Deep learning technology has been widely used in synthetic aperture radar automatic target recognition (SAR ATR) tasks due to its good performance and high efficiency. However, the development of semiconductor technology cannot completely satisfy the demand for electronic hardware with high computational capability to conduct deep neural networks. To solve this dilemma, we propose an optronic convolutional neural network (OPCNN) that can perform SAR ATR tasks directly in optical platform. In OPCNN, all computational operations are implemented in optics with high speed and low energy cost. Electronic platforms are used only to control devices and transmit data information without a massive computational burden. As in digital CNNs, the convolutional layer, downsampling layer, nonlinear activation layer, and fully connected layer are all contained in OPCNN. The simulations demonstrate the feasibility of our OPCNN in solving SAR ATR problems. The good performance in experiments, which achieves 87.4% and 93.8% recognition accuracy on original and denoised moving and stationary target acquisition and recognition dataset, validates the application ability of OPCNN in practical scenario. In one-time recognition tasks, the processing time of our OPCNN is only 0.26 s with the speed of light and the power consumption of our prototype is also far less than digital processer, which is 954 W. Through analysis, our OPCNN obtains the higher processing speed and lower energy cost than digital CNNs with the same structure due to the advantages of optical technology. Also, the scalability of optical structure contributes to build more complex networks to solve complicated dataset without the demand of advanced electronic hardware.
Scattering media would scratch light propagation, and images would degenerate into unrecognizable speckle patterns. Conventional target recognition through scattering media is composed of two steps, i.e., reconstruction and recognition. Here, combining the compressive sensing with feature extraction, a method of efficient speckle based compressive target recognition through scattering media is proposed. In the paper, autocorrelation of speckles is proved to have the same singular values as that of their corresponding objects, and then speckle based recognition is introduced. Compressive sensing can be used to retrieve signals with measurements fewer than those required by Nyquist-Shannon theory. With the proposed method, scattered object recognition can be replaced with speckle recognition, bypassing the conventional object reconstruction procedure. Performances are validated through relevant experiments. Besides, benefited from the conclusion, domain adaption based support vector regression method is proposed and utilized for imaging through scattering media then. Domain adaption is introduced to transfer leaning samples and testing samples into a new space where the distance between them is much closer, leading to high reconstruction fidelity in the followed support vector regression based inverse scattering stage. Principle component analysis is also considered to help decrease dimension and thus improving efficiency. Experiments validate that the presented technique owns a higher image reconstruction efficiency and fidelity, compared with our previous researches. Since the target recognition and reconstruction is mainly based on ground truth images, the work is valuable and meaningful for remote sensing applications, especially for object detection or monitoring when scattering is occurred.
A clear image of an observed object may deteriorate into unrecognizable speckle when encountering heterogeneous scattering media, thus it is necessary to recover the object image from the speckle. A method combining least square and semidefinite programming is proposed, which can be used for imaging through scattering media. The proposed method consists of two main stages, that is, media scattering characteristics (SCs) estimation and image reconstruction. SCs estimation is accomplished through LS concept after establishing a database of known object-and-speckle pairs. Image reconstruction is realized by solving an SDP problem to obtain the product of the unknown object image and its Hermitian transposition. Finally, the unknown object image can be reconstructed by extracting the largest rank-1 component of the product. Structural similarity (SSIM) index is employed as a performance indicator in speckle prediction and image reconstruction. Numerical simulations and physical experiments are performed to verify the feasibility and practicality of the proposed method. Compared with the existing phase shift interferometry mean square optimization method and the single-shot phase retrieval algorithm, the proposed method is the most precise to obtain the best reconstruction results with highest SSIM index value. The work can be used for exploring the potential applications of scattering media, especially for imaging through turbid media in biomedical, scattering property measurement, and optical image encryption.
Motion compensation (MOCO) is an essential procedure for synthetic aperture radar under a complicated flight path. Wide swath and low-precision navigation data limit traditional MOCO algorithms. We present a space-variant MOCO method to solve two problems: (1) estimation of the deviations of radar during data acquisition and (2) range envelope correction (REC) before range cell migration correction. In the first problem, we apply phase-gradient autofocus with correlate input and weighted total least square jointly. In the second one, the chirp-scaling-based (CS) REC is proposed. Finally, both simulation and real data experiments validate the proposed method.
Scattering media would deteriorate an object image into unrecognizable speckle pattern. Machine learning is introduced to reconstruct the object image from speckle pattern. In the proposed method, a database containing two groups (i.e., face image-and-speckle-pattern pairs, non-face image-and-speckle-pattern pairs) is firstly established. Then support vector classification (SVC) is introduced to classify a given unknown speckle pattern into which group it belongs to. Taking advantage of support vector regression (SVR), the object image corresponding to the unknown speckle pattern can be reconstructed. Experiments are conducted to verify the effectiveness of the proposed method, as well as the necessity of the introduction of SVC.
Target object image would deteriorate into unrecognizable speckle pattern when encountering with scattering media. It’s necessary to reconstruct the target object image from captured speckle. In this paper, a method combining correlation method and oversampling smoothness is proposed. It is used for target object reconstruction from scattered speckle pattern. The reconstruction is based on the Fourier transform of the target object. The Fourier amplitude of the target object can be calculated through an inverse Fourier transform of the autocorrelation of captured speckle pattern. The Fourier phase can be recovered with oversampling smoothness method. Experiments were used to comparing the proposed C-OSS method with the C-ERHIO method. The results indicate that the proposed method improves the reconstruction with lower background components than the other. The proposed method can also be applied to optical image encryption et al.
A scheme is presented for ground moving target indication in multichannel synthetic aperture radar (SAR) systems. After the effect of different Doppler centroids is compensated, the spatial spectrum is symmetric around zero for a stationary scatterer but asymmetric around zero for a moving scatterer. Thus, moving targets can be detected through measuring the asymmetry of their spatial spectra. Moreover, since the asymmetry of the spatial spectrum can be measured directly in the spatial domain, the procedure of estimating the spatial spectrum is avoided and the computational efficiency is improved greatly. The effectiveness of this scheme is verified by both simulated and real SAR data.
Convolutional neural network (CNN), as a vital part of the deep learning research field, has shown powerful potential for automatic target recognition (ATR) of synthetic aperture radar (SAR). However, the high complexity caused by the deep structure of CNN makes it difficult to generalize. An improved form of CNN with higher generalization capability and less probability of overfitting, which further improves the efficiency and robustness of the SAR ATR system, is proposed. The convolution layers of CNN are combined with a two-dimensional principal component analysis algorithm. Correspondingly, the kernel support vector machine is utilized as the classifier layer instead of the multilayer perceptron. The verification experiments are implemented using the moving and stationary target acquisition and recognition database, and the results validate the efficiency of the proposed method.
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 configuration of multiple uniformly spaced channels in azimuth can achieve high-resolution and wide-swath images for synthetic aperture radar (SAR) systems. The unambiguous Doppler spectrum can be reconstructed via digital beamforming (DBF) techniques in the azimuth multichannel SAR system. However, the performance of DBF deteriorates significantly because of the inevitable channel errors in practical applications. Since the SAR antennas are generally unweighted in azimuth, the power of the SAR signal is symmetrically distributed around the Doppler centroid in the azimuth frequency domain. Based on this observation, the covariance matrix of multichannel output in the range-Doppler domain can be regarded as a real matrix when there is no existence of channel errors and the correction of the baseband Doppler centroid is applied. Consequently, we propose a fast calibration algorithm to calculate channel phase errors. The proposed method can acquire the estimation of phase errors directly from the covariance matrix. Thus, the proposed method has the properties of high robustness and low computation load. Theoretical analysis and experiments validate the performance and the efficiency of the proposed algorithm.
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.
A scheme is presented for ground moving target indication with multichannel synthetic aperture radar (SAR). After the effects of different phase centroids are compensated, the images from different channels are used to form an interferogram. Then the probability distribution function of the interferometric phase is estimated as a function of the interferometric magnitude, thus the detection threshold is derived as a function of the interferometric magnitude. Since the detection threshold is magnitude dependent, this scheme has a better performance than the conventional schemes, especially for slow, weak moving targets. The effectiveness of this scheme is verified by both simulated and real SAR data.
A scheme is presented in this paper for ground moving target indication of multichannel synthetic aperture radar (SAR) systems. “Dominant-velocity” is chosen as a valuable metric to describe the velocity map of the observed scene and the “dominant-velocity image” (DVI) can be generated via the developed spatial spectral processing technique. The mean μ and the standard deviation σ of each dominant-velocity are estimated from its neighborhood. Two different methods are proposed to derive the detection threshold in the (μ,σ) plane: one is a nonparametric histogram approximation approach and the other is a parametric polynomial curve-fitting approach. The proposed ground moving target indication approach is a multistage one: the first stage implements the preliminary detection in the (μ,σ) plane and a clustering technique is utilized to indicate potential moving targets, while the second stage implements the fine detection via a velocity estimation method based on maximum signal-to-interference ratio for the tagged targets. Finally, the effectiveness of the proposed method is verified by both simulated and real airborne SAR data.
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.
This paper presents a novel simulator to obtain single-look complex (SLC) image pair from the distributed target for
interferometric synthetic aperture radar (InSAR). From conventional works, two simulation levels are derived: one is
raw signal level (RSL) which means using raw signal to obtain SLC image pairs, the other is SLC image level (SIL)
which means obtaining the SLC image pairs directly from existing SAR images. Conventional simulators only work on
one simulation level, use complicated backscattering models, have high computational load on RSL and mismatch the
real data on SIL. The novel simulator can robustly work on both RSL and SIL. It not only simplified the backscattering
model, but also reduces the computational load on RSL. Moreover, the novel simulator creatively uses complex
backscattering coefficient (CBC) pair to generate SLC image pair on SIL, which makes the result more accurately
match real data. Finally, the improvements of this novel simulator are demonstrated by experimental results.
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