Track-Before-Detect Procedures in AM Radio-Based Passive Radar

In amplitude modulation radio-based passive radar, the track-before-detect (TBD) procedures are performed to process long time observation data. This work mainly focuses on the tracking of the time-Doppler and time-azimuth traces of multiple target under real scenarios such as low signal-to-noise ratio, hybrid clutter, severe breaking points, and intersecting traces. A new original approach to deal with the TBD problem of this work is developed. Two types of linear equations according to the simplified radio wave propagation model are formulated, which is the key point of the proposed method. We make the theoretical analysis about how the linear equations can be used to track multiple target, and how multiple target’s constant velocities and initial positions can be estimated, which is an additional parameter estimation capability of the proposed method. Both the simulated data and real experimental data are performed with the proposed method and some conventional TBD methods. Several comparisons of the results are given to verify the effectiveness of the proposed method.

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Robust Weighted l1,2 Norm Filtering in Passive Radar Systems

Sensors ◽

10.3390/s20113270 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3270 ◽

Cited By ~ 1

Author(s):

Baris Satar ◽

Gokhan Soysal ◽

Xue Jiang ◽

Murat Efe ◽

Thiagalingam Kirubarajan

Keyword(s):

Target Detection ◽

Impulsive Noise ◽

Simulated Data ◽

Real Data ◽

Detection Performance ◽

Passive Radar ◽

Mobile Telecommunication ◽

Telecommunication System ◽

Conventional Methods ◽

Non Gaussian

Conventional methods such as matched filtering, fractional lower order statistics cross ambiguity function, and recent methods such as compressed sensing and track-before-detect are used for target detection by passive radars. Target detection using these algorithms usually assumes that the background noise is Gaussian. However, non-Gaussian impulsive noise is inherent in real world radar problems. In this paper, a new optimization based algorithm that uses weighted l 1 and l 2 norms is proposed as an alternative to the existing algorithms whose performance degrades in the presence of impulsive noise. To determine the weights of these norms, the parameter that quantifies the impulsiveness level of the noise is estimated. In the proposed algorithm, the aim is to increase the target detection performance of a universal mobile telecommunication system (UMTS) based passive radars by facilitating higher resolution with better suppression of the sidelobes in both range and Doppler. The results obtained from both simulated data with α stable distribution, and real data recorded by a UMTS based passive radar platform are presented to demonstrate the superiority of the proposed algorithm. The results show that the proposed algorithm provides more robust and accurate detection performance for noise models with different impulsiveness levels compared to the conventional methods.

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Multilag Correlation Estimators for Polarimetric Radar Measurements in the Presence of Noise

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-11-00010.1 ◽

2012 ◽

Vol 29 (6) ◽

pp. 772-795 ◽

Cited By ~ 24

Author(s):

Lei Lei ◽

Guifu Zhang ◽

Richard J. Doviak ◽

Robert Palmer ◽

Boon Leng Cheong ◽

...

Keyword(s):

Theoretical Analysis ◽

Data Quality ◽

Signal To Noise Ratio ◽

Simulated Data ◽

Radar Data ◽

Polarimetric Radar ◽

Signal To Noise ◽

Spectral Moments ◽

Radar Measurements

Abstract The quality of polarimetric radar data degrades as the signal-to-noise ratio (SNR) decreases. This substantially limits the usage of collected polarimetric radar data to high SNR regions. To improve data quality at low SNRs, multilag correlation estimators are introduced. The performance of the multilag estimators for spectral moments and polarimetric parameters is examined through a theoretical analysis and by the use of simulated data. The biases and standard deviations of the estimates are calculated and compared with those estimates obtained using the conventional method.

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Machine Learning Based Track Classification and Estimation using Kalman Filter

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a2616.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 1700-1704

Keyword(s):

Machine Learning ◽

Kalman Filter ◽

Learning Algorithm ◽

Signal To Noise Ratio ◽

Minimum Mean Square Error ◽

Velocity Model ◽

Supervised Machine Learning ◽

Multiple Target ◽

Low Snr

Classification of target from a mixture of multiple target information is quite challenging. In This paper we have used supervised Machine learning algorithm namely Linear Regression to classify the received data which is a mixture of target-return with the noise and clutter. Target state is estimated from the classified data using Kalman filter. Linear Kalman filter with constant velocity model is used in this paper. Minimum Mean Square Error (MMSE) analysis is used to measure the performance of the estimated track at various Signal to Noise Ratio (SNR) levels. The results state that the error is high for Low SNR, for High SNR the error is Low

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The development of non-coherent passive radar techniques for space situational awareness with the Murchison Widefield Array

Publications of the Astronomical Society of Australia ◽

10.1017/pasa.2020.1 ◽

2020 ◽

Vol 37 ◽

Cited By ~ 1

Author(s):

Steve Prabu ◽

Paul J. Hancock ◽

Xiang Zhang ◽

Steven J. Tingay

Keyword(s):

Situational Awareness ◽

Signal To Noise Ratio ◽

Space Station ◽

Passive Radar ◽

Space Situational Awareness ◽

Direct Path ◽

Orbital Parameters ◽

Ratio Spectrum ◽

Murchison Widefield Array ◽

Future Work

Abstract The number of active and non active satellites in Earth orbit has dramatically increased in recent decades, requiring the development of novel surveillance techniques to monitor and track them. In this paper, we build upon previous non-coherent passive radar space surveillance demonstrations undertaken using the Murchison Widefield Array (MWA). We develop the concept of the Dynamic Signal to Noise Ratio Spectrum (DSNRS) in order to isolate signals of interest (reflections of FM transmissions of objects in orbit) and efficiently differentiate them from direct path reception events. We detect and track Alouette-2, ALOS, UKube-1, the International Space Station, and Duchifat-1 in this manner. We also identified out-of-band transmissions from Duchifat-1 and UKube-1 using these techniques, demonstrating the MWA’s capability to look for spurious transmissions from satellites. We identify an offset from the locations predicted by the cataloged orbital parameters for some of the satellites, demonstrating the potential of using MWA for satellite catalog maintenance. These results demonstrate the capability of the MWA for Space Situational Awareness and we describe future work in this area.

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The Solar-Induced Chlorophyll Fluorescence Imaging Spectrometer (SIFIS) Onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1): Specifications and Prospects

Sensors ◽

10.3390/s20030815 ◽

2020 ◽

Vol 20 (3) ◽

pp. 815 ◽

Cited By ~ 1

Author(s):

Shanshan Du ◽

Liangyun Liu ◽

Xinjie Liu ◽

Xinwei Zhang ◽

Xianlian Gao ◽

...

Keyword(s):

Chlorophyll Fluorescence ◽

Signal To Noise Ratio ◽

Simulated Data ◽

Terrestrial Ecosystem ◽

Global Scale ◽

Terrestrial Vegetation ◽

Atmospheric Transmission ◽

Imaging Spectrometer ◽

Ecosystem Carbon ◽

Carbon Inventory

The global monitoring of solar-induced chlorophyll fluorescence (SIF) using satellite-based observations provides a new way of monitoring the status of terrestrial vegetation photosynthesis on a global scale. Several global SIF products that make use of atmospheric satellite data have been successfully developed in recent decades. The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1), the first Chinese terrestrial ecosystem carbon inventory satellite, which is due to be launched in 2021, will carry an imaging spectrometer specifically designed for SIF monitoring. Here, we use an extensive set of simulated data derived from the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) and Soil Canopy Observation Photosynthesis and Energy (SCOPE) models to evaluate and optimize the specifications of the SIF Imaging Spectrometer (SIFIS) onboard TECIS for accurate SIF retrievals. The wide spectral range of 670−780 nm was recommended to obtain the SIF at both the red and far-red bands. The results illustrate that the combination of a spectral resolution (SR) of 0.1 nm and a signal-to-noise ratio (SNR) of 127 performs better than an SR of 0.3 nm and SNR of 322 or an SR of 0.5 nm and SNR of 472 nm. The resulting SIF retrievals have a root-mean-squared (RMS) diff* value of 0.15 mW m−2 sr−1 nm−1 at the far-red band and 0.43 mW m−2 sr−1 nm−1 at the red band. This compares with 0.20 and 0.26 mW m−2 sr−1 nm−1 at the far-red band and 0.62 and 1.30 mW m−2 sr−1 nm−1 at the red band for the other two configurations described above. Given an SR of 0.3 nm, the increase in the SNR can also improve the SIF retrieval at both bands. If the SNR is improved to 450, the RMS diff* will be 0.17 mW m−2 sr−1 nm−1 at the far-red band and 0.47 mW m−2 sr−1 nm−1 at the red band. Therefore, the SIFIS onboard TECIS-1 will provide another set of observations dedicated to monitoring SIF at the global scale, which will benefit investigations of terrestrial vegetation photosynthesis from space.

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Configuration and Registration of Multi-Camera Spectral Image Database of Icon Paintings

Computation ◽

10.3390/computation7030047 ◽

2019 ◽

Vol 7 (3) ◽

pp. 47

Author(s):

Arash Mirhashemi

Keyword(s):

Dynamic Range ◽

Specular Reflection ◽

Signal To Noise Ratio ◽

Simulated Data ◽

Image Database ◽

High Dynamic Range ◽

Registration Method ◽

Spatial Registration ◽

Feature Based ◽

The Cost

At the cost of added complexity and time, hyperspectral imaging provides a more accurate measure of the scene’s irradiance compared to an RGB camera. Several camera designs with more than three channels have been proposed to improve the accuracy. The accuracy is often evaluated based on the estimation quality of the spectral data. Currently, such evaluations are carried out with either simulated data or color charts to relax the spatial registration requirement between the images. To overcome this limitation, this article presents an accurately registered image database of six icon paintings captured with five cameras with different number of channels, ranging from three (RGB) to more than a hundred (hyperspectral camera). Icons are challenging topics because they have complex surfaces that reflect light specularly with a high dynamic range. Two contributions are proposed to tackle this challenge. First, an imaging configuration is carefully arranged to control the specular reflection, confine the dynamic range, and provide a consistent signal-to-noise ratio for all the camera channels. Second, a multi-camera, feature-based registration method is proposed with an iterative outlier removal phase that improves the convergence and the accuracy of the process. The method was tested against three other approaches with different features or registration models.

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Assessment of Effective Network Connectivity among MEG None Contaminated Epileptic Transitory Events

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6406362 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Abir Hadriche ◽

Ichrak Behy ◽

Amal Necibi ◽

Abdennaceur Kachouri ◽

Chokri Ben Amar ◽

...

Keyword(s):

Inverse Problem ◽

Clinical Performance ◽

Signal To Noise Ratio ◽

Statistical Parametric Mapping ◽

Simulated Data ◽

Network Connectivity ◽

Propagation Delay ◽

Connection Strength ◽

Advanced Technique ◽

Epileptiform Discharges

Characterizing epileptogenic zones EZ (sources responsible of excessive discharges) would assist a neurologist during epilepsy diagnosis. Locating efficiently these abnormal sources among magnetoencephalography (MEG) biomarker is obtained by several inverse problem techniques. These techniques present different assumptions and particular epileptic network connectivity. Here, we proposed to evaluate performances of distributed inverse problem in defining EZ. First, we applied an advanced technique based on Singular Value Decomposition (SVD) to recover only pure transitory activities (interictal epileptiform discharges). We evaluated our technique’s robustness in separation between transitory and ripples versus frequency range, transitory shapes, and signal to noise ratio on simulated data (depicting both epileptic biomarkers and respecting time series and spectral properties of realistic data). We validated our technique on MEG signal using detector precision on 5 patients. Then, we applied four methods of inverse problem to define cortical areas and neural generators of excessive discharges. We computed network connectivity of each technique. Then, we confronted obtained noninvasive networks to intracerebral EEG transitory network connectivity using nodes in common, connection strength, distance metrics between concordant nodes of MEG and IEEG, and average propagation delay. Coherent Maximum Entropy on the Mean (cMEM) proved a high matching between MEG network connectivity and IEEG based on distance between active sources, followed by Exact low-resolution brain electromagnetic tomography (eLORETA), Dynamical Statistical Parametric Mapping (dSPM), and Minimum norm estimation (MNE). Clinical performance was interesting for entire methods providing in an average of 73.5% of active sources detected in depth and seen in MEG, and vice versa, about 77.15% of active sources were detected from MEG and seen in IEEG. Investigated problem techniques succeed at least in finding one part of seizure onset zone. dSPM and eLORETA depict the highest connection strength among all techniques. Propagation delay varies in this range [18, 25]ms, knowing that eLORETA ensures the lowest propagation delay (18 ms) and the closet one to IEEG propagation delay.

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Estimativa do campo de vento em alta resolução pelo Modelo Wind Ninja para estimativa em Energia Eólica

Ciência e Natura ◽

10.5902/2179460x53215 ◽

2020 ◽

Vol 42 ◽

pp. e33

Author(s):

César Magno Leite de Oliveira Júnior ◽

Nivea Maria Barreto Nunes Oleques ◽

Fabricio Pereira Harter ◽

Jonas Da Costa Carvalho ◽

Marcelo Romero de Moraes

Keyword(s):

Wind Farm ◽

Weather Forecasting ◽

Wrf Model ◽

Simulated Data ◽

Momentum Conservation ◽

Simulation Software ◽

Observation Data ◽

Wind Fields ◽

Numerical Weather Forecasting ◽

Conservation Model

In this work it was proposed to predict high resolution wind fields in order to simulate the wind speed and direction variables in the Cerro Chato wind complex located in the city of Santana do Livramento / RS. For this, numerical weather forecasting techniques were used, using the \ textit {Weather Research and Forecasting} (WRF) model and a microscale mass and momentum conservation model, known as Windninja. In this study, the simulated data was compared with the observation data collected by the towers of the wind farm in question, using statistical procedures. The results presented by the Windninja simulation software were efficient, but not very effective.

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Improved deconvolution of very weak confocal signals

F1000Research ◽

10.12688/f1000research.11773.1 ◽

2017 ◽

Vol 6 ◽

pp. 787 ◽

Cited By ~ 6

Author(s):

Kasey J. Day ◽

Patrick J. La Rivière ◽

Talon Chandler ◽

Vytas P. Bindokas ◽

Nicola J. Ferrier ◽

...

Keyword(s):

Background Noise ◽

Signal To Noise Ratio ◽

Simulated Data ◽

Time Lapse ◽

Confocal Image ◽

Signal To Noise ◽

4D Imaging ◽

Fluorescence Images ◽

Gaussian Blur ◽

Imaging Conditions

Deconvolution is typically used to sharpen fluorescence images, but when the signal-to-noise ratio is low, the primary benefit is reduced noise and a smoother appearance of the fluorescent structures. 3D time-lapse (4D) confocal image sets can be improved by deconvolution. However, when the confocal signals are very weak, the popular Huygens deconvolution software erases fluorescent structures that are clearly visible in the raw data. We find that this problem can be avoided by prefiltering the optical sections with a Gaussian blur. Analysis of real and simulated data indicates that the Gaussian blur prefilter preserves meaningful signals while enabling removal of background noise. This approach is very simple, and it allows Huygens to be used with 4D imaging conditions that minimize photodamage.

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Moving Target Detection in Multi-Static GNSS-Based Passive Radar Based on Multi-Bernoulli Filter

Remote Sensing ◽

10.3390/rs12213495 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3495

Author(s):

HongCheng Zeng ◽

Jie Chen ◽

PengBo Wang ◽

Wei Liu ◽

XinKai Zhou ◽

...

Keyword(s):

Target Detection ◽

Sequential Monte Carlo ◽

Signal To Noise Ratio ◽

Power Level ◽

Estimation Method ◽

Satellite System ◽

Moving Target ◽

Passive Radar ◽

Moving Target Detection ◽

Global Navigation Satellite

Over the past few years, the global navigation satellite system (GNSS)-based passive radar (GBPR) has attracted more and more attention and has developed very quickly. However, the low power level of GNSS signal limits its application. To enhance the ability of moving target detection, a multi-static GBPR (MsGBPR) system is considered in this paper, and a modified iterated-corrector multi-Bernoulli (ICMB) filter is also proposed. The likelihood ratio model of the MsGBPR with range-Doppler map is first presented. Then, a signal-to-noise ratio (SNR) online estimation method is proposed, which can estimate the fluctuating and unknown map SNR effectively. After that, a modified ICMB filter and its sequential Monte Carlo (SMC) implementation are proposed, which can update all measurements from multi-transmitters in the optimum order (ascending order). Moreover, based on the proposed method, a moving target detecting framework using MsGBPR data is also presented. Finally, performance of the proposed method is demonstrated by numerical simulations and preliminary experimental results, and it is shown that the position and velocity of the moving target can be estimated accurately.

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