Iterative reweighted proximal projection based DOA estimation algorithm for monostatic MIMO radar

We consider the problem of single snapshot direction-of-arrival (DOA) estimation of multiple targets in monostatic multiple-input multiple-output (MIMO) radar. When only a single snapshot is used, the sample covariance matrix of the data becomes non-invertible and, therefore, does not permit application of Capon-based DOA estimation techniques. On the other hand, low-resolution techniques, such as the conventional beamformer, suffer from biased estimation and fail to resolve closely spaced sources. In this paper, we propose a new Capon-based method for DOA estimation in MIMO radar using a single radar pulse. Assuming that the angular locations of the sources are known a priori to be located within a certain spatial sector, we employ multiple transmit beams to focus the transmit energy of multiple orthogonal waveforms within the desired sector. The transmit weight vectors are carefully designed such that they have the same transmit power distribution pattern. As compared to the standard MIMO radar, the proposed approach enables transmitting an arbitrary number of orthogonal waveforms. By using matched-filtering at the receiver, the data associated with each beam is extracted yielding a virtual data snapshot. The total number of virtual snapshots is equal to the number of transmit beams. By choosing the number of transmit beams to be larger than the number of receive elements, it becomes possible to form a full-rank sample covariance matrix. The Capon beamformer is then applied to estimate the DOAs of the targets of interest. The proposed method is shown to have improved DOA estimation performance as compared to conventional single-snapshot DOA estimation methods.

In this study, the authors consider the direction of arrival (DOA) estimation problem for a monostatic multiple-input multiple-output (MIMO) radar. A DOA estimation algorithm for the monostatic MIMO radar using Capon and the reduced-dimension transformation is proposed. The proposed algorithm can achieve lower computational complexity than the Capon algorithm, while not debasing the performance of the angle estimation. The proposed algorithm has better angle estimation performance than the Capon algorithm and estimation of signal parameters via rotational invariance techniques algorithm, and it has close angle estimation performance to the Cramér–Rao bound (CRB). The variance of the estimation error and the CRB of the DOA estimation are derived. Simulation results present the usefulness of the proposed algorithm.

MIMO radar can provide enhanced spatial resolution and achieve better target detection performance. Hence the parameter identifiability of the system can be significantly improved. In this paper, the issue of direction of arrival (DOA) estimation in multiple-input multiple-output (MIMO) radar is discussed, and several algorithms for DOA tracking based on monostatic MIMO radar in impulsive noise background are proposed and studied. These tracking algorithms are based on the concept of robust covariation (ROC), fractional lower order moment (FLOM), fractional lower order cyclic (FLOC) and similar normalization. The transmit and receive arrays of a monostatic narrowband MIMO radar are assumed to be close to each other and eigenstructure based MUSIC algorithm at the receiver is used to identify the DOA's of the targets of interest. Simulation results verify the usefulness of the proposed algorithms. The comparison of these four tracking algorithms show that the algorithm based on the concept of similar normalization has better performance than the others.

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.

Recently, 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.

The sparse signal recovery-based direction of arrival (DOA) estimation has received a great deal of attention over the past decade. From the sparse representation point of view, $$\ell _0$$ -norm is the best choice to evaluate the sparsity of a vector. However, solving an $$\ell _0$$ -norm minimization problem is non-deterministic polynomial hard (NP-hard). Thus, The common idea for many sparse DOA estimation methods is to use the $$\ell _1$$ -norm as the sparsity metric. However, its sparse solution may not coincide with the solution resulting from the $$\ell _0$$ -norm thus deteriorating the DOA estimation performance. In this paper, we propose a new sparse method based on $$\ell _p$$ ( $$0<p<1$$ ) regularization for DOA estimation to achieve a sparser solution than $$\ell _1$$ regularization. In particular, we use the Taylor expansion to convert the $$\ell _p$$ -norm minimization problem to a weighted $$\ell _1$$ -norm problem. Then, a two-step iterative method is employed to achieve the DOA estimate. The $$\ell _p$$ ( $$0<p<1$$ ) regularization is able to improve the angle resolution, leading to an improved performance in low SNR and correlated signal scenarios. Numerical results show that our proposed method has better estimation performance than many other methods do.

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.

In order to solve the problem that the channel gain-phase mismatch would deteriorate the performance of direction of arrival (DOA) estimation in actual direction finding (DF) system, a novel efficient gain-phase calibration algorithm based on the self-checking signal is proposed in this paper. By injecting a self-checking signal into the actual DF system, the whole calibration process could divide into the off-line pre-calibration and the auto-calibration part. Thus, it could realize the real-time auto-calibration efficiently during the DOA estimation process. Moreover, in order to verify the effectiveness of the proposed method, a practical DF signal processor based on the FPGA and DSP is implemented in this paper, and the experiments are carried out in the anechoic chamber. The experiment result shows that the proposed method could calibrate the gain-phase mismatch in real-time and have higher DOA estimation accuracy.

The advanced radar detection method which combines Tx beamforming and Rx array processing with highresolution direction of arrival (DOA) estimation is proposed. The proposed method associates DOA estimation direction with Tx beamforming direction (TxD-DOA) for a high accuracy detection for multiple targets in millimeter-wave radar system. Computer simulation and experiment evaluation verify the improvement effects of the TxD-DOA method. We have confirmed that the suppression of multiple radar reflection waves by the transmission beam scanning brings the better DOA estimation accuracy and higher angular resolution of the TxD-DOA for multiple human targets.

DOA (Direction of Arrival) estimation is a major problem in array signal processing applications. Recently, compressive sensing algorithms, including convex relaxation algorithms and greedy algorithms, have been recognized as a kind of novel DOA estimation algorithm. However, the success of these algorithms is limited by the RIP (Restricted Isometry Property) condition or the mutual coherence of measurement matrix. In the DOA estimation problem, the columns of measurement matrix are steering vectors corresponding to different DOAs. Thus, it violates the mutual coherence condition. The situation gets worse when there are two sources from two adjacent DOAs. In this paper, an algorithm based on OMP (Orthogonal Matching Pursuit), called ILS-OMP (Iterative Local Searching-Orthogonal Matching Pursuit), is proposed to improve DOA resolution by Iterative Local Searching. Firstly, the conventional OMP algorithm is used to obtain initial estimated DOAs. Then, in each iteration, a local searching process for every estimated DOA is utilized to find a new DOA in a given DOA set to further decrease the residual. Additionally, the estimated DOAs are updated by substituting the initial DOA with the new one. The simulation results demonstrate the advantages of the proposed algorithm.

In this paper, the issue of direction of arrival (DOA) estimation is discussed, and a partial angular sparse representation (SR)-based method using a sparse separate nested acoustic vector sensor (SSN-AVS) array is developed. Traditional AVS array is improved by separating the pressure sensor array and velocity sensor array into two different sparse array geometries with nested relationship. This improved array geometry can achieve large degrees of freedom (DOF) after the extended vectorization of the cross-covariance matrix, and only partial SR of the angle is required by exploiting the cyclic phase ambiguity caused by the large inter-element spacing of the virtual array. Joint sparse recovery is developed to amend the grid offset and unitary transformation is utilized to transform the complex atoms into real-valued ones. After sparse recovery, the sparse vector can simultaneously provide high-resolution but ambiguous angle estimation and unambiguous reference angle estimation embedded in the AVS array, and they are combined to obtain unique and high-resolution DOA estimation. Compared to other state-of-the-art DOA estimation methods using the AVS array, the proposed algorithm can provide better DOA estimation performance while requiring lower complexity. Multiple simulation results verify the effectiveness of the approach.

Compared with other systems with a single transmit antenna, multiple-input multiple-output (MIMO) radar systems have additional degrees of freedom that can enhance space resolution, improve parameter identifiability and enhance flexibility for transmit beam pattern design. The computational complexity for direction of arrival (DOA) estimation using sensor arrays increases very rapidly with the number of channels. The polynomial-rooting version of multiple signal classification algorithm (root-MUSIC) is computationally more efficient than spectral MUSIC. However, this algorithm can only be applied to uniform linear arrays. The manifold separation technique allows arrays of any geometry to be used with fast DOA estimators designed for linear arrays. In this study, a DOA estimation method that uses manifold separation and polynomial rooting technique is presented for monostatic MIMO radar with arbitrary array configuration. The algorithm offers a low computation complexity and an improved resolution capability for closely spaced sources as compared to conventional spectral MUSIC. Moreover, the wavefield modelling for monostatic MIMO radar and the number of mode selection of the proposed method are also analysed in the study. Finally, the simulation results are presented and the performances of the proposed algorithm are investigated and discussed.

The Direction of Arrival (DoA) estimation using linear sensor arrays is a critical task in array signal processing, with applications spanning SONAR, RADAR, and more. Traditional methods often struggle with the non-linear and high-dimensional nature of the data involved. This paper proposes a novel approach that leverages the strengths of both deep learning and machine learning classifiers. By combining an overcomplete Auto Encoder (AE) with a Support Vector Machine (SVM), the proposed method effectively extracts intricate features from the high-dimensional input signals, enabling accurate DoA estimation. The model was trained and evaluated using field data collected from the Indian Ocean. Sensor delays were introduced experimentally to the collected single-channel data by positioning acoustic sources at various angles within 0 to 180-degree relative to a linear microphone array with 16 channels. In DOA estimation, where experimental data are often noisy or missing features, the AE learns a detailed latent space representation that helps to smooth the data before it is classified. While AE + SVM is not new, its application to DoA estimation with noisy, incomplete, or sparse experimental data is novel. The overcomplete AE addresses the unique challenges of DoA estimation by improving feature quality and enabling more accurate, robust predictions. Experimental results demonstrate the efficacy of this unified model, achieving a remarkable 94.7% accuracy in DoA estimation tasks.

Direction of arrival (DOA) estimation has emerged as a promising technology for many military and civilian applications, such as radio astronomy and wireless communications. However, it remains very challenging to estimate DOA for distributed sources within impulsive noise environments. As is widely known, the utilization of the subspace technology in array signal processing can enable high-resolution DOA estimation. In many instances, most of these algorithms assume that the noise is Gaussian. Considering the impulsive noise environments, the derivative of the error function is derived as the iteratively weighted factor in the objective function of low-rank decomposition. Two iterative algorithms are proposed to compute the signal subspace to implement DOA estimation for coherently distributed sources. To further improve the DOA estimation accuracy and increase the number of estimated sources, the virtual data samples are reconstructed from the extended co-prime array. Simulation results confirm that the two proposed impulsive noise suppression algorithms provide better performance than the existing algorithms. The virtual data samples that are reconstructed from the extended co-prime array can be utilized as the effective inputs for subspace-like algorithms.

Direction of arrival (DOA) estimation is an important issue for monostatic MIMO radar. A DOA estimation method for monostatic MIMO radar based on unitary root-MUSIC is presented in this article. In the presented method, a reduced-dimension matrix is first utilised to transform the high dimension of received signal data into low dimension one. Then, a low-dimension real-value covariance matrix is obtained by forward–backward (FB) averaging and unitary transformation. The DOA of targets can be achieved by unitary root-MUSIC. Due to the FB averaging of received signal data and the eigendecomposition of the real-valued matrix covariance, the proposed method owns better angle estimation performance and lower computational complexity. The simulation results of the proposed method are presented and the performances are investigated and discussed.