22 research outputs found
Simplified and enhanced multiple level nested arrays exploiting high order difference co-arrays
Based on the high order difference co-array concept, an enhanced four level nested array (E-FL-NA) is first proposed, which optimizes the consecutive lags at the fourth order difference co-array stage. To simplify the formulations for sensor locations for comprehensive illustration and also convenient structure construction, a simplified and enhanced four level nested array (SE-FL-NA) is then proposed, whose performance is compromised but still better than the four level nested array (FL-NA). This simplified structure is further extended to the higher order case with multiple sub-arrays, referred to as simplified and enhanced multiple level nested arrays (SE-ML-NAs), where significantly increased degrees of freedom (DOFs) can be provided and exploited for underdetermined DOA estimation. Simulation results are provided to verify the superior performance of the proposed E-FL-NA, while a higher number of detectable sources is achieved by the SE-ML-NA with a limited number of physical sensors
ICAR, a tool for Blind Source Separation using Fourth Order Statistics only
International audienceThe problem of blind separation of overdetermined mixtures of sources, that is, with fewer sources than (or as many sources as) sensors, is addressed in this paper. A new method, named ICAR (Independent Component Analysis using Redundancies in the quadricovariance), is proposed in order to process complex data. This method, without any whitening operation, only exploits some redundancies of a particular quadricovariance matrix of the data. Computer simulations demonstrate that ICAR offers in general good results and even outperforms classical methods in several situations: ICAR ~(i) succeeds in separating sources with low signal to noise ratios, ~(ii) does not require sources with different SO or/and FO spectral densities, ~(iii) is asymptotically not affected by the presence of a Gaussian noise with unknown spatial correlation, (iv) is not sensitive to an over estimation of the number of sources
Convolutive Blind Source Separation Methods
In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks
Joint smoothed l0-norm DOA estimation algorithm for multiple measurement vectors in MIMO radar
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed l0-norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar. To eliminate the white or colored Gaussian noises, the new method first obtains a low-complexity high-order cumulants based data matrix. Then, the proposed algorithm designs a joint smoothed function tailored for the MMV case, based on which joint smoothed l0-norm sparse representation framework is constructed. Finally, for the MMV-based joint smoothed function, the corresponding gradient-based sparse signal reconstruction is designed, thus the DOA estimation can be achieved. The proposed method is a fast sparse representation algorithm, which can solve the MMV problem and perform well for both white and colored Gaussian noises. The proposed joint algorithm is about two orders of magnitude faster than the l1-norm minimization based methods, such as l1-SVD (singular value decomposition), RV (real-valued) l1-SVD and RV l1-SRACV (sparse representation array covariance vectors), and achieves better DOA estimation performance
Nested Arrays: A Novel Approach to Array Processing With Enhanced Degrees of Freedom
A new array geometry, which is capable of significantly
increasing the degrees of freedom of linear arrays, is
proposed. This structure is obtained by systematically nesting two
or more uniform linear arrays and can provide O(N^2) degrees
of freedom using only physical sensors when the second-order
statistics of the received data is used. The concept of nesting is
shown to be easily extensible to multiple stages and the structure
of the optimally nested array is found analytically. It is possible to
provide closed form expressions for the sensor locations and the
exact degrees of freedom obtainable from the proposed array as a
function of the total number of sensors. This cannot be done for
existing classes of arrays like minimum redundancy arrays which
have been used earlier for detecting more sources than the number
of physical sensors. In minimum-input–minimum-output (MIMO)
radar, the degrees of freedom are increased by constructing a
longer virtual array through active sensing. The method proposed
here, however, does not require active sensing and is capable of
providing increased degrees of freedom in a completely passive
setting. To utilize the degrees of freedom of the nested co-array, a
novel spatial smoothing based approach to DOA estimation is also
proposed, which does not require the inherent assumptions of the
traditional techniques based on fourth-order cumulants or quasi
stationary signals. As another potential application of the nested
array, a new approach to beamforming based on a nonlinear
preprocessing is also introduced, which can effectively utilize the
degrees of freedom offered by the nested arrays. The usefulness of
all the proposed methods is verified through extensive computer
simulations
Statistical Nested Sensor Array Signal Processing
Source number detection and direction-of-arrival (DOA) estimation are two major applications of sensor arrays. Both applications are often confined to the use of uniform linear arrays (ULAs), which is expensive and difficult to yield wide aperture. Besides, a ULA with N scalar sensors can resolve at most N − 1 sources. On the other hand, a systematic approach was recently proposed to achieve O(N 2 ) degrees of freedom (DOFs) using O(N) sensors based on a nested array, which is obtained by combining two or more ULAs with successively increased spacing.
This dissertation will focus on a fundamental study of statistical signal processing of nested arrays. Five important topics are discussed, extending the existing nested-array strategies to more practical scenarios. Novel signal models and algorithms are proposed.
First, based on the linear nested array, we consider the problem for wideband Gaussian sources. To employ the nested array to the wideband case, we propose effective strategies to apply nested-array processing to each frequency component, and combine all the spectral information of various frequencies to conduct the detection and estimation. We then consider the practical scenario with distributed sources, which considers the spreading phenomenon of sources.
Next, we investigate the self-calibration problem for perturbed nested arrays, for which existing works require certain modeling assumptions, for example, an exactly known array geometry, including the sensor gain and phase. We propose corresponding robust algorithms to estimate both the model errors and the DOAs. The partial Toeplitz structure of the covariance matrix is employed to estimate the gain errors, and the sparse total least squares is used to deal with the phase error issue.
We further propose a new class of nested vector-sensor arrays which is capable of significantly increasing the DOFs. This is not a simple extension of the nested scalar-sensor array. Both the signal model and the signal processing strategies are developed in the multidimensional sense. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors.
Last but not least, in order to make full use of the available limited valuable data, we propose a novel strategy, which is inspired by the jackknifing resampling method. Exploiting numerous iterations of subsets of the whole data set, this strategy greatly improves the results of the existing source number detection and DOA estimation methods
Compressive Sensing Based Estimation of Direction of Arrival in Antenna Arrays
This thesis is concerned with the development of new compressive sensing (CS) techniques both in element space and beamspace for estimating the direction of arrival of various types of sources, including moving sources as well as fluctuating sources, using one-dimensional antenna arrays. The problem of estimating the angle of arrival of a plane electromagnetic wave is referred to as the direction of arrival (DOA) estimation problem. Such algorithms for estimating DOA in antenna arrays are often used in wireless communication network to increase their capacity and
throughput. DOA techniques can be used to design and adapt the directivity of the array antennas. For example, an antenna array can be designed to detect a number of incoming signals and accept signals from certain directions only, while rejecting signals that are declared as interference. This spatio-temporal estimation and filtering
capability can be exploited for multiplexing co-channel users and rejecting harmful co-channel interference that may occur because of jamming or multipath effects.
In this study, three CS-based DOA estimation methods are proposed, one in the element space (ES), and the other two in the beamspace (BS). The proposed techniques do not require a priori knowledge of the number of sources to be estimated. Further, all these techniques are capable of handling both non-fluctuating and fluctuating source signals as well as moving signals. The virtual array concept is utilized in order to be able to identify more number of sources than the number of the sensors used.
In element space, an extended version of the least absolute shrinkage and selection operator (LASSO) algorithm, the adaptable LASSO (A-LASSO), is presented. A-LASSO is utilized to solve the DOA problem in compressive sensing framework. It is shown through extensive simulations that the proposed algorithm outperforms the classical DOA estimation techniques as well as LASSO using a small number of snapshots. Furthermore, it is able to estimate coherent as well as spatially-close sources. This technique is then extended to the case of DOA estimation of the sources in unknown noise fields.
In beamspace, two compressive sensing techniques are proposed for DOA estimation, one in full beamspace and the other in multiple beam beamspace. Both these techniques are able to estimate correlated source signals as well as spatially-close sources using a small number of snapshots. Furthermore, it is shown that the computational
complexity of the two beamspace-based techniques is much less than that of the element-space based technique. It is shown through simulations that the performance of the DOA estimation techniques in multiple beam beamspace is superior to that of the other two techniques proposed in this thesis, in addition to having the lowest computational complexity.
Finally, the feasibility for real-time implementation of the proposed CS-based DOA estimation techniques, both in the element-space and the beamspace, is examined. It is shown that the execution time of the proposed algorithms on Raspberry Pi board are compatible for real-time implementation
Advances in parameter estimation, source enumeration, and signal identification for wireless communications
Parameter estimation and signal identification play an important role in modern wireless
communication systems. In this thesis, we address different parameter estimation
and signal identification problems in conjunction with the Internet of Things (IoT),
cognitive radio systems, and high speed mobile communications.
The focus of Chapter 2 of this thesis is to develop a new uplink multiple access
(MA) scheme for the IoT in order to support ubiquitous massive uplink connectivity
for devices with sporadic traffic pattern and short packet size. The proposed uplink
MA scheme removes the Media Access Control (MAC) address through the signal
identification algorithms which are employed at the gateway.
The focus of Chapter 3 of this thesis is to develop different maximum Doppler
spread (MDS) estimators in multiple-input multiple-output (MIMO) frequency-selective
fading channel. The main idea behind the proposed estimators is to reduce the computational
complexity while increasing system capacity.
The focus of Chapter 4 and Chapter 5 of this thesis is to develop different antenna
enumeration algorithms and signal-to-noise ratio (SNR) estimators in MIMO timevarying
fading channels, respectively. The main idea is to develop low-complexity
algorithms and estimators which are robust to channel impairments.
The focus of Chapter 6 of this thesis is to develop a low-complexity space-time
block codes (STBC)s identification algorithms for cognitive radio systems. The goal
is to design an algorithm that is robust to time-frequency transmission impairments