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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.

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 problem of adaptive detection of subspace signals embedded in thermal noise and clutter that depends on the transmitted signal. To this end, at the design stage, we assume that the signal-dependent (SD) clutter shares the same subspace as the target signals. As customary, a set of secondary data, free of signal components, is also assumed available. Two adaptive detectors, referred to as the SD Rao and SD Wald, are proposed by resorting to the Rao test and Wald test design criteria. Unlike the classical Rao and Wald tests, which are derived by dividing the complex parameter into the real and imaginary parts, the proposed detectors treat the complex parameter as a single quantity to reduce the computational burden. Moreover, we derive the theoretical false alarm probabilities and detection probabilities and show that both the two proposed detectors exhibit the constant false alarm rate property. Numerical results show that the proposed detectors achieve a detection performance improvement over the conventional multidimensional detectors.

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 paper deals with the problem of detecting distributed target in Gaussian disturbance with unknown but persymmetric structured covariance matrix. The partially-homogeneous environment is considered and two receivers based on the Rao test and the Wald test design criteria are derived at the design stage. The performance assessment conducted by resorting to both simulated data and real data, also in comparison to the previously proposed detectors, has confirmed the effectiveness of the newly proposed detectors. Two detectors based on the Rao and Wald tests are devised.The disturbance has unknown but persymmetric structured covariance matrix.The proposed detectors achieve matched detection performance improvement.

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.

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.

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.