The issue of two-dimensional (2D) direction-of-departure and direction-of-arrival estimation for bistatic multiple-input multiple-output (MIMO) radar with a coprime electromagnetic vector sensor ...(EMVS) is addressed in this paper, and a tensor-based subspace algorithm is proposed. Firstly, the covariance measurement of the received data is arranged into a fourth-order tensor, which can maintain the multi-dimensional characteristic of the received data. Then, the higher-order singular value decomposition is followed to get an accurate signal subspace. By utilizing the uniformity of the subarrays in coprime EMVS–MIMO radar, the rotation invariant technique is adopted to achieve ambiguous elevation angle estimation. Thereafter, the unambiguous elevation angles are recovered by exploring the coprime characteristic of the subarrays. Finally, all azimuth angles are achieved by using the vector cross-product strategy. The tensor nature inherited from the array measurement is fully explored, and the coprime geometry enables EMVS–MIMO radar to achieve larger array aperture than the existing uniform linear configuration; thus, the proposed method offers better estimation performance than current state-of-the-art algorithms. Several computer simulations validate the effectiveness of the proposed algorithm.
Unmanned aerial vehicle (UAV) swarms-enabled mobile edge computing system can be deployed in critical industrial zones for monitoring. Meanwhile, its malicious use may bring great threat to the ...security, and the accurate detection, and localization are important. UAV swarms show characteristics of the high density, small radar cross section, far range, and time-varying motion, and have posed formidable challenges to the accurate detection and localization. In this article, the accurate detection and localization of UAV swarms are investigated, and an effective method is proposed based on the Dechirp-keystone transform, and frequency-selective reweighted trace minimization. It inherits high robustness of the coherent long-time integration technique and superresolution of the gridless sparse technique. Mathematical analyzes and numerical simulations validate its superiorities in accurate detection and localization of UAV swarms.
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.
In this letter, a novel robust block sparse recovery algorithm by using the weighted subspace fitting (WSF) is proposed to deal with the direction-of-arrival (DOA) problem under the condition of ...unknown mutual coupling. Firstly, a novel block sparse representation signal model based on the WSF is established to settle the effect of unknown mutual coupling. Then, the sparse constraint problem is investigated, and a regularization criterion between the sparsity penalty and subspace fitting error is given. Finally, the DOA estimation problem can be converted into a block sparse recovery problem. Some experimental results are carried out to prove the performance of proposed method in the case of unknown mutual coupling.
In the new era of integrated computing with intelligent devices and system, moving aerial targets can be tracked flexibly. The estimation performance of traditional matched filter-based methods would ...deteriorate dramatically for multiple targets tracking, since the weak target is masked by the strong target or the strong sidelobes. In order to solve the problems mentioned above, this paper aims at developing a joint range-Doppler-angle estimation solution for an intelligent tracking system with a commercial frequency modulation radio station (noncooperative illuminator of opportunity) and a uniform linear array. First, a gridless sparse method is proposed for simultaneous angle-range-Doppler estimation with atomic norm minimization. Based on the integrated computing, multiple workstations or servers of the data process center in the intelligent tracking system can cooperate with each other to accelerate the data process. Then a suboptimal method, which estimates three parameters in a sequential way, is proposed based on grid sparse method. The range-Doppler of each target is iteratively estimated by exploiting the joint sparsity in multiple surveillance antennas. A simple beamforming method is used to estimate the angles in turn by exploiting the angle information in the joint sparse coefficients. Simulation result and real test show that the proposed solution can effectively detect weak targets in an iterative manner.
The sudden outbreak of COVID-19 brings many unpredictable situations to human travel, such as temporarily closed highways, parking lots, etc. The scenarios mentioned above will lead to a large ...backlog of vehicles, and the requirements of Internet of vehicle (IoV) applications increase sharply in a period of short time correspondingly. Mobile edge computing (MEC) is a key enabling technology that can guarantee the diverse requirements of IoV applications through the optimization of resource scheduling. However, the sharp increasing in requirements of IoV applications caused by the congestion of highways or parking lots still bring great challenges to the deployment of traditional MEC. Therefore, in this paper, we construct an unmanned aerial vehicle (UAV) enabled MEC system, in which the data generated from IoV applications is processed by offloading to UAVs with MEC servers to ensure the efficiency of data processing and the response time of IoV applications. In order to approximate real-world UAV enabled MEC system, we consider the stochastic offloading and downloading processing time. Moreover, the priority constraints of sensors from the same vehicle are taken into consideration since they have different importance degrees. Then, we propose an Markov network-based cooperative evolutionary algorithm (MNCEA) to search out the optimal UAV scheduling solution to guarantee the shortest response time, in which the solution space is divided into multiple sub-solution spaces with the help of MN structure and parameters. Finally, we construct multiple simulation experiments with different probability distributions to simulate uncertainty factors. The simulation results verify the validity of MNCEA compared with the state-of-the-art methods, which is reflected by the shortest response time of requirements of IoV applications.
The traffic congestion and accidents can be relieved by deploying the software defined internet of vehicles (SDN-IoV). However, the traffic of pedestrians and vehicles is particularly heavy near ...commercial streets and campuses. In particular scenarios, the SDN-IoV may not ensure the quality of service (QoS) for pedestrians and vehicles. In this paper, we construct a novel system architecture consisting of multiple non-cooperative unmanned aerial vehicles (UAVs) and a SDN-IoV. The non-cooperative UAV is equipped with an antenna array to receive the signals from the vehicles and pedestrians of SDN-IoV. In order to locate the positions of vehicles and pedestrians, two source enumeration methods are proposed in a complex SDN-IoV environment with color noise. The projection matrix of the low dimensional signal subspace is constructed by the proposed criterion based on signal subspace projection (SSP). The sequence of the projected difference values of the local covariance matrix is applied to estimate the number of vehicles and pedestrians. The eigenvalues can be grouped to construct different subspaces by the proposed eigen-subspace projection (ESP). By projecting a new covariance matrix into the eigen-subspaces, the variance of values represents the projection difference can be exploited to estimate the number of vehicles and pedestrians. Simulation results and real system test verify the validity of the two proposed methods by comparing them with the state-of-the-art methods. Both of the methods have excellent estimation performance especially in color noise.
In the environment of unknown mutual coupling, many works on direction-of-arrival (DOA) estimation with sensor array are prone to performance degradation or even failure. Moreover, there are few ...literatures on off-grid direction finding using regularized sparse recovery technology. Therefore, the scenario of off-grid DOA estimation in sensor array with unknown mutual coupling is investigated, and then a reweighted off-grid Sparse Spectrum Fitting (Re-OGSpSF) approach is developed in this article. Inspired by the selection matrix, an undisturbed array output is formed to remove the unknown mutual coupling effect. Subsequently, a refined off-grid SpSF (OGSpSF) recovery model is structured by integrating the off-grid error term obtained from the first-order Taylor approximation of the higher-order term into the underlying on-grid sparse representation model. After that, a novel Re-OGSpSF framework is formulated to recover the sparse vectors, where a weighted matrix is developed by the MUSIC-like spectrum function to enhance the solution's sparsity. Ultimately, off-grid DOA estimation can be realized with the help of the recovered sparse vectors. Thanks to the off-grid representation and reweighted strategy, the proposed method can effectively and efficiently achieve high-precision continuous DOA estimation, making it favorable for real-time direction finding. The simulation results validate the superiority of the proposed method.
In this paper, an improved localization method named three-uniform-linear-array localization is proposed for patient track systems. Three receivers adopting a smart antenna technique cooperate with ...each other to locate the patients using the angulation positioning method. In order to be able to track patients in environment with high patient density, a high-resolution direction-of-arrival (DOA) estimation algorithm for the coexistence of noncircular and circular signals is proposed. First, the maximal and common noncircularity rated signals are preliminarily estimated. Second, based on the noise space block matrix, the DOAs of these signals are re-estimated with high accuracy. Then, the covariance matrix of the maximal and common noncircularity rated signals is reconstructed. The contributions of these signals are eliminated after performing a subtraction operation on the covariance matrix of the received data and only those of circular signals remain. Finally, the DOAs of circular signals are obtained. Results of simulations and real tests demonstrate the effectiveness and performance of the proposed algorithm.
Intelligent transportation systems (ITSs) of industrial systems have played an important role in Internet of things (IOT). The assistant calibration system (ACS) of vehicles is an emerging ...technology, which services the driver to drive the vehicle safely. To solve some existing problems in ACS such as frequency pairing, vehicle localization judgment, and driving in the curve road, two direction-of-arrival (DOA) estimation-based approaches are proposed to resolve these problems. However, the performance of most conventional DOA estimation algorithms is affected by the mutual coupling among the elements. The special structure of the mutual coupling matrix of the uniform linear array is applied to eliminate the effect of mutual coupling. Then, a novel on-grid DOA estimation algorithm based on compressive sensing (CS) strategies is proposed in the presence of unknown mutual coupling. In order to compensate the aperture loss of discarding information that the array receives, the array aperture is extended by the vectorization operator. In order to deal with the effect of grid mismatch, an off-grid DOA estimation algorithm based on sparse Bayesian learning (SBL) is proposed in this paper. The temporal correlation between the neighboring snapshot numbers is considered in the off-grid algorithm. The computer simulation verifies the effectiveness of the proposed algorithms.
