scholarly journals Cramér-Rao Lower Bound of Target Localization Method Based on TOA Measurements

2019 ◽  
Vol 37 (5) ◽  
pp. 871-877
Author(s):  
Jinxiang Du ◽  
Benmao Zhang
Keyword(s):  
Measurement Errors ◽  
Signal To Noise Ratio ◽  
Target Localization ◽  
Time Of Arrival ◽  
Echo Signal ◽  
Signal To Noise ◽  
Localization Method ◽  
Monte Carlo Experiments ◽  
Distributed Process ◽  
Cramer Rao Bound

The Cramér-Rao bound of target localization method based on time-of-arrival measurements is analyzed. For the localization error analysis, the CRLB is derived under the assumptions that the measurement errors are independent and characterized by zero-mean Gaussian distributed process with identical variance, which is not satisfied in the situation of localizing noncooperative targets. Cramér-Rao bound is deduced by using the fact that the variances of TOA measurements of different sensors are affected by the signal-to-noise ratio of the echo signal and are different from each other. Simulations of Monte Carlo experiments are carried out so as to verify the analytical results.

Subspace-based estimation of time of arrival and Doppler shift for a signal of known waveform

2009 ◽  
Vol 1 (3) ◽  
pp. 209-214
Author(s):  
V.V. Latyshev
Keyword(s):  
Doppler Shift ◽  
Signal To Noise Ratio ◽  
Time Of Arrival ◽  
Computationally Efficient ◽  
Signal To Noise ◽  
Sufficient Statistics ◽  
Ml Estimation ◽  
Generalized Eigenvectors ◽  
Low Dimensional ◽  
Estimation Of Time

The subspace-based technique is used for the estimation of the time of arrival and Doppler shift of a signal of known waveform. The tool to find required subspaces is a special orthogonal decomposition of received data. It allows one to concentrate Fisher information on the desired parameter in just a few of the first terms of the decomposition. This approach offers a low-dimensional vector of sufficient statistics. It leads to computationally efficient Bayesian estimation. Besides, it results in expansion of the signal-to-noise ratio (SNR) range for effective maximum likelihood (ML) estimation. Finally, we can obtain independent time arrival and Doppler shift estimations based on generalized eigenvectors.

scholarly journals X-Ray Pulsar Profile Recovery Based on Tracking-Differentiator

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Dapeng Zhang ◽  
Wei Zheng ◽  
Yidi Wang ◽  
Lu Zhang
Keyword(s):  
Simulation Experiment ◽  
Signal To Noise Ratio ◽  
Time Of Arrival ◽  
Signal To Noise ◽  
Important Work ◽  
X Ray ◽  
Tracking Differentiator ◽  
Cascade Structure ◽  
Simulation Results ◽  
Application Requirements

The profile recovery is an important work in X-ray pulsar-based navigation. It is a key step for the analysis on the pulsar signal’s characteristic and the computing of time of arrival (TOA). This paper makes an argument for an algorithm based on the tracking-differentiator (TD) to recover the profile from the low Signal-to-Noise Ratio (SNR) signals. In the method, a TD filter with cascade structure is designed which has very low phase delay and amplitude distortion. In the simulation experiment, two typical pulsars (PSR B0531+21 and PSR B1937+21) are used to verify the algorithm’s performance. The simulation results show that the method satisfies the application requirements in the aspects of SNR and profile fidelity. By processing the data collected by the Rossi X-Ray Timing Explorer (RXTE) satellite in space, similar results can also be achieved.

Frequency Domain Design Method of Wavelet Basis Based on Pulsar Signal

2020 ◽  
Vol 73 (6) ◽  
pp. 1223-1236
Author(s):  
Sihai You ◽  
Hongli Wang ◽  
Yiyang He ◽  
Qiang Xu ◽  
Lei Feng
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Design Method ◽  
Signal To Noise Ratio ◽  
Frequency Domain Analysis ◽  
Time Of Arrival ◽  
Frequency Noise ◽  
Wavelet Basis ◽  
Signal To Noise ◽  
Noise Ratio

During pulsar navigation, the high-frequency noise carried by the pulsar profile signal reduces the accuracy of the pulse TOA (Time of Arrival) estimation. At present, the main method to remove signal noise by using wavelet transform is to redesign the function of the threshold and level of wavelet transform. However, the signal-to-noise ratio and other indicators of the filtered signal need to be further optimised, so a more appropriate wavelet basis needs to be designed. This paper proposes a wavelet basis design method based on frequency domain analysis to improve the denoising effect of pulsar signals. This method first analyses the pulsar contour signal in the frequency domain and then designs a Crab pulsar wavelet basis (CPn, where n represents the wavelet basis length) based on its frequency domain characteristics. In order to improve the real-time performance of the algorithm, a wavelet lifting scheme is implemented. Through simulation, this method analyses the pulsar contour signal data at home and abroad. Results show the signal-to-noise ratio can be increased by 4 dB, the mean square error is reduced by 61% and the peak error is reduced by 45%. Therefore, this method has better filtering effect.

scholarly journals An eLoran Signal Cycle Identification Method Based on Joint Time–Frequency Domain

2022 ◽  
Vol 14 (2) ◽  
pp. 250
Author(s):  
Wenhe Yan ◽  
Ming Dong ◽  
Shifeng Li ◽  
Chaozhong Yang ◽  
Jiangbin Yuan ◽  
...  
Keyword(s):  
Success Rate ◽  
Frequency Domain ◽  
Measurement Errors ◽  
Signal To Noise Ratio ◽  
Global Navigation Satellite Systems ◽  
Time Of Arrival ◽  
Satellite Systems ◽  
Time Frequency ◽  
Identification Method ◽  
The Time Domain

The eLoran system is an international standardized positioning, navigation, and timing service system, which can complement global navigation satellite systems to cope with navigation and timing warfare. The eLoran receiver measures time-of-arrival (TOA) through cycle identification, which is key in determining timing and positioning accuracy. However, noise and skywave interference can cause cycle identification errors, resulting in TOA-measurement errors that are integral multiples of 10 μs. Therefore, this article proposes a cycle identification method in the joint time–frequency domain. Based on the spectrum-division method to determine the cycle identification range, the time–domain peak-to-peak ratio and waveform matching are used for accurate cycle identification. The performance of the method is analyzed via simulation. When the signal-to-noise ratio (SNR) ≥ 0 dB and skywave-to-groundwave ratio (SGR) ≤ 23 dB, the success rate of cycle identification is 100%; when SNR ≥ −13 dB and SGR ≤ 23 dB, the success rate exceeds 75%. To verify its practicability, the method was implemented in the eLoran receiver and tested at three test sites within 1000 km using actual signals emitted by an eLoran system. The results show that the method has a high identification probability and can be used in modern eLoran receivers to improve TOA-measurement accuracy.

scholarly journals An Improved BAQ Encoding and Decoding Method for Improving the Quantized SNR of SAR Raw Data

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4221
Author(s):  
Wei Ji ◽  
Xiaolan Qiu ◽  
Xuejiao Wen ◽  
Lijia Huang
Keyword(s):  
Power Loss ◽  
Signal To Noise Ratio ◽  
Practical Application ◽  
Scattering Mechanism ◽  
Echo Signal ◽  
Signal To Noise ◽  
Raw Data ◽  
Look Up Table ◽  
Adaptive Quantization ◽  
Simulation Results

When the original echo data of SAR are saturated for quantization, the performance of the commonly used block adaptive quantization (BAQ) algorithm will be degraded, which will degrade the imaging quality. This article proposes an improved Llody-Max codec method, which only needs to change the codec look-up table to get better quantization performance when the original echo is saturated. The simulation results show that the proposed method can reduce the quantization power loss, improve the echo signal-to-noise ratio (SNR), and reduce the influence of quantization saturation on the scattering mechanism of polarized SAR data, which have good practical application value.

Analysis of parameters for smoothing electrocardiographic signals Aleksandr A. Fedotov

2019 ◽  
pp. 47-51
Author(s):  
A.A. Fedotov
Keyword(s):  
Measurement Error ◽  
Wavelet Transforms ◽  
Measurement Errors ◽  
Signal To Noise Ratio ◽  
Ecg Signal ◽  
Optimal Parameters ◽  
Signal To Noise ◽  
Optimal Method ◽  
Rr Intervals ◽  
Noise Ratio

The article is devoted to the consideration of the features of smoothing filtering of ECG signal against the background of electromyographic distortions of various magnitude. The main goal of the research is comparative analysis of various options for the implementation of smoothing filtering of an ECG signal contaminated by myographic interference in order to determine the optimal approach in terms of minimizing biosignal distortions and measurement errors of its amplitude-time characteristics. To obtain quantitative characteristics of effectiveness of various methods for smoothing filtering of the ECG signal, we used an approach based on simulation models of the ECG signal and distortions. A criterion for choosing the optimal parameters for smoothing filtering of the ECG signal based on minimizing the errors in determining the durations of RR-intervals and distortions of the ECG signal was proposed. Various options for smoothing filters are considered: low-pass filtering, multiscale wavelet transform, Savitzky–Golay filtering, moving average filtering. The optimal parameters for each type of filter are determined in terms of minimizing the distortion of the ECG signal and the measurement error of the durations of RR-intervals. The dependences of the change in the measurement error of the durations of RR-intervals on the signal-to-noise ratio, the dependences of the change in the signal distortion coefficient on the signal-to-noise ratio, the plots of processing the noisy fragment of ECG signal by various types of filters are presented. Research have shown that multiscale wavelet transforms of ECG signal with myographic interference is the optimal method for processing an ECG signal, providing minimal measurement errors of RR-intervals with minimal distortion of the ECG signal.

scholarly journals Experimental Results of a Differential Angle-of-Arrival Based 2D Localization Method Using Signals of Opportunity

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
M. A. Aziz ◽  
C. T. Allen
Keyword(s):  
Signal To Noise Ratio ◽  
Angle Of Arrival ◽  
Signal To Noise ◽  
Localization Accuracy ◽  
Localization Method ◽  
Precise Knowledge ◽  
Fm Radio ◽  
Signal Characteristics ◽  
Radio Signals ◽  
Signals Of Opportunity

This paper presents a study of differential AoA (Angle-of-Arrival) based 2D localization method utilizing FM radio signals (88 MHz–108 MHz) as Signals of Opportunity (SOP). Given prior knowledge of the transmitters’ position and signal characteristics, the proposed technique utilizes triangulation to localize receiver’s 2D position. Dual antenna interferometry provides the received signals’ AoA required for triangulation. Reliance on precise knowledge of antenna system’s orientation is removed by utilizing differential Angle of Arrivals (dAoAs). The 2D localization accuracy is improved by utilizing colocated transmitters, a concept proposed in this paper as supertowers. Analysis, simulation, and ground-based experiments have been presented; results showed that when the SNR (Signal-to-Noise Ratio) is higher than 45 dB, the proposed method localizes the receiver’s 2D position with an error of less than 15 m.

Novel Acoustic Source Localization Method in WSN Based on LSSVR Regression Modeling

2012 ◽  
Vol 468-471 ◽  
pp. 2296-2303
Author(s):  
Xiao Ping Zhang ◽  
Yang Wang
Keyword(s):  
Source Localization ◽  
Feature Vector ◽  
Signal To Noise Ratio ◽  
Parameters Optimization ◽  
Support Vector ◽  
Acoustic Source ◽  
Signal To Noise ◽  
Localization Method ◽  
Acoustic Source Localization ◽  
Noise Ratio

To solve the problem of acoustic source localization in wireless sensor networks (WSN) under interference of environmental noise, a novel acoustic source localization method in WSN based on Least Square Support Vector Regression (LSSVR) modeling (ASL-LRM) was proposed. The ideal measured values of acoustic sensors were used to compose feature vector at first. Then LSSVR models were built by LSSVR modeling on the mapping relation between feature vector and acoustic source coordinate. The acoustic source was then located by inputting feature vector composed of real measured values of the sensors into LSSVR models. The modeling parameters optimization method based on localization effect in sample locations was also discussed. Experiments were performed in 100 test locations. RMSE values by ASL-LRM method in 72-76 test locations were less than MLE method and reduced by 60%-74% at most. In lower signal-to-noise ratio case, there were 87 test locations where RMSE values by ASL-LRM method were less than 2 meters, while there were only 12 test locations by MLE method. It shows ASL-LRM method achieves better localization effects in a large part of the region surrounded by sensor nodes. It especially has advantage on the occasions like lower signal-to-noise ratio or high precision localization.

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