Direction-of-arrival (DOA) estimation using distributed arrays has emerged as a promising technique for autonomous vehicle (AV) positioning in Internet of Vehicles (IoV) systems. This paper proposes a novel DOA estimation method based on orthogonal distributed arrays for accurate AV localization. Specifically, the covariance matrix between orthogonal arrays is exploited to construct two univariate polynomials for DOA estimation. As the polynomial degree depends on the array aperture and the discontinuous sensor layout, the resulting polynomials are often high-order and lacunary. To efficiently solve these polynomials, an improved differential evolution (DE) algorithm is developed, featuring an adaptive mutation strategy to reduce computational cost and a counter-based mechanism to escape local optima. In addition, a covariance-based cost function is designed for 2D angle pairing. The array aperture is further extended to enable simultaneous DOA estimation of multiple vehicles when the orthogonal arrays share a common sensor. Simulation results demonstrate that, compared with existing methods, the proposed approach achieves higher estimation accuracy and lower computational complexity, offering a promising solution for vehicle positioning in IoV environments.

In this paper, we present a Situational Awareness based Resource Allocation (SA-RA) strategy for multi-target tracking (MTT) in distributed radar networks, where both the resource utilization and overall MTT accuracy can be improved. The fusion rule with probabilistic data association (PDA) is used to associate cumulative data with measurement data from radar nodes. We also further derive the Bayesian Cramér-Rao Lower Bound (BCRLB) under the PDA fusion rule. For the proposed SA-RA strategy, the targets behavior is analyzed to determine their importance weights, expected tracking accuracy, and the allocated beams for each target. Utilizing the PDA-BCRLB and the importance weights, the objective function of the SA-RA strategy is formulated as a weighted sum of target utility functions. By addressing this objective function, the optimal transmission power is determined. Simulation results verify the superiority both in terms of tracking performance and resource allocation.

Integrated sensing and massive multiple-input-multiple-output (MIMO) communication (mMIMO-ISAC) at terahertz (THz) bands can provide vast spatial degrees of freedom and abundant bandwidth resources. However, the employment of a massive number of antennas will pose prominent challenges to both target sensing and channel training in THz-mMIMO-ISAC. In this article, our goal is to integrate the target sensing functionality into the channel estimation stage and develop a channel-training-aided target sensing framework to facilitate the efficient resource sharing of THz-mMIMO-ISAC. Specifically, by exploiting the sparse characteristics of THz mMIMO channels, we build up the intrinsic connection between the channel parameters and the target parameters in angular, delay, and Doppler dimensions. Then, we propose a shared channel training pattern accommodating the hybrid architecture constraints of THz transceiver. Both the channel estimation and the target sensing can be formulated as two structured tensor decomposition problems and then concurrently addressed at the UE and BS sides, respectively. Next, we propose a tensor-based parameter estimation algorithm to acquire the target and channel parameters, where the associated angles of arrival/departure, time delays, Doppler shifts, and coefficients can be extracted from the estimated factor matrices. In addition, we present the detailed derivation of the Cramér-Rao bound (CRB) for the considered parameter estimation problem in THz-mMIMO-ISAC. Numerical results demonstrate that the proposed algorithm can achieve the target parameters estimation performance close to their corresponding CRB, and recover the high-dimensional THz mMIMO channels with substantially reduced training overhead.

In this letter, we propose the Bi-nuclear Schatten-p norm minimization (BSNM) for coprime array to achieve the efficient direction of arrival (DOA) estimation. Specifically, BSNM factorizes a large Hermitian Toeplitz covariance matrix as the product of two small matrices, which not only improves the computational efficiency but also captures the low-rank property of the interpolated covariance matrix. This BSNM problem of the Hermitian Toeplitz matrix is then solved by a parallel alternating optimization algorithm. Finally, the recovered covariance matrix is applied to estimate the DOA via the MUSIC algorithm. Numerical simulation illustrates the superiority of the proposed BSNM method over state-of-the-art methods in terms of computational efficiency.

This paper presents hole-free lengthened coprime arrays with reduced mutual coupling (rLCAs). Based on the$k$-times extended coprime array structure, the rLCAs are designed by positioning two sparse subarrays outside the original aperture. An enhanced rLCA is further derived via locating and rearranging an inessential sensor. This enhanced rLCA is able to increase the number of degrees of freedom (DOFs). Compared with other arrays, the proposed rLCAs exhibit more DOFs and significantly reduced mutual coupling. The achievable DOFs of the rLCAs are derived and analyzed in detail. Finally, numerical examples show that the proposed rLCAs provide reduced mutual coupling and higher estimation accuracy.

The holes in difference coarrays (DCA) of coprime arrays (CA) limit the extension of the aperture thus causing the waste of resources. In this paper, we propose a generalized hole-filling strategy for hole-existing CAs with overlapping subarrays, which allows to extend the aperture completely and achieves a larger hole-free DCA. First, we summarize a generalized CA configuration based on the existing CAs with overlapping subarrays and derive the symmetric relationship between holes and non-consecutive lags in DCA. Then, we propose a hole-free coprime array by arranging a third subarray outside the original configurations, where the first element location is determined by the symmetric relationship. Furthermore, we derive the optimal parameters that produce the largest hole-free DCA with a given number of sensors. Theoretical derivations and simulations are provided to demonstrate that the proposed hole-free coprime configurations overcome the original coprime arrays in terms of uniform degrees of freedom and direction-of-arrival estimation performance.

In this paper, we propose a novel reduced-dimensional decomposition method of coherent sources with arbitrary rectangle arrays, namely RD-MUSIC. Compared with the orientational invariance structure of two-dimensional spatial smoothing methods, the proposed RD-MUSIC utilizes the orthogonally similar transformation to achieve the real-valued decomposition only with a half dimension, which can be extended to arbitrary rectangle arrays. Simulation results demonstrate the satisfactory estimation accuracy and improved resolution.

It is well-known that the multiple signal classification (MUSIC) algorithm is computationally time-consuming because it requires a complex-valued full-dimension eigenvalue decomposition (EVD) and a complex-valued spectral searching. In this paper, we exploit the virtual signal model of forward/backward average of array covariance matrix (FBACM) to show that its real part (R-FBACM) is a real symmetric matrix. Based on that, we prove that by evaluating two half-dimension EVD after an orthogonally similar transformation performed on the estimated R-FBACM, we are able to reconstruct the original eigenspace whereas the maximum number of estimated sources is reduced as compared to the upper limit$\mathit{M}-\text{1}$for original MUSIC. Numerical results show that the proposed method provides satisfactory estimation accuracy and improved resolution with reduced complexity.