scholarly journals Support Detection for SAR Tomographic Reconstructions from Compressive Measurements

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Alessandra Budillon ◽  
Gilda Schirinzi
Keyword(s):  
Detection Method ◽  
Probability Of Detection ◽  
Synthetic Aperture ◽  
Generalized Likelihood Ratio Test ◽  
Ratio Test ◽  
Detection Techniques ◽  
Probability Of False Alarm ◽  
Support Set ◽  
Sparse Vector ◽  
Reliable Solution

The problem of detecting and locating multiple scatterers in multibaseline Synthetic Aperture Radar (SAR) tomography, starting from compressive measurements and applying support detection techniques, is addressed. Different approaches based on the detection of the support set of the unknown sparse vector, that is, of the position of the nonzero elements in the unknown sparse vector, are analyzed. Support detection techniques have already proved to allow a reduction in the number of measurements required for obtaining a reliable solution. In this paper, a support detection method, based on a Generalized Likelihood Ratio Test (Sup-GLRT), is proposed and compared with the SequOMP method, in terms of probability of detection achievable with a given probability of false alarm and for different numbers of measurements.

scholarly journals Tree Copula Theory Based Fusion And Compressive Sensing For Activity Detection using Multi-Modal Data

2019 ◽  
Vol 8 (11) ◽  
pp. 4152-4158
Keyword(s):  
False Alarm ◽  
Compressive Sensing ◽  
Detection Method ◽  
Probability Of Detection ◽  
Stochastic Gradient Descent ◽  
Activity Detection ◽  
Copula Theory ◽  
Probability Of False Alarm ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Activity detection based on likelihood ratio in the presence of high dimensional multimodal data acts as a challenging problem as the estimation of joint probability density functions (pdfs) with intermodal dependence is tedious. The existing method with above expectations fails due to poor performance in the presence of strongly dependent data. This paper proposes a Compressive Sensing Based Detection method in the Multi-sensor signal using the deep learning method. The proposed Tree copula- Grasshopper optimization based Deep Convolutional Neural Network (TC-GO based DCNN) detection method comprises of three main steps, such as compressive sensing, fusion and detection. The signals are initially collected from the sensors in order to subject them under tensor based compressive sensing. The compressed signals are then fused together using tree copula theory, and the parameters are estimated with the Grasshopper optimization algorithm (GOA). The activity detection is finally performed using DCNN, which is trained with the Stochastic Gradient Descent (SGD) Optimizer. The performance of the proposed method is evaluated based on the evaluation metrics, such as probability of detection and probability of false alarm. The highest probability of detection and least probability of false alarm are obtained as 0.9083, and 0.0959, respectively using the proposed method that shows the effectiveness of the proposed method in activity detection.

scholarly journals Super-Resolution Multi-Look Detection in SAR Tomography

2018 ◽  
Vol 10 (12) ◽  
pp. 1894 ◽  
Author(s):  
Cosmin Dănișor ◽  
Gianfranco Fornaro ◽  
Antonio Pauciullo ◽  
Diego Reale ◽  
Mihai Datcu
Keyword(s):  
Super Resolution ◽  
Synthetic Aperture ◽  
Generalized Likelihood Ratio Test ◽  
Ratio Test ◽  
Sidelobe Suppression ◽  
Persistent Scatterers ◽  
Detection Strategy ◽  
Detection Capabilities ◽  
System Acquisition ◽  
A Minor

Synthetic Aperture Radar (SAR) Tomography (TomoSAR) allows extending the 2-D focusing capabilities of SAR to the elevation direction, orthogonal to the azimuth and range. The multi-dimensional extension (along the time) also enables the monitoring of possible scatterer displacements. A key aspect of TomoSAR is the identification, in the presence of noise, of multiple persistent scatterers interfering within the same 2-D (azimuth range plane) pixel. To this aim, the use of multi-look has been shown to provide tangible improvements in the detection of single and double interfering persistent scatterers at the expense of a minor spatial resolution loss. Depending on the system acquisition characteristics, this operation may require also the detection of multiple scatterers interfering at distances lower than the Rayleigh resolution (super-resolution). In this work we further investigated the use of multi-look in TomoSAR for the detection of multiple scatterers located also below the Rayleigh resolution. A solution relying on the Capon filtering was first analyzed, due to its improved capabilities in the separation of the responses of multiple scatterers and sidelobe suppression. Moreover, in the framework of the Generalized Likelihood Ratio Test (GLRT), the single-look support based detection strategy recently proposed in the literature was extended to the multi-look case. Experimental results of tests carried out on two datasets acquired by TerraSAR-X and COSMO-SkyMED sensors are provided to show the performances of the proposed solution as well as the effects of the baseline span of the dataset for the detection capabilities of interfering scatterers.

scholarly journals Detección óptica en 3D de un objeto flotante en una superficie marina agitada

2020 ◽  
pp. 7-16
Author(s):  
Ana Karen Paredes-Perez ◽  
Victor Golikov ◽  
Hussain Alazki
Keyword(s):  
A Priori ◽  
Computer Software ◽  
Probability Of Detection ◽  
Sea Surface ◽  
Ratio Test ◽  
Gaussian Random Process ◽  
Probability Of False Alarm ◽  
Floating Object ◽  
The Difference ◽  
Object Parameters

In this paper we realize a comparison between two detectors: Matched Subspace Detector (MSD) and Modify Matched Subspace Detector (MMSD) when there is a images secuence (3D detection), where the parameters of sea surface and the parameters of floating object are priori unknown in computer simulation, with help of computer software MATLAB. Both detectors (MSD and MMSD) are based in the General Likelihood Ratio Test (GLRT); this method helps solve detection problems when the sea surface and floating object parameters are unknown. The sea surface is simulated as a Gaussian random process, and the floating object is simulated as a priori unknown deterministic process. The paper considers the dependence of the probability of detection with a fixed probability of false alarm on the difference between the average values of reflections from the sea surface and from a floating object with different ratios of the power of fluctuations of reflections from the object and from the sea Surface.

scholarly journals Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3255
Author(s):  
Waqas Bin Abbas ◽  
Fuhu Che ◽  
Qasim Zeeshan Ahmed ◽  
Fahd Ahmed Khan ◽  
Temitope Alade
Keyword(s):  
Detection Method ◽  
Detection Threshold ◽  
Analytical Framework ◽  
Impulse Radio ◽  
Probability Of Detection ◽  
Probability Of False Alarm ◽  
Frame Length ◽  
Simulation Results ◽  
Device Free ◽  
System Operating

In this paper, an analytical framework is presented for device detection in an impulse radio (IR) ultra-wide bandwidth (UWB) system and its performance analysis is carried out. The Neyman–Pearson (NP) criteria is employed for this device-free detection. Different from the frequency-based approaches, the proposed detection method utilizes time domain concepts. The characteristic function (CF) is utilized to measure the moments of the presence and absence of the device. Furthermore, this method is easily extendable to existing device-free and device-based techniques. This method can also be applied to different pulse-based UWB systems which use different modulation schemes compared to IR-UWB. In addition, the proposed method does not require training to measure or calibrate the system operating parameters. From the simulation results, it is observed that an optimal threshold can be chosen to improve the ROC for UWB system. It is shown that the probability of false alarm, PFA, has an inverse relationship with the detection threshold and frame length. Particularly, to maintain PFA<10−5 for a frame length of 300 ns, it is required that the threshold should be greater than 2.2. It is also shown that for a fix PFA, the probability of detection PD increases with an increase in interference-to-noise ratio (INR). Furthermore, PD approaches 1 for INR >−2 dB even for a very low PFA i.e., PFA=1×10−7. It is also shown that a 2 times increase in the interference energy results in a 3 dB improvement in INR for a fixed PFA=0.1 and PD=0.5. Finally, the derived performance expressions are corroborated through simulation.

scholarly journals An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1581
Author(s):  
Xiaolong Chen ◽  
Jian Li ◽  
Shuowen Huang ◽  
Hao Cui ◽  
Peirong Liu ◽  
...  
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Multiple Scales ◽  
Point Clouds ◽  
Depth Image ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Concrete Crack ◽  
3D Point Clouds ◽  
2D Images

Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

scholarly journals Adaptive Waveform Design with Multipath Exploitation Radar in Heterogeneous Environments

2021 ◽  
Vol 13 (9) ◽  
pp. 1628
Author(s):  
Seden Hazal Gulen Yilmaz ◽  
Chiara Zarro ◽  
Harun Taha Hayvaci ◽  
Silvia Liberata Ullo
Keyword(s):  
Secondary Data ◽  
Waveform Design ◽  
Second Step ◽  
Point Estimate ◽  
Generalized Likelihood Ratio Test ◽  
Final Decision ◽  
Ratio Test ◽  
Heterogeneous Environments ◽  
Range Cell ◽  
Extensive Performance

The problem of detecting point like targets over a glistening surface is investigated in this manuscript, and the design of an optimal waveform through a two-step process for a multipath exploitation radar is proposed. In the first step, a non-adaptive waveform is transmitted anda constrained Generalized Likelihood Ratio Test (GLRT) detector is deduced at reception which exploits multipath returns in the range cell under test by modelling the target echo as a superposition of the direct plus the multipath returns. Under the hypothesis of heterogeneous environments, thus by assuming a compound-Gaussian distribution for the clutter return, this latter is estimated in the range cell under test through the secondary data, which are collected from the out-of-bin cells. The Fixed Point Estimate (FPE) algorithm is applied in the clutter estimation, then used to design the adaptive waveform for transmission in the second step of the algorithm, in order to suppress the clutter coming from the adjacent cells. The proposed GLRT is also used at the end of the second transmission for the final decision. Extensive performance evaluation of the proposed detector and adaptive waveform for various multipath scenarios is presented. The performance analysis prove that the proposed method improves the Signal-to-Clutter Ratio (SCR) of the received signal, and the detection performance with multipath exploitation.

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