scholarly journals An Angle Recognition Algorithm for Tracking Moving Targets Using WiFi Signals with Adaptive Spatiotemporal Clustering

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 276
Author(s):  
Liping Tian ◽  
Liangqin Chen ◽  
Zhimeng Xu ◽  
Zhizhang Chen
Keyword(s):  
Clustering Algorithm ◽  
Estimation Error ◽  
Estimation Algorithm ◽  
Joint Estimation ◽  
Moving Targets ◽  
Angle Of Arrival ◽  
Data Set ◽  
Angle Estimation ◽  
Spatiotemporal Clustering ◽  
Average Angle

An angle estimation algorithm for tracking indoor moving targets with WiFi is proposed. First, phase calibration and static path elimination are proposed and performed on the collected channel state information signals from different antennas. Then, the angle of arrival information is obtained with the joint estimation algorithm of the angle of arrival (AOA) and time of flight (TOF). To deal with the multipath effects, we adopt the DBscan spatiotemporal clustering algorithm with adaptive parameters. In addition, the time-continuous angle of arrival information is obtained by interpolating and supplementing points to extract the dynamic signal paths better. Finally, the least-squares method is used for linear fitting to obtain the final angle information of a moving target. Experiments are conducted with the tracking data set presented with Tsinghua’s Widar 2.0. The results show that the average angle estimation error with the proposed algorithm is smaller than Widar2.0. The average angle error is about 7.18° in the classroom environment, 3.62° in the corridor environment, and 12.16° in the office environment; they are smaller than the errors of the existing system.

IMPROVED SPECTRUM SEARCHING GENERALIZED–ESPRIT ALGORITHM FOR JOINT DOD AND DOA ESTIMATION IN MIMO RADAR WITH NON-UNIFORM LINEAR ARRAYS

2014 ◽  
Vol 23 (08) ◽  
pp. 1450106 ◽  
Author(s):  
WEIYANG CHEN ◽  
XIAOFEI ZHANG
Keyword(s):  
Multiple Input Multiple Output ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Joint Estimation ◽  
Angle Estimation ◽  
Linear Arrays ◽  
Half Wavelength ◽  
Direction Of Departure ◽  
Esprit Algorithm

This paper investigates the problem of angle estimation for bistatic multiple-input multiple-output (MIMO) radar with non-uniform linear arrays, and proposes an improved spectrum searching generalized estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation algorithm in bistatic MIMO radar. The proposed algorithm obtains initial estimation of angles obtained from the signal subspace, and uses the 1D local searchings to achieve the joint estimation of DOD and DOA. Compared to the spectrum searching generalized-ESPRIT algorithm which requires the global searchings and additional pairing, the proposed algorithm just needs the local searchings and obtains automatically paired 2D angle estimation. The angle estimation performance of the proposed algorithm is almost the same as that of the generalized-ESPRIT algorithm, and better than ESPRIT-like algorithm. Furthermore, the proposed algorithm is suitable for irregular array geometry, has much lower complexity than the spectrum searching generalized-ESPRIT algorithm, and imposes less constraint on the transmit/receive sensor spacing, which need not be limited to a half-wavelength strictly. The simulation results verify the effectiveness of the algorithm.

Fast Angle Estimation Algorithm of Coherently Distributed Targets for Bistatic MIMO Radar

2011 ◽  
Vol 33 (7) ◽  
pp. 1684-1688
Author(s):  
Yi-duo Guo ◽  
Yong-shun Zhang ◽  
Lin-rang Zhang ◽  
Ning-ning Tong
Keyword(s):  
Estimation Algorithm ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Bistatic Mimo Radar

scholarly journals SACN: A Novel Rotating Face Detector Based on Architecture Search

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 558
Author(s):  
Anping Song ◽  
Xiaokang Xu ◽  
Xinyi Zhai
Keyword(s):  
Face Detection ◽  
Human Face ◽  
Angle Error ◽  
Rotation Invariant ◽  
Convolutional Network ◽  
Data Set ◽  
Practical Applications ◽  
Model Size ◽  
Average Angle ◽  
Face Detector

Rotation-Invariant Face Detection (RIPD) has been widely used in practical applications; however, the problem of the adjusting of the rotation-in-plane (RIP) angle of the human face still remains. Recently, several methods based on neural networks have been proposed to solve the RIP angle problem. However, these methods have various limitations, including low detecting speed, model size, and detecting accuracy. To solve the aforementioned problems, we propose a new network, called the Searching Architecture Calibration Network (SACN), which utilizes architecture search, fully convolutional network (FCN) and bounding box center cluster (CC). SACN was tested on the challenging Multi-Oriented Face Detection Data Set and Benchmark (MOFDDB) and achieved a higher detecting accuracy and almost the same speed as existing detectors. Moreover, the average angle error is optimized from the current 12.6° to 10.5°.

An approach for document retrieval using cluster-based inverted indexing

2021 ◽  
pp. 016555152110184
Author(s):  
Gunjan Chandwani ◽  
Anil Ahlawat ◽  
Gaurav Dubey
Keyword(s):  
High Performance ◽  
Clustering Algorithm ◽  
Pearson Correlation ◽  
Relevant Information ◽  
Document Retrieval ◽  
Bhattacharyya Distance ◽  
Data Set ◽  
Query Matching ◽  
Inverted Indexing ◽  
Query Optimisation

Document retrieval plays an important role in knowledge management as it facilitates us to discover the relevant information from the existing data. This article proposes a cluster-based inverted indexing algorithm for document retrieval. First, the pre-processing is done to remove the unnecessary and redundant words from the documents. Then, the indexing of documents is done by the cluster-based inverted indexing algorithm, which is developed by integrating the piecewise fuzzy C-means (piFCM) clustering algorithm and inverted indexing. After providing the index to the documents, the query matching is performed for the user queries using the Bhattacharyya distance. Finally, the query optimisation is done by the Pearson correlation coefficient, and the relevant documents are retrieved. The performance of the proposed algorithm is analysed by the WebKB data set and Twenty Newsgroups data set. The analysis exposes that the proposed algorithm offers high performance with a precision of 1, recall of 0.70 and F-measure of 0.8235. The proposed document retrieval system retrieves the most relevant documents and speeds up the storing and retrieval of information.

Multi-frame DOA Estimation Algorithm for Weak Moving Targets

2020 ◽  
Author(s):  
Penghui Zhang ◽  
Kezhu Liu ◽  
Wujun Li ◽  
Wei Yi ◽  
Xiaobo Yang
Keyword(s):  
Estimation Algorithm ◽  
Doa Estimation ◽  
Moving Targets

Empirical Evaluation of Genetic Clustering Methods Using Multilocus Genotypes From 20 Chicken Breeds

Genetics ◽  
2001 ◽  
Vol 159 (2) ◽  
pp. 699-713
Author(s):  
Noah A Rosenberg ◽  
Terry Burke ◽  
Kari Elo ◽  
Marcus W Feldman ◽  
Paul J Freidlin ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Population Structure ◽  
Clustering Algorithm ◽  
Empirical Evaluation ◽  
Unknown Origin ◽  
Clustering Methods ◽  
Genetic Cluster ◽  
Data Set ◽  
Multilocus Genotypes ◽  
Chicken Breeds

Abstract We tested the utility of genetic cluster analysis in ascertaining population structure of a large data set for which population structure was previously known. Each of 600 individuals representing 20 distinct chicken breeds was genotyped for 27 microsatellite loci, and individual multilocus genotypes were used to infer genetic clusters. Individuals from each breed were inferred to belong mostly to the same cluster. The clustering success rate, measuring the fraction of individuals that were properly inferred to belong to their correct breeds, was consistently ~98%. When markers of highest expected heterozygosity were used, genotypes that included at least 8–10 highly variable markers from among the 27 markers genotyped also achieved >95% clustering success. When 12–15 highly variable markers and only 15–20 of the 30 individuals per breed were used, clustering success was at least 90%. We suggest that in species for which population structure is of interest, databases of multilocus genotypes at highly variable markers should be compiled. These genotypes could then be used as training samples for genetic cluster analysis and to facilitate assignments of individuals of unknown origin to populations. The clustering algorithm has potential applications in defining the within-species genetic units that are useful in problems of conservation.

scholarly journals Accelerating the HyperLogLog Cardinality Estimation Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Cem Bozkus ◽  
Basilio B. Fraguela
Keyword(s):  
Large Data ◽  
Estimation Algorithm ◽  
Xeon Phi ◽  
Memory Usage ◽  
Intel Xeon Phi ◽  
Data Set ◽  
Internet Routers ◽  
Multicore System ◽  
Sequential Implementation ◽  
New Algorithms

In recent years, vast amounts of data of different kinds, from pictures and videos from our cameras to software logs from sensor networks and Internet routers operating day and night, are being generated. This has led to new big data problems, which require new algorithms to handle these large volumes of data and as a result are very computationally demanding because of the volumes to process. In this paper, we parallelize one of these new algorithms, namely, the HyperLogLog algorithm, which estimates the number of different items in a large data set with minimal memory usage, as it lowers the typical memory usage of this type of calculation from O(n) to O(1). We have implemented parallelizations based on OpenMP and OpenCL and evaluated them in a standard multicore system, an Intel Xeon Phi, and two GPUs from different vendors. The results obtained in our experiments, in which we reach a speedup of 88.6 with respect to an optimized sequential implementation, are very positive, particularly taking into account the need to run this kind of algorithm on large amounts of data.

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