An Adaptive Moments Estimation Technique Applied to MST Radar Echoes

2005 ◽  
Vol 22 (4) ◽  
pp. 396-408 ◽  
Author(s):  
V. K. Anandan ◽  
P. Balamuralidhar ◽  
P. B. Rao ◽  
A. R. Jain ◽  
C. J. Pan
Keyword(s):  
Conventional Method ◽  
Signal To Noise Ratio ◽  
Radar Data ◽  
Adaptive Method ◽  
Radar Signal ◽  
Signal Spectrum ◽  
Estimation Technique ◽  
Low Snr ◽  
Wind Speeds ◽  
Mst Radar

Abstract An adaptive spectral moments estimation technique has been developed for analyzing the Doppler spectra of the mesosphere–stratosphere–troposphere (MST) radar signals. The technique, implemented with the MST radar at Gadanki (13.5°N, 79°E), is based on certain criteria, set up for the Doppler window, signal-to-noise ratio (SNR), and wind shear parameters, which are used to adaptively track the signal in the range–Doppler spectral frame. Two cases of radar data, one for low and the other for high SNR conditions, have been analyzed and the results are compared with those from the conventional method based on the strongest peak detection in each range gate. The results clearly demonstrate that by using the adaptive method the height coverage can be considerably enhanced compared to the conventional method. For the low SNR case, the height coverage for the adaptive and conventional methods is about 22 and 11 km, respectively; the corresponding heights for the high SNR case are 24 and 13 km. To validate the results obtained through the adaptive method, the velocity profile is compared with global positioning system balloon sounding (GPS sonde) observations. The results of the adaptive method show excellent agreement with the GPS sonde measured wind speeds and directions throughout the height profile. To check the robustness and reliability of the adaptive algorithm, data taken over a diurnal cycle at 1-h intervals were analyzed. The results demonstrate the reliability of the algorithm in extracting wind profiles that are self-consistent in time. The adaptive method is thus found to be of considerable advantage over the conventional method in extracting information from the MST radar signal spectrum, particularly under low SNR conditions that are free from interference and ground clutter.

scholarly journals A Radar Signal Recognition Approach via IIF-Net Deep Learning Models

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ji Li ◽  
Huiqiang Zhang ◽  
Jianping Ou ◽  
Wei Wang
Keyword(s):  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Software Radio ◽  
Radar Signal ◽  
Signal Recognition ◽  
Electromagnetic Environment ◽  
Time Frequency ◽  
Low Snr ◽  
Radar Signals

In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures. In this paper, USRP N210, USRP-LW N210, and other general software radio peripherals are used to simulate the transmitting and receiving process of radar signals, and a total of 8 radar signals, namely, Barker, Frank, chaotic, P1, P2, P3, P4, and OFDM, are produced. The signal obtains time-frequency images (TFIs) through the Choi–Williams distribution function (CWD). According to the characteristics of the radar signal TFI, a global feature balance extraction module (GFBE) is designed. Then, a new IIF-Net convolutional neural network with fewer network parameters and less computation cost has been proposed. The signal-to-noise ratio (SNR) range is −10 to 6 dB in the experiments. The experiments show that when the SNR is higher than −2 dB, the signal recognition rate of IIF-Net is as high as 99.74%, and the signal recognition accuracy is still 92.36% when the SNR is −10 dB. Compared with other methods, IIF-Net has higher recognition rate and better robustness under low SNR.

scholarly journals Spectral Analysis of MST Radar Signal using Maximum Likelihood Estimation Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 3372-3379
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Radar Data ◽  
Low Rank ◽  
Radar Signal ◽  
Complex Signal ◽  
Complex Data ◽  
Mst Radar ◽  
Frequency Components

In this work, we propose Maximum likelihood estimation of low- rank Toeplitz covariance matrix (MELT) with reduced complexity algorithm for computing the power spectral density of mesosphere-stratosphere-troposphere (MST) radar data. MELT is designed based on the method of majorization-minimization and it is an iterative algorithm to update the powers in each successive step. We tested MELT algorithm for complex signal, which contain multiple frequency components in existence of different noise conditions. For simulated complex data, it can be seen that MELT works much better for low Signal to Noise Ratio (SNR) conditions and also effectively detects the frequency components with a fine resolution in the existence with high noise impact. At last, MELT algorithm is applied to the radar data received from MST radar established at National Atmospheric Research laboratory (NARL), Gadhanki. MELT algorithm estimates the accurate Doppler spectra and thus in turn, estimate the wind parameters using Doppler profiles. For the purpose of validation, the obtained radar results through MELT are compared with the Global Positioning System (GPS) radiosonde.

scholarly journals Evaluation of wind profiles from the NERC MST radar, Aberystwyth, UK

2014 ◽  
Vol 7 (9) ◽  
pp. 3113-3126 ◽  
Author(s):  
C. F. Lee ◽  
G. Vaughan ◽  
D. A. Hooper
Keyword(s):  
Wind Speed ◽  
Sampling Error ◽  
Signal To Noise Ratio ◽  
Weather Prediction ◽  
Horizontal Wind ◽  
Mountain Waves ◽  
Wind Speeds ◽  
Mst Radar ◽  
Wind Profiles ◽  
Aspect Sensitivity

Abstract. This study quantifies the uncertainties in winds measured by the Aberystwyth Mesosphere–Stratosphere–Troposphere (MST) radar (52.4° N, 4.0° W), before and after its renovation in March 2011. A total of 127 radiosondes provide an independent measure of winds. Differences between radiosonde and radar-measured horizontal winds are correlated with long-term averages of vertical velocities, suggesting an influence from local mountain waves. These local influences are an important consideration when using radar winds as a measure of regional conditions, particularly for numerical weather prediction. For those applications, local effects represent a source of sampling error additional to the inherent uncertainties in the measurements themselves. The radar renovation improved the signal-to-noise ratio (SNR) of measurements, with a corresponding improvement in altitude coverage. It also corrected an underestimate of horizontal wind speeds attributed to beam formation problems, due to pre-renovation component failure. The root mean square error (RMSE) in radar-measured horizontal wind components, averaged over half an hour, increases with wind speed and altitude, and is 0.8–2.5 m s−1 (6–12% of wind speed) for post-renovation winds. Pre-renovation values are typically 0.1 m s−1 larger. The RMSE in radial velocities is <0.04 m s−1. Eight weeks of special radar operation are used to investigate the effects of echo power aspect sensitivity. Corrections for echo power aspect sensitivity remove an underestimate of horizontal wind speeds; however aspect sensitivity is azimuthally anisotropic at the scale of routine observations (≈1 h). This anisotropy introduces random error into wind profiles. For winds averaged over half an hour, the RMSE is around 3.5% above 8 km, but as large as 4.5% in the mid-troposphere.

scholarly journals Radar signal recognition based on triplet convolutional neural network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Lutao Liu ◽  
Xinyu Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Signal To Noise Ratio ◽  
Poor Performance ◽  
Radar Signal ◽  
Time Frequency ◽  
Low Snr ◽  
Triplet Loss ◽  
Recognition Success ◽  
Fully Connected

AbstractRecently, due to the wide application of low probability of intercept (LPI) radar, lots of recognition approaches about LPI radar signal modulations have been proposed. However, facing the increasingly complex electromagnetic environment, most existing methods have poor performance to identify different modulation types in low signal-to-noise ratio (SNR). This paper proposes an automatic recognition method for different LPI radar signal modulations. Firstly, time-domain signals are converted to time-frequency images (TFIs) by smooth pseudo-Wigner–Ville distribution. Then, these TFIs are fed into a designed triplet convolutional neural network (TCNN) to obtain high-dimensional feature vectors. In essence, TCNN is a CNN network that triplet loss is adopted to optimize parameters of the network in the training process. The participation of triplet loss can ensure that the distance between samples in different classes is greater than that between samples with the same label, improving the discriminability of TCNN. Eventually, a fully connected neural network is employed as the classifier to recognize different modulation types. Simulation shows that the overall recognition success rate can achieve 94% at − 10 dB, which proves the proposed method has a strong discriminating capability for the recognition of different LPI radar signal modulations, even under low SNR.

scholarly journals Radar Signal Modulation Recognition Based on Sep-ResNet

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7474
Author(s):  
Yongjiang Mao ◽  
Wenjuan Ren ◽  
Zhanpeng Yang
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Morphological Processing ◽  
Radar Signal ◽  
Modulation Recognition ◽  
Time Frequency ◽  
Low Snr ◽  
Modulated Signals ◽  
Intelligent Recognition

With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in space, causing difficult problems in terms of accurately identifying radar-modulated signals in low signal-to-noise ratio (SNR) environments. To address this problem, in this paper, we propose an intelligent recognition method that combines time–frequency (T–F) analysis and a deep neural network to identify radar modulation signals. The T–F analysis of the complex Morlet wavelet transform (CMWT) method is used to extract the characteristics of signals and obtain the T–F images. Adaptive filtering and morphological processing are used in T–F image enhancement to reduce the interference of noise on signal characteristics. A deep neural network with the channel-separable ResNet (Sep-ResNet) is used to classify enhanced T–F images. The proposed method completes high-accuracy intelligent recognition of radar-modulated signals in a low-SNR environment. When the SNR is −10 dB, the probability of successful recognition (PSR) is 93.44%.

scholarly journals Comparison of the performance of different radar pulse compression techniques in an incoherent scatter radar measurement

2009 ◽  
Vol 27 (2) ◽  
pp. 797-806 ◽  
Author(s):  
B. Damtie ◽  
M. S. Lehtinen
Keyword(s):  
Adaptive Filtering ◽  
Pulse Compression ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Radar Signal ◽  
Incoherent Scatter Radar ◽  
Low Snr ◽  
Incoherent Scatter ◽  
Scatter Radar

Abstract. Improving an estimate of an incoherent scatter radar signal is vital to provide reliable and unbiased information about the Earth's ionosphere. Thus optimizing the measurement spatial and temporal resolutions has attracted considerable attention. The optimization usually relies on employing different kinds of pulse compression filters in the analysis and a matched filter is perhaps the most widely used one. A mismatched filter has also been used in order to suppress the undesirable sidelobes that appear in the case of matched filtering. Moreover, recently an adaptive pulse compression method, which can be derived based on the minimum mean-square error estimate, has been proposed. In this paper we have investigated the performance of matched, mismatched and adaptive pulse compression methods in terms of the output signal-to-noise ratio (SNR) and the variance and bias of the estimator. This is done by using different types of optimal radar waveforms. It is shown that for the case of low SNR the signal degradation associated to an adaptive filtering is less than that of the mismatched filtering. The SNR loss of both matched and adaptive pulse compression techniques was found to be nearly the same for most of the investigated codes for the case of high SNR. We have shown that the adaptive filtering technique is a compromise between matched and mismatched filtering method when one evaluates its performance in terms of the variance and the bias of the estimator. All the three analysis methods were found to have the same performance when a sidelobe-free matched filter code is employed.

Improved SNR Estimation Technique for BPSK and QPSK Signals

2012 ◽  
Vol 239-240 ◽  
pp. 994-999
Author(s):  
Guang Zu Liu ◽  
Jian Xin Wang
Keyword(s):  
Phase Shift ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Estimation Accuracy ◽  
Estimation Technique ◽  
Phase Shift Keying ◽  
Low Snr ◽  
Mathematical Relation ◽  
Shift Keying ◽  
Binary Phase Shift Keying

To improve the estimation accuracy of non-data-aided (NDA) signal-to-noise ratio (SNR) estimators at low SNR value, A novel estimation technique for binary phase-shift keying and quadrature phase-shift keying signals in complex additive white Gaussian noise channel is proposed. The mathematical relation between SNR and the ratio of two simple statistical computations is derived, then SNR is determined by looking up a table. Its accuracy surpasses other NDA estimators, approaching closely to the Cramer-Rao lower bound at SNR > 5dB.

scholarly journals Detection of Polyphase Codes Radar Signals in Low SNR

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Runlan Tian ◽  
Guoyi Zhang ◽  
Rui Zhou ◽  
Wei Dong
Keyword(s):  
Hough Transform ◽  
Signal To Noise Ratio ◽  
Superior Performance ◽  
Radar Signal ◽  
Time Frequency ◽  
Low Snr ◽  
Radar Signals ◽  
Core Idea ◽  
Code Type ◽  
Electronic Intelligence

A novel effective detection method is proposed for electronic intelligence (ELINT) systems detecting polyphase codes radar signal in the low signal-to-noise ratio (SNR) scenario. The core idea of the proposed method is first to calculate the time-frequency distribution of polyphase codes radar signals via Wigner-Ville distribution (WVD); then the modified Hough transform (HT) is employed to cumulate all the energy of WVD’s ridges effectively to achieve signal detection. Compared with the generalised Wigner Hough transform (GWHT) method, the proposed method has a superior performance in low SNR and is not sensitive to the code type. Simulation results verify the validity of the proposed method.

Modal frequencies of bridges from GNSS (GPS) monitoring data: Experimental, statistical evidence

2021 ◽  
Vol 17 (1-2) ◽  
pp. 3-14
Author(s):  
Stathis C. Stiros ◽  
F. Moschas ◽  
P. Triantafyllidis
Keyword(s):  
Signal To Noise Ratio ◽  
Vertical Axis ◽  
Bridge Monitoring ◽  
Low Snr ◽  
True Value ◽  
Gps Satellites ◽  
Wide Range ◽  
Spectral Peaks ◽  
Monitoring Survey ◽  
True Frequency

GNSS technology (known especially for GPS satellites) for measurement of deflections has proved very efficient and useful in bridge structural monitoring, even for short stiff bridges, especially after the advent of 100 Hz GNSS sensors. Mode computation from dynamic deflections has been proposed as one of the applications of this technology. Apart from formal modal analyses with GNSS input, and from spectral analysis of controlled free attenuating oscillations, it has been argued that simple spectra of deflections can define more than one modal frequencies. To test this scenario, we analyzed 21 controlled excitation events from a certain bridge monitoring survey, focusing on lateral and vertical deflections, recorded both by GNSS and an accelerometer. These events contain a transient and a following oscillation, and they are preceded and followed by intervals of quiescence and ambient vibrations. Spectra for each event, for the lateral and the vertical axis of the bridge, and for and each instrument (GNSS, accelerometer) were computed, normalized to their maximum value, and printed one over the other, in order to produce a single composite spectrum for each of the four sets. In these four sets, there was also marked the true value of modal frequency, derived from free attenuating oscillations. It was found that for high SNR (signal-to-noise ratio) deflections, spectral peaks in both acceleration and displacement spectra differ by up to 0.3 Hz from the true value. For low SNR, defections spectra do not match the true frequency, but acceleration spectra provide a low-precision estimate of the true frequency. This is because various excitation effects (traffic, wind etc.) contribute with numerous peaks in a wide range of frequencies. Reliable estimates of modal frequencies can hence be derived from deflections spectra only if excitation frequencies (mostly traffic and wind) can be filtered along with most measurement noise, on the basis of additional data.

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