Jie Xiong

Dot-Shaped Range-Angle Beampattern Synthesis for Frequency Diverse Array

The frequency diverse array (FDA) using linearly increasing frequency increment yields an “S”-shape range-angle beampattern, which provides the potential capability to suppress range-dependent interferences. However, the FDA transmit beampattern is coupled in range and angle dimensions, which may degrade the output signal-to-interference-plus-noise ratio performance. In this letter, we propose a symmetrical FDA beampattern synthesis approach using multicarrier frequency increments and convex optimization, named convex-multilog-FDA, to achieve dot-shaped transmit beampatterns. Both single-dot and multi-dot shaped beampatterns can be synthesized. Numerical results show that, the proposed approach outperforms the existing log-FDA using logarithmically increasing frequency increments in transmit energy focusing, sidelobe suppression, and array resolution performance.

Frequency Diverse Array Transmit Beampattern Optimization With Genetic Algorithm

Due to its range-angle-dependent transmit beampattern, frequency diverse array (FDA) provides potential applications for joint range and angle estimation of targets and range-dependent interference suppression. However, a standard FDA using linearly increasing frequency increments will generate range- and angle-coupled S-shaped beampattern. This letter proposes a focused beampattern synthesis by optimizing the frequency increments with genetic algorithm. Both single-dot and multidot-shaped transmit beampatterns can be synthesized in this way. Numerical results show that the proposed method outperforms the existing method using logarithmically increasing frequency increments in suppressing undesired sidelobes, which implies that better target location performance can be achieved for the proposed method.

Directional Modulation Using Frequency Diverse Array For Secure Communications

In this paper, to obtain a better physical layer security for wireless communications, a directional modulation (DM) using frequency diverse array (FDA) is proposed. Different from the traditional directional modulation that achieves only angle dependent directional modulation, our proposed scheme achieves DM in both angle and range dimensions. The transmitted signal symbols only could be recovered successfully at the desired receiver, while they are distorted to be unrecoverable at the undesired receivers. Therefore, it will increases the security of data transmission to against the eavesdroppers. This FDA-based DM scheme can be potentially applied in the point-to-point security communications and the effectiveness is validated by numerical results.

Cognitive target tracking using FDA radar for increased SINR performance

Different from a phased-array, frequency diverse array (FDA) offers a range-dependent beampattern. By jointly utilizing the advantages of cognitive radar in environment sensing and FDA with range-dependent transmit beampattern, this paper proposes a cognitive target tracking scheme using FDA radar for increased output signal-to-interferences plus noise ratio (SINR) performance due to its range-dependent interference suppression capability. The proposed method can avoid undesired strong interferences and focus to the desired targets by optimizing the frequency increment via adaptive target tracking to maximize the output SINR performance. Simulation results verify that the method yields much better SINR and significantly suppresses range-dependent interferences, which is beneficial for target detection, localization and tracking applications.

Compressive sensing-based range and angle estimation for nested FDA radar

This paper proposes a nested frequency diverse array (FDA) design scheme, which jointly utilizes FDA angle-range-dependent beampattern and nested array increased degrees-of-freedom (DOFs). In this case, the commonly used MUSIC algorithm fail to estimate multiple sources because of the source correlation. Furthermore, a compressive sensing-based range and angle estimation algorithm is proposed. While compared with traditional spatial smoothing (SS) based localization method, our proposed method requires less snapshots and better performance.

FDA-MIMO Radar Range–Angle Estimation: CRLB, MSE, and Resolution Analysis

Multiple-input multiple-output (MIMO) radar enjoys the advantage of increased degrees-of-freedom and spatial diversity gain, but it cannot effectively resolves the targets closely spaced in the same angle cell (but different range cells). Frequency diverse array (FDA)-MIMO radar can handle this problem by exploiting its range-dependent beampattern. FDA-MIMO radar was, thus, suggested for range–angle estimation of targets. Nevertheless, it is necessary to provide theoretical performance analysis for such a relatively new radar technique. Since multiple signal classification (MUSIC) algorithm is widely adopted in most of the FDA-MIMO literature, this paper derives the Cramér–Rao lower bound and mean square error expressions in MUSIC-based range–angle estimation algorithms for a general FDA-MIMO radar. Furthermore, the corresponding range and angle resolution thresholds in target detection and localization are also derived. Numerical results verify that the FDA-MIMO indeed outperforms conventional MIMO radar in both range–angle estimation and resolution threshold performance.

Sparse reconstruction-based beampattern synthesis for multi-carrier frequency diverse array antenna

Frequency diverse array (FDA) antenna using uniform frequency increments produces a S-shaped range-angle-dependent transmit beampattern, which can provide more degrees of freedom (DOFs) to suppress the interferences with different ranges. However, the S-shaped beampattern is coupled in the range-angle dimension and consequently results in inaccurate target localizations. In this letter, we proposed a multi-carrier FDA scheme to generate a dot-shaped transmit beampattern. To further improve the range-angle resolution capability, the sparse reconstruction respective of compressive sensing is also adopted. Theoretical analysis and simulation results demonstrate that the proposed approach produces a decoupled high-resolution dot-shaped beampattern. Moreover, both single-dot and multi-dot shaped beampatterns can be synthesized in this way.

Bayesian information criterion for multidimensional sinusoidal order selection

Detecting the sinusoidal order is a prerequisite step for parametric multidimensional sinusoidal frequency estimation methods, whose applications range from radar and wireless communications to nuclear magnetic resonance spectroscopy. Although the Bayesian information criterion (BIC) has been commonly applied for model order selection, its application to sinusoidal order estimation is recent. By means of estimation of Fisher information matrix, we extend the 1-D BIC to multidimensional case for multidimensional sinusoidal order selection. The multidimensional BIC is shown in simulations to outperform the state-of-the-art algorithms in terms of probability of correct detection.

Optimization of frequency increments via CRLB minimization for frequency diverse array

Different from the phased-array using the same radiation frequency for each array element, frequency diverse array (FDA) uses a small frequency increment across its array elements to produce range, time and angle dependent transmit beampattern. Nevertheless, it is necessary to optimally design the frequency increments for the specific applications. In this paper, we propose a frequency increments design strategy by minimizing the Cramér-Rao lower bound (CRLB) for range and angle estimations of targets. The formulated CRLB minimization is a constrained optimization problem, which is solved by the genetic algorithm (GA). Numerical results show that, the proposed method achieves lower CRLBs for target localization and more focused transmit beampattern than existing FDA design strategies.

Tensor-based sparsity order estimation for big data applications

In Big Data Processing we typically face very large data sets that are highly structured. To save the computation and storage cost, it is desirable to extract the essence of the data from a reduced number of observations. One example of such a structural constraint is sparsity. If the data possesses a sparse representation in a suitable domain, it can be recovered from a small number of linear projections into a low-dimensional space. In this case, the degree of sparsity, referred to as sparsity order, is of high interest. It has recently been shown that if the measurement matrix obey certain structural constraints, one can estimate the sparsity order directly from the compressed data. The rich structure of the measurement matrix allows to rearrange the multiple-snapshot measurement vectors into a fourth-order tensor with rank equal to the desired sparsity order. In this paper, we exploit the multilinear structure of the data for accurate sparsity order estimation with improved identifiability. We discuss the choice of the parameters, i.e., the block size, block offset, and number of blocks, to maximize the sparsity order that can be inferred from a certain number of observations, and compare state-of-the-art order selection algorithms for sparsity order estimation under the chosen parameter settings. By performing an extensive campaign of simulations, we show that the discriminant function based method and the random matrix theory algorithm outperform other approaches in small and large snapshot-number scenarios, respectively.