A Target Localization Method for UAV Image Sequences Based on DEM Matching

2016 ◽  
Author(s):  
Jiajing Zhuo ◽  
Lanqiong Sun ◽  
Yi Yang ◽  
Xinglong Zhao
Keyword(s):  
Image Sequences ◽  
Target Localization ◽  
Localization Method ◽  
Uav Image

Method of Optimization for Target Localization Model Parameters Based on LSSVR

2011 ◽  
Vol 268-270 ◽  
pp. 934-939
Author(s):  
Xue Wen He ◽  
Gui Xiong Liu ◽  
Hai Bing Zhu ◽  
Xiao Ping Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Target Localization ◽  
Likelihood Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

scholarly journals UAV IMAGE BLUR – ITS INFLUENCE AND WAYS TO CORRECT IT

2015 ◽  
Vol XL-1/W4 ◽  
pp. 33-39 ◽  
Author(s):  
T. Sieberth ◽  
R. Wackrow ◽  
J. H. Chandler
Keyword(s):  
Image Processing ◽  
Target Identification ◽  
Image Sequences ◽  
Identification Measurements ◽  
Filtering Technique ◽  
Coordinate Measurements ◽  
Active Research ◽  
Uav Image ◽  
Blurred Images ◽  
Degradation Effect

Unmanned aerial vehicles (UAVs) have become an interesting and active research topic in photogrammetry. Current research is based on image sequences acquired by UAVs which have a high ground resolution and good spectral resolution due to low flight altitudes combined with a high-resolution camera. One of the main problems preventing full automation of data processing of UAV imagery is the unknown degradation effect of blur caused by camera movement during image acquisition. <br><br> The purpose of this paper is to analyse the influence of blur on photogrammetric image processing, the correction of blur and finally, the use of corrected images for coordinate measurements. It was found that blur influences image processing significantly and even prevents automatic photogrammetric analysis, hence the desire to exclude blurred images from the sequence using a novel filtering technique. If necessary, essential blurred images can be restored using information of overlapping images of the sequence or a blur kernel with the developed edge shifting technique. The corrected images can be then used for target identification, measurements and automated photogrammetric processing.

Conflict analytics through the vehicle safety space in mixed traffic flows using UAV image sequences

2020 ◽  
Vol 119 ◽  
pp. 102744
Author(s):  
Albert Y. Chen ◽  
Yen-Lin Chiu ◽  
Meng-Hsiu Hsieh ◽  
Po-Wei Lin ◽  
Ohay Angah
Keyword(s):  
Image Sequences ◽  
Vehicle Safety ◽  
Mixed Traffic ◽  
Traffic Flows ◽  
Uav Image

scholarly journals 3D target localization based on multi–unmanned aerial vehicle cooperation

2020 ◽  
pp. 002029402092226
Author(s):  
Cheng Xu ◽  
Chanjuan Yin ◽  
Daqing Huang ◽  
Wei Han ◽  
Dongzhen Wang
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Target Localization ◽  
Weight Matrix ◽  
Target Point ◽  
Observation Data ◽  
Remote Sensing Images ◽  
Localization Method ◽  
Ground Target ◽  
Aerial Vehicle

Ground target three-dimensional positions measured from optical remote-sensing images taken by an unmanned aerial vehicle play an important role in related military and civil applications. The weakness of this system lies in its localization accuracy being unstable and its efficiency being low when using a single unmanned aerial vehicle. In this paper, a novel multi–unmanned aerial vehicle cooperative target localization measurement method is proposed to overcome these issues. In the target localization measurement stage, three or more unmanned aerial vehicles simultaneously observe the same ground target and acquire multiple remote-sensing images. According to the principle of perspective projection, the target point, its image point, and the camera’s optic center are collinear, and nonlinear observation equations are established. These equations are then converted to linear equations using a Taylor expansion. Robust weighted least-squares estimation is used to solve the equations with the objective function of minimizing the weighted square sum of re-projection errors from target points to multiple pairs of images, which can make the best use of the effective information and avoid interference from the observation data. An automatic calculation strategy using a weight matrix is designed, and the weight matrix and target-position coordinate value are updated in each iteration until the iteration stopping condition is satisfied. Compared with the stereo-image-pair cross-target localization method, the multi–unmanned aerial vehicle cooperative target localization method can use more observation information, which results in higher rendezvous accuracy and improved performance. Finally, the effectiveness and robustness of this method is verified by numerical simulation and flight testing. The results show that the proposed method can effectively improve the precision of the target’s localization and demonstrates great potential for providing more accurate target localization in engineering applications.

scholarly journals Bionic target localization method based on range-azimuth joint dictionary

2021 ◽  
Vol 39 (3) ◽  
pp. 471-476
Author(s):  
Changsheng Yang ◽  
Hangbo Li ◽  
Liping Hu ◽  
Hong Liang
Keyword(s):  
Joint Estimation ◽  
Target Localization ◽  
Sparse Signal ◽  
High Angle ◽  
Localization Method ◽  
Big Brown Bats ◽  
Sonar System ◽  
Random Array ◽  
Sparse Signal Representation ◽  
Array Aperture

The traditional underwater sonar system usually achieve high angle resolution by increasing array aperture and the number of array elements, but this method will inevitably lead to complex system and high cost. Given that big brown bats have obtained surprisingly high resolution using a simple system, this paper proposes a bionic target localization method. First, a range-azimuth joint dictionary was constructed based on the bionic system of multi-harmonic emission and double random array reception. Then, the coherence characteristic of the dictionary was analyzed and the range and azimuth of the target were estimated, and at last the experimental verification was completed. The results show that the bionic range-azimuth joint estimation based on sparse signal representation can achieve high-precision target localization under the condition of echo high aliasing.

scholarly journals Wireless Sensor Network Target Localization Algorithm Based on Two- and Three-Dimensional Delaunay Partitions

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Chenguang Shao
Keyword(s):  
Three Dimensional ◽  
Target Localization ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Target Node ◽  
Localization Accuracy ◽  
Localization Method ◽  
Anchor Nodes ◽  
2D And 3D ◽  
The Relationship

The target localization algorithm is critical in the field of wireless sensor networks (WSNs) and is widely used in many applications. In the conventional localization method, the location distribution of the anchor nodes is fixed and cannot be adjusted dynamically according to the deployment environment. The resulting localization accuracy is not high, and the localization algorithm is not applicable to three-dimensional (3D) conditions. Therefore, a Delaunay-triangulation-based WSN localization method, which can be adapted to two-dimensional (2D) and 3D conditions, was proposed. Based on the location of the target node, we searched for the triangle or tetrahedron surrounding the target node and designed the localization algorithm in stages to accurately calculate the coordinate value of the target. The relationship between the number of target nodes and the number of generated graphs was analysed through numerous experiments, and the proposed 2D localization algorithm was verified by extending it the 3D coordinate system. Experimental results revealed that the proposed algorithm can effectively improve the flexibility of the anchor node layout and target localization accuracy.

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