Phase Tracking Problem Research Articles

Carleman framework-based filtering for a nonlinear phase tracking problem

Carleman framework-based filtering for a nonlinear phase tracking problem

Effective Phase Tracking for Bioinspired Undulations of Robotic Fish Models: A Learning Control Approach

Robotic models have been used as one of the approaches to study fish locomotion. Therefore, this paper proposes an effective control scheme that enables robotic models to mimic fin-ray undulation kinematics of live fish. We found in the experiments of robotic fin undulation that the difference between the desired and actual trajectories can be significant. It is believed that the difference might be caused by the phase lagging effect. To tackle the phase tracking problem, a modified iterative learning control (ILC) scheme is proposed and implemented on the robotic fish model. Furthermore, a memory clearing operator is proposed to satisfy the Lipschitz condition. This is necessary for the convergence and feasibility of the ILC scheme. Finally, experimental results illustrate the effectiveness of the proposed learning control approach, including the memory clearing operator.

The On-Grid Photovoltaic Power Generation Simulate System Design

The photovoltaic power generation, as the effective complement of the existing grid, usually adopts the on-grid method，which needs to solve the frequency and phase tracking problem; otherwise, once the frequency and phase of the electric energy in the photovoltaic power generation system are different from those in the grid, the solar power will become an interference source of the existing grid. In the on-grid photovoltaic power generation simulate system, use a DC power supply and a resistance to simulate solar cells. The microcontroller outputs SPWM signals to control the status of the high-frequency power tubes in the inverter circuit，thus changing the DC energy power into AC power with the same frequency and phase of the grid. On the other hand, because the light and temperature will affect the outputting features of the solar cells, usually we require the solar cells work at the maximum power output point. In the system, we use the microcontroller to realize the maximum power point tracking algorithm. Seen from the experimental results, the quality of the output wave is good; by testing, the efficiency of the whole machine is above 80%.

Power-efficient M-ary PSSK Communications with Uncertain Phase Errors

M-ary phase-silence-shift-keying (PSSK), one of power-efficient modulation schemes, has enhanced symbol error rate (SER) performance by 6 dB compared with that of the conventional phase-shift-keying (PSK). Phase tracking problem of the PSSK with phase-locked-loop (PLL) is discussed in this paper. Capacity of the PSSK for the additive white Gaussian noise (AWGN) channel and Rayleigh fading channel is analyzed. Capacity loss of the PSSK due to the PLL is small, which tells us that the PSSK demodulator can be practically implemented.

Applications of New Fuzzy Inference-Based Tracking Loops for Kinematic GPS Receiver

Carrier phase measurement is essential for high-accuracy measurement in kinematic global positioning system (GPS) applications. For GPS receiver design, a narrow noise bandwidth is desired to decrease phase jitter due to thermal noise. However, this bandwidth will deteriorate the capability of the tracking loop and result in cycle slipping. Based on bandwidth adjustment criteria, a novel intelligent GPS receiver is proposed for solving the carrier phase tracking problem in the presence of high dynamic environments. A phase error estimator is developed in the carrier loop to conduct the phase error signals; i.e., frequency and frequency ramp errors. Two kinds of fuzzy inference (FI)-based approaches, fuzzy logic control and adaptive neuro-fuzzy control methods, that are simple and have easy realization properties are designed to perform rapid and accurate control of the digital frequency phase-locked loop (FPLL). A new design procedure for kinematic GPS receiver development is also presented. The computer results show that the FI-based receivers achieve faster settling time and wider pull-in range than the conventional tracking loops while also preventing the occurrence of cycle slips.

Optimal Recursive Non-Linear Filtering: Convergence of Parallel Algorithms with Application to Detection Hypothesis and Phase Tracking Problems

This paper sumes up several results obtained for the numerical approximation of optimal non-linear filters. It presents convergence results for numerical algorithms arising from an analysis of non-linear partial differential equations. It extends these results to a class of parallel algorithms describing the evolution of the unnormalised conditional density of probability. Several examples in detection and phase tracking specify the region of application of this type of solution and show the performances and stability that one can expect for such a filter through a statistical analysis of a numerical implementation.Using developments in partial differential equation theory, this paper describes a class of algorithms the implementation of which on a multiprocessor parallel computer is specially easy. Results of convergence guarantee the numerical stability of this solution and a Monte Carlo analysis shows what is in practice the exact meaning of this stability.Two types of application are presented. The first one concerns the detection of hypothesis from a set of equiprobable situations. This leads to performing algorithms for one-line identification of discrete valued parameters in linear and non-linear systems. The second one gives a stable and accurate solution to the phase tracking problem and the associated coding and decoding problem when the noise over signal ratio is too high for classical solution to be efficient. This pair of applications shows what is the area of efficiency of these approximation algorithms and what performances can be a priori expected.

Fourier series and estimation on the circle with applications to synchronous communication--II: Implementation

The practical implementation of the infinite-dimensional optimal estimation results presented in Part I of this series is considered. Several techniques are described in detail. Included among these is the so-called assumed density approximation technique. Finite-dimensional suboptimal filtering equations based on this method are derived for several of the phase-tracking/demodulation problems studied in Part I. Finally, these techniques are applied to a phase tracking problem of importance in navigation systems such as Omega, and simulation results are reported that favorably compare a system designed using these techniques to an optimal phase-lock loop and an optimal linear system.