Tianyao Huang

Cognitive random stepped frequency radar with sparse recovery

Random stepped frequency (RSF) radar, which transmits random-frequency pulses, can suppress the range ambiguity, improve convert detection, and possess excellent electronic counter-countermeasures (ECCM) ability [1]. In this paper, we apply a sparse recovery method to estimate the range and Doppler of targets. We also propose a cognitive mechanism for RSF radar to further enhance the performance of the sparse recovery method. The carrier frequencies of transmitted pulses are adaptively designed in response to the observed circumstance. We investigate the criterion to design carrier frequencies, and efficient methods are then devised. Simulation results demonstrate that the adaptive frequency-design mechanism significantly improves the performance of target reconstruction in comparison with the nonadaptive mechanism.

Adaptive matching pursuit with constrained total least squares

Compressive sensing (CS) can effectively recover a signal when it is sparse in some discrete atoms. However, in some applications, signals are sparse in a continuous parameter space, e.g., frequency space, rather than discrete atoms. Usually, we divide the continuous parameter into finite discrete grid points and build a dictionary from these grid points. However, the actual targets may not exactly lie on the grid points no matter how densely the parameter is grided, which introduces mismatch between the predefined dictionary and the actual one. In this article, a novel method, namely adaptive matching pursuit with constrained total least squares (AMP-CTLS), is proposed to find actual atoms even if they are not included in the initial dictionary. In AMP-CTLS, the grid and the dictionary are adaptively updated to better agree with measurements. The convergence of the algorithm is discussed, and numerical experiments demonstrate the advantages of AMP-CTLS.Compressive sensing (CS) can effectively recover a signal when it is sparse in some discrete atoms. However, in some applications, signals are sparse in a continuous parameter space, e.g., frequency space, rather than discrete atoms. Usually, we divide the continuous parameter into finite discrete grid points and build a dictionary from these grid points. However, the actual targets may not exactly lie on the grid points no matter how densely the parameter is grided, which introduces mismatch between the predefined dictionary and the actual one. In this article, a novel method, namely adaptive matching pursuit with constrained total least squares (AMP-CTLS), is proposed to find actual atoms even if they are not included in the initial dictionary. In AMP-CTLS, the grid and the dictionary are adaptively updated to better agree with measurements. The convergence of the algorithm is discussed, and numerical experiments demonstrate the advantages of AMP-CTLS.

Fundamental Limits of HRR Profiling and Velocity Compensation for Stepped-Frequency Waveforms

A stepped-frequency (SF) waveform is effective in achieving high-range resolution (HRR) in modern radars. In this paper, we determine various fundamental limits of the SF waveform regarding the ambiguity, stability, and accuracy of stationary target profiling and the velocity compensation accuracy for moving targets. The investigation reveals that by using the information contained in both the phase and envelope of the echo signal, SF radars can achieve HRR profiles without ambiguity under a looser criterion and can compensate the range shift caused by a targets radial velocity. The results of this paper can aid in SF waveform design and in the development of processing algorithms for HRR profiling and velocity compensation.

Randomized stepped frequency ISAR imaging

Linearly stepped frequency (LSF) waveforms are widely equipped in inverse synthetic aperture radar (ISAR), where the frequency is linearly altered with a fixed frequency step in each burst. ISAR employing randomized stepped frequency (RSF) modulation improves the rang-Doppler resolution, widens the unambiguous Doppler window, and enhances performance in electronic counter-countermeasures (ECCM). However in RSF radar, range profiles can severely spread when the radial velocity is unknown. In this paper, we analytically derive the return signal model of a target rectilinearly moving at invariable speed, and devise a new algorithm to estimate the velocity and reconstruct the image of target. Numerical results demonstrate the effectiveness of the algorithm.

Adaptive Compressed Sensing via Minimizing Cramer–Rao Bound

This letter considers the problem of observation strategy design for compressed sensing. An adaptive method, based on Cramer-Rao bound minimization, is proposed to design the sensing matrix. Simulation results demonstrate that the adaptively constructed sensing matrix can lead to much lower recovery errors than those of traditional Gaussian matrices and some existing adaptive approaches.

Randomized step frequency radar with adaptive compressed sensing

We develop a novel algorithm for range and velocity joint estimating in randomized stepped frequency radar. By exploiting sparseness of the targets and invoking compressed sensing (CS) theory, higher resolution can be achieved. However, in actual radar system, some previous CS methods probably suffer from off-grid point effect, which causes performance degradation of the methods. We produce an adaptive CS method to mitigate the off-grid point effect. The performance of the proposed algorithm on accuracy and resolution of range-velocity joint estimate are illustrated by simulations and field experiments.

A novel target motion compensation method for randomized stepped frequency ISAR

In this paper, we focus on motion compensation of moving target with a constant acceleration in randomized stepped frequency inverse synthetic radar (RSF ISAR). A novel target motion compensation method called minimum entropy of accumulated Doppler spectrum method is proposed. The method compensates the quadratic phase first, and then estimates radial velocity. The method can overcome the influence on radial velocity estimation caused by radial acceleration, and keep the benefit of overcome range profile de-focus problem. Simulation results show that the proposed method is effective for motion compensation and robust to the signal-noise ratio (SNR).