Detection of the number of signals in noise with banded covariance matrices

Chen, W.; Reilly, J.P.; Wong, K.M.

doi:10.1049/ip-rsn:19960512

Cited by 41 publications

(15 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, Chen et al [28] use two sets of receiver antennas X 1 (n) and X 2 (n) with N 1 > p, (respectively N 2 > p), components. The main idea of Chen's algorithm consists on using the rank of a covariance matrix Z :…”

Section: Estimation Of Source Numbermentioning

confidence: 99%

Enhancement of acoustic tomography using spatial and frequency diversities

Mansour

2012

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

This article introduces several contributions to enhance an important application such as acoustic tomography (AT), using mainly the spatial and spectral diversities of underwater acoustic signals. Due to their inherited properties, (i.e. spareness, non-stationarity or cyclostationarity, wide-band frequency range, wide range of power, etc.), the process of underwater acoustic signals becomes a real challenge for many scientists and engineers who are involved in studies related to the ocean. For various applications, these studies require huge and daily information. AT techniques remain fast and cheap ways to obtain such data. Nowadays, active acoustic tomography (AAT), is communally used to generate powerful and repetitive acoustic sources. Recently, researchers have been attracted by an alternative way, called passive acoustic tomography (PAT), which uses acoustic opportune signals of their environment. PAT techniques are mainly used for ecological, economical and other reasons such as military applications. With PAT, no signal is emitted; therefore, problems become more challenging. The number and positions of existent sources are unknown, and sensors measure mixtures of available sources. Algorithms based on time or frequency domains are widely deployed to classify, identify, and study received signals in AAT applications. For PAT, researchers employ multiple sensors in order to add an extra dimension, (such as space). This article focuses on approaches used in space along with time or frequency to extract information, improve performances, and simplify the overall architecture. This article explains the use of signal processing and statistical approaches to solve problems raised using PAT and discusses the experimental results. The review of the literature offers a big variety of algorithms to deal with classic AAT problems. Therefore, only problems related to PAT have been considered herein.

show abstract

Section: Estimation Of Source Numbermentioning

confidence: 99%

Enhancement of acoustic tomography using spatial and frequency diversities

Mansour

2012

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…The main kinds of the source number estimation algorithm include [1] information theoretic criteria (ITC) [2][3][4][5] , spatial smoothing algorithm [6][7] , matrix decomposition approach [7][8] , Gerschgorin Disk [9][10] and generalized correlation decomposition algorithm [11][12] . All these algorithms are based on the comparison between the eigenvalues [13] of the covariance matrixes of the observation vectors.…”

Section: Introductionmentioning

confidence: 99%

A new source number estimation method based on the beam eigenvalue

et al. 2007

View full text Add to dashboard Cite

Most source number estimation methods based on the eigenvalues are decomposed by covariance matrix in MUSIC algorithm. To develop the source number estimation method which has lower signal to noise ratio and is suitable to both correlated and uncorrelated impinging signals, a new source number estimation method called beam eigenvalue method (BEM) is proposed in this paper. Through analyzing the space power spectrum and the correlation of the line array, the covariance matrix is constructed in a new way, which is decided by the line array shape when the signal frequency is given. Both of the theory analysis and the simulation results show that the BEM method can estimate the source number for correlated signals and can be more effective at lower signal to noise ratios than the normal source number estimation methods.

show abstract

“…It successfully solves the problem of MDL suffered at high SNR, but it is still not good at low SNR. In [7], a good technique to estimate frequency signals based on CFAR has been developed. This idea inspires us, but this technique is not adaptable to estimate the order of channel because the amplitudes of several paths are often small, especially when the order of channel is large.…”

Section: Introductionmentioning

confidence: 99%

“…For simplicity, assume that,, c,aD are asymptotic Gaussian. The threshold can be determined by the following formula [9] PCFAR Ierfc KT )>-, (14) Hi]2 H.,-3' (6) Then, average R(i) over N OFDM symbols to get a better estimation of the channel correlation matrix as i NS RA =-YR(i) (7) Perform eigenvalue decomposition to R. and get …”

Section: Introductionmentioning

confidence: 99%

An Improved Algorithm for Order Detection of Multipath Channel

Chen

2006

TENCON 2006 - 2006 IEEE Region 10 Conference

View full text Add to dashboard Cite

Detecting the order of Channel is an important content in parametric channel estimation. In this paper, an improved algorithm based on constant false alarm rate with forward-backward averaging (FB-CFAR) is proposed to detect the order of multipath channel. Simulation results in an OFDM system show that the proposed scheme can improve the performance of correct detection effectively. I. INTRODUCTIONThe wireless channel can often be modeled by a few dominant power paths which have been widely applied in the GSM communications to improve the estimator performance [1]. These channel estimators use algorithms mostly based on some information theoretic criteria as the standard first step to estimate the channel order. They are Akaike information theoretic criterion (AIC) and minimum description length (MDL) [2]. However, the development of these information theoretic criteria is based on the assumptions that successive data vectors are identical and independently distributed (i.i.d) zero mean Gaussian random vectors and that the noise is white Gaussian and uncorrelated from the signal. They are sensitive to variations in signal to noise (SNR) and not robust enough for realistic applications [3], [4]. And in [5], an improved algorithm based on linear prediction filter theory was presented, but the decision of the threshold is much complicated.The algorithm based on forward-backward averaging MDL (FB-MDL) was adopted in [6], to estimate the order of the channel. It successfully solves the problem of MDL suffered at high SNR, but it is still not good at low SNR. In [7], a good technique to estimate frequency signals based on CFAR has been developed. This idea inspires us, but this technique is not adaptable to estimate the order of channel because the amplitudes of several paths are often small, especially when the order of channel is large.

show abstract

Detection of the number of signals in noise with banded covariance matrices

Cited by 41 publications

References 9 publications

Enhancement of acoustic tomography using spatial and frequency diversities

Enhancement of acoustic tomography using spatial and frequency diversities

A new source number estimation method based on the beam eigenvalue

An Improved Algorithm for Order Detection of Multipath Channel

Contact Info

Product

Resources

About