Synthetic Aperture Radar is widely used in civil and military fields, and there are many efficient digital imaging algorithms in synthetic aperture radar imaging field. While in practice, the phase error in the echo signal will lead to the degradation of the imaging quality. In addition, with the development of high-resolution synthetic aperture radar, the traditional motion compensation is not enough to meet the imaging requirements. Based on the existing research results at home and abroad, this paper proposes a fine phase error compensation technology of synthetic aperture radar adaptive optical imaging system. In this paper, the application of deformable mirror and wavefront sensor in synthetic aperture radar optical imaging system is studied. The wavefront sensor detects the distortion generated in the radar echo data and sends the distortion signal to the control computer. The control computer calculates the conjugate signal according to the distorted wavefront phase and sends it to the deformable mirror, which conducts distortion correction according to the signal sent by the control computer. Furthermore, the deformable mirror adjusts the surface shape in real time through the wavefront phase feedback of the wavefront sensor to reduce the phase error, which is of great significance for improving the imaging quality and improving the adaptive ability of the imaging system.
Due to the presence of atmospheric turbulence, motion error (ME) arises and causes residual azimuth phase error (APE) during synthetic aperture radar (SAR) data acquisition. APE can degrade SAR images, especially for light-weight SAR. Moreover, different kinds of APE have different impacts on the image, which makes it hard to compensate for. A parametric autofocus based on a cost metric consisting of the modified entropy and the residual entropy (MERE) is developed to compensate the APE. This approach using the optimization transfer method aims to minimize the MERE. The polynomial decomposition is applied to fit the low-order APE while inverse discrete cosine transform model is adopted for the high-frequency case. Additionally, we also design a modified adaptive-order search strategy, and it helps to remarkably reduce the computational load while maintaining accuracy. In the case of correcting high-frequency APE, the MERE metric could effectively avoid the over-fitted problem that arises in entropy-based autofocus. The real airborne SAR data experiments and comparisons demonstrate the validity and effectiveness of the proposed autofocus.
The problem of waveform optimization design for cognitive radar (CR) in the presence of extended target with unknown target impulse response (TIR) is investigated. On the premise of ensuring the TIR estimation precision, a flexible waveform-constrained optimization design method taking both target detection and range resolution into account is proposed. In this method, both the estimate of TIR and transmitted waveform can be updated according to the environment information fed back by the receiver. Moreover, rather than optimizing waveforms for a single design criterion, the framework can synthesize waveforms that provide a trade-off between competing design criteria. The trade-off is determined by the parameter settings, which can be adjusted according to the requirement of radar performance in each cycle of CR. Simulation results demonstrate that CR with the proposed waveform performs better than a traditional radar system with a fixed waveform and offers more flexibility and practicability.
Multichannel synthetic aperture radar systems are usually employed to suppress azimuth ambiguity and realize high-resolution wide-swath imaging. However, unavoidable array errors will significantly degrade the performance of ambiguity suppression and imaging quality. This paper presents an array error estimation method based on cross correlation. First, unambiguous Doppler spectra are obtained by selecting a short length of range profiles from strong targets. Then, array errors are estimated by a proposed cross-correlation method. Finally, a preprocessing method to improve the estimation accuracy is proposed. The proposed method takes full advantage of the training samples from strong targets and estimates array errors by the coherent integration technique, which improves the estimation accuracy and robustness. Theoretical analysis and experiments based on simulations and measurements showed the validity of the proposed method, especially in low signal-to-noise ratios.
The problem of adaptive waveform design for target detection in cognitive radar (CR) is investigated. This problem is analyzed in signal-dependent interference, as well as additive channel noise for extended target with unknown target impulse response (TIR). In order to estimate the TIR accurately, the Kalman filter is used in target tracking. In each Kalman filtering iteration, a flexible online waveform spectrum optimization design taking both detection and range resolution into account is modeled in Fourier domain. Unlike existing CR waveform, the proposed waveform can be simultaneously updated according to the environment information fed back by receiver and radar performance demands. Moreover, the influence of waveform spectral phase to radar performance is analyzed. Simulation results demonstrate that CR with the proposed waveform performs better than a traditional radar system with a fixed waveform and offers more flexibility and suitability. In addition, waveform spectral phase will not influence tracking, detection, and range resolution performance but will greatly influence waveform forming speed and peak-to-average power ratio.
We investigate optimal waveform design using fractional Fourier transform in signal-dependent interference, as well as additive channel noise for stochastic extended target. Within constraints on waveform energy and bandwidth, optimal waveform design in fractional Fourier domain based on the signal-to-interference-plus-noise ratio criterion, probability of detection criterion, and mutual information criterion are modeled, respectively. In addition, the relationship between the optimal waveforms that are designed based on the three criteria is discussed. Simulations are conducted to illustrate that for all of the three criteria, the energy of optimal waveform can be distributed in some narrow bands where the target power is large and the interference power is small in fractional Fourier domain. Finally, the fractional Fourier domain waveform design method is proved more flexible and effective than traditional Fourier domain waveform design method, especially when the spectral density of target response and interference are relatively dispersed and flat.
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.
KEYWORDS: Radar, Signal detection, Target detection, Interference (communication), Signal to noise ratio, Doppler effect, Radar signal processing, Detection and tracking algorithms, Signal processing, Receivers
An optimal radar waveform-design method is proposed to detect moving targets in the presence of clutter and noise. The clutter is split into moving and static parts. Radar-moving target/clutter models are introduced and combined with Neyman–Pearson criteria to design optimal waveforms. Results show that optimal waveform for a moving target is different with that for a static target. The combination of simple-frequency signals could produce maximum detectability based on different noise-power spectrum density situations. Simulations show that our algorithm greatly improves signal-to-clutter plus noise ratio of radar system. Therefore, this algorithm may be preferable for moving target detection when prior information on clutter and noise is available.
A computational method for suppressing clutter and generating clear microwave images of targets is proposed in this paper, which combines synthetic aperture radar (SAR) principles with recursive method and waveform design theory, and it is suitable for SAR for special applications. The nonlinear recursive model is introduced into the SAR operation principle, and the cubature Kalman filter algorithm is used to estimate target and clutter responses in each azimuth position based on their previous states, which are both assumed to be Gaussian distributions. NP criteria-based optimal waveforms are designed repeatedly as the sensor flies along its azimuth path and are used as the transmitting signals. A clutter suppression filter is then designed and added to suppress the clutter response while maintaining most of the target response. Thus, with fewer disturbances from the clutter response, we can generate the SAR image with traditional azimuth matched filters. Our simulations show that the clutter suppression filter significantly reduces the clutter response, and our algorithm greatly improves the SINR of the SAR image based on different clutter suppression filter parameters. As such, this algorithm may be preferable for special target imaging when prior information on the target is available.
The performance of synthetic aperture radar (SAR) image interpretation directly depends on the image quality. However, conventional SAR image-quality indicators are measured to check whether the SAR system has maintained its performance specifications, not to assess how well the SAR image can serve image interpretation. An SAR image-quality assessment method based on the modulation transfer function (MTF) is proposed for image interpretation, which jointly reflects the resolution and the contrast of the SAR image. In addition, it describes the imaging performance of the SAR system at different spatial frequencies. Specifically, we propose a feasible MTF test field and realize it in Shanghai Jiao Tong University, Shanghai, China. Then, we give the MTF measurement procedure and utilize the MTF curve to evaluate SAR image quality. Simulation results demonstrate that the proposed MTF-based method is more accurate to assess the SAR image quality than the conventional methods. In addition, the spaceborne SAR experiments are carried out by the TerraSAR-X sensor and the experimental results are given to confirm the benefits of the proposed method.
With the high programmability of a spatial light modulator (SLM), a newly developed synthetic aperture radar (SAR) optronic processor is capable of focusing SAR data with different parameters. The embedded SLM, encoding SAR data into light signal in the processor, has a limited loading resolution of 1920×1080. When the dimension of processed SAR data increases to tens of thousands in either range or azimuth direction, SAR data should be input and focused block by block. And then, part of the imaging results is mosaicked to offer a full-scene SAR image. In squint mode, however, Doppler centroid will shift signal spectrum in the azimuth direction and make phase filters, loaded by another SLM, unable to cover the entire signal spectrum. It brings about a poor imaging result. Meanwhile, the imaging result, shifted away from the center of light output, will cause difficulties in subsequent image mosaic. We present an SAR image formation algorithm designed to solve these problems when processing SAR data of a large volume in low-squint case. It could not only obtain high-quality imaging results, but also optimize the subsequent process of image mosaic with optimal system cost and efficiency. Experimental results validate the performance of this proposed algorithm in optical full-scene SAR imaging.
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