Pub Date : 2020-05-01DOI: 10.23919/icins43215.2020.9133936
A. Sholokhov, S. Berkovich, N. Kotov
A new solution to the estimation problem is proposed in which unknown parameters depend nonlinearly on available measurements. The final estimate is formed by the point-mass method as a weighted sum of partial estimates obtained at probable values of unknown parameters. The peculiarity of the solution is an additional account of the covariance of weight coefficients for partial estimates. In contrast to known approaches, a priori probabilities of values of unknown parameters are not assumed as the known given data. The considered approach is effective in solving nonlinear estimation problems characterized by low accuracy of available measurement data and (or) their few number.
{"title":"Nonlinear Estimation of Navigation and Geodetic Parameter on the Basis of the Point-Mass Method Taking into Account the Statistical Relationship of Node Weights","authors":"A. Sholokhov, S. Berkovich, N. Kotov","doi":"10.23919/icins43215.2020.9133936","DOIUrl":"https://doi.org/10.23919/icins43215.2020.9133936","url":null,"abstract":"A new solution to the estimation problem is proposed in which unknown parameters depend nonlinearly on available measurements. The final estimate is formed by the point-mass method as a weighted sum of partial estimates obtained at probable values of unknown parameters. The peculiarity of the solution is an additional account of the covariance of weight coefficients for partial estimates. In contrast to known approaches, a priori probabilities of values of unknown parameters are not assumed as the known given data. The considered approach is effective in solving nonlinear estimation problems characterized by low accuracy of available measurement data and (or) their few number.","PeriodicalId":127936,"journal":{"name":"2020 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130044554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-01DOI: 10.23919/icins43215.2020.9133810
V. Busurin, K. A. Korobkov, L. A. Shleenkin, N. A. Makarenkova
An optoelectronic acceleration compensation converter based on optical tunneling is proposed. The structural diagram of the acceleration converter is developed and a study of the influence of design parameters on its characteristics is carried out. The dynamic characteristics of the acceleration converter are investigated. The influence of temperature and lateral acceleration on the characteristics of the converter is analyzed.
{"title":"Compensation Linear Acceleration Converter Based on Optical Tunneling","authors":"V. Busurin, K. A. Korobkov, L. A. Shleenkin, N. A. Makarenkova","doi":"10.23919/icins43215.2020.9133810","DOIUrl":"https://doi.org/10.23919/icins43215.2020.9133810","url":null,"abstract":"An optoelectronic acceleration compensation converter based on optical tunneling is proposed. The structural diagram of the acceleration converter is developed and a study of the influence of design parameters on its characteristics is carried out. The dynamic characteristics of the acceleration converter are investigated. The influence of temperature and lateral acceleration on the characteristics of the converter is analyzed.","PeriodicalId":127936,"journal":{"name":"2020 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129943164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-01DOI: 10.23919/icins43215.2020.9133839
O. Zlatkin, V. G. Ignatyev, A. Kirichenko, V. I. Chumachenko, V.V. Zlatkina, Yu.A. Kuznyetsov
The methodology of calibrating a strapdown inertial navigation system (SINS) on a three-axis motion simulator is described. SINS is considered on the basis of serial fiber-optic gyroscopes (FOG) and pendulum accelerometers. The mathematical relations connect output signals of sensors with their main errors. The factory test results of SINS developed at the Research and Production Enterprise Hartron-Arkos, Ltd are presented.
{"title":"Technology for Automated Iterative Calibration of FOG-based SINS on a Three-Axis Motion Simulator","authors":"O. Zlatkin, V. G. Ignatyev, A. Kirichenko, V. I. Chumachenko, V.V. Zlatkina, Yu.A. Kuznyetsov","doi":"10.23919/icins43215.2020.9133839","DOIUrl":"https://doi.org/10.23919/icins43215.2020.9133839","url":null,"abstract":"The methodology of calibrating a strapdown inertial navigation system (SINS) on a three-axis motion simulator is described. SINS is considered on the basis of serial fiber-optic gyroscopes (FOG) and pendulum accelerometers. The mathematical relations connect output signals of sensors with their main errors. The factory test results of SINS developed at the Research and Production Enterprise Hartron-Arkos, Ltd are presented.","PeriodicalId":127936,"journal":{"name":"2020 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129224298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-01DOI: 10.23919/icins43215.2020.9133763
Xiaokang Yang, G. Yan, Sihai Li
With the development of MEMS (Micro-electromechanical Systems) manufacturing technology, MEMS inertial sensors have been widely applied in military industry and civil industry due to its advantages of low cost, low power consumption and small size. Although MIMU (MEMS-Inertial Measurement Unit) cannot meet the requirements of pure inertial navigation because of its low precision, it can be qualified for some specific navigation tasks by combining external data such as GNSS (Global Navigation Satellite System) and magnetic information with the fusion algorithms. MIMU is usually taken as the core sensor of AHRS (Attitude and Heading Reference System), meanwhile triaxial magnetometer is used to assist measuring attitude and heading with the gradient descent method. However, in the common gradient descent attitude estimation algorithm, the update step is unit size or just related to angular velocity. Hence, the estimated value of attitude converges slowly when the platform is stationary and the estimation result is unstable under the large angular velocity condition. In order to solve these problems, an estimation algorithm of attitude and heading based on improved gradient descent method is proposed in this paper. An inexact search method is adopted to obtain the optimal step length in each update, that improves the speed and stability of attitude estimation. The simulation results show that the estimation attitude of the improved algorithm can quickly converge to an accurate result in the condition of large initial error and the estimation precision is higher than conventional algorithm.
随着MEMS(微机电系统)制造技术的发展，MEMS惯性传感器以其低成本、低功耗、体积小等优点在军事工业和民用工业中得到了广泛的应用。虽然MIMU (MEMS-Inertial Measurement Unit, mems -惯性测量单元)精度不高，不能满足纯惯性导航的要求，但通过融合算法将GNSS (Global navigation Satellite System)、磁信息等外部数据结合起来，可以胜任某些特定的导航任务。AHRS(姿态航向参考系统)通常以MIMU为核心传感器，同时采用梯度下降法，利用三轴磁强计辅助测量姿态和航向。然而，在常用的梯度下降姿态估计算法中，更新步长是单位大小或仅与角速度相关。因此，平台静止时姿态估计值收敛缓慢，大角速度条件下估计结果不稳定。为了解决这些问题，本文提出了一种基于改进梯度下降法的姿态航向估计算法。采用非精确搜索方法在每次更新中获得最优步长，提高了姿态估计的速度和稳定性。仿真结果表明，在初始误差较大的情况下，改进算法的估计姿态可以快速收敛到准确的结果，估计精度高于常规算法。
{"title":"An Estimation Algorithm of Attitude and Heading Under Homogenous Field Based on Improved Gradient Descent Method","authors":"Xiaokang Yang, G. Yan, Sihai Li","doi":"10.23919/icins43215.2020.9133763","DOIUrl":"https://doi.org/10.23919/icins43215.2020.9133763","url":null,"abstract":"With the development of MEMS (Micro-electromechanical Systems) manufacturing technology, MEMS inertial sensors have been widely applied in military industry and civil industry due to its advantages of low cost, low power consumption and small size. Although MIMU (MEMS-Inertial Measurement Unit) cannot meet the requirements of pure inertial navigation because of its low precision, it can be qualified for some specific navigation tasks by combining external data such as GNSS (Global Navigation Satellite System) and magnetic information with the fusion algorithms. MIMU is usually taken as the core sensor of AHRS (Attitude and Heading Reference System), meanwhile triaxial magnetometer is used to assist measuring attitude and heading with the gradient descent method. However, in the common gradient descent attitude estimation algorithm, the update step is unit size or just related to angular velocity. Hence, the estimated value of attitude converges slowly when the platform is stationary and the estimation result is unstable under the large angular velocity condition. In order to solve these problems, an estimation algorithm of attitude and heading based on improved gradient descent method is proposed in this paper. An inexact search method is adopted to obtain the optimal step length in each update, that improves the speed and stability of attitude estimation. The simulation results show that the estimation attitude of the improved algorithm can quickly converge to an accurate result in the condition of large initial error and the estimation precision is higher than conventional algorithm.","PeriodicalId":127936,"journal":{"name":"2020 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124384040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-01DOI: 10.23919/icins43215.2020.9134001
Y. Liu, Tijing Cai, Li-Ming Wu
In order to improve the alignment accuracy and reduce time for the initial alignment of SINS, an improved CKF method is proposed. SINS nonlinear error model with large initial misalignment angles is built up. Based on the basic algorithm of CKF, multiple fading factors are introduced to the covariance matrix of the prediction errors to modulate gain matrix online in real-time for each data channel, which can improve the accuracy and robustness of the algorithm; Singular Value Decomposition is used instead of the traditional Cholesky decomposition of CKF to improve the stability of the algorithm. Experiment results show that the alignment time for azimuth angle of improved CKF is 100 seconds shorter than CKF, the alignment accuracy improved by 40% compared with CKF, and the alignment accuracy of azimuth angle is less than 0.1°. The experimental results show that the improved CKF effectively improves the alignment accuracy under the premise of higher speed, which better fits SINS initial alignment for large misalignment angles.
{"title":"Application of Improved CKF in SINS Initial Alignment with Large Misalignment Angles","authors":"Y. Liu, Tijing Cai, Li-Ming Wu","doi":"10.23919/icins43215.2020.9134001","DOIUrl":"https://doi.org/10.23919/icins43215.2020.9134001","url":null,"abstract":"In order to improve the alignment accuracy and reduce time for the initial alignment of SINS, an improved CKF method is proposed. SINS nonlinear error model with large initial misalignment angles is built up. Based on the basic algorithm of CKF, multiple fading factors are introduced to the covariance matrix of the prediction errors to modulate gain matrix online in real-time for each data channel, which can improve the accuracy and robustness of the algorithm; Singular Value Decomposition is used instead of the traditional Cholesky decomposition of CKF to improve the stability of the algorithm. Experiment results show that the alignment time for azimuth angle of improved CKF is 100 seconds shorter than CKF, the alignment accuracy improved by 40% compared with CKF, and the alignment accuracy of azimuth angle is less than 0.1°. The experimental results show that the improved CKF effectively improves the alignment accuracy under the premise of higher speed, which better fits SINS initial alignment for large misalignment angles.","PeriodicalId":127936,"journal":{"name":"2020 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122406544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-01DOI: 10.23919/icins43215.2020.9133944
D. Wei, B. Guan, S. Li, Q. Fu
Initial alignment technology is one of the key technologies of strapdown inertial navigation system and its precision will affect the navigation result. In order to improve the accuracy of initial alignment and shorten the alignment time, this paper introduces the rotary modulation technology into the strapdown inertial navigation system, designs an IMU translocation scheme of continuous rotary alignment, expounds the principle of rotary modulation initial alignment, and establishes the static reference alignment error model for strapdown inertial navigation system based on Kalman filtering. The simulation results show that compared with the traditional single position alignment and two-position alignment, the scheme of rotational modulation initial alignment can improve the alignment accuracy effectively.
{"title":"Study on Strapdown Inertial Navigation Initial Alignment Method Based on Uniaxial Rotation Modulation","authors":"D. Wei, B. Guan, S. Li, Q. Fu","doi":"10.23919/icins43215.2020.9133944","DOIUrl":"https://doi.org/10.23919/icins43215.2020.9133944","url":null,"abstract":"Initial alignment technology is one of the key technologies of strapdown inertial navigation system and its precision will affect the navigation result. In order to improve the accuracy of initial alignment and shorten the alignment time, this paper introduces the rotary modulation technology into the strapdown inertial navigation system, designs an IMU translocation scheme of continuous rotary alignment, expounds the principle of rotary modulation initial alignment, and establishes the static reference alignment error model for strapdown inertial navigation system based on Kalman filtering. The simulation results show that compared with the traditional single position alignment and two-position alignment, the scheme of rotational modulation initial alignment can improve the alignment accuracy effectively.","PeriodicalId":127936,"journal":{"name":"2020 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117136636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-01DOI: 10.23919/icins43215.2020.9133878
N. Al Bitar, A. Gavrilov
The integrated navigation system consists of Inertial Navigation System (INS) and receiver of Global Navigation Satellite System (GNSS). Aiming to improve position and velocity precision of the INS/GNSS system during GNSS outages, a novel method that combines unscented Kalman filter (UKF) and nonlinear autoregressive neural networks with external inputs (NARX) is proposed (namely NARX aided UKF). The NARX-based module is used to predict the measurements for UKF during GNSS signal outages. A new method for choosing inputs of NARX networks is suggested. This method is based on mutual information criterion (MI) for identifying the inputs that influence each of outputs and lag-space estimation (LSE) for investigating the dependency of these outputs on the past values of inputs and outputs. The performance of the proposed methodology is experimentally verified using data acquired from simulated flight trips, in which the measurement model of MEMS-based INS is used.
{"title":"Neural Networks Aided Unscented Kalman Filter for Integrated INS/GNSS Systems","authors":"N. Al Bitar, A. Gavrilov","doi":"10.23919/icins43215.2020.9133878","DOIUrl":"https://doi.org/10.23919/icins43215.2020.9133878","url":null,"abstract":"The integrated navigation system consists of Inertial Navigation System (INS) and receiver of Global Navigation Satellite System (GNSS). Aiming to improve position and velocity precision of the INS/GNSS system during GNSS outages, a novel method that combines unscented Kalman filter (UKF) and nonlinear autoregressive neural networks with external inputs (NARX) is proposed (namely NARX aided UKF). The NARX-based module is used to predict the measurements for UKF during GNSS signal outages. A new method for choosing inputs of NARX networks is suggested. This method is based on mutual information criterion (MI) for identifying the inputs that influence each of outputs and lag-space estimation (LSE) for investigating the dependency of these outputs on the past values of inputs and outputs. The performance of the proposed methodology is experimentally verified using data acquired from simulated flight trips, in which the measurement model of MEMS-based INS is used.","PeriodicalId":127936,"journal":{"name":"2020 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123673489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-01DOI: 10.23919/icins43215.2020.9134008
Ruiyang Zhou, N. Konstantin, Selezneva Mariya, Ryazanova Natalya, Xinke Zhang
The authors studied the task of processing the information from the optical system when the UAV is landing on a moving unmanned vehicle. Generally, color image analysis algorithms are very accurate, but they cannot work in real time or need to enhance the performance of professional computers. A compact high-speed color image recognition algorithm is developed basing on a pre-processing method—a «downsample» function for decimation; HSV model; Otsu's method - an algorithm for calculating the binary threshold of grayscale images, and method for isolating connected components-Two-Pass method. The simulation results demonstrated the operating capability and high enough efficiency of the developed algorithm. It is possible to achieve a significant reduction in the implementation time of the algorithm by using the decimation function and the HSV model.
{"title":"Motion Algorithm for Unmanned Aerial Vehicle Landing on a Car","authors":"Ruiyang Zhou, N. Konstantin, Selezneva Mariya, Ryazanova Natalya, Xinke Zhang","doi":"10.23919/icins43215.2020.9134008","DOIUrl":"https://doi.org/10.23919/icins43215.2020.9134008","url":null,"abstract":"The authors studied the task of processing the information from the optical system when the UAV is landing on a moving unmanned vehicle. Generally, color image analysis algorithms are very accurate, but they cannot work in real time or need to enhance the performance of professional computers. A compact high-speed color image recognition algorithm is developed basing on a pre-processing method—a «downsample» function for decimation; HSV model; Otsu's method - an algorithm for calculating the binary threshold of grayscale images, and method for isolating connected components-Two-Pass method. The simulation results demonstrated the operating capability and high enough efficiency of the developed algorithm. It is possible to achieve a significant reduction in the implementation time of the algorithm by using the decimation function and the HSV model.","PeriodicalId":127936,"journal":{"name":"2020 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129836941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-01DOI: 10.23919/icins43215.2020.9133965
A. V. Molodenkov, S. Perelyaev, T. Molodenkova, Y. Sapunkov
The exact solution of the Riccati-type approximate quaternion equation has made it possible to solve the problem of determining the quaternion of orientation of a rigid body for an arbitrary angular velocity and small angle of rotation of a rigid body with the help of quadratures (the truncated Darboux problem is actually solved). Proceeding from this solution, the following approach to the design of a new algorithm for computation of strapdown INS orientation is proposed.
{"title":"The Exact Solution of the Riccati-Type Approximate Kinematic Equation and its Application to Construct a Quaternion Algorithm for Determining Orientation of a Strapdown INS","authors":"A. V. Molodenkov, S. Perelyaev, T. Molodenkova, Y. Sapunkov","doi":"10.23919/icins43215.2020.9133965","DOIUrl":"https://doi.org/10.23919/icins43215.2020.9133965","url":null,"abstract":"The exact solution of the Riccati-type approximate quaternion equation has made it possible to solve the problem of determining the quaternion of orientation of a rigid body for an arbitrary angular velocity and small angle of rotation of a rigid body with the help of quadratures (the truncated Darboux problem is actually solved). Proceeding from this solution, the following approach to the design of a new algorithm for computation of strapdown INS orientation is proposed.","PeriodicalId":127936,"journal":{"name":"2020 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131644158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-01DOI: 10.23919/icins43215.2020.9133858
A. Kumarin, I. Kudryavtsev, S. Shafran
Software defined radio-based GNSS receivers are getting more and more popular. Signal tracking is the most time-consuming part of signal processing in such receivers. The main purpose was to design FPGA-based signal tracking module of a System-on-Chip-based receiver. Its hardware processor system is used for signal acquisition and position computation, while the FPGA part is used for signal tracking. The design includes data buffers with shared memory, resource-efficient signal multipliers, set of correlators, discriminators and special multichannel memory-based C/A code generator. All the units are implemented using System Verilog hardware description language.
{"title":"Implementation of a GNSS Receiver Signal Tracking Module","authors":"A. Kumarin, I. Kudryavtsev, S. Shafran","doi":"10.23919/icins43215.2020.9133858","DOIUrl":"https://doi.org/10.23919/icins43215.2020.9133858","url":null,"abstract":"Software defined radio-based GNSS receivers are getting more and more popular. Signal tracking is the most time-consuming part of signal processing in such receivers. The main purpose was to design FPGA-based signal tracking module of a System-on-Chip-based receiver. Its hardware processor system is used for signal acquisition and position computation, while the FPGA part is used for signal tracking. The design includes data buffers with shared memory, resource-efficient signal multipliers, set of correlators, discriminators and special multichannel memory-based C/A code generator. All the units are implemented using System Verilog hardware description language.","PeriodicalId":127936,"journal":{"name":"2020 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116133648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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