scholarly journals Enhancement of Matched Filter Response for Chirp Radar Signals using Signal Recovery Technique

2019 ◽  
Vol 9 (2) ◽  
pp. 561-565
Keyword(s):  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Side Lobe ◽  
Detection Performance ◽  
Signal Recovery ◽  
Reconstruction Process ◽  
New Approach ◽  
Recovery Technique ◽  
Radar Signals ◽  
Filter Response

The matched filter is one among many effective techniques used to maximize the signal –to- noise ratio (SNR) of chirp radar signals. Besides this enhancement in SNR, it has a drawback due to sidelobe levels which degrade the filter response. There are many additional techniques which reduce these levels such as window techniques and inverse filter. In this paper, a new approach is utilized to completely cancel the level of matched filter side lobes based on the signal recovery of the compressive sensing (CS) theory. The reconstruction process of CS is based on the CAMP algorithm which applied to the response of the matched filter. The recovered chirp radar signals are achieved with completely side lobes cancellation compared to the traditional side lobe reduction technique based on the widow method. The comparison between the proposed and traditional methods is achieved according to the detection performance using Receiver Operating Characteristic (ROC) curve. Besides the detection performance, a resolution in the range is another comparison aspect between these algorithms.

scholarly journals Modified Radar Signal Model using NLFM

2019 ◽  
Vol 8 (2S3) ◽  
pp. 513-516
Keyword(s):  
Interference Mitigation ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Nonlinear Function ◽  
Side Lobe ◽  
Radar Signal ◽  
Target Range ◽  
Nonlinear Frequency ◽  
Good Resolution ◽  
Signal Model

Intended to the setback of high side lobes of the linear frequency modulation (LFM) signal, we put forward a new signal model using nonlinear frequency (NLFM) signal to overcome the issue. NLFM is a promising way for achieving lower signal to noise ratio, good resolution and better interference mitigation. The novel signal model is designed to enhance the target range estimation and to reduce the side lobe levels. In this paper, a new signal model is designed based on the principle of fusion of two stages. First stage is exponential based nonlinear function and the second stage is a linear function. The simulations were performed for the designed signal model and are compared with the NLFM signal designed using two stage LFM functions. Simulation results show that the designed signal has significant reduction in side lobe levels of the matched filter response

scholarly journals New approach for localization and smart data transmission inside underground mine environment

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Ankita RayChowdhury ◽  
Ankita Pramanik ◽  
Gopal Chandra Roy
Keyword(s):  
Data Transmission ◽  
Signal To Noise Ratio ◽  
Visible Light Communication ◽  
Underground Mine ◽  
Control Room ◽  
Time Data ◽  
New Approach ◽  
Mine Environment ◽  
Surface Control ◽  
Smart Data

AbstractThis paper presents an approach to access real time data from underground mine. Two advance technologies are presented that can improve the adverse environmental effect of underground mine. Visible light communication (VLC) technology is incorporated to estimate the location of miners inside the mine. The distribution of signal to noise ratio (SNR) for VLC system is also studied. In the second part of the paper, long range (LoRa) technology is introduced for transmitting underground information to above the surface control room. This paper also includes details of the LoRa technology, and presents comparison of ranges with existing above the surface technologies.

scholarly journals Radiant measurement accuracy of micrometeors detected by the Arecibo 430 MHz Dual-Beam Radar

2004 ◽  
Vol 4 (3) ◽  
pp. 621-626 ◽  
Author(s):  
D. Janches ◽  
M. C. Nolan ◽  
M. Sulzer
Keyword(s):  
Measurement Accuracy ◽  
Signal To Noise Ratio ◽  
Side Lobe ◽  
Beam Axis ◽  
Main Beam ◽  
High Signal ◽  
Precise Knowledge ◽  
Dual Beam ◽  
Arecibo Observatory ◽  
Meteoric Material

Abstract. Precise knowledge of the angle between the meteor vector velocity and the radar beam axis is one of the largest source of errors in the Arecibo Observatory (AO) micrometeor observations. In this paper we study ~250 high signal-to-noise ratio (SNR) meteor head-echoes obtained using the dual-beam 430 MHz AO Radar in Puerto Rico, in order to reveal the distribution of this angle. All of these meteors have been detected first by the radar first side lobe, then by the main beam and finally seen in the side lobe again. Using geometrical arguments to calculate the meteor velocity in the plane perpendicular to the beam axis, we find that most of the meteors are travelling within ~15° with respect to the beam axis, in excellent agreement with previous estimates. These results suggest that meteoroids entering the atmosphere at greater angles may deposit their meteoric material at higher altitudes explaining at some level the missing mass inconsistency raised by the comparisson of meteor fluxes derived from satellite and traditional meteor radar observations. They also may be the source of the observed high altitude ions and metalic layers observed by radars and lidars respectively.

Compressed wavefield extrapolation

Geophysics ◽  
2007 ◽  
Vol 72 (5) ◽  
pp. SM77-SM93 ◽  
Author(s):  
Tim T. Lin ◽  
Felix J. Herrmann
Keyword(s):  
Wave Propagation ◽  
Compressed Sensing ◽  
Signal Recovery ◽  
New Paradigm ◽  
New Approach ◽  
Measurement Basis ◽  
Recent Developments ◽  
Data Volume ◽  
Extrapolation Problem ◽  
Wavefield Extrapolation

An explicit algorithm for the extrapolation of one-way wavefields is proposed that combines recent developments in information theory and theoretical signal processing with the physics of wave propagation. Because of excessive memory requirements, explicit formulations for wave propagation have proven to be a challenge in 3D. By using ideas from compressed sensing, we are able to formulate the (inverse) wavefield extrapolation problem on small subsets of the data volume, thereby reducing the size of the operators. Compressed sensing entails a new paradigm for signal recovery that provides conditions under which signals can be recovered from incomplete samplings by nonlinear recovery methods that promote sparsity of the to-be-recovered signal. According to this theory, signals can be successfully recovered when the measurement basis is incoherent with the representa-tion in which the wavefield is sparse. In this new approach, the eigenfunctions of the Helmholtz operator are recognized as a basis that is incoherent with curvelets that are known to compress seismic wavefields. By casting the wavefield extrapolation problem in this framework, wavefields can be successfully extrapolated in the modal domain, despite evanescent wave modes. The degree to which the wavefield can be recovered depends on the number of missing (evanescent) wavemodes and on the complexity of the wavefield. A proof of principle for the compressed sensing method is given for inverse wavefield extrapolation in 2D, together with a pathway to 3D during which the multiscale and multiangular properties of curvelets, in relation to the Helmholz operator, are exploited. The results show that our method is stable, has reduced dip limitations, and handles evanescent waves in inverse extrapolation.

scholarly journals A Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2742
Author(s):  
Yuwei Ge ◽  
Tao Zhang ◽  
Haihua Liang ◽  
Qingfeng Jiang ◽  
Dan Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Detection Performance ◽  
Hidden Information ◽  
Adaptive Steganography ◽  
Image Steganalysis ◽  
Channel Correlation ◽  
Cyberspace Security ◽  
And Training ◽  
Training Efficiency ◽  
Good Detection

Image steganalysis is a technique for detecting the presence of hidden information in images, which has profound significance for maintaining cyberspace security. In recent years, various deep steganalysis networks have been proposed in academia, and have achieved good detection performance. Although convolutional neural networks (CNNs) can effectively extract the features describing the image content, the difficulty lies in extracting the subtle features that describe the existence of hidden information. Considering this concern, this paper introduces separable convolution and adversarial mechanism, and proposes a new network structure that effectively solves the problem. The separable convolution maximizes the residual information by utilizing its channel correlation. The adversarial mechanism makes the generator extract more content features to mislead the discriminator, thus separating more steganographic features. We conducted experiments on BOSSBase1.01 and BOWS2 to detect various adaptive steganography algorithms. The experimental results demonstrate that our method extracts the steganographic features effectively. The separable convolution increases the signal-to-noise ratio, maximizes the channel correlation of residuals, and improves efficiency. The adversarial mechanism can separate more steganographic features, effectively improving the performance. Compared with the traditional steganalysis methods based on deep learning, our method shows obvious improvements in both detection performance and training efficiency.

scholarly journals Code Optimisation Framework for Multicode Sonar Systems

2021 ◽  
Author(s):  
◽  
Muhammad Rashed
Keyword(s):  
Transmission Loss ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Welch Bound ◽  
Optimisation Algorithm ◽  
Sonar System ◽  
Channel Effects ◽  
Sonar Systems ◽  
Mismatched Filters ◽  
Quadratically Constrained

<p>The ocean is a temporally and spatially varying environment, the characteristics of which pose significant challenges to the development of effective underwater wireless communications and sensing systems.  An underwater sensing system such as a sonar detects the presence of a known signal through correlation. It is advantageous to use multiple transducers to increase surveying area with reduced surveying costs and time. Each transducers is assigned a dedicated code. When using multiple codes, the sidelobes of auto- and crosscorrelations are restricted to theoretical limits known as bounds. Sets of codes must be optimised in order to achieve optimal correlation properties, and, achieve Sidelobe Level (SLL)s as low as possible.  In this thesis, we present a novel code-optimisation method to optimise code-sets with any number of codes and up to any length of each code. We optimise code-sets for a matched filter for application in a multi-code sonar system. We first present our gradient-descent based algorithm to optimise sets of codes for flat and low crosscorrelations and autocorrelation sidelobes, including conformance of the magnitude of the samples of the codes to a target power profile. We incorporate the transducer frequency response and the channel effects into the optimisation algorithm. We compare the correlations of our optimised codes with the well-known Welch bound. We then present a method to widen the autocorrelation mainlobe and impose monotonicity. In many cases, we are able to achieve SLLs beyond the Welch bound.  We study the Signal to Noise Ratio (SNR) improvement of the optimised codes for an Underwater Acoustic (UWA) channel. During its propagation, the acoustic wave suffers non-constant transmission loss which is compensated by the application of an appropriate Time Variable Gain (TVG). The effect of the TVG modifies the noise received with the signal. We show that in most cases, the matched filter is still the optimum filter. We also show that the accuracy in timing is very important in the application of the TVG to the received signal.  We then incorporate Doppler tolerance into the existing optimisation algorithm. Our proposed method is able to optimise sets of codes for multiple Doppler scaling factors and non-integer delays in the arrival of the reflection, while still conforming to other constraints.  We suggest designing mismatched filters to further reduce the SLLs, firstly using an existing Quadratically Constrained Qaudratic Program (QCQP) formulation and secondly, as a local optimisation problem, modifying our basic optimisation algorithm.</p>

scholarly journals Sparse Recovery Algorithm for Compressed Sensing Using Smoothed l0 Norm and Randomized Coordinate Descent

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 834
Author(s):  
Jin ◽  
Yang ◽  
Li ◽  
Liu
Keyword(s):  
Compressed Sensing ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Sparse Recovery ◽  
Coordinate Descent ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Recovery Algorithm ◽  
Core Concepts

Compressed sensing theory is widely used in the field of fault signal diagnosis and image processing. Sparse recovery is one of the core concepts of this theory. In this paper, we proposed a sparse recovery algorithm using a smoothed l0 norm and a randomized coordinate descent (RCD), then applied it to sparse signal recovery and image denoising. We adopted a new strategy to express the (P0) problem approximately and put forward a sparse recovery algorithm using RCD. In the computer simulation experiments, we compared the performance of this algorithm to other typical methods. The results show that our algorithm possesses higher precision in sparse signal recovery. Moreover, it achieves higher signal to noise ratio (SNR) and faster convergence speed in image denoising.

scholarly journals Poly-phase signal generation and optimizationof LPI Radar: A new approach

2018 ◽  
Vol 7 (2.6) ◽  
pp. 147
Author(s):  
Jayshree Kamble ◽  
I A Pasha ◽  
M Madhavilatha
Keyword(s):  
Spread Spectrum ◽  
Pulse Compression ◽  
Detection Performance ◽  
Complex Signal ◽  
New Approach ◽  
Frequency Hopping Spread Spectrum ◽  
Phase Signal ◽  
The Difference ◽  
Processing Techniques ◽  
Digital Receivers

Low Probability of Intercept (LPI) Radar own certain positive characteristics make them nearly undetectable by Intercept Receivers. In a battle field, this present a considerable strategic problem. New digital receivers required complex signal processing techniques to detect these types of Radar. This paper address the problem of constructing a new hybrid waveform design using Poly-Phase modulation technique to optimize the detection performance of LPI Radar. Phase coded Pulse compression waveforms using Frequency Hopping Spread Spectrum (FHSS) are designed to evaluate the detection performance of LPI radar in terms of  Discrimination factor (DF).The difference in DF of the Poly-phase coded and Binary phase coded signals is increasing with the increase in the phase values.The effect of noise on Hybrid Poly-Phase waveforms examined using the signal to noise ratios of -10dB,-15dB and -20dB  and extract the parameter necessary for the LPI Radar system. 

scholarly journals A Soft-Threshold Filtering Approach for Tomography Reconstruction from a Limited Number of Projections with Bilateral Edge Preservation

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2346
Author(s):  
Tiago Wirtti ◽  
Evandro Salles
Keyword(s):  
Objective Function ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Edge Preservation ◽  
Edge Preserving ◽  
Reconstruction Process ◽  
Regularization Term ◽  
Computational Performance

In X-ray tomography image reconstruction, one of the most successful approaches involves a statistical approach with l 2 norm for fidelity function and some regularization function with l p norm, 1 < p < 2 . Among them stands out, both for its results and the computational performance, a technique that involves the alternating minimization of an objective function with l 2 norm for fidelity and a regularization term that uses discrete gradient transform (DGT) sparse transformation minimized by total variation (TV). This work proposes an improvement to the reconstruction process by adding a bilateral edge-preserving (BEP) regularization term to the objective function. BEP is a noise reduction method and has the purpose of adaptively eliminating noise in the initial phase of reconstruction. The addition of BEP improves optimization of the fidelity term and, as a consequence, improves the result of DGT minimization by total variation. For reconstructions with a limited number of projections (low-dose reconstruction), the proposed method can achieve higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) results because it can better control the noise in the initial processing phase.

Optimal level and order detection in wavelet decomposition for PCG signal denoising

2019 ◽  
Vol 64 (2) ◽  
pp. 163-176 ◽  
Author(s):  
Mohamed Rouis ◽  
Abdelkrim Ouafi ◽  
Salim Sbaa
Keyword(s):  
Signal To Noise Ratio ◽  
Optimal Level ◽  
Signal Denoising ◽  
Discrete Wavelet ◽  
Clinical Environment ◽  
Mother Wavelet ◽  
New Approach ◽  
The Mean ◽  
Pcg Signals ◽  
Selection Of

Abstract The recorded phonocardiogram (PCG) signal is often contaminated by different types of noises that can be seen in the frequency band of the PCG signal, which may change the characteristics of this signal. Discrete wavelet transform (DWT) has become one of the most important and powerful tools of signal representation, but its effectiveness is influenced by the issue of the selected mother wavelet and decomposition level (DL). The selection of the DL and the mother wavelet are the main challenges. This work proposes a new approach for finding an optimal DL and optimal mother wavelet for PCG signal denoising. Our approach consists of two algorithms designed to tackle the problems of noise and variability caused by PCG acquisition in a real clinical environment for different categories of patients. The results obtained are evaluated by examining the coherence analysie (Coh) correlation coefficient (Corr) and the mean square error (MSE) and signal-to-noise ratio (SNR) in simulated noisy PCG signals. The experimental results show that the proposed method can effectively reduce noise.

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