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Progri I.F.,Giftet Inc.
Proceedings of the 2017 International Technical Meeting of The Institute of Navigation, ITM 2017 | Year: 2017

This paper examines the VBOC1(α) generalized multidimensional geolocation modulation (GMGM) waveforms, their time domain representation, the autocorrelation function (ACF), power spectral density (PSD) function, and the ACF pure signal optimization (PSO) for the generalized subcarrier parameter frequency, (p = {1,⋯, 8}). I.e., this paper is an extension of the work that was performed for VBOC1(α) GMGM waveforms and ACF PSO for the generalized subcarrier parameter frequency, (p = {1, 2, 3, 4}). There are a number of reasons why the extension of this work is important. First, it is not really obvious or intuitive how this extension is done. Second, so far no one has produced results to clearly demonstrate that this extension does not change the performance results VBOC1(α) GMGM waveforms and ACF PSO aside from my preliminary investigation. Third, this extension now serves as building block. One, should be able to easily extend this work for values of p = {9, 10, ..., ∝}; i.e., this work is really the closes thing to a complete poof that we can give. Afterwards, by employing any of the optimization algorithms such as sum, product, and MMSE on GPS L1C BOC(6,1) signals such as VBOC1(6,1,0.5) we are able to produce ACFs that are one hundred percent more efficient than the corresponding ACFs of the GPS L1C BOC(6,1) signals. Hence, we have the provision for a new L1C signal: TMVBOC(1,1,29/33,0.5) on the data and TMVBOC(6,1,4/33,0.5) on the pilot. I am also making provision for the new signal in the military code of the current M-code from the current BOC(2,1) to the TMVBOC(8,1,4/33,0.5) in which the data is TMVBOC(2,1,29/33,0.5). © 2017, Institute of Navigation. All rights reserved.


Progri I.F.,Giftet Inc.
Proceedings of the 2017 International Technical Meeting of The Institute of Navigation, ITM 2017 | Year: 2017

In this paper an advanced anti-jam indoor adaptive GNSS signal acquisition and tracking algorithm is considered. Initially, we were able to determine that double-dwell structure (DDS) reduces the processing time penalty caused by false alarm. Nevertheless, the double-dwell structure is still vulnerable to interference and jamming. In order to determine a suitable advanced anti-jam indoor adaptive GNSS signal acquisition and tracking algorithm for DDS we first perform the Bayesian parameter estimation; i.e., we analytically compute the posterior Bayes probability density function (pdf) and cumulative distribution function (cdf) by applying the Bayes theorem in three steps. First, we compute the complex signal distribution and complex matrix variate signal distribution. This is an original new result never published before. Second, we provide an introduction of the complex Bessel or parabolic function interference distribution due to earlier assumptions of the DDS interference distribution models. We further explain that the process that is required to produce Complex Matrix Variate Bessel Interference Distribution or Complex Matrix Variate Parabolic Function Interference Distribution is an extremely daunting task let alone the Complex Matrix Variate DDS Bayesian posterior density it would seem at the moment nearly an impossible task because it may require the computation of functions such as Kampé de Fériet function and Jack functions of matrix arguments. Third, instead of performing the second step, we relax the assumption of interference to normal distribution. In both the scalar case and complex matrix variate cases we observe that the complex matrix variate Bayesian posterior pdf or cdf is invariant of the observation data or is identical to the prior complex matrix variate signal distribution model. This is an original new and very powerful result never published before. Why this result is so powerful is because up until now we never had a complete theoretical validation of our GNSS receiver design based on either autocorrelation or cross-correlation properties since, the complex matrix variate Bayesian posterior pdf or cdf is invariant of the observation data or is identical to the prior complex matrix variate signal distribution model. Future simulation results will illustrate that an anti-jam indoor adaptive DDS stable detection structure reduces by half the average acquisition time and significantly outperform its predecessor against interference and jamming. Moreover, future simulation results will show further advancements of the Giftet Inc. MATLAB library capability to perform advanced numerical computations based on closed form expressions of the generalized Bessel function distributions and generalized parabolic cylinder function distribution models via Kampé de Fériet function and Jack functions. © 2017, Institute of Navigation. All rights reserved.


Wang G.H.,Yantai Naval Aeronautical and Astronautical University | Chen L.,Yantai Naval Aeronautical and Astronautical University | Jia S.Y.,Yantai Naval Aeronautical and Astronautical University | Progri I.,Giftet Inc.
Journal of Navigation | Year: 2013

For mobile 3-D radar installed on a gyro-stabilized platform, its measurements are usually contaminated by the systematic biases which contain radar offset biases (i.e., range, azimuth and elevation biases) and attitude biases (i.e., yaw, pitch and roll biases) of the platform because of the errors in the Inertial Measurement Units (IMU). Systematic biases can NOT be removed by a single radar itself; however, fortunately, they can be estimated by using two different radar measurements of the same target. The process of estimating systematic biases and then compensating radar measurements is called error registration. In this paper, the registration models are established first, then, the equivalent radar measurement error expressions caused by the attitude biases are derived and the dependencies among attitude biases and offset biases are analysed by using the observable matrix criterion. Based on the analyses above, an Optimized Bias Estimation Model (OBEM) is proposed for registration. OBEM uses the subtraction of azimuth and yaw bias as one variable and omits roll and pitch biases in the state vector, which decreases the dimension of the state vector from fourteen of the All Augmented Model (AAM), (which uses all the systematic biases of both radars as state vector) to eight and has about 80% reduction in calculation costs. Also, OBEM can decrease the coupling influences of roll and pitch biases and improve the estimation performance of radar elevation bias. Monte Carlo experiments were made. Numerical results showed that the bias estimation accuracies and the rectified radar raw measurement accuracies can be improved. Copyright © The Royal Institute of Navigation 2012.


Chen L.,Yantai Naval Aeronautical and Astronautical University | Wang G.H.,Yantai Naval Aeronautical and Astronautical University | He Y.,Yantai Naval Aeronautical and Astronautical University | Progri I.,Giftet Inc.
Journal of Navigation | Year: 2014

For mobile radars installed on a gyro-stabilised platform (GSP) that can steadily follow an East-North-Up (ENU) frame, attitude biases (ABs) of the platform and offset biases (OBs) of the radar are linear dependent variables. Therefore ABs and OBs are unobservable in the linearized registration equations; however, when combining them as new variables, the system becomes observable, and this model has been called the unified registration model (URM). Unlike GSP mobile radars, un-stabilised GSP (or UGSP) mobile radars are installed on the platform directly and rotate with the platform simultaneously. For UGSP, it is testified that both types of biases are independent and observable because the time-varying attitude angles (AAs) 1 of the platform are included in the registration equations, which destroy the dependencies of both kinds of biases and lead us to propose a completely different linearized registration model- the All Augmented Model (AAM). AAM employs all OBs and ABs in the state vector and a Kalman filter (KF) to produce their estimates. Numerical simulation results show that the estimated performance of AAM is close to the Cramér-Rao lower bound (CRLB) and that the Root Mean Square Errors (RMSEs) of the rectified measurements by using AAM are more than 500Â m smaller than by URM in all directions. Copyright © The Royal Institute of Navigation 2013 Â.


Chen L.,Yantai Naval Aeronautical and Astronautical University | Wang G.H.,Yantai Naval Aeronautical and Astronautical University | Jia S.Y.,Yantai Naval Aeronautical and Astronautical University | Progri I.,Giftet Inc.
Journal of Navigation | Year: 2012

Besides offset biases (such as range, the gain of range, azimuth, and elevation biases), for mobile radars, platform attitude biases (such as yaw, pitch, and roll biases) induced by the accumulated errors of the Inertial Measurement Units (IMU) of the Inertial Navigation System (INS) can also influence radar measurements. Both kinds of biases are coupled. Based on the analyses of the coupling influences and the observability of 3-D radars error registration model, in the article, an Attitude Bias Conversion Model (ABCM) based on Square Root Unscented Kalman Filter (SRUKF) is proposed. ABCM can estimate 3-D radars absolute offset biases under the influences of platform attitude biases. It converts platform attitude biases into radar measurement errors, by which the target East-North-Up (ENU) coordinates can be obtained from radar measurements directly without using the rotation transformation, which was usually used in the transition from platform frame to ENU considering attitude biases. In addition, SRUKF can avoid the inaccurate estimations caused by linearization, and it can weaken the adverse influences of the poor attitude bias estimation results in the application of ABCM. Theoretical derivations and simulation results show that 1) ABCM-SRUKF can improve elevation bias estimate accuracy to about 0•8 degree in the mean square error sense; 2) linearization is not the main reason for poor estimation of attitude biases; and 3) unobservability is the main reason. © 2012 The Royal Institute of Navigation.


Progri I.F.,Giftet Inc. | Huang P.,University of Electronic Science and Technology of China | Pi Y.,University of Electronic Science and Technology of China | Xia X.,Huazhong University of Science and Technology
Institute of Navigation International Technical Meeting 2016, ITM 2016 | Year: 2016

Indoors, GNSS signal encounters severe multipath power loss and fading which leads to significant signal degradation of the amplitude and phase to perform GPS (or GNSS) signal acquisition. To overcome these effects, piling up received GPS (or GNSS) data is a traditional (or conventional) method; however, it exhibits two shortcomings (drawbacks or limitations): instable detection performance, such as the probability of false alarm (PFA) fluctuates due to changes of the signal-to-noise ratio (SNR); and elongated acquisition time caused by extended accumulation duration and over repetitive FA occurrence (or "penalty"). To overcome the first shortcoming, an adaptive structure is employed in GNSS signal acquisition that enables constant FA rate (CFAR) criteria to guarantee a stable detection performance on the GNSS signal acquisition. To overcome the second shortcoming, an adaptive determination on accumulation length is employed to minimize the accumulation duration; therefore, a double-dwell structure (DDS) is used to reduce the processing time "penalty" caused by FA. Simulation results illustrate that an adaptive, stable detection implementation and DDS reduce the average acquisition time by almost fifty percent (or by half or a factor of two). Other important significant unique accomplishments found in this paper are as follows: Dr. Progri for the first time provides the closed form expressions of generalized modified Bessel function distributions of the cumulative distribution functions (cdf) of the first and second kinds and for the first time introduces the very powerful utility of the Kampé de Fériet function and parabolic cylinder function in the navigation community. © 2016 by Institute of Navigation. All right reserved.


Progri I.F.,Giftet Inc.
Proceedings of the Annual Precise Time and Time Interval Systems and Applications Meeting, PTTI | Year: 2014

This paper presents the complete original definition of first generation Variable Binary Offset Carrier generalized multidimensional geolocation modulation waveforms, to improve the standardization of the United States DoD GPS, European Galileo, Russian GLONASS, Chinese Compass, Indian IRNSS in the L-band (1-2 GHz), and the United Nations International Telecommunications Union (ITU) GNSS or geolocation waveforms in the S-band (2-4 GHz) and C-band (4-8 GHz). In the paper it is argued that the selection of BOC(1,1) on the GPS L1 civil data code and BOC(10,5) (or the military code or M-Code) on both GPS L1 and L2 frequencies is entirely arbitrary because BOC modulation is a special case of for or ; hence, all the current state-of-the-art GNSS waveforms exhibit sub-optimal signal design performance even at the end-user when generalized global objective functions are applied. pure signal design or broad definition of generalized autocorrelation function (ACF) and power spectral density (PSD) offers a unique signal design methodology and provides the necessary framework for ACF pure signal optimization to fill in substantial signal design gaps; hence, improving the GNSS signal design and standardization. Index Terms-Pulse generation, pulse amplitude modulation, pulse width modulation, multidimensional sequences, signal design, signal analysis, generalized functions, time-frequency analysis, minimization methods, optimization methods.


Progri I.F.,Giftet Inc.
Institute of Navigation International Technical Meeting 2015, ITM 2015 | Year: 2015

This paper examines common optimization algorithms, such as sum and mean square sense applied to the optimization of the autocorrelation functions (ACF) of GPS M-code like signals such as the variable binary offset carrier (VBOC). Before the different versions are examined for efficiency, on GPS M-code like signals such as VBOC2 which vary with the parameter of signal design and optimization alpha, they are checked to make sure that their corresponding ACFs are valid via a set of conditions known as continuity theorems. Afterwards, by employing any of the optimization algorithms such as sum and mean square sense on GPS M-code like signals such as VBOC2 we are able to produce ACFs that are one ninety four percent more efficient than the corresponding ACFs of the GPS M-code signals.


Progri I.F.,Giftet Inc.
Institute of Navigation International Technical Meeting 2015, ITM 2015 | Year: 2015

This paper presents the complete original definition of first generation Variable Binary Offset Carrier VB0C2(α, 1-α) generalized multidimensional geolocation modulation waveforms, to improve the standardization of the United States DoD GPS, European Galileo, Russian GLONASS, Chinese Compass, Indian IRNSS in the L-band (1-2 GHz), and the United Nations International Telecommunications Union (ITU) GNSS or geolocation waveforms in the S-band (2-4 GHz) and C-band (4-8 GHz). In the paper it is argued that the selection of BOC(10,5) (or the military code or M-Code) on both GPS LI and L2 frequencies is entirely arbitrary because BOC modulation is a special case of VBOC2(α, 1-α) for α = 0 or α - 1; hence, all the current state-of-the-art GNSS waveforms exhibit sub-optimal signal design performance even at the end-user when generalized global objective functions are applied. VB0C2(α, 1 - α) pure signal design or broad definition of generalized autocorrelation function (ACF) and power spectral density (PSD) offers a unique signal design methodology and provides the necessary framework for VBOC2(α, 1 - α) ACF pure signal optimization to fill in substantial signal design gaps; hence, improving the GNSS signal design and standardization.


Progri I.F.,Giftet Inc.
Institute of Navigation International Technical Meeting 2016, ITM 2016 | Year: 2016

This paper examines common optimization algorithms, such as sum, product, and minimum mean square error (MMSE) sense applied to the optimization of the autocorrelation functions (ACF) of GPS L1C and M-code like signals such as the variable binary offset carrier (VBOC). Before the different versions are examined for efficiency, on GPS L1C and M-code like signals such as VBOC1 which vary with the parameter of signal design and optimization alpha, they are checked to make sure that their corresponding ACFs are valid via a set of conditions known as continuity theorems. Afterwards, by employing any of the optimization algorithms such as sum, product, and MMSE on GPS L1C and M-code like signals such as VBOC1 we are able to produce ACFs that are one hundred percent more efficient than the corresponding ACFs of the GPS L1C and M-code signals. © 2016 by Institute of Navigation. All right reserved.

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