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.

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.

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.