High Direction Of Arrival Estimation Accuracy Research Articles

Grid-less wideband direction of arrival estimation based on variational Bayesian inference.

Many recent works have addressed the problem of wideband direction of arrival (DOA) estimation using grid-less sparse techniques, and these methods have been shown to outperform the traditional wideband DOA estimation methods. However, these methods often suffer from the problem of requiring manual parameter tuning or high computational complexity, which reduces their practicality. To alleviate this problem, a grid-less wideband DOA estimation method based on variational Bayesian inference is proposed in this paper. The method approximates the posterior probability density function of DOA with the help of variational Bayesian inference, which does not require manual adjustment of parameters and can obtain accurate DOA estimation results with low computational complexity. Numerical simulations and real measurement data processing show that the proposed method has a higher DOA estimation accuracy than other grid-less wideband methods while providing higher computational speed.

IoMT-Net: Blockchain-Integrated Unauthorized UAV Localization Using Lightweight Convolution Neural Network for Internet of Military Things

Unmanned aerial vehicle (UAV) contributes substantial strategic benefits on the Internet-of-military-things (IoMT). However, the untrusted party’s misuse of the UAV may violate the security and even demolish the critical operation in the IoMT system. In addition, data manipulation and falsification using unauthorized access are the significant challenges of the IoMT system. In response to this problem, this study proposes a blockchain integrated convolution neural network (CNN)-based intelligent framework named IoMT-Net for identification and tracking illegal UAV in the IoMT system. Blockchain technology prevents illicit access, data manipulation, and illegal intrusions, as well as stored data on the central control server (CCS). Concurrently, the proposed CNN analyzed the radio frequency signal sent by the antenna array element to determine the direction of arrival (DoA) for the localization of the illegal UAV. Therefore, a signal model is designed to processes the received signal array through IoMT-Net. Moreover, the proposed CNN model is designed with two different functional modules, such as the resource accuracy trade-off (RAT) module and the unique feature extraction and accuracy boosting (UAB) module, by adopting depth-wise and grouped convolution layers. These sparsely connected convolution layers offer high DoA estimation accuracy while maintaining the network more lightweight. In addition, the skip connection is also leveraged into the sub-units of RAT and UAB modules for sharing features and handling the vanishing gradients problem. Based on the simulation results, the proposed network achieves superior DoA estimation accuracy (approximately 97.63% accuracy at 10 dB SNR) and outperforms other state-of-the-art models.

Ternary redundant sparse linear array design with high robustness

Sparse linear array is prone to element failure affected by environmental and other factors in practical application, resulting in the decline of degrees of freedom and the attenuation or even failure of direction of arrival (DOA) estimation performance. To address this problem, a ternary redundant sparse array composed of three uniform linear arrays with different spacings at specific distances is designed, and the analytical expression of its degrees of freedom is derived. The configuration rules of the array are concise, and the difference coarrays of the array are hole-free. Mathematically proves that all virtual array elements can be covered with a weight of not less than 3 except the 4 farthest. The array has lower sensors importance and less generalized k-fragility. The array design takes into account the maximum degrees of freedom, redundancy and sparsity. In case of sensors failure, compared with the other sparse linear arrays, the array has more stable difference coarrays and higher uniform degrees of freedom. Compared with multiple-fold redundancy arrays, the array has lower mutual coupling. Simulation results show that the ternary redundant array has superior robustness and higher DOA estimation accuracy under sensors failure.

Performance analysis of deep neural networks for direction of arrival estimation of multiple sources

AbstractRecently, popular machine learning algorithms have successfully been applied to the direction of arrival (DOA) estimation. An implementation of determination of DOA estimation is presented based on deep neural networks (DNNs) to reduce the computational complexity of traditional superresolution DOA estimation methods. The classical DOA estimation algorithms have limitations due to unforeseen effects, such as array perturbations. Instead of computing an inverse mapping based on the incomplete forward mapping that relates the signal directions to the array outputs, the DOA problem is approached as a mapping, which can be approximated using a suitable DNN trained with input output pairs. The neural network architecture is based on a multilayer perception and a group of parallel DNNs to perform detection and DOA estimation, respectively. Simulation results are performed to investigate the effect of network parameters on estimation accuracy so that they can be roughly determined in the case of one signal scenario. Based on a set of simulations and experimental measurements, the performance of the optimum network is also assessed and compared to that of the classical DOA estimation methods for multiple signals. It has been shown that the proposed method can not only achieve reasonably high DOA estimation accuracy, but also dramatically reduce the computational complexity and the memory space.

A novel DOA estimation method for an antenna array under strong interference

Strong interference will affect direction of arrival (DOA) estimation of weak desired signal and even cause DOA estimation failure. This paper investigates the weak signal DOA estimation for an antenna array under strong interference signals, and proposed a novel DOA estimation method for strong interference source suppression and weighted l1-norm sparse representation. A parallel adaptive beamforming algorithm based on power inversion is used to suppress strong interference and form new array data. To reduce spurious peaks in the spectrum under strong interference, a weighted matrix is determined by the optimized subspace algorithm for the subspace projection. Then, the DOA estimation, which is calculated by weighted l1-norm sparse representation, is formed by the weighted matrix and new array data. In this paper, the superiority of the proposed algorithm is illustrated by an example of unmanned aerial vehicle (UAV) video signal DOA estimation under strong interference signals. The simulated results of an orthogonal frequency division multiplexing signal indicate that the proposed algorithm shows merits of fewer snapshots, a sharper main lobe, a lower average noise spectrum value, higher DOA estimation accuracy and success rate. For validation, an outdoor experiment was conducted which demonstrated that the proposed algorithm is superior to other algorithms and can be used for DOA estimation of UAV video signals under strong WiFi interference. Both the simulations and experiments verify that the proposed algorithm can effectively suppress strong interference and achieve better DOA estimation performance for weak signals.

TB-NET: A Two-Branch Neural Network for Direction of Arrival Estimation under Model Imperfections

For direction of arrival (DoA) estimation, the data-driven deep-learning method has an advantage over the model-based methods since it is more robust against model imperfections. Conventionally, networks are based singly on regression or classification and may lead to unstable training and limited resolution. Alternatively, this paper proposes a two-branch neural network (TB-Net) that combines classification and regression in parallel. The grid-based classification branch is optimized by binary cross-entropy (BCE) loss and provides a mask that indicates the existence of the DoAs at predefined grids. The regression branch refines the DoA estimates by predicting the deviations from the grids. At the output layer, the outputs of the two branches are combined to obtain final DoA estimates. To achieve a lightweight model, only convolutional layers are used in the proposed TB-Net. The simulation results demonstrated that compared with the model-based and existing deep-learning methods, the proposed method can achieve higher DoA estimation accuracy in the presence of model imperfections and only has a size of 1.8 MB.

Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNR

Signal source number detection is an essential issue for the direction of arrival (DOA) estimation in satellite communication systems. The performances of conventional and deep-learning-based signal source number detection methods will deteriorate when the signal-to-noise ratio is low or coherent signals exist. This paper proposes a DOA detection network (DTN) combined with the root weighted subspace fitting (root-WSF) method to tackle this challenge. The DTN uses the constructed deep neural networks (DNN) to denoise the received signals and captures the nonlinear mapping relationship between the received signals and the number of signal sources. The received signals in the complex-valued domain are directly treated as DTN’s input, and the label of DTN is the one-hot encoding of the source number. It solves the issue that the classifier cannot well-handle the coherent signals and extends the values of discrete features to Euclidean space. Accordingly, the trained DTN can detect the signal source numbers with an average detection accuracy of 96.6%, and the root-WSF algorithm is applied as the rear stage of DOA estimation. Compared with the traditional DOA methods, the proposed DTN incorporated with the root-WSF algorithm features superior robustness, high DOA estimation accuracy, and enhanced resolution.

Max-MUSIC: A Low-Complexity High-Resolution Direction Finding Method for Sparse MIMO Radars

In this paper, we propose a low-complexity high-resolution direction-of-arrival (DoA) method (max-MUSIC) by exploiting multiple subarrays of a sparse multiple-input multiple-output (MIMO) radar. In this method, specific subarrays for different MIMO configurations are selected according to a proposed selection criterion for generating diverse MUSIC spatial spectrums. Subsequently, we design a new spatial spectrum function that can perform high DoA estimation accuracy and resolution without spatial aliasing, by leveraging the advantages of the diverse spatial spectrums. With a given number of antenna elements, enhancements in array aperture size can improve estimation accuracy while causing severe aliasing, which cannot be mitigated. To define a legitimate MIMO configuration for aliasing suppression, a criterion that offers an optimal compromise between DoA estimation performance and anti-aliasing ability is provided. Simulations are provided to verify the effectiveness of the proposed method.

Deep Neural Network for Estimation of Direction of Arrival With Antenna Array

For many object tracking systems, how to quickly and efficiently estimate the direction of arrival (DOA) of radio waves impinging on the antenna array is an urgent task. In this paper, a new efficient DOA estimation approach based on the deep neural networks (DNN) is proposed, in which a nonlinear mapping that relates the outputs of the receiving antennas with its associated DOAs is learned by using the DNN-based network. The novel network architecture is divided into two phases, the detection phase and the DOA estimation phase. Additional detection network dramatically reduces the size of the training set and the process of the training data preparation is discussed in detail. After finishing the training phase, the corresponding DOAs can be identified based on current input data during testing phase. It has been shown that the proposed method can not only achieve reasonably high DOA estimation accuracy, but also reduce the computational complexity required by traditional superresolution DOA estimation methods such as multiple signal classification (MUSIC) and estimation of signal parameters via rotation invariance (ESPRIT). The computer simulation results are performed to investigate the generalization and effectiveness of the proposed approach in different scenarios.

Monopulse forward‐looking imaging algorithm based on Levenberg–Marquardt optimisation

With precise angle measurement, traditional monopulse techniques for forward-looking imaging can acquire exact angle information of one single target within a radar beam. However, when multiple targets exist in a beam, it is difficult to resolve them. To address this problem, a novel monopulse forward-looking imaging algorithm based on Levenberg–Marquardt (LM) optimisation is proposed. The core idea of this algorithm is to solve maximum likelihood estimation problem based on LM optimisation (ML-LM) to obtain the direction of arrival (DOA) estimation of unresolved targets. First, the echo model of two targets within a forward-looking radar beam is established, then the imaging framework of the proposed algorithm is introduced. Finally, the advantages of ML-LM are illustrated based on a series of evaluation criterion, including angle measurement error for various values of signal-to-noise ratio (SNR), angle interval adaptability and computational efficiency. The simulation results show that two targets within a forward-looking radar beam can be resolved and relocated accurately utilising the proposed algorithm. Meanwhile, the comparison with other algorithms shows it has higher DOA estimation accuracy, less computational complexity and a wider range of angle interval adaptability.

DOA estimation for wideband signals based on weighted Squared TOPS

This paper introduces a new direction-of-arrival (DOA) estimation method for wideband signal sources. The new method estimates the DOA of wideband signal sources based on squared test of orthogonality of projected subspaces (Squared TOPS) which is an improved method of TOPS. TOPS and Squared TOPS use the signal and noise subspaces of multiple frequency components of wideband signal sources. Although coherent wideband method, such as coherent signal subspace method (CSSM), performs high DOA estimation accuracy, it requires the initial estimate of signal source directions. On the other hand, TOPS and Squared TOPS can provide good performance of DOA estimation without the initial value of signal sources; however, some false peaks appear in spatial spectrum based on these methods. The proposed method, called weighted Squared TOPS (WS-TOPS), uses the modified squared matrix and selective weighted averaging process to improve DOA estimation performance. The performance of WS-TOPS is compared with those of TOPS, Squared TOPS, incoherent MUSIC, and test of orthogonality of frequency spaces (TOFS) through computer simulations. The simulation results show that WS-TOPS can suppress all false peaks in spatial spectrum and improve DOA estimation accuracy and also keep the same resolution performance as Squared TOPS.

Low complexity direction-of-arrival estimation of coherent noncircular sources

Conventional direction-of-arrival (DOA) estimation methods for coherent noncircular signals may reduce the effective aperture of sensor arrays and thus have low estimation accuracy. To address this problem, a low complexity algorithm for DOA estimation of noncircular coherent signals, the extended ESPRIT-like (EEL) algorithm, is proposed in this paper. The proposed EEL algorithm has fully utilized the effective array aperture and the number of the sources that can be detected by the proposed algorithm now can attain the two-third of the total number of the impinging sources. Compared with the existing DOA estimation methods for noncircular coherent signals, the proposed EEL algorithm has lower computational complexity and higher DOA estimation accuracy. Simulation results are presented to show the superior performance of the proposed method.

A Low Energy Consumption DOA Estimation Approach for Conformal Array in Ultra-Wideband

Most direction-of-arrival (DOA) estimation approaches for conformal array suffer from high computational complexity, which cause high energy loss for the direction finding system. Thus, a low energy consumption DOA estimation algorithm for conformal array antenna is proposed in this paper. The arbitrary baseline direction finding algorithm is extended to estimate DOA for a conformal array in ultra-wideband. The rotation comparison method is adopted to solve the ambiguity of direction finding. The virtual baseline approach is used to construct the virtual elements. Theoretically, the virtual elements can be extended in the space flexibility. Four elements (both actual and virtual elements) can be used to obtain a group of solutions. The space angle estimation can be obtained by using sub-array divided technique and matrix inversion method. The stability of the proposed algorithm can be guaranteed by averaging the angles obtained by different sub-arrays. Finally, the simulation results verify the effectiveness of the proposed method with high DOA estimation accuracy and relatively low computational complexity.