Yesheng Gao

Improved Channel Error Calibration Algorithm for Azimuth Multichannel SAR Systems

Multichannel synthetic aperture radar systems in azimuth can effectively suppress azimuth ambiguity and are promising in high-resolution wide-swath imaging. However, unavoidable channel errors will significantly degrade the performance of ambiguity suppression. Conventional subspace calibration methods usually estimate phase error via decomposing a Doppler-variant covariance matrix from one Doppler bin, and then average these errors estimated from several Doppler bins to improve the estimation accuracy, which will result in a large computational load. This letter presents an improved channel error calibration method, which works on the undersampled data of the individual azimuth channel. By a proposed matrix transformation method, the Doppler-variant covariance matrices will be transformed into a constant covariance matrix. Therefore, the improved calibration algorithm needs to estimate and decompose the new covariance matrix only once. The computation load could be greatly reduced. Moreover, the new covariance matrix can be estimated by training samples not only from range bins but also from Doppler bins, which will improve the estimation accuracy. Theoretical analysis and experiments based on simulations and measurements showed the high accuracy, efficiency, and robustness of the improved method, particularly in low signal-to-noise ratio.

An automatic SAR-GMTI algorithm based on DPCA

An automatic DPCA technique is presented for SAR Ground Moving Target Indication (GMTI). We note that there exists a shift and a phase difference between the images from two channels. Therefore, SAR-GMTI can be implemented in the following steps: Image registration, phase compensation, image subtraction, and CFAR detection. In our technique, these steps are carried out automatically, and thus no precise information is needed about the length of the baseline and the velocity of the platform. We utilize a set of real data to demonstrate the accuracy and the robustness of our algorithm.

Optimal waveform design oriented toward cognitive radar in fractional Fourier domain

In this paper the problem of waveform design using Fractional Fourier Transform (FRFT) in signal-dependent interference, as well as additive channel noise for stochastic extended target is investigated. Within constraints on waveform energy and duration, the optimum waveform design in fractional Fourier domain based on the signal to interference plus noise ratio (SINR) criterion is modeled. Simulations conducted to illustrate that by changing angle variable, the energy of optimal waveform designed in fractional Fourier domain can be distributed in some narrow bands where the target power is large and the interference power is small. In addition, the waveform designed in fractional Fourier domain is proved more flexible and effective than that in Fourier domain, especially when the spectral density of target response and interference are relatively dispersed and flat.

Modified reconstruction algorithm based on space-time adaptive processing for multichannel synthetic aperture radar systems in azimuth

Abstract. A spectrum reconstruction algorithm based on space–time adaptive processing (STAP) can effectively suppress azimuth ambiguity for multichannel synthetic aperture radar (SAR) systems in azimuth. However, the traditional STAP-based reconstruction approach has to estimate the covariance matrix and calculate matrix inversion (MI) for each Doppler frequency bin, which will result in a very large computational load. In addition, the traditional STAP-based approach has to know the exact platform velocity, pulse repetition frequency, and array configuration. Errors involving these parameters will significantly degrade the performance of ambiguity suppression. A modified STAP-based approach to solve these problems is presented. The traditional array steering vectors and corresponding covariance matrices are Doppler-variant in the range-Doppler domain. After preprocessing by a proposed phase compensation method, they would be independent of Doppler bins. Therefore, the modified STAP-based approach needs to estimate the covariance matrix and calculate MI only once. The computation load could be greatly reduced. Moreover, by combining the reconstruction method and a proposed adaptive parameter estimation method, the modified method is able to successfully achieve multichannel SAR signal reconstruction and suppress azimuth ambiguity without knowing the above parameters. Theoretical analysis and experiments showed the simplicity and efficiency of the proposed methods.

An automatic and programmable optical SAR data processor

Synthetic Aperture Radar now is widely used in remote imaging which have many kinds of system. But as SAR data become larger and larger, the traditional Digital Signal Processing processor can not afford the high speed and high resolution process in rated size and power. But our optical processor for SAR can finish the data process in high speed because it can perform the Fourier transformation at the speed of light with low power consumption. Besides, this system is automatic and programmable which can be realized by computer and spatial light modulator.

Estimating target-induced azimuth envelope for SAR image formation and feature extraction

Conventional SAR imaging algorithms form SAR image based on imaging geometries, the resulting image indicates the spatial location of illuminated targets with respect to radar platform. Scattering characteristics of targets are overlooked by these algorithms. We construct a model of received SAR signal in terms of (1) envelope, (2) phase, (3) duration, (4) arrival time, (5) frequency center and (6) chirp rate, and the envelope shape can be controlled by two parameters. Arrival time, frequency center and chirp rate indicate the spatial location and motion dynamics of the targets, while information of scattering characteristics can be analyzed from the other parameters. The parameters of the model are then estimated via atomic decomposition. In this paper, the impact of target-induced envelope on data focusing is analyzed, furthermore, target feature can be extracted from a signal-level point of view.

Suppression of Azimuth Ambiguities of Strong Point-Like Targets for Multichannel SAR Systems

Multichannel synthetic aperture radar (SAR) signal reconstruction methods can effectively suppress azimuth ambiguities and achieve high-resolution wide-swath imaging. However, due to the characteristics of the practical antenna patterns, there exist non-bandlimited Doppler spectra, which will result in residual azimuth ambiguities, especially for strong targets. This letter presents a novel method for the suppression of the azimuth ambiguities of the strong point-like targets. First, we find out the positions of the strong point-like targets from the multichannel reconstructed SAR image. Then, we locate the ambiguous range history of each strong point-like target. Finally, the ambiguous components in the range history are filtered out by an orthogonal projection method. Therefore, the spectra of the strong point-like targets will be converted into the bandlimited spectra, and then, the azimuth ambiguities can be effectively suppressed by the conventional multichannel SAR signal reconstruction methods. Theoretical analysis and experiments demonstrate the feasibility of the proposed methods.

Robust Channel Phase Error Calibration Algorithm for Multichannel High-Resolution and Wide-Swath SAR Imaging

High-resolution and wide-swath synthetic aperture radar (SAR) imaging can be achieved by the azimuth multichannel system. The minimum variance distortionless response (MVDR) beamformer can be utilized to suppress azimuth ambiguities. However, the presence of channel phase errors significantly deteriorates the performance of the azimuth multichannel SAR system. Instead of employing subspace techniques, this letter proposes a robust channel phase error calibration algorithm via maximizing the MVDR beamformer output power. Compared with the conventional subspace-based calibration methods, there is no redundancy of channels required to estimate the subspaces in the proposed algorithm. Also, the proposed algorithm is relatively robust, because it avoids the subspace swap phenomenon, which probably takes place at low signal-to-noise ratios for the subspace techniques. Moreover, the proposed method has the advantage of estimating the channel phase errors without covariance matrix decomposition, which reduces the computation load. The simulation experiments and the real data processing validate the effectiveness of the proposed calibration method.

A optronic SAR processor with high-speed and high-precision phase modulation

Synthenic Aperture Radar (SAR) has an important role in the field of remote sensing. As optical SAR data processor has the outstanding advantage of high speed, it performs processing prospect in real-time SAR imaging field. Liquid crystal based spatial light modulators (SLMs) are widely used to make phase modulation in optical SAR processor. However, in some phase-sensing or real-time applications such as SAR imaging process, low phase precision of SLM may cause phase error and low data refresh rate of SLM may restrict processing speed. This paper proposes a new optical SAR data processor with high-speed and high-precision phase modulation. The core of this optical processor is a phase modulation module by using a digital micromirror device (DMD) which can achieve high-speed and high-precision phase modulation because of its high data refresh rate of 9500Hz and phase resolution of 0.002 rad. The principle of phase modulation with DMD and the structure of the new SAR optical processor are presented. The images processed by it are also presented.

Optimal radar waveform design for moving target

Radar performance improvement through waveform optimization has been an ongoing topic of research recent years. In this paper, we use the optimal waveform design method to deal with the moving target in the clutter and noise. Neyman-Pearson detector criterion is used to maximize the probability of target detection. The optimal waveform is then designed theoretically corresponding to the velocity of target and clutter/noise power spectrum density. Simple CW signals can produce maximum detectability based on different noise PSD situations. Simulated results are presented based on our method and improvement in image is approached. Finally, the conclusions are drawn based on our analysis and simulations.