scholarly journals Adaptive Doppler Compensation for Mitigating Range Dependence in Forward-Looking Airborne Radar

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1896
Author(s):  
Muhammad Bilal Khan ◽  
Ahmed Hussain ◽  
Umar Anjum ◽  
Channa Babar Ali ◽  
Xiaodong Yang
Keyword(s):  
Parameter Estimation ◽  
Real Time ◽  
Covariance Matrix ◽  
Synthetic Data ◽  
Weather Conditions ◽  
Airborne Radar ◽  
Signal Processing Algorithm ◽  
Implementation Scheme ◽  
Doppler Compensation ◽  
Forward Looking

In this paper, we present ground moving target indication (GMTI) signal processing algorithm encompassing clutter suppression, target detection and parameter estimation. One of the most significant yet least publicized is the need of the GMTI mode for a forward-looking airborne radar. The integration of GMTI mode in a forward-looking airborne radar allows reconnaissance and surveillance operations in all weather conditions. In this context, space time adaptive processing (STAP) offers a unique prospect of enabling the GMTI mode in forward looking airborne radar. STAP is a two-dimensional filter designed to suppress platform motion-induced clutter Doppler spread. Interference is characterized by a covariance matrix. In the case of a forward-looking airborne radar, the clutter Doppler is dependent on range. Clutter Doppler dependency on the range renders the training cells heterogeneous. The heterogeneity effects are particularly prominent in the near range bins. Non-homogeneous training cells have a deleterious effect on STAP performance. In this study, we propose an adaptive Doppler compensation to mitigate the degraded STAP performance in the near range bins. The adaptivity feature circumvents the need for the availability of radar parameters in real-time. The real time implementation of STAP is impeded by requirements of a large number of training samples and covariance matrix inversion. Therefore, there is a dire need to devise a framework to detect and estimate target parameters within the STAP. In this regard, we propose an efficient STAP algorithm to detect and estimate target parameters. STAP weights are applied to the input data to obtain a 3D array. The range projection of the 3D array is utilized to detect and estimate the range of the target, while the angle–Doppler projection is used to estimate spatial and temporal parameters of the target. Most of the literature on STAP is geared towards a known covariance matrix. The assumption of a known covariance matrix may degrade STAP performance because of the inherent mismatches between the actual and assumed target steering vectors. In this study, we estimate the covariance matrix based on the synthetic data generated from a model of an airborne phased array radar. The developed STAP algorithms closely mimic a real-time implementation scheme in an airborne radar platform. The results of the proposed algorithm are validated through target parameter estimation and STAP metrics on synthetic data.

REAL CONDITION AND PROSPECTS OF DEVELOPMENT OF THE RADAR STATIONS OF THE COUNTER BATTERY FIGHTINGV

2019 ◽  
Vol 1 (12) ◽  
pp. 30-40
Author(s):  
V. Нolovan ◽  
V. Gerasimov ◽  
А. Нolovan ◽  
N. Maslich
Keyword(s):  
Comparative Analysis ◽  
Real Time ◽  
Weather Conditions ◽  
Time Of Day ◽  
Current Position ◽  
Real Condition ◽  
Leading Role ◽  
Artillery Shell

Fighting in the Donbas, which has been going on for more than five years, shows that a skillful counter-battery fight is an important factor in achieving success in wars of this kind. Especially in conditions where for the known reasons the use of combat aviation is minimized. With the development of technical warfare, the task of servicing the counter-battery fight began to rely on radar stations (radar) to reconnaissance the positions of artillery, which in modern terms are called counter-battery radar. The principle of counter-battery radar is based on the detection of a target (artillery shell, mortar mine or rocket) in flight at an earlier stage and making several measurements of the coordinates of the current position of the ammunition. According to these data, the trajectory of the projectile's flight is calculated and, on the basis of its prolongation and extrapolation of measurements, the probable coordinates of the artillery, as well as the places of ammunition falling, are determined. In addition, the technical capabilities of radars of this class allow you to recognize the types and caliber of artillery systems, as well as to adjust the fire of your artillery. The main advantages of these radars are:  mobility (transportability);  inspection of large tracts of terrain over long distances;  the ability to obtain target's data in near real-time;  independence from time of day and weather conditions;  relatively high fighting efficiency. The purpose of the article is to determine the leading role and place of the counter-battery radar among other artillery instrumental reconnaissance tools, to compare the combat capabilities of modern counter-battery radars, armed with Ukrainian troops and some leading countries (USA, China, Russia), and are being developed and tested in Ukraine. The method of achieving this goal is a comparative analysis of the features of construction and combat capabilities of modern models of counter-battery radar in Ukraine and in other countries. As a result of the conducted analysis, the directions of further improvement of the radar armament, increasing the capabilities of existing and promising counter-battery radar samples were determined.

Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions

2021 ◽  
Vol 40 (3) ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Yuxiao Zhou ◽  
Yifei Tian ◽  
Jun-Hai Yong ◽  
Feng Xu
Keyword(s):  
Real Time ◽  
Synthetic Data ◽  
Real Data ◽  
Depth Image ◽  
Real Time System ◽  
The Real ◽  
Time Performance ◽  
Contact Constraint ◽  
Object Shapes ◽  
Object Interactions

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.

scholarly journals Fast Split Bregman Based Deconvolution Algorithm for Airborne Radar Imaging

2020 ◽  
Vol 12 (11) ◽  
pp. 1747 ◽  
Author(s):  
Yin Zhang ◽  
Qiping Zhang ◽  
Yongchao Zhang ◽  
Jifang Pei ◽  
Yulin Huang ◽  
...  
Keyword(s):  
Real Time ◽  
Computing Time ◽  
Real Data ◽  
Radar Imaging ◽  
Matrix Inversion ◽  
Airborne Radar ◽  
Split Bregman ◽  
Regularization Problem ◽  
Azimuth Resolution ◽  
Split Bregman Algorithm

Deconvolution methods can be used to improve the azimuth resolution in airborne radar imaging. Due to the sparsity of targets in airborne radar imaging, an L 1 regularization problem usually needs to be solved. Recently, the Split Bregman algorithm (SBA) has been widely used to solve L 1 regularization problems. However, due to the high computational complexity of matrix inversion, the efficiency of the traditional SBA is low, which seriously restricts its real-time performance in airborne radar imaging. To overcome this disadvantage, a fast split Bregman algorithm (FSBA) is proposed in this paper to achieve real-time imaging with an airborne radar. Firstly, under the regularization framework, the problem of azimuth resolution improvement can be converted into an L 1 regularization problem. Then, the L 1 regularization problem can be solved with the proposed FSBA. By utilizing the low displacement rank features of Toeplitz matrix, the proposed FSBA is able to realize fast matrix inversion by using a Gohberg–Semencul (GS) representation. Through simulated and real data processing experiments, we prove that the proposed FSBA significantly improves the resolution, compared with the Wiener filtering (WF), truncated singular value decomposition (TSVD), Tikhonov regularization (REGU), Richardson–Lucy (RL), iterative adaptive approach (IAA) algorithms. The computational advantage of FSBA increases with the increase of echo dimension. Its computational efficiency is 51 times and 77 times of the traditional SBA, respectively, for echoes with dimensions of 218 × 400 and 400 × 400 , optimizing both the image quality and computing time. In addition, for a specific hardware platform, the proposed FSBA can process echo of greater dimensions than traditional SBA. Furthermore, the proposed FSBA causes little performance degradation, when compared with the traditional SBA.

REAL-TIME 2.5D INVERSION OF LWD RESISTIVITY MEASUREMENTS USING DEEP LEARNING FOR GEOSTEERING APPLICATIONS ACROSS FAULTED FORMATIONS

2021 ◽  
Author(s):  
Kyubo Noh ◽  
◽  
Carlos Torres-Verdín ◽  
David Pardo ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Fault Plane ◽  
Synthetic Data ◽  
Field Measurements ◽  
Geological Structure ◽  
Inversion Method ◽  
Adaptive Finite Element ◽  
Resistivity Measurements ◽  
Lwd Resistivity

We develop a Deep Learning (DL) inversion method for the interpretation of 2.5-dimensional (2.5D) borehole resistivity measurements that requires negligible online computational costs. The method is successfully verified with the inversion of triaxial LWD resistivity measurements acquired across faulted and anisotropic formations. Our DL inversion workflow employs four independent DL architectures. The first one identifies the type of geological structure among several predefined types. Subsequently, the second, third, and fourth architectures estimate the corresponding spatial resistivity distributions that are parameterized (1) without the crossings of bed boundaries or fault plane, (2) with the crossing of a bed boundary but without the crossing of a fault plane, and (3) with the crossing of the fault plane, respectively. Each DL architecture employs convolutional layers and is trained with synthetic data obtained from an accurate high-order, mesh-adaptive finite-element forward numerical simulator. Numerical results confirm the importance of using multi-component resistivity measurements -specifically cross-coupling resistivity components- for the successful reconstruction of 2.5D resistivity distributions adjacent to the well trajectory. The feasibility and effectiveness of the developed inversion workflow is assessed with two synthetic examples inspired by actual field measurements. Results confirm that the proposed DL method successfully reconstructs 2.5D resistivity distributions, location and dip angles of bed boundaries, and the location of the fault plane, and is therefore reliable for real-time well geosteering applications.

New LWD Image Improvement Method to Mitigate Interpretation Risks

2021 ◽  
Author(s):  
Tianhua Zhang ◽  
Shiduo Yang ◽  
Chandramani Shrivastava ◽  
Adrian A ◽  
Nadege Bize-Forest
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Best Practice ◽  
Synthetic Data ◽  
Image Resolution ◽  
Drilling Process ◽  
Drilling Tool ◽  
Stick Slip ◽  
Geological Interpretation ◽  
Wellbore Temperature

Abstract With the advancement of LWD (Logging While Drilling) hardware and acquisition, the imaging technology becomes not only an indispensable part of the drilling tool string, but also the image resolution increases to map layers and heterogeneity features down to less than 5mm scale. This shortens the geological interpretation turn-around time from wireline logging time (hours to days after drilling) to semi-real time (drilling time or hours after drilling). At the same time, drilling motion is complex. The depth tracking is on the surface referenced to the surface block movement. The imaging sensor located downhole can be thousands of feet away from the surface. Mechanical torque and drag, wellbore friction, wellbore temperature and weight on bit can make the downhole sensor movement motion not synchronized with surface pipe depth. This will cause time- depth conversion step generate image artifacts that either stop real-time interpretation of geological features or mis-interpret features on high resolution images. In this paper, we present several LWD images featuring distortion mechanism during the drilling process using synthetic data. We investigated how heave, depth reset and downhole sensor stick/slip caused image distortions. We provide solutions based on downhole sensor pseudo velocity computation to minimize the image distortion. The best practice in using Savitsky-Golay filter are presented in the discussion sections. Finally, some high-resolution LWD images distorted with drilling-related artifacts and processed ones are shown to demonstrate the importance of image post-processing. With the proper processed images, we can minimize interpretation risks and make drilling decisions with more confidence.

scholarly journals How Can Existing Ground-Based Profiling Instruments Improve European Weather Forecasts?

2019 ◽  
Vol 100 (4) ◽  
pp. 605-619 ◽  
Author(s):  
A. J. Illingworth ◽  
D. Cimini ◽  
A. Haefele ◽  
M. Haeffelin ◽  
M. Hervo ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Positive Impact ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Time Data ◽  
Weather Forecasts ◽  
Forecast Models ◽  
Wind Lidar ◽  
Microwave Radiometers

Abstract To realize the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog, and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few kilometers are hardly accessible by satellite, especially in dynamically active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real time to forecast centers. The three classes of instruments are i) automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog, and volcanic ash, the last two being especially important for air traffic control; ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind gusts, and low-level jets; and iii) microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. The project includes collaboration from 22 European countries and 15 European national weather services, which involves the implementation of common operating procedures, instrument calibrations, data formats, and retrieval algorithms. Currently, data from 265 ceilometers in 19 countries are being distributed in near–real time to national weather forecast centers; this should soon rise to many hundreds. One wind lidar is currently delivering real time data rising to 5 by the end of 2019, and the plan is to incorporate radiometers in 2020. Initial data assimilation tests indicate a positive impact of the new data.

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