gate structure” to address the long-term memory, which allows it to have the pattern of selectively remembering for a long time.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the LSTM-RNN is incorporated in MEMS IMU gyroscope raw signal de-noising. LSTM-RNN has performed excellently in time series signal processing, for instance stock price prediction, speech single processing, and others [ 37 , 38 , 39 , 40 , 41 ]. A MEMS Inertial Measurement Unit (IMU) manufactured by MT Microsystems Company known as MSI2000 IMU is employed in the experiments for testing [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

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

Jiang

Shuai

Chen

et al. 2018

Sensors

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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.

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“…Recently, machine learning has been attracting much research interest from various fields due to numerous successful applications to solve significant practical problems [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. Most of the conventional approaches in communication system design rely on maximizing or minimizing the objective functions, i.e., optimization-driven approaches.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems

Lin¹,

Shin

Joung

2019

Entropy

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In this paper, a support vector machine (SVM) technique has been applied to an antenna allocation system with multiple antennas in multiuser downlink communications. Here, only the channel magnitude information is available at the transmitter. Thus, a subset of transmit antennas that can reduce multiuser interference is selected based on such partial channel state information to support multiple users. For training, we generate the feature vectors by fully utilizing the characteristics of the interference-limited setup in the multiuser downlink system and determine the corresponding class label by evaluating a key performance indicator, i.e., sum rate in multiuser communications. Using test channels, we evaluate the performance of our antenna allocation system invoking the SVM-based allocation and optimization-based allocation, in terms of sum-rate performance and computational complexity. Rigorous testing allowed for a comparison of a SVM algorithm design between one-vs-one (OVO) and one-vs-all (OVA) strategies and a kernel function: (i) OVA is preferable to OVO since OVA can achieve almost the same sum rate as OVO with significantly reduced computational complexity, (ii) a Gaussian function is a good choice as the kernel function for the SVM, and (iii) the variance (kernel scale) and penalty parameter (box constraint) of an SVM kernel function are determined by 21.56 and 7.67, respectively. Further simulation results revealed that the designed SVM-based approach can remarkably reduce the time complexity compared to a traditional optimization-based approach, at the cost of marginal sum rate degradation. Our proposed framework offers some important insights for intelligently combining machine learning techniques and multiuser wireless communications.

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

Cited by 39 publications

References 17 publications

A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing

A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing

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

Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems

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