DoA estimation of highly correlated stochastic sources using neural model

2018 ◽  
Vol 38 (8) ◽  
pp. 500-516
Author(s):  
Zoran Stanković ◽  
Nebojsa Dončov ◽  
Ivan Milovanović ◽  
Bratislav Milovanović
Keyword(s):  
Neural Model ◽  
Doa Estimation ◽  
Highly Correlated ◽  
Stochastic Sources

scholarly journals DOA Estimation for Highly Correlated and Coherent Multipath Signals with Ultralow SNRs

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Li Cheng ◽  
Yang Li ◽  
Lianying Zou ◽  
Yong Qin
Keyword(s):  
Multipath Propagation ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Doa Estimation ◽  
Time Shift ◽  
Direct Path ◽  
Multipath Signal ◽  
Highly Correlated ◽  
Multipath Signals ◽  
Cramer Rao Bound

In a typical multipath propagation environment, there exists a strong direct path signal accompanying with several weak multipath signals. Due to the strong direct path interference and other masking effects, the Direction-of-Arrival (DOA) of a weak multipath signal is hard to be estimated. In this paper, a novel method is proposed to estimate the DOA of multipath signals with ultralow signal-to-noise ratio (SNR). The main idea is to increase the SNR and signal-to-interference ratio (SIR) of the desired multipath signal in time-delay domain before DOA estimation processing. Firstly, the cross-correlation functions of the direct path signal and the received array signal are calculated. Then, they are combined and constructed to an enhanced array signal. Under certain conditions, the SNR and SIR of the desired signal can be significantly increased. Finally, the DOAs of multipath signals can be estimated by conventional technologies, and the associated time delays can be measured on the DOA-time-shift map. The SNR and SIR gains of the desired signal are analyzed theoretically, and theoretical analysis also indicates that the Cramer–Rao bound can be reduced. Simulation examples are presented to verify the advantages of the proposed method.

scholarly journals Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 208
Author(s):  
Oluwole John Famoriji ◽  
Thokozani Shongwe
Keyword(s):  
Antenna Array ◽  
Frequency Estimation ◽  
Time Windows ◽  
Doa Estimation ◽  
Single Frequency ◽  
Computationally Efficient ◽  
Spherical Antenna ◽  
Line Of Sight Propagation ◽  
The Difference ◽  
Highly Correlated

Direction-of-arrival (DoA) estimation of electromagnetic (EM) waves impinging on a spherical antenna array in short time windows is examined in this paper. Reflected EM signals due to non-line-of-sight propagation measured with a spherical antenna array can be coherent and/or highly correlated in a snapshot. This makes spectral-based methods inefficient. Spectral methods, such as maximum likelihood (ML) methods, multiple signal classification (MUSIC), and beamforming methods, are theoretically and systematically investigated in this study. MUSIC is an approach used for frequency estimation and radio direction finding, ML is a technique used for estimating the parameters of an assumed probability distribution for given observed data, and PWD applies a Fourier transform to the capture response and produces them in the frequency domain. Although they have been previously adapted and used to estimate DoA of EM signals impinging on linear and planar antenna array configurations, this paper investigates their suitability and effectiveness for a spherical antenna array. Various computer simulations were conducted, and plots of root-mean-square error (RMSE) against the square root of the Cramér–Rao lower bound (CRLB) were generated and used to evaluate the performance of each method. Numerical experiments and results from measured data show the degree of appropriateness and efficiency of each method. For instance, the techniques exhibit identical performance to that in the wideband scenario when the frequency f = 8 GHz, f = 16 GHz, and f = 32 GHz, but f = 16 GHz performs best. This indicates that the difference between the covariance matrix of the signal is coherent and that the steering vectors of signals impinging from that angle are small. MUSIC and PWD share the same problems in the single-frequency scenario as in the wideband scenario when the delay sample d = 0. Consequently, the DoA estimation obtained with ML techniques is more suitable, less biased, and more robust against noise than beamforming and MUSIC techniques. In addition, deterministic ML (DML) and weighted subspace fitting (WSF) techniques show better DoA estimation performance than the stochastic ML (SML) technique. For a large number of snapshots, WSF is a better choice because it is more computationally efficient than DML. Finally, the results obtained indicate that WSF and ML methods perform better than MUSIC and PWD for the coherent or partially correlated signals studied.

scholarly journals A Two-Step Compressed Sensing Approach for Single-Snapshot DOA Estimation of Closely Spaced Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shuang Wei ◽  
Yanhua Long ◽  
Rui Liu ◽  
Ying Su
Keyword(s):  
Compressed Sensing ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Measurement Matrix ◽  
Chaotic Sequences ◽  
Overcomplete Dictionary ◽  
Highly Correlated ◽  
Single Snapshot

Single-snapshot direction-of-arrival (DOA) estimation plays an important role in dynamic target detection and tracking applications. Because a single-snapshot signal provides few information for statistics calculation, recently compressed sensing (CS) theory is applied to solve single-snapshot DOA estimation, instead of the traditional DOA methods based on statistics. However, when the unknown sources are closely located, the spatial signals are highly correlated, and its overcomplete dictionary is made up of dense grids, which leads to a serious decrease in the estimation accuracy of the CS-based algorithm. In order to solve this problem, this paper proposed a two-step compressed sensing-based algorithm for the single-snapshot DOA estimation of closely spaced signals. The overcomplete dictionaries with coarse and refined grids are used in the two steps, respectively. The measurement matrix is constructed by using a very sparse projection scheme based on chaotic sequences because chaotic sequences have determinism and pseudo-randomness property. Such measurement matrix is mainly proposed for compressing the overcomplete dictionary in preestimation step, while it is well designed by choosing the steering vectors of true DOA in the accurate estimation step, in which the neighborhood information around the true DOAs partly solved in the previous step will be used. Monte Carlo simulation results demonstrate that the proposed algorithm can perform better than other existing single-snapshot DOA estimation methods. Especially, it can work well to solve the issues caused by closely spaced signals and single snapshot.

scholarly journals Combining MUSIC Spatial Sampling and Bootstrap to Estimate Closed Space DOA for Few Samples

2018 ◽  
Vol 3 (3) ◽  
pp. 125-132
Author(s):  
SIDI MOHAMMED HADJ IRID ◽  
SAMIR KAMECHE
Keyword(s):  
Sample Size ◽  
Array Processing ◽  
Doa Estimation ◽  
Classification Algorithm ◽  
Spatial Sampling ◽  
Signal Classification ◽  
Closed Space ◽  
Multiple Signal Classification ◽  
Highly Correlated ◽  
Increase Sample Size

DOA estimation in array processing uses MUSIC (Multiple Signal Classification) algorithm, mainly. It’s the most investigated technique and is very attractive because of its simplicity. However, it meets drawbacks and fails when only very few samples are available and the sources are very close or highly correlated. In these conditions, the problem is more intricate and the detection of targets becomes arduous. To overcome these problems, a new algorithm is developed in this paper. We combine bootstrap technique to increase sample size, spatial sampling and MUSIC method to improve resolution. Through different simulations, the performance and the effectiveness of the proposed approach, referred as spatial Sampling and Bootstrapped technique ‘’SSBoot’’, are demonstrated.

Cannabis use in Switzerland: The role of attribution of drug use to friends, urbanization and repression 1The study was supported by the Swiss Federal Office of Public Health (Bundesamt für Gesundheit BAG) Grant 316.96.5980.

2001 ◽  
Vol 60 (2) ◽  
pp. 99-107 ◽  
Author(s):  
Holger Schmid
Keyword(s):  
Drug Use ◽  
Urban Areas ◽  
Hierarchical Linear Model ◽  
Cannabis Use ◽  
School Aged Children ◽  
Structural Variables ◽  
Hbsc Study ◽  
Nationally Representative ◽  
Highly Correlated ◽  
Individual Use

Cannabis use does not show homogeneous patterns in a country. In particular, urbanization appears to influence prevalence rates, with higher rates in urban areas. A hierarchical linear model (HLM) was employed to analyze these structural influences on individuals in Switzerland. Data for this analysis were taken from the Switzerland survey of Health Behavior in School-Aged Children (HBSC) Study, the most recent survey to assess drug use in a nationally representative sample of 3473 15-year-olds. A total of 1487 male and 1620 female students indicated their cannabis use and their attributions of drug use to friends. As second level variables we included address density in the 26 Swiss Cantons as an indicator of urbanization and officially recorded offences of cannabis use in the Cantons as an indicator of repressive policy. Attribution of drug use to friends is highly correlated with cannabis use. The correlation is even more pronounced in urban Cantons. However, no association between recorded offences and cannabis use was found. The results suggest that structural variables influence individuals. Living in an urban area effects the attribution of drug use to friends. On the other hand repressive policy does not affect individual use.

The Difficulties in Emotion Regulation Scale (DERS)

2013 ◽  
Vol 72 (1) ◽  
pp. 5-11 ◽  
Author(s):  
Elise S. Dan-Glauser ◽  
Klaus R. Scherer
Keyword(s):  
Emotion Regulation ◽  
Well Being ◽  
French Translation ◽  
French Version ◽  
Inappropriate Behaviors ◽  
Initial Validation ◽  
Difficulties In Emotion Regulation ◽  
Instrument Measuring ◽  
Highly Correlated ◽  
Psychopathological Disorders

Successful emotion regulation is a key aspect of efficient social functioning and personal well-being. Difficulties in emotion regulation lead to relationship impairments and are presumed to be involved in the onset and maintenance of some psychopathological disorders as well as inappropriate behaviors. Gratz and Roemer (2004 ) developed the Difficulties in Emotion Regulation Scale (DERS), a comprehensive instrument measuring emotion regulation problems that encompasses several dimensions on which difficulties can occur. The aim of the present work was to develop a French translation of this scale and to provide an initial validation of this instrument. The French version was created using translation and backtranslation procedures and was tested on 455 healthy students. Congruence between the original and the translated scales was .98 (Tucker’s phi) and internal consistency of the translation reached .92 (Cronbach’s α). Moreover, test-retest scores were highly correlated. Altogether, the initial validation of the French version of the DERS (DERS-F) offers satisfactory results and permits the use of this instrument to map difficulties in emotion regulation in both clinical and research contexts.

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