As a promising research area in Internet of Things (IoT), Internet of Vehicles (IoV) has attracted much attention in wireless communication and network. In general, vehicle localization can be achieved by the Global Positioning Systems (GPS). However, in some special scenarios, such as cloud cover, tunnels or some places where the GPS signals are weak, GPS cannot perform well. The continuous and accurate localization services cannot be guaranteed. In order to improve the accuracy of vehicle localization, an assistant vehicle localization method based on Direction-of-Arrival (DOA) estimation is proposed in this paper. The assistant vehicle localization system is composed of three Base Stations (BS) equipped with a Multiple Input Multiple Output (MIMO) array. The locations of vehicles can be estimated if the positions of the three BSs and the DOAs of vehicles estimated by the BSs are known. However, the DOA estimated accuracy maybe degrade dramatically when the electromagnetic environment is complex. In the proposed method, a Sparse Bayesian Learning (SBL) based robust DOA estimation approach is first proposed to achieve the off-grid DOA estimation of the target vehicles under the condition of non-uniform noise, where the covariance matrix of non-uniform noise is estimated by a Least Squares (LS) procedure, and a grid refinement procedure implemented by finding the roots of a polynomial is performed to refine the grid points to reduce the off-grid error. Then, according to the DOA estimation results, the target vehicle is cross-located once by each two BSs in the localization system. Finally, robust localization can be realized based on the results of three-time cross-location. Plenty of simulation results demonstrate the effectiveness and superiority of the proposed method.

Forest fire monitoring plays an important role in forest resource protection. Although satellite remote sensing is an effective way for forest fire monitoring, satellite-based methods can only monitor huge scale forest areas, and they are weak in predicting the specific areas of forest fires. In this paper, we first propose an unmanned aerial vehicle (UAV)-enabled system architecture consisting of multiple industrial internet of things (IIoTs), in which the collected data by sensors in IIoTs can be delivered to UAVs for processing directly. As the sensors of IIoTs are deployed to monitor different indexes of forest fires, fully considering the priority constraints among sensors can guarantee a quick response of forest fire monitoring. Thus, the priority constraints among the sensors are taken into consideration in this system architecture, and the objective to minimize the maximum response time of forest fire monitoring. To search for the optimal UAV resource allocation strategy, a learning based cooperative particle swarm optimization algorithm (LCPSO) with Markov random field (MRF) based decomposition strategy is proposed. The solution space of UAV resource allocation is decomposed into sub solution space according to the decomposed decision variables by the MRF network structure, and the optimal resource allocation strategy is searched by LCPSO in multiple sub solution space cooperatively. Three simulation experiments on two datasets are designed, and the simulation results compared with the state-of-the-art methods verify the validity of LCPSO, which are reflected by the quickest response time of forest fire monitoring.

As an indispensable part of the Internet of Vehicles (IoV), unmanned aerial vehicles (UAVs) can be deployed for target positioning and navigation in the space–air–ground-integrated network (SAGIN) environment. Maritime target positioning is very important for the safe navigation of ships, hydrographic surveys, and marine resource exploration. Traditional methods typically exploit satellites to locate marine targets in the SAGIN environment, and the location accuracy does not satisfy the requirements of modern ocean observation missions. In order to localize the marine target, we develop a system architecture in this article, which contains UAVs integrated with monostatic multiple-input–multiple-output (MIMO) radars. The main thrust is to estimate the direction-of-arrival (DOA) via MIMO radar. Herein, we consider a general scenario that unknown mutual coupling exist and a novel sparse reconstruction algorithm is proposed. The mutual coupling matrix (MCM) is adopted with the help of its special structure, we formulate the data model as a sparse representation form. Then, two novel matrices, a weighted matrix, and a reduced-dimensional matrix are constructed to reduce the computational complexity and enhance the sparsity, respectively. Thereafter, a sparse constraint model is constructed using the concept of optimal weighted subspace fitting (WSF). Finally, the DOA estimation of maritime targets can be achieved by reconstructing the support of a block sparse matrix. Based on the DOA estimation results, multiple UAVs are used to cross-locate marine targets multiple times, and an accurate marine target position is achieved in the SAGIN environment. Numerical results are carried out, which demonstrates the effectiveness of the proposed DOA estimator, and the multi-UAV cooperative localization system can realize accurate target localization.

The polarization millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system has been deployed in next-generation wireless communication since it can provide a high data stream and high space efficiency simultaneously. Polarization channel parameter estimation for polarized mmWave massive MIMO systems is extremely important for directional beamforming with data transmissionS. In this paper, the base station (BS) equipped with a large polarization-sensitive array is considered in massive MIMO systems. The polarization channel consists of direction-of-arrival (DOA) and polarization parameters that are estimated from the coexistence of circular and non-circular signals. Based on the unconjugated covariance matrix, the initialized polarization channel estimation is achieved by multiple signal classification (MUSIC). Then, the high-accuracy polarization channel estimation for general non-circular rate signals is performed by reconstructing the corresponding noise matrix. The high-accuracy polarization channel estimation for circular signals is obtained based on covariance matrix differencing. Moreover, the dimension of parameter search is reduced based on the partial derivative of the spectrum function with respect to the non-circular phase. The high-accuracy polarization channel estimation for the maximum non-circular rate signal is finally achieved based on the initialized parameter estimation of the polarization channel. The proposed algorithm estimates different kinds of signals separately. The effect of different kinds of signals is reduced significantly, which means that the resolution probability of different kinds of signals can be dramatically improved. Numerical examples are provided to demonstrate the performance of the proposed algorithm, especially in small angular distances.

The electromagnetic vector sensor (EMVS) embedded multiple-input multiple-output (MIMO) radar, named EMVS-MIMO radar, offers a unique perspective on target localization, providing both two-dimensional (2D) angle and polarization status estimations of targets. However, the current frameworks limit the EMVS-MIMO radar's ability to achieve high-resolution 2D direction finding while maintaining low complexity. To address this limitation, this paper proposes a uniform rectangular array-based methodology with the inter-sensor spacing of the receiving array greater than half-wavelength, along with a high-resolution estimation strategy based on a parallel factor (PARAFAC) model. To optimize the identifiability of the PARAFAC model, the array measurement is first arranged into a third-order tensor, and the 6 possible PARAFAC tensor models are established. Factor matrices are then estimated via PARAFAC decomposition, and 2D direction finding is achieved by combining the spatial rotational invariance technique with the normalized vector-cross product technique. The proposed PARAFAC algorithm can be extended to other sparse uniform geometries, such as L-shaped arrays and parallel linear arrays. The larger array aperture and inherent tensor gain of the proposed PARAFAC approaches offer superior estimation performance compared to existing frameworks. Additionally, the flexibility of the PARAFAC model may make it suitable for single snapshot scenarios. Finally, the improvements of the proposed approaches are validated by numerical simulations.

It is a serious problem that the performance loss is suffered by traditional Direction-of-Arrival (DOA) estimation methods in non-ideal environment, such as mutual coupling of array elements, coherent sources, colored noise and plethora targets. A data-driven robust DOA estimation framework is proposed for MIMO radar via deep neural networks (DNN), so as to overcome the problems mentioned before. The framework consists of an autoencoder, a feedforward network, a network parameters database and a series of parallel directed acyclic graph networks (DAGN). Assisted with feedforward network for target-number determination, matching parameters of networks will be loaded from database. The autoencoder acts like a noise filter, it reconstructs the noise-free covariance from the noisy signal and thus the generalization burden of the subsequent DOA estimation DAGN will be decreased. Each sub-network of the parallel DAGN consists of a convolutional neural network (CNN) and two bidirectional long short-term memory (BiLSTM) networks, from which the estimation of DOA will be obtained by regression. The simulation results show that the proposed method is superior to the traditional methods in a non-ideal environment, and can also perform well when the number of targets reaches the upper limitation of the degrees of freedom of MIMO radar.