scholarly journals A Data Fusion Orientation Algorithm Based on the Weighted Histogram Statistics for Vector Hydrophone Vertical Array

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5619 ◽  
Author(s):  
Yan Liang ◽  
Zhou Meng ◽  
Yu Chen ◽  
Yichi Zhang ◽  
Mingyang Wang ◽  
...  
Keyword(s):  
Data Fusion ◽  
Signal To Noise Ratio ◽  
Beam Width ◽  
Least Square ◽  
Vertical Array ◽  
Music Algorithm ◽  
Fusion Algorithm ◽  
Sea Trial ◽  
Vector Hydrophone ◽  
Weighted Histogram

In this paper, we propose a data fusion algorithm based on the weighted histogram statistics (DF-WHS) to improve the performance of direction-of-arrival (DOA) estimation for the vector hydrophone vertical array (VHVA). The processing frequency band is firstly divided into multiple sub-bands, and the high-resolution multiple signal classification (MUSIC) algorithm is applied to estimate the azimuth of each sub-band for each vector hydrophone. Then, the weighted least square (WLS) data fusion technique is used to fuse the sub-band estimation results of multiple sensors. Finally, the weighted histogram statistics method is employed to obtain the synthesis results in the frequency domain. We carried out a simulation and sea trial of the 16-element VHVA to evaluate the performance of the proposed algorithm. Compared to several traditional processing algorithms, the beam width of the proposed approach is significantly narrower, the side lobes are considerably lower, and the mean square error (MSE) is effectively smaller. In addition, the DF-WHS method is more suitable to accurately estimate the target azimuth with a low signal-to-noise ratio (SNR) because the noise sub-band is suppressed in the weighted histogram statistics step. The DF-WHS method in this article provides a new approach to improve the performance of deep-sea target detection for the VHVA.

scholarly journals Steering Acoustic Intensity Estimator Using a Single Acoustic Vector Hydrophone

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Guang Pu Zhang ◽  
Ce Zheng ◽  
Wang Sheng Lin
Keyword(s):  
Signal To Noise Ratio ◽  
Azimuth Angle ◽  
Acoustic Energy ◽  
Multiple Sources ◽  
Acoustic Intensity ◽  
Angle Estimation ◽  
Sea Trial ◽  
Vector Hydrophone ◽  
Global Positioning ◽  
Intensity Estimator

Azimuth angle estimation using a single vector hydrophone is a well-known problem in underwater acoustics. In the presence of multiple sources, a conventional complex acoustic intensity estimator (CAIE) cannot distinguish the azimuth angle of each source. In this paper, we propose a steering acoustic intensity estimator (SAIE) for azimuth angle estimation in the presence of interference. The azimuth angle of the interference is known in advance from the global positioning system (GPS) and compass data. By constructing the steering acoustic energy fluxes in the x and y channels of the acoustic vector hydrophone, the azimuth angle of interest can be obtained when the steering azimuth angle is directed toward the interference. Simulation results show that the SAIE outperforms the CAIE and is insensitive to the signal-to-noise ratio (SNR) and signal-to-interference ratio (SIR). A sea trial is presented that verifies the validity of the proposed method.

Application of multi-resolution data fusion algorithm

2010 ◽  
Vol 30 (9) ◽  
pp. 2556-2558 ◽  
Author(s):  
Ming-bo SHI ◽  
Ji-hong CHEN ◽  
Zheng-zheng JIANG
Keyword(s):  
Data Fusion ◽  
Fusion Algorithm ◽  
Resolution Data

scholarly journals Phenological Analysis of Sub-Alpine Forest on Jeju Island, South Korea, Using Data Fusion of Landsat and MODIS Products

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 286
Author(s):  
Sang-Jin Park ◽  
Seung-Gyu Jeong ◽  
Yong Park ◽  
Sang-hyuk Kim ◽  
Dong-kun Lee ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Data Fusion ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Study Region ◽  
Modis Imagery ◽  
Increasing Temperature

Climate change poses a disproportionate risk to alpine ecosystems. Effective monitoring of forest phenological responses to climate change is critical for predicting and managing threats to alpine populations. Remote sensing can be used to monitor forest communities in dynamic landscapes for responses to climate change at the species level. Spatiotemporal fusion technology using remote sensing images is an effective way of detecting gradual phenological changes over time and seasonal responses to climate change. The spatial and temporal adaptive reflectance fusion model (STARFM) is a widely used data fusion algorithm for Landsat and MODIS imagery. This study aims to identify forest phenological characteristics and changes at the species–community level by fusing spatiotemporal data from Landsat and MODIS imagery. We fused 18 images from March to November for 2000, 2010, and 2019. (The resulting STARFM-fused images exhibited accuracies of RMSE = 0.0402 and R2 = 0.795. We found that the normalized difference vegetation index (NDVI) value increased with time, which suggests that increasing temperature due to climate change has affected the start of the growth season in the study region. From this study, we found that increasing temperature affects the phenology of these regions, and forest management strategies like monitoring phenology using remote sensing technique should evaluate the effects of climate change.

scholarly journals An efficient intelligent data fusion algorithm for wireless sensor network

2021 ◽  
Vol 183 ◽  
pp. 418-424
Author(s):  
Haitao Wang ◽  
Lihua Song ◽  
Jue Liu ◽  
Tingting Xiang
Keyword(s):  
Wireless Sensor Network ◽  
Data Fusion ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Fusion Algorithm

scholarly journals Development and Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2480
Author(s):  
Isidoro Ruiz-García ◽  
Ismael Navarro-Marchal ◽  
Javier Ocaña-Wilhelmi ◽  
Alberto J. Palma ◽  
Pablo J. Gómez-López ◽  
...  
Keyword(s):  
Data Fusion ◽  
Reference System ◽  
Inertial Sensor ◽  
Low Cost ◽  
Sampling Rate ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Improve Technique ◽  
Worst Case ◽  
Sensor Data Fusion

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

Model Optimization and Stability of Hemoglobin Analysis in Human Soluble Blood Samples by FTIR/ATR Spectroscopy

2011 ◽  
Vol 55-57 ◽  
pp. 1168-1171
Author(s):  
Tao Pan ◽  
Ai Hong Peng ◽  
Wen Jie Huang
Keyword(s):  
Signal To Noise Ratio ◽  
Attenuated Total Reflection ◽  
Partial Least Square ◽  
Least Square ◽  
Model Optimization ◽  
Optimal Model ◽  
Blood Samples ◽  
Quantification Method ◽  
Atr Spectroscopy ◽  
Pls Method

Using Fourier transform infrared spectroscopy (FTIR), attenuated total reflection (ATR) technology and partial least square (PLS) method, the rapid quantification method of hemoglobin (HGB) in human soluble blood samples was established. Based on the distribution of samples’ HGB chemical value and absorbance on 1543 cm-1 which had the highest signal to noise ratio for HGB, all samples were divided into calibration set and prediction set for 50 times. PLS models were established for all divisions, based on the average data RMSEPAve, the stable optimal model was selected, the corresponding PLS factor, RMSEPAve and RP,Ave were 2, 6.81 g/L and 0.943 respectively.

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