Study on high resolution and high repeatability target localization algorithm in development of national level standard

2015 ◽  
Author(s):  
Yao Huang ◽  
Weichen Wang ◽  
Zi Xue
Keyword(s):  
High Resolution ◽  
National Level ◽  
Target Localization ◽  
Localization Algorithm ◽  
High Repeatability

scholarly journals seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day

2018 ◽  
Vol 10 (1) ◽  
pp. 235-249 ◽  
Author(s):  
Cristian Lussana ◽  
Tuomo Saloranta ◽  
Thomas Skaugen ◽  
Jan Magnusson ◽  
Ole Einar Tveito ◽  
...  
Keyword(s):  
High Resolution ◽  
Daily Precipitation ◽  
Spatial Scales ◽  
National Level ◽  
Calibration Procedure ◽  
Geographical Information ◽  
Optimal Interpolation ◽  
Small Scale ◽  
Meteorological Forcing ◽  
Atmospheric Sciences

Abstract. The conventional climate gridded datasets based on observations only are widely used in atmospheric sciences; our focus in this paper is on climate and hydrology. On the Norwegian mainland, seNorge2 provides high-resolution fields of daily total precipitation for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The dataset constitutes a valuable meteorological input for snow and hydrological simulations; it is updated daily and presented on a high-resolution grid (1 km of grid spacing). The climate archive goes back to 1957. The spatial interpolation scheme builds upon classical methods, such as optimal interpolation and successive-correction schemes. An original approach based on (spatial) scale-separation concepts has been implemented which uses geographical coordinates and elevation as complementary information in the interpolation. seNorge2 daily precipitation fields represent local precipitation features at spatial scales of a few kilometers, depending on the station network density. In the surroundings of a station or in dense station areas, the predictions are quite accurate even for intense precipitation. For most of the grid points, the performances are comparable to or better than a state-of-the-art pan-European dataset (E-OBS), because of the higher effective resolution of seNorge2. However, in very data-sparse areas, such as in the mountainous region of southern Norway, seNorge2 underestimates precipitation because it does not make use of enough geographical information to compensate for the lack of observations. The evaluation of seNorge2 as the meteorological forcing for the seNorge snow model and the DDD (Distance Distribution Dynamics) rainfall–runoff model shows that both models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The seNorge2 dataset 1957–2015 is available at https://doi.org/10.5281/zenodo.845733. Daily updates from 2015 onwards are available at http://thredds.met.no/thredds/catalog/metusers/senorge2/seNorge2/provisional_archive/PREC1d/gridded_dataset/catalog.html.

scholarly journals Negative Pressure Waves Based High Resolution Leakage Localization Method Using Piezoceramic Transducers and Multiple Temporal Convolutions

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1990 ◽  
Author(s):  
Guangmin Zhang ◽  
Siu Chun Michael Ho ◽  
Linsheng Huo ◽  
Junxiao Zhu
Keyword(s):  
High Resolution ◽  
Lead Zirconate Titanate ◽  
Negative Pressure ◽  
Lead Zirconate ◽  
Signal Duration ◽  
Leakage Detection ◽  
Localization Algorithm ◽  
Large Area ◽  
Leakage Area ◽  
Better Than

The negative pressure wave (NPW) signals generated by a pipeline leakage often have a long signal duration. When these signals are utilized to compute the leakage position, the long signal duration will result in a large area being considered as leakage area. The localization resolution is low. A novel high-resolution localization algorithm is developed for pipeline leakage detection using piezoceramic transducers in this paper. The proposed algorithm utilizes multiple temporal convolutions to decrease the localization functional values at the points close to the leakage, in order to reduce the range of the leakage area revealed by the proposed algorithm. As a result, the localization resolution is improved. A measured experiment was conducted to study the proposed algorithm. In the experiment, the proposed algorithm was used to monitor a 55.8 m pressurized pipeline with two controllable valves and two Lead Zirconate Titanate (PZT) sensors. With the aid of the piezoceramic sensor, the experimental results show that the proposed algorithm results in a resolution which is better than that of the traditional method.

scholarly journals Energy-Level Jumping Algorithm for Global Optimization in Compressive Sensing-Based Target Localization

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2502
Author(s):  
Tianjing Wang ◽  
Xinjie Guan ◽  
Xili Wan ◽  
Guoqing Liu ◽  
Hang Shen
Keyword(s):  
Global Optimization ◽  
Energy Level ◽  
Initial Point ◽  
Target Localization ◽  
Global Minimizer ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Simulation Results ◽  
Sparse Solutions ◽  
Recovery Problem

Target localization is one of the essential tasks in almost applications of wireless sensor networks. Some traditional compressed sensing (CS)-based target localization methods may achieve low-precision target localization because of using locally optimal sparse solutions. Solving global optimization for the sparse recovery problem remains a challenge in CS-based target localization. In this paper, we propose a novel energy-level jumping algorithm to address this problem, which achieves high-precision target localization by solving the globally optimal sparse solution of l p -norm ( 0 < p < 1 ) minimization. By repeating the process of energy-level jumping, our proposed algorithm establishes a global convergence path from an initial point to the global minimizer. Compared with existing CS-based target localization methods, the simulation results show that our localization algorithm obtain more accurate locations of targets with the significantly reduced number of measurements.

High-Resolution Imaging Sonar and Video Technologies for Detection and Classification of Underwater Munitions

2011 ◽  
Vol 45 (6) ◽  
pp. 62-74 ◽  
Author(s):  
Pierre-Philippe J. Beaujean ◽  
Lisa N. Brisson ◽  
Shahriar Negahdaripour
Keyword(s):  
High Resolution ◽  
Template Matching ◽  
Metal Alloy ◽  
Acoustic Energy ◽  
Target Localization ◽  
Imaging Systems ◽  
Optical Systems ◽  
Test Tank ◽  
Range Limitation

AbstractThe detection of and response to underwater munitions will undoubtedly require the appropriate combinations of fully integrated sensors and imaging systems and platforms, as well as navigation and positioning technologies, to handle the variability in bottom conditions, water clarity and depth, size and type of munitions of interest, whether they are buried or proud. Where visibility allows, practically no sensing modality matches the details and information content from optical imaging systems for target localization, discrimination and identification. The significant disadvantage of optical systems for underwater applications is the range limitation. Sonar imaging systems are of limited resolution but do not have such a severe range limitation, as acoustic energy propagates well through turbid waters.In this study, we have explored two aspects of the munitions detection and classification process: (1) high-resolution mapping of an environment using a high-frequency sonar system to determine footprints of areas with munitions present and target localization in a wide-area survey and to perform detailed surveys for individual detected items during a re-acquisition process and (2) Multiple-Aspect Fixed-Range Template Matching (MAFR-TM) for detection and classification of the potential target.The MAFR-TM approach was tested using (1) a singular target scene collected in a test tank, (2) a cluttered scene acquired in the same test tank, and (3) a cluttered scene obtained in a realistic field environment (a marina). The munitions-like targets were cylinders made of steel or aluminum. The clutter was a collection of PVC tubes. Biological growth surrounded the target and artificial clutter in the marina. The experimental results indicate that the detection algorithm performs fairly well with the tank data (100% of the targets are detected) and cluttered tank data (94.44%). The classification between metals and plastics, proper orientation and target localization is also of good quality: 94.4% of the detected targets are properly classified as metal alloy if no clutter is present versus 82.35% in the presence of clutter. The algorithm performance in the marina is reasonably good, even though the overall performance drops: 61.11% of the targets are detected, and 68.18% of the detected targets are properly classified as metal alloy.

scholarly journals A Fast and High-Resolution Multi-Target Localization Approach in MIMO Radar

10.5772/56651 ◽  
2013 ◽  
Vol 10 (9) ◽  
pp. 322
Author(s):  
Yu Zhang ◽  
Hong Jiang ◽  
Ying-Chun Wei ◽  
Hai-Jing Cui
Keyword(s):  
High Resolution ◽  
Mimo Radar ◽  
Target Localization ◽  
Localization Approach

Algorithm for Target Tracking Using Passive Radar

2012 ◽  
Vol 58 (4) ◽  
pp. 345-350 ◽  
Author(s):  
Mateusz Malanowski
Keyword(s):  
Target Tracking ◽  
Real Data ◽  
Tracking Algorithm ◽  
Target Localization ◽  
Passive Radar ◽  
Localization Algorithm ◽  
Two Stage ◽  
Cartesian Coordinates ◽  
The Real ◽  
University Of Technology

Abstract In the paper the problem of target tracking in passive radar is addressed. Passive radar measures bistatic parameters of a target: bistatic range and bistatic velocity. The aim of the tracking algorithm is to convert the bistatic measurements into Cartesian coordinates. In the paper a two-stage tracking algorithm is presented, using bistatic and Cartesian tracking. In addition, a target localization algorithm is applied to initialize Cartesian tracks from bistatic measurements. The tracking algorithm is tested using simulated and real data. The real data were obtained from an FM-based passive radar called PaRaDe, developed at Warsaw University of Technology.

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