Search

Deconvolution technique can be utilized in the forward-looking radar (FLR). However, the forward-looking imaging performance degenerates greatly due to the effect of high-speed movement of the platform. In this article, an efficient Bayesian forward-looking superresolution imaging algorithm based on Doppler deconvolution in expanded beam space is proposed. First, the Doppler phase information caused by the high-speed platform is fully exploited and the Doppler matrix is integrated with the antenna pattern. The Doppler convolution model of the echo signal for forward-looking is derived in this article. Then, the Doppler phase information is adopted to perform the Doppler deconvolution. Moreover, an expanded beam space is constructed to enhance the sparsity of the imaging scene. The complex Gaussian distribution and the Laplace distribution have been used to model the distribution characteristics of noise and targets in the imaging scene, respectively. Finally, based on the Bayesian framework, the forward-looking imaging problem is converted into the convex optimization problem. The performance assessment based on simulated and experimental data, also in comparison to the conventional real beam, truncated singular value decomposition (TSVD), iterative adaptive approach (IAA) methods, has demonstrated the effectiveness of our proposed algorithm under high-speed platform scenarios.

This paper addresses the adaptive detection of subspace signals in the noise whose covariance matrix is unknown. The partially homogeneous scenario, where the primary data have the same noise covariance matrix with the training data up to an unknown scaling factor is considered. We exploit the persymmetric structure of the noise covariance matrix to enhance the matched detection performance in the case of limited number of training data. Three persymmetric subspace detectors are proposed by applying the generalized likelihood ratio (GLR), Rao and Wald design criteria, respectively. It is proved that the three persymmetric subspace detectors can ensure the constant false alarm rate (CFAR) property. Experimental results show that the new persymmetric subspace detectors significantly outperform the conventional subspace detector in terms of the matched detection performance. Compared with the persymmetric rank-one signal detectors, the proposed persymmetric subspace detectors are more robust in the mismatched signal case.

This letter addresses the problem of target relocation in wide-area ground moving target indication system. The antenna pattern information is exploited as prior knowledge to enhance the performance of airborne radar. Firstly, the ideal target relocation model for moving targets based on the dual-channel radar system is analysed and the mismatch model of the receiving channel is constructed. Then the reason for poor relocation performance is analysed and the channel error model is constructed. Based on this fact, the problem of target relocation is converted into a problem of channel phase estimation. Moreover, a least-squares method is utilised to estimate the phase error by exploiting the real antenna pattern information and a modified mono-pulse curve (MPC) is derived. Finally, the real geometry location of the moving target can be achieved with the modified MPC. Airborne radar experiments are given to verify the effectiveness of the proposed algorithm.

The problem of real-time imaging in synthetic aperture radar (SAR) system is addressed here. Sub-aperture processing algorithm is usually used in the engineering applications. However, high azimuth sidelobes and edge aliasing problem usually occur in conventional sub-aperture algorithm, which degenerates the quality of the SAR image. To solve this problem, the reason for high azimuth sidelobes and edge aliasing problem is analysed, and a modified sub-aperture processing algorithm is proposed. The proposed algorithm minimises the effect of the phase discontinuity to improve the SAR image quality. Therefore, the azimuth sidelobes can be improved, and the edge aliasing phenomenon will be eliminated. Real measured airborne SAR datasets demonstrate the effectiveness of the proposed algorithm.

In order to solve synthetic aperture radar image-focus problem when space-variant motion errors could not be ignored, this study proposes a partitioned phase gradient autofocus (PGA) method. In this method, it is most important whether the dominant scatters is in the partitioned images or not. In order to find the dominant scatters, energy gradient factor is defined and energy inflection point is determined. Then, the judgment is made by energy gradient factor and its threshold. Based on measured data processing, it is indicated that the method could distinguish the partitioned images with significant dominant scatters, un-significant dominant scatters, and without dominant scatters. PGA is implemented on all blocks with significant dominant scatters and partial blocks with un-significant dominant scatters. There is no need to use PGA for the blocks without dominant scatters.

Airborne radar can be used to surveillance wide area and monitor interesting targets, which makes it a good choice for military and civil applications. However, the ground clutter is spread over a region in Doppler due to the movement of the platform, and potential slowly moving target may be obscured by the heavy clutter. To solve this problem, a knowledge-aided Space-time Adaptive Processing (KA-STAP) algorithm is proposed. In particular, the prior knowledge of clutter range-variance property as well as the information extracted from the radar data have been fully utilized. Firstly, the blocked sample selection strategy in range is utilized to decrease the range-variance effect. Then, low threshold CFAR strategy is exploited to choose proper samples in the secondary data, which is quite useful to eliminate the potential targets or jammings. By exploiting this prior knowledge information, the accuracy of the estimated covariance matrix can be well improved. Therefore, the clutter suppression performance can be enhanced in heterogeneous environment. Finally, the performance of different clutter suppression methods are analyzed by resorting to airborne experimental results, which further confirms the effectiveness of the proposed algorithm.

This paper addresses the problem of ground moving target relocation (GMTR) for airborne dual-channel wide-area radar systems. The monopulse technique can be utilized to perform GMTR. However, in real conditions, the GMTR performance degrades greatly due to the effect of channel mismatch. To tackle this problem, prior knowledge of the antenna pattern information is fully utilized to improve the GMTR performance, and a knowledge-aided GMTR algorithm (KA-GMTR) for airborne dual-channel wide-area radar is proposed in this paper. First, the GMTR model for the two receiving channels is analyzed. The channel mismatch model is constructed, and its expression is derived. Then, the channel mismatch phase error is well estimated by exploiting the prior antenna pattern information based on the least squares (LS) method. Meanwhile, the knowledge-aided monopulse curve (KA-MPC) is derived to perform the direction of arrival (DOA) estimation for potential targets. Finally, KA-GMTR, based on the KA-MPC, is performed to estimate the azimuth offsets and relocate the geometry positions of the potential targets when channel mismatch occurs. Moreover, the target relocation performance is analyzed, and the intrinsic reason that degrades the target relocation accuracy is figured out. The performance assessment based on airborne real-data, also in comparison to the conventional GMTR method, has demonstrated that our proposed KA-GMTR algorithm offers preferable target relocation results under channel mismatch scenarios.

Azimuth super-resolution is an efficient method to enhance the angular resolution of scanning radar in forward-looking area. The existing super-resolution methods are limited in a simple scene, which mainly consider a single clutter environment. For some complex scenes, the distribution of clutter is more complex than single clutter, such as sea-surface scene or the junction scene of sea-surface and ground. In this paper, we present a sparse Bayesian learning method to promote the azimuth resolution in a complex forward-looking scene. First, we use the Gaussian mixture model (GMM) to express the statistical character of the clutter and noise in the complex scene. Second, we present Laplace hierarchical prior as the prior information to model the sparse target. Then, the joint distribution of the clutter and target is derived as an optimized problem under the Bayesian framework. Finally, the solution is solved by the expectation maximization (EM) based maximum a posterior (MAP) method. The simulation and semi-real data results show that the proposed algorithm provides better angular resolution than traditional methods in complex scene.

This paper deals with the forward-looking imaging problem under high-speed platform for the airborne single-channel forward-looking radar (ASFLR). Biphase-based synthetic bandwidth azimuth modulation imaging radar (Bi-SBAMIR) can be used to increase the azimuth resolution. However, the performance degrades significantly when the velocity of the platform is high. To solve this problem, a more generalized SBAMIR framework based on linear frequency modulation (LFM) waveform (LFM-SBAMIR) is proposed. The LFM waveform is more Doppler tolerant than the biphase waveform, which makes it a good choice for high speed platform. In LFM-SBAMIR, the synthetic bandwidth of the received signal has two parts: the modulation bandwidth and the Doppler bandwidth. Therefore, large synthetic bandwidth can be achieved by increasing the modulation bandwidth even in the case of small Doppler bandwidth, and high azimuth resolution can be achieved after matched filtering. Moreover, it is firstly proved that the azimuth resolution is proportional to the time bandwidth product (TBP) of the modulated LFM waveform. Finally, the azimuth resolution performance under different TBPs, SNRs and velocities are analyzed. The performance assessment conducted by resorting to simulated data, also in comparison to the previously proposed Bi-SBAMIR method, has confirmed the effectiveness of the newly proposed algorithm in high-speed platform scenarios.

This letter addresses the problem of cross-range superresolution in Doppler beam sharpening (DBS). The coherence of echoes in the azimuth direction and the sparsity of the DBS image in the Doppler domain are fully exploited; thus, a superresolution DBS imaging framework using aperture-extrapolated sparse representation (SR) is proposed. In this framework, aperture extrapolation based on the autoregressive model is utilized to predict the forward and backward information in the azimuth direction, and SR is exploited to extract the Doppler spectrum information. In addition, the resolution ability with different coherent processing intervals is analyzed. The sharpening ratio in this proposed algorithm can be improved by a factor of two or four theoretically in comparison with the conventional DBS imaging method. Experimental results demonstrate that the proposed framework can lead to noticeable performance improvement.

In this study, the authors present an efficient time-dimension-reduced track-before-detect (TR-TBD) processor for slow-moving weak multi-targets detection in strong clutter environment. In their proposed framework, they elaborate observations from multiple frames (or scans) and resample them in time direction, then distinguish the slow-moving targets from the clutter in the radon parameter domain by exploiting the fact that different velocities of targets have different skewing angles corresponding to their tracks in the range-time (range-pulse) plane. To further enlarge the skewing angles differences between the slow-moving targets and the clutter, TR-TBD is proposed by incorporating the time-dimension reduction operator. This is very helpful to amplify the skewing angle of slow-moving targets, while the improvement is very small for the clutter. Therefore, it is much convenient to figure out the slow-moving weak targets from heavy clutter environment based on their amplified skewing angle differences by setting proper threshold. After detecting the targets, CLEAN-based track recovery method is proposed to eliminate the false tracks and recover the true tracks. Experimental results on real-data demonstrate that the proposed algorithm can detect the closely spaced targets and eliminate the false tracks under low signal-to-noise ratio and signal-to-clutter ratio.

An efficient airborne single-channel forward-looking radar (ASFLR) imaging framework to improve the azimuth resolution performance with synthetic bandwidth azimuth modulation imaging radar (SBAMIR) is proposed. During the framework, we incorporate azimuth modulation to the transmitted signal with a coded sequence in azimuth direction, and derive the SBAMIR imaging model for the first time. In SBAMIR, the synthetic bandwidth of the received echo is not only determined by the Doppler bandwidth, but also by the modulation bandwidth. Even though the Doppler bandwidth has little contribution to the synthetic bandwidth in ASFLR, the modulation bandwidth can be utilized to increase the synthetic bandwidth. Accordingly, a high azimuth resolution can be achieved after azimuth matched filtering. Achievable azimuth resolution and performance to SNR are analyzed later. Simulation results are given to verify the effectiveness of the proposed algorithm. Synthetic bandwidth azimuth modulation imaging radar (SBAMIR) is proposed.Azimuth modulation will bring in additional modulation bandwidth.Synthetic bandwidth is determined by the Doppler bandwidth and modulation bandwidth.

This study deals with the problem of adaptive detection in the presence of mismatch signals in partially homogeneous environment. To enhance the selectivity of the detector, the authors add a fictitious signal under the null hypothesis. The fictitious signal is orthogonal to the nominal signal steering vector in the quasi-whitened observation space. Two generalised likelihood ratio test-based mismatch selective detectors are derived. Simulation results show that the one-step mismatch selective detector coincides with the adaptive coherence estimator and the two-step mismatch selective detector has a better mismatch rejection capability than the generalised adaptive matched filter.

Detecting and resolving the space group targets in the main beam of radar is an urgent requirement for the air-defense and anti-missile radar system. Ground-based radar, as an important instrument for space surveillance, can be used to detect and track the space targets like grouped aircrafts, warheads and the decoys of the missiles. However, it is difficult to detect and resolve the dense targets due to the limit of the radar resolving power. To solve this problem, a space group targets detecting and resolving algorithm based on ultra-low sidelobe filtering is proposed. By exploiting the convex optimization into the pulse-Doppler radar, the problem of ultra-low sidelobe is converted into the problem of optimization. The key of this algorithm is to minimize the peak to sidelobe level (PSL) of the range sidelobes with a constraint of signal to noise ratio (SNR) loss. Then the ultra-low sidelobe filtering results are used to detect and resolve the space group targets in the main beam. Numerical and experimental results demonstrate the effectiveness of the proposed algorithm.

In Doppler beam sharpening (DBS) imaging, the imaging scene is characterised corresponding to the Doppler band, and the Doppler band occupies only a small part compared with the whole frequency domain. Accordingly, the DBS image is sparse in the frequency domain. Motivated by the sparsity, the authors propose a novel framework of DBS formation via sparse representation to perform super-resolution. In the framework, by exploiting the fact that the ground scene is sparse in frequency domain, they perform the super-resolution formation by incorporating the sparsity constraint with respect to a redundant time–frequency dictionary. The recovered sparse coefficients are utilised to form the final DBS image in frequency domain. Since the dictionary is redundant with more columns than rows, a thinner Doppler frequency resolution and a higher sharpening ratio can be achieved. Experimental results on real measured data verify the effectiveness of the new super-resolution algorithm.

A knowledge-aided mono-pulse forward-looking imaging algorithm to enhance the performance of airborne radar is proposed. Firstly, the ideal mono-pulse imaging model based on the dual-channel radar system is analysed and the mismatch model of the receiving channel is constructed. Then the mismatch phase error and a modified mono-pulse curve (MPC) are derived based on the least square (LS) method by taking advantage of a prior antenna pattern information. Finally, the modified MPC is utilised to perform the direction-of-arrival (DOA) estimation of each scattering centres in mono-pulse imaging. Real airborne radar data experiments are given to verify the effectiveness of the proposed algorithm.

The efficient scan moving target indication (MTI) mode is implemented in the wide-area ground MTI systems for the purpose of wide-area surveillance. However, because of the scanning movement of the radar antenna, it is difficult to acquire enough coherent pulses in a single azimuth direction in the Doppler beam sharpening (DBS), so the sharpening ratio is greatly limited. To mitigate this problem, a combined super-resolution algorithm with aperture extrapolation for DBS imaging is proposed to enhance the sharpening ratio in this study. In this algorithm, aperture extrapolation technique is utilised to increase the data length in azimuth direction and the amplitude and phase estimation of a sinusoid, which can acquire accurate spectral estimation with much lower side lobes and narrower spectral peaks, is applied to replace the fast Fourier transform to perform the Doppler analysis. In this way, the sharpening ratio in the new algorithm could be enhanced by one time. Experimental results on simulation data and real data verify the effectiveness of the new super-resolution algorithm, and demonstrate that the proposed algorithm can provide sharp and clear scene information with lower side lobes and narrower peaks.

This paper deals with the problem of high cross-range resolution Doppler beam sharpening (DBS) imaging for airborne wide-area surveillance (WAS) radar under short dwell time situations. A knowledge-aided DBS (KA-DBS) imaging algorithm is proposed. In the proposed KA-DBS framework, the DBS imaging model for WAS radar is constructed and the cross-range resolution is analyzed. Since the radar illuminates the imaging scene continuously through the scanning movement of the antenna, there is strong spatial coherence between adjacent pulses. Based on this fact, forward and backward pulse information can be predicted, and the equivalent number of pulses in each coherent processing interval (CPI) will be doubled based on the autoregressive (AR) technique by taking advantage of the spatial continuity property of echoes. Finally, the predicted forward and backward pulses are utilized to merge with the initial pulses, then the newly merged pulses in each CPI are utilized to perform the DBS imaging. Since the number of newly merged pulses in KA-DBS is twice larger than that in the conventional DBS algorithm with the same dwell time, the cross-range resolution in the proposed KA-DBS algorithm can be improved by a factor of two. The imaging performance assessment conducted by resorting to real airborne data set, has verified the effectiveness of the proposed algorithm.

The triplet Markov field (TMF) model recently proposed is suitable for tackling the nonstationary image segmentation. In this letter, we propose a hierarchical TMF (HTMF) model for unsupervised synthetic aperture radar (SAR) image segmentation. In virtue of the Bayesian inference on the quadtree, the HTMF model captures the global and local image characteristics more precisely in the bottom-up and top-down probability computations. In this way, the underlying spatial structure information is effectively propagated. To model the SAR data related to radar backscattering sources, generalized Gamma distribution is utilized. The effectiveness of the proposed HTMF model is demonstrated by application to simulated data and real SAR image segmentation.

The stitching quality of Doppler Beam Sharpening (DBS) image will be greatly affected due to the non-ideal movement of the radar platform. A novel method based on affine transformation is proposed, which can correct and stitch the DBS image effectively. The change of attitude of the carrier aircraft could be corrected with rotation angle and the change of position of the carrier aircraft could be corrected with offset with the help of the affine transformation. Processing results of measured data show effectiveness of the method. (4 pages)