In acoustic sensing systems, acoustic vector sensor (also known as vector hydrophone for underwater applications) arrays are widely used. Most of the acoustic vector sensor array signal processing methods presume the ...
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In acoustic sensing systems, acoustic vector sensor (also known as vector hydrophone for underwater applications) arrays are widely used. Most of the acoustic vector sensor array signal processing methods presume the minimum spacing between two adjacent sensors or sensor components to be within a half-wavelength in order to avoid azimuth-elevation angle estimation aliasing. This would limit the effective array aperture, thereby reducing the potential estimation accuracy. Furthermore, they are unapplicable to the underdetermined scenarios, where the number of sources exceeds that of sensor components. Exploiting the recently proposed nested array concept, we present a new type of nested array, termed as Sparse nested spatially spread Square Acoustic Vector sensor array (SNSAVA) to realize underdetermined 2-D direction finding with increased estimation accuracy. In SNSAVA, interspacing of two sensors and two components of a sensor can be spread to be much higher than a half-wavelength so that the effective array aperture will be significantly extended. An unambiguous angle estimation method is further derived to make this fully sparse array configuration practically feasible. Performance studies focused on underdetermined high-accuracy azimuth-elevation angle estimation are provided via numerical examples. The estimation performance for the SNSAVA is also compared with that of the nested acoustic vector sensor arrays, proposed in [33], and with the Cramer-Rao bound.
The recent criteria for designing sparse arrays used in many applications are the number of degrees of freedom (DoFs) and mutual coupling. In this paper, two optimized sparse nested arrays, called OSNAI and OSNA-II, a...
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The recent criteria for designing sparse arrays used in many applications are the number of degrees of freedom (DoFs) and mutual coupling. In this paper, two optimized sparse nested arrays, called OSNAI and OSNA-II, are proposed for the direction of arrival (DoA) estimation of non-circular signals. The proposed structures aim to enhance the DoFs and mitigate mutual coupling. The OSNA-I and OSNA-II are obtained by rearranging physical sensors of the conventional nested array. Specifically, for the proposed OSNA-I, its dense uniform linear array (ULA) is exactly the same as the dense ULA of the conventional nested array, while the physical sensors of its sparse ULA are located at n (2 N 1 + 1) d. For the proposed OSNA-II, it is composed of two subarrays plus one separate sensor that is appropriately placed. The OSNAII keeps the dense array of the conventional nested array untouched except the sensor at N 1 d being removed, while its sparse array is deployed according to n (2 N 1 ) d. The separate sensor is set apart with the rightmost sensor of the sparse array by a space of 3 N 1 d. The closed-form expressions are derived for the actual sensor locations, number of uniform DoFs and available DoFs, respectively. Besides, it is also proven that OSNA-I and OSNA-II have small weight values due to their sparse structures, which means that OSNA-I and OSNA-II can achieve a good balance between DoFs and weight values. Simulation results demonstrate the superiorities of the proposed structures in terms of robustness and estimation accuracy.(c) 2022 Elsevier B.V. All rights reserved.
Orthogonal time frequency space (OTFS) modulation has been considered as one of the most promising candidates to support reliable data transmission especially in high-mobility networks, wherein the performance of comm...
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Orthogonal time frequency space (OTFS) modulation has been considered as one of the most promising candidates to support reliable data transmission especially in high-mobility networks, wherein the performance of communications strongly relies on timely and accurate tracking of the relevant user state parameters. In this context, the problem of integrated sensing and communication (ISAC) assisted user state refinement is addressed in the framework of OTFS systems. In particular, by exploiting the initial yet coarse angle estimate provided by the typical codebook-based user state sensing algorithm, we judiciously design a hybrid digital-analog architecture to output the nested array structured low dimensional observations. In this way, the corresponding nested array based technique is employed to perform angle refinement by fully utilizing the degrees of freedom provided by the measurements. Next, based on the refined angle estimate, we develop a two-stage joint delay and Doppler shifts estimation scheme to update the corresponding coarse estimates. Numerical results validate the effectiveness of the proposed algorithm in various scenarios, showing that our well designed user state refinement scheme is able to improve the performance of the considered ISAC-assisted OTFS systems in term of both radar and communication metrics.
Hybrid beamforming (HBF) is emerging as a key technology for future wireless networks, particularly in millimeter wave (mmWave) bands. In this paper, we present a non-uniform sub-connected hybrid beamforming (NSC-HBF)...
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Hybrid beamforming (HBF) is emerging as a key technology for future wireless networks, particularly in millimeter wave (mmWave) bands. In this paper, we present a non-uniform sub-connected hybrid beamforming (NSC-HBF) system that enables the implementation of the difference co-array for the underdetermined angle-of-arrival (AOA) estimation. We propose a broad beam synthesis technique to mitigate performance degradation caused by uneven analog combining gains. To further enhance estimation efficiency, we modified the co-array least mean squares (co-array LMS) method by directly solving the least-squares problem to determine the filter weights. The proposed method reduces computational complexity and eliminates iterative procedures for faster processing. Our numerical results demonstrate the pseudo-spectrum and detection performance for both pencil and broad beams, highlighting the importance of analog combining codebook design. Additionally, the proposed method achieves higher estimation accuracy compared to the co-array root-LMS.
In the pursuit of enabling the unprecedented capabilities of the sixth-generation (6G) technology, this paper endeavours to advance the state-of-the-art in the direction of arrival (DOA) estimation techniques for dyna...
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In the pursuit of enabling the unprecedented capabilities of the sixth-generation (6G) technology, this paper endeavours to advance the state-of-the-art in the direction of arrival (DOA) estimation techniques for dynamic scenarios. This work introduces an innovative adaptive compressive sensing (CS) technique, termed the BS weighted-CSKF algorithm. This approach integrates CS principles with a CS-oriented Kalman filter (KF), providing enhanced adaptability to fluctuating and moving source signals. Comparative analysis against existing CS-based DOA estimation methods demonstrates the superior performance of the proposed algorithm, particularly in low signal-to-noise ratio (SNR) environments. Notably, the BS weighted-CSKF algorithm operates effectively even in unknown noise field scenarios, eliminating the requirement for orthogonality between the signal and subspace noise or singular value decomposition. This capability enables accurate DOA estimation without prior knowledge of the number of signal sources. Additionally, investigations into rank-one updates of the covariance matrix highlight the algorithm's ability to estimate a higher number of sources than sensors employed without imposing constraints on source properties. The algorithm's versatility extends to coherent and spatially correlated sources, further enhancing its applicability in diverse scenarios. Moreover, employing BS CS-based DOA estimation techniques yields a significant computational load reduction, exceeding 35% compared to the conventional element-space (ES) CS-based approach. Leveraging the proposed technique, fluctuating moving source signals can be efficiently detected and tracked using fewer snapshots, facilitating real-time monitoring and analysis in dynamic environments.
The features of quasi-stationary signals (QSS) are considered to be in a direct position determination (DPD) framework, and a real-valued DPD algorithm of QSS for nested arrays is proposed. By stacking the vectorizati...
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The features of quasi-stationary signals (QSS) are considered to be in a direct position determination (DPD) framework, and a real-valued DPD algorithm of QSS for nested arrays is proposed. By stacking the vectorization form of the signal's covariance for different frames and further eliminating noise, a new noise-eliminated received signal matrix is obtained first. Then, the combination of the Khatri-Rao subspace method and subspace data fusion method was performed to form the cost function. High complexity can be reduced by matrix reconstruction, including the modification of the dimension-reduced matrix and unitary transformation. Ultimately, the advantage of lower complexity, compared with the previous algorithm, is verified by complexity analysis, and the superiority over the existing algorithms, in terms of the maximum number of identifiable sources, estimation accuracy, and resolution, are corroborated by some simulation results.
In this paper, the problem of passive direction finding is addressed using an acoustic vector sensor array (AVS), which may be deployed either in free space or near a reflecting boundary. Building upon the 4x1 vector ...
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In this paper, the problem of passive direction finding is addressed using an acoustic vector sensor array (AVS), which may be deployed either in free space or near a reflecting boundary. Building upon the 4x1 vector field measured by an AVS, the particle-velocity coarray augmentation (PVCA) is proposed to admit the underdetermined direction finding using the spatial difference coarray derived from the vectorization of the array covariance matrix. Unlike the widely used spatial coarray Toeplitz recovery technique, the PVCA is applicable to arbitrary array geometries and imposes no reduction of the spatial difference coarray aperture. For the array located at or near a reflecting boundary, the PVCA allows resolving up to 13 sources, while for the array located in free space, the PVCA can identify 9 sources at most. By applying to the systematically designed nonuniform arrays, such as coprime arrays and nested arrays, the PVCA can be coupled with the spatial smoothing technique to get the number of resolvable sources multiplied. Finally, the effectiveness of the PVCA is verified by numerical simulations.
For a polarization sensitive array (PSA) composed of electromagnetic vector sensors (EMVSs), all component-antennas of an EMVS are spatially collocated at a same location in the PSA, and mutual coupling introduced has...
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For a polarization sensitive array (PSA) composed of electromagnetic vector sensors (EMVSs), all component-antennas of an EMVS are spatially collocated at a same location in the PSA, and mutual coupling introduced has an adverse effect on parameter estimation. The mutual coupling is not only among antennas in adjacent elements, namely, inter-element coupling (IEC), but also among component-antennas which are collocated to give an EMVS [namely inter-polarization coupling (IPC)]. In existing array design, component-antennas of each EMVS are spatially separated into different elements for IPC reduction, and then the spacing between EMVSs is increased for IEC reduction. However, such designed arrays are usually implemented as 2-D arrays which are large compared with linear arrays, and provide only O(N) degrees of freedom (DOFs) with N antennas. To overcome such problems, this paper proposes two kinds of spatially separated augmented nested vector-sensor arrays (SS-ANVSAs), which are linear arrays, can reduce both IEC and IPC, and provide O(N-2) DOFs with N antennas. Specifically, we introduce a scalar-sensor array and propose a sparse scalar-sensor array for IEC reduction, and then propose the SS-ANVSAs by restricting physical space and EMVSs' number for IPC reduction. Theoretical analysis and simulation results are given to illustrate the superior performance of the SS-ANVSAs.
Traditional DOA tracking algorithms have poor performance when considering source time-varying and impulsive noise interference. To address these issues, an improved multisource multi-Bernoulli (IMSMB) particle filter...
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Traditional DOA tracking algorithms have poor performance when considering source time-varying and impulsive noise interference. To address these issues, an improved multisource multi-Bernoulli (IMSMB) particle filtering algorithm based on phased fractional lower order moment is proposed. In the IMSMB algorithm, an unscented transform method is used to operate the surviving particles, which increases the diversity of resampling particles. Moreover, the proposed method is extended to nested array to improve tracking accuracy. Simulation results verify the superiority of the proposed method compared to the existing DOA tracking algorithm.
A quaternion-based method is developed to estimate direction-of-arrival (DOA) and degree-of-polarization (DOP) of multiple partially polarized (PP) electromagnetic (EM) source signals, based on the data collected by a...
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A quaternion-based method is developed to estimate direction-of-arrival (DOA) and degree-of-polarization (DOP) of multiple partially polarized (PP) electromagnetic (EM) source signals, based on the data collected by a linear nested cross-dipole sensor array. The basic insight of the new method for DOA estimation is to form a quaternion vector by exploiting the vectorization of horizontal and vertical polarization data correlation matrices to formulate a quaternion version of MUSIC algorithm. Based on the estimation of DOAs, the DOPs of signals are subsequently estimated by using the Stokes vectors. The performance of the presented method is evaluated and compared with that of conventional complex-based methods.
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