Source Estimation Using Coprime Array: A Sparse Reconstruction Perspective

Zhang

et al. 2020

Sensors

In this paper, a type of effective electronic counter-countermeasures (ECCM) technique for suppressing the high-power deception jamming using an orthogonal frequency division multiplexing (OFDM) radar is proposed. Concerning the velocity deception jamming, the initial phases of the pulses transmitted in a coherent processing interval (CPI) are designed to minimize the jamming power within a specific range, forming a notch around the jamming in the Doppler spectrum. For the purpose of suppressing the range deception jamming and the joint range-velocity deception jamming, the phase codes of the subcarriers belonging to the OFDM pulses are optimized to minimize the jamming power, distributing some specific bands in the range and the range-velocity domain, respectively. According to Parseval's theorem, the phase encoding, acting as the coding manner of the OFDM subcarriers can ensure that the energy of each OFDM symbol stays the same. It is worth noticing that the phase codes of the OFDM subcarriers can influence the peak-to-average power ratio (PAPR). Thus, an optimization problem is formulated to optimize the phase codes of the subcarriers under the constraint of global PAPR, which can regulate the PAPRs of multiple OFDM symbols at the same time. The proposed problem is non-convex; therefore, it is a huge challenge to tackle. Then we present a method named by the phase-only alternating direction method multipliers (POADMM) to solve the aforementioned optimization problem. Some necessary simulation results are provided to demonstrate the effectiveness of the proposed radar signaling strategy 4 of 26 norm. In addition, arg{x} denotes the phase vector of x.|·|, {·} and {·} are the magnitude, real part, and image part of a complex-valued input. The operators ⊕ and ⊗ are the Hadamard operator and Kronecker product operator, respectively. Let I, 1, and 0 denote the identity matrix, matrix of 1 and zero matrix (their size is determined from the context). The operators, Diag(·) and diag(·), are, respectively, the diagonal matrices formed by the entries of the vector argument and the column vector containing the principal diagonal of the matrix argument. denotes the gradient operator and vec(·) represents the vectorization of an input matrix.

Section: Introductionmentioning

confidence: 99%

ECCM Schemes against Deception Jamming Using OFDM Radar with Low Global PAPR

Zhang

et al. 2020

Sensors

“…Many sparse signal recovery algorithms have been proposed in CS literature, which can be applied to estimate the DOAs by exploiting the spatial sparsity [20][21][22][23][24]. FOCUSS (FOcal Underdetermined System Solver) [25] uses an iterative method which is based on weighted norm minimization.…”

Section: Introductionmentioning

confidence: 99%

Grid Reconfiguration Method for Off-Grid DOA Estimation

Ling

Gao

et al. 2019

Electronics

Off-grid algorithms for direction of arrival (DOA) estimation have become attractive because of their advantages in resolution and efficiency over conventional ones. In this paper, we propose a grid reconfiguration direction of arrival (GRDOA) estimation method based on sparse Bayesian learning. Unlike other off-grid methods, the grid points of GRDOA are treated as dynamic parameters. The number and position of the grid points are varied iteratively via a root method and a fission process. Then, the grid gets reconfigured through some criteria. By iteratively updating the reconfigured grid, DOAs are estimated completely. Since GRDOA has fewer grid points, it has better computational efficiency than the previous methods. Moreover, GRDOA can achieve better resolution and relatively higher accuracy. Numerical simulation results validate the effectiveness of GRDOA.

“…These subspace based estimators often rely on the statistical properties of the sampled data, thus a large number of snapshots are required for precision estimation. With the development of sparse signal reconstruction (SSR) [5], [6], several SSRbased DOA estimation methods exploit the spatial sparse property of direction information to further improve the estimation performance [7][8][9][10][11][12], and these methods show strong robustness at the cases of limited snapshots.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Super-Resolution DOA Estimator Based on Grid Learning

Li¹,

Liu²,

Wang³

et al. 2019

RADIOENGINEERING

Direction-of-arrival (DOA) estimation based on sparse signal reconstruction (SSR) is always vulnerable to off-grid error. To address this issue, an efficient superresolution DOA estimation algorithm is proposed in this work. Utilizing the Taylor series expansion, the sparse dictionary matrix is constructed under the off-grid model. Then, a polynomial optimization function is established based on the orthogonality principle. By minimizing the given objective function, we derive an efficient closed-form solution of the off-grid errors. Using the estimated off-grid errors, the discretized grid can be iteratively learned and approaches the true DOAs. With the newly learned grid, accurate DOA estimations can be achieved through the SSR scheme. The proposed algorithm converges fast and achieves precise DOA estimations even the step size of the discretized grid is large. The superior performance of the proposed algorithm is demonstrated by the simulation results.