scholarly journals Direct Position Determination for Augmented Coprime Arrays via Weighted Subspace Data Fusion Method

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yang Qian ◽  
Zhongtian Yang ◽  
Haowei Zeng
Keyword(s):  
Data Fusion ◽  
Positioning Accuracy ◽  
Position Determination ◽  
Propagator Method ◽  
Coprime Arrays ◽  
Optimal Weighting ◽  
Array Aperture ◽  
Coprime Array ◽  
Poor Stability ◽  
Better Than

Direct position determination (DPD) for augmented coprime arrays is investigated in this paper. Augmented coprime array expands degree of freedom and array aperture and improves positioning accuracy. Because of poor stability and noise sensitivity of the subspace data fusion (SDF) method, we propose two weighted subspace data fusion (W-SDF) algorithms for direct position determination. Simulation results show that two W-SDF algorithms have a prominent promotion in positioning accuracy than SDF, Capon, and propagator method (PM) algorithm for augmented coprime arrays. SDF based on optimal weighting (OW-SDF) is slightly better than SDF based on SNR weighting (SW-SDF) in positioning accuracy. The performance for DPD of the W-SDF method with augmented coprime arrays is better than that of the W-SDF method with uniform arrays.

scholarly journals Direct Position Determination of Noncircular Sources with Multiple Nested Arrays: Reduced Dimension Subspace Data Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yang Qian ◽  
Dalin Zhao ◽  
Haowei Zeng
Keyword(s):  
Data Fusion ◽  
Linear Array ◽  
Degree Of Freedom ◽  
Search Problem ◽  
Positioning Accuracy ◽  
Position Determination ◽  
Spatial Degree ◽  
Reduced Dimension ◽  
Nested Arrays

Direct position determination (DPD) of noncircular (NC) sources with multiple nested arrays (NA) is investigated in this paper. Noncircular sources are used to expand the dimension of the received signal matrix, so the number of identifiable information sources and the accuracy of direct position determination are improved. Furthermore, nested array increases spatial degree of freedom. In this paper, the high-dimensional search problem of noncircular sources is investigated. Therefore, we propose algorithm dimension reduction subspace data fusion (RD-SDF) to reduce complexity and increase positioning accuracy. Simulation results show that the proposed RD-SDF algorithm for multiple nested arrays with noncircular sources has improved positioning accuracy with higher spatial degree of freedom than SDF, Capon, and two-step algorithms with uniform linear array and circular sources (CS).

scholarly journals DOA estimation method of spatial spectrum product for distributed coprime linear array

2021 ◽  
Vol 39 (5) ◽  
pp. 1130-1138
Author(s):  
Na WANG ◽  
Xuanzhi ZHAO ◽  
Zengli LIU ◽  
Jingjing ZHANG
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Spatial Spectrum ◽  
Estimation Accuracy ◽  
Low Snr ◽  
Distributed Array ◽  
Coprime Arrays ◽  
Coprime Array ◽  
Mathematical Derivation ◽  
Better Than

Coprime array isAsparse array composed of two uniform linear arrays with different spacing. When the two subarrays are inAnon-coherent distributed configuration, the direction of arrival (DOA) method based on the covariance analysis of the complete coprime array is no longer effective. According to the essential attribute that the distance between the elements of two subarrays can eliminate the angle ambiguity, based on the mathematical derivation, Aspatial spectral product DOA estimation method for incoherent distributed coprime arrays is proposed. Firstly, the spatial spectrum of each subarray is calculated by using the snapshot data of each subarray, and then the DOA estimation is realized by multiplying the spatial spectrum of each subarray. The simulation results show that the estimation accuracy and angle resolution of the present method are better than those of the traditional ambiguity resolution methods, and the estimation performance is good in the mutual coupling and low SNR environment, with the good adaptability and stability. Moreover, by using the flexibility of distributed array, the matching error is effectively solved through the rotation angle.

Analysis on Signal-in-Space Range Error and Positioning Accuracy of BDS-3

2021 ◽  
Author(s):  
Weiping Liu ◽  
Bo Jiao ◽  
Jinming Hao ◽  
Zhiwei Lv ◽  
Jiantao Xie ◽  
...  
Keyword(s):  
Navigation System ◽  
Convergence Time ◽  
Asia Pacific ◽  
Single Frequency ◽  
Earth Orbit ◽  
Positioning Accuracy ◽  
Point Positioning ◽  
Range Error ◽  
Space Range ◽  
Better Than

Abstract Being the first mixed-constellation global navigation system, the global BeiDou navigation system (BDS-3) designs new signals, the service performance of which has attracted extensive attention. In the present study, the Signal-in-space range error (SISRE) computation method for different types of navigation satellites was presented. And the differential code bias (DCB) correction method for BDS-3 new signals was deduced. Based on these, analysis and evaluation were done by adopting the actual measured data after the official launching of BDS-3. The results showed that BDS-3 performed better than the regional navigation satellite system (BDS-2) in terms of SISRE. Specifically, the SISRE of the BDS-3 medium earth orbit (MEO) satellites reached 0.52 m, slightly inferior compared to 0.4 m from Galileo, marginally better than 0.57 m from GPS, and significantly better than 2.33 m from GLONASS. And the BDS-3 inclined geostationary orbit (IGSO) satellites achieved the SISRE of 0.90 m, on par with that of the QZSS IGSO satellites. However, the average SISRE of BDS-3 geostationary earth orbit (GEO) satellites was 1.15 m, which was marginally inferior to that of the QZSS GEO satellite (0.91m). In terms of positioning accuracy, the overall three-dimensional single-frequency standard point positioning (SPP) accuracy of BDS-3 B1C, B2a, B1I, and B3I gained an accuracy level better than 5 m. Moreover, the B1I signal exhibited the best positioning accuracy in the Asian-Pacific region, while the B1C signal set forth the best positioning accuracy in the other regions. Owing to the advantage in signal frequency, the dual-frequency SPP accuracy of B1C+B2a surpassed that of the transitional signal of B1I+B3I. Since there are more visible satellites in Asia-Pacific, the positioning accuracy of BDS-3 was moderately superior to that of GPS. The precise point positioning (PPP) accuracy of BDS-3 B1C+B2a or B1I+B3I converged to the order of centimeters, marginally inferior to that of the GPS L1+L2. However, these three combinations had a similar convergence time of approximately 30 minutes.

scholarly journals Channel state information–based multi-level fingerprinting for indoor localization with deep learning

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880671 ◽  
Author(s):  
Tao Li ◽  
Hai Wang ◽  
Yuan Shao ◽  
Qiang Niu
Keyword(s):  
Deep Learning ◽  
Channel State Information ◽  
Deep Neural Networks ◽  
Training Phase ◽  
Channel State ◽  
Positioning Accuracy ◽  
State Information ◽  
Filtering Method ◽  
Multi Level ◽  
Better Than

With the rapid growth of indoor positioning requirements without equipment and the convenience of channel state information acquisition, the research on indoor fingerprint positioning based on channel state information is increasingly valued. In this article, a multi-level fingerprinting approach is proposed, which is composed of two-level methods: the first layer is achieved by deep learning and the second layer is implemented by the optimal subcarriers filtering method. This method using channel state information is termed multi-level fingerprinting with deep learning. Deep neural networks are applied in the deep learning of the first layer of multi-level fingerprinting with deep learning, which includes two phases: an offline training phase and an online localization phase. In the offline training phase, deep neural networks are used to train the optimal weights. In the online localization phase, the top five closest positions to the location position are obtained through forward propagation. The second layer optimizes the results of the first layer through the optimal subcarriers filtering method. Under the accuracy of 0.6 m, the positioning accuracy of two common environments has reached, respectively, 96% and 93.9%. The evaluation results show that the positioning accuracy of this method is better than the method based on received signal strength, and it is better than the support vector machine method, which is also slightly improved compared with the deep learning method.

scholarly journals Monitoring Aircraft Position Using EGNOS Data for the SBAS APV Approach to the Landing Procedure

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1945 ◽  
Author(s):  
Kamil Krasuski ◽  
Damian Wierzbicki
Keyword(s):  
Research Method ◽  
Satellite System ◽  
Civil Aviation ◽  
International Civil Aviation Organization ◽  
Positioning Accuracy ◽  
Research Findings ◽  
Global Navigation Satellite ◽  
Service Data ◽  
Computational Strategy ◽  
Better Than

The aim of this paper is to present the problem of the implementation of the EGNOS (European Geostationary Navigation Overlay Service) data for the processing of aircraft position determination. The main aim of the research is to develop a new computational strategy which might improve the performance of the EGNOS system in aviation, based on navigation solutions of an aircraft position, using several GNSS (Global Navigation Satellite System) onboard receivers. The results of an experimental test conducted by the Cessna 172 at EPDE (European Poland Deblin) (ICAO (International Civil Aviation Organization) code, N51°33.07’/E21°53.52’) aerodrome in Dęblin are presented and discussed in this paper. Two GNSS navigation receivers with the EGNOS positioning function for monitoring changes in the parameters of the aircraft position in real time during the landing phase were installed onboard a Cessna 172. Based on obtained research findings, it was discovered that the positioning accuracy was not higher than 2.1 m, and the integrity of positioning did not exceed 19 m. Moreover, the availability parameter was found to equal 1 (or 100%); also, no intervals in the continuity of the operation of the EGNOS system were recorded. In the paper, the results of the air test from Dęblin were compared with the parameters of positioning quality from the air test conducted in Chełm (ICAO code: EPCD, N51°04’57.8” E23°26’15”). In the air test in Chełm, the obtained parameters of EGNOS quality positioning were: better than 4.9 m for accuracy, less than 35.5 m for integrity, 100% for availability, and no breaks in continuity. Based on the results of the air tests in Dęblin and Chełm, it was concluded that the parameters of the EGNOS positioning quality in aviation for the SBAS (Satellite Based Augmentation System) APV (Approach to Vertical guidance) procedure were satisfied in accordance with the ICAO (International Civil Aviation Organization) requirements. The presented research method can be utilized in the SBAS APV landing procedure in Polish aviation. In this paper, the results of PDOP (Position Dilution of Precision) are presented and compared to the two air tests in Dęblin and Chełm. The maximum results of PDOP amounted to 1.4 in the air test in Dęblin, whereas they equaled 4.0 in the air test in Chełm. The paper also shows how the EGNOS system improved the aircraft position in relation to the only GPS solution. In this context, the EGNOS system improved the aircraft position from about 78% to 95% for each ellipsoidal coordinate axis.

scholarly journals Crime Prediction with Historical Crime and Movement Data of Potential Offenders Using a Spatio-Temporal Cokriging Method

2020 ◽  
Vol 9 (12) ◽  
pp. 732
Author(s):  
Hongjie Yu ◽  
Lin Liu ◽  
Bo Yang ◽  
Minxuan Lan
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Prediction Models ◽  
Crime Data ◽  
Crime Prediction ◽  
Movement Data ◽  
Environment Variables ◽  
Spatio Temporal ◽  
Fine Resolution ◽  
Better Than

Crime prediction using machine learning and data fusion assimilation has become a hot topic. Most of the models rely on historical crime data and related environment variables. The activity of potential offenders affects the crime patterns, but the data with fine resolution have not been applied in the crime prediction. The goal of this study is to test the effect of the activity of potential offenders in the crime prediction by combining this data in the prediction models and assessing the prediction accuracies. This study uses the movement data of past offenders collected in routine police stop-and-question operations to infer the movement of future offenders. The offender movement data compensates historical crime data in a Spatio-Temporal Cokriging (ST-Cokriging) model for crime prediction. The models are implemented for weekly, biweekly, and quad-weekly prediction in the XT police district of ZG city, China. Results with the incorporation of the offender movement data are consistently better than those without it. The improvement is most pronounced for the weekly model, followed by the biweekly model, and the quad-weekly model. In sum, the addition of offender movement data enhances crime prediction, especially for short periods.

Experimental Study on the Cutting Performance of Ultra-Precision Micro Milling Machine

2010 ◽  
Vol 97-101 ◽  
pp. 2546-2549
Author(s):  
Peng Zhang ◽  
Li Hua Lu ◽  
Bo Wang ◽  
Ying Chun Liang
Keyword(s):  
Aluminum Alloy ◽  
Pure Copper ◽  
Cutting Performance ◽  
Micro Milling ◽  
Step Response ◽  
Milling Machine ◽  
Positioning Accuracy ◽  
Ultra Precision ◽  
Scale Step ◽  
Better Than

To meet the requirement for the machining of the ultra-precision, ultra-smooth and micro-structure surface, an ultra-precision three axes micro milling machine was developed with the positioning accuracy better than ±0.25μm and the repetitive positioning accuracy better than ±0.2μm of all the three axes. The machine is proved to achieve the nanometer scale step response. Through milling experiments with micro-diameter tungsten carbide milling tool, the cutting performance has been further proved: the milling accuracy of 50μm-high step on the workpiece of aluminum alloy is better than ±0.3μm; and the 3D surface of pure copper workpiece is as smooth as mirror, with a roughness reaching 40nm. At last, the thin-walled structure of 10μm thickness on the workpiece of aluminum alloy is milled.

Dynamic Localization of Car-like Vehicles using Data Fusion of Redundant ABS Sensors

2003 ◽  
Vol 56 (3) ◽  
pp. 429-441 ◽  
Author(s):  
Ph. Bonnifait ◽  
P. Bouron ◽  
D. Meizel ◽  
P. Crubillé
Keyword(s):  
Data Fusion ◽  
Fusion Process ◽  
Dynamic Localization ◽  
Localization System ◽  
Gps Measurement ◽  
Driving Wheel ◽  
Noticeable Reduction ◽  
Using Data ◽  
Better Than

A localization system using GPS, ABS sensors and a driving wheel encoder is described and tested through real experiments. A new odometric technique using the four ABS sensors is presented. Due to the redundancy of measurements, the precision is better than the method using differential odometry on the rear wheels only. The sampling is performed when necessary and when a GPS measurement is performed. This implies a noticeable reduction of the GPS latency, simplifying the data-fusion process and improving the quality of its results.

scholarly journals ANALYSIS OF SPOT-6 DATA FUSION USING GRAM-SCHMIDT SPECTRAL SHARPENING ON RURAL AREAS

2014 ◽  
Vol 10 (2) ◽  
Author(s):  
Danang Surya Candra
Keyword(s):  
Data Fusion ◽  
Spatial Resolution ◽  
Rural Areas ◽  
Signal To Noise Ratio ◽  
Test Site ◽  
Multispectral Images ◽  
Fused Image ◽  
Pc Method ◽  
Better Than

Image fusion is a process to generate higher spatial resolution multispectral images by fusion of lower resolution multispectral images and higher resolution panchromatic images. It is used to generate not only visually appealing images but also provide detailed images to support applications in remote sensing field, including rural area. The aim of this study was to evaluate the performance of SPOT-6 data fusion using Gram-Schmidt Spectral Sharpening (GS) method on rural areas. GS method was compared with Principle Component Spectral Sharpening (PC) method to evaluate the reliability of GS method. In this study, the performance of GS was presented based on multispectral and panchromatic of SPOT-6 images. The spatial resolution of the multispectral (MS) image was enhanced by merging the high resolution Panchromatic (Pan) image in GS method. The fused image of GS and PC were assessed visually and statistically. Relative Mean Difference (RMD), Relative Variation Difference (RVD), and Peak Signal to Noise Ratio (PSNR) Index were used to assess the fused image statistically. The test sites of rural areas were devided into four main areas i.e., whole area, rice field area, forest area, and settlement. Based on the results, the visual quality of the fused image using GS method was better than using PC method. The color of the fused image using GS was better and more natural than using PC. In the statistical assessment, the RMD results of both methods were similar. In the RVD results, GS method was better then PC method especially in band 1 and band 3. GS method was better than PC method in PSNR result for each test site. It was observed that the Gram-Schmidt method provides the best performance for each band and test site. Thus, GS was a robust method for SPOT-6 data fusion especially on rural areas.

Comparative Efficiency Analysis of Data Fusion Methods for Vehicle Trajectory Reconstruction

2019 ◽  
Vol 886 ◽  
pp. 182-187
Author(s):  
Anuthep Chomputawat ◽  
Watchara Chatwiriya
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Linear Interpolation ◽  
Data Loss ◽  
Comparative Efficiency ◽  
Fusion Methods ◽  
Vehicle Trajectory ◽  
Sudden Transition ◽  
Detection And Localization ◽  
Better Than

This article compares the efficiency of vehicle trajectory analysis methods based on data fusion from multiple cameras, monitoring the same area from different views under the condition having detection errors, which causes incorrectly localized and, in some cases, undetected vehicle during the movement. The experiment used the simulation of detection and localization of vehicle moving in straight, curved, zigzag and arbitrary trajectories, with localization errors and multi-level loss of data. By comparing Kalman-filter-based method and Linear-interpolation-based method for analyzing and reconstructing vehicle trajectory, the result shows that the data loss robustness of Kalman-filter-based method is higher than that of Linear-interpolation-based method, with data loss around 97% 97% and 90% for straight, curved and zigzag trajectories respectively. However, for arbitrary trajectory, the Linear-interpolation-based method is better than Kalman-filter-based method in all levels of data loss. In conclusion, Kalman-filter-based method is effective in the case of unchanged or slight transition of direction, while Linear-interpolation-based method is effective in the case of sudden transition of direction.

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