Due to existence of the long along-track baseline (ATB) among the different satellites in a distributed space-based radar (DSBR) system, a large number of grating lobes appear in radar returns, causing the non-continuous detection phenomenon of an air moving target (AMT). To solve this problem, in this paper, a novel approach for ATB distribution design in a DSBR system is proposed. In the proposed algorithm, to reduce the amount of spatial ambiguity points located at the main-lobe region and achieve the best air moving target indication (AMTI), the maximum target detectability ratio (TDR) in the main-lobe region is chosen to be the criteria for the optimal ATB design. The effectiveness of the proposed method is verified by the simulated multi-channel radar data in a DSBR system.

Employing azimuth multi-channel array configuration can effectively enhance the high-resolution and wide swath - synthetic aperture radar (HRWS-SAR) imaging performance by increasing the spatial sampling frequency along the azimuth direction. 1However, the ground moving targets with unknown radial velocities, may induce azimuth "ghost" points in a well-focused HRWS-SAR image with the imaging function related to ground static scene. To address this issue, the paper introduces a novel ground moving target imaging method in a multi-channel HRWS-SAR system. The multichannel HRWS-SAR simulation results verify the effectiveness and feasibility of the proposed method.

Mosaic mode synthetic aperture radar (SAR) combines the spotlight or sliding spotlight mode with a ScanSAR mode to accomplish the high azimuth resolution and large imaging swath SAR imaging. This paper studies the problem of the frequency modulated continuous wave (FMCW) SAR imaging with a Mosaic mode, where the imaging algorithms for pulse radars are not suitable for a FMCW SAR. To deal with this issue, this paper proposes a FMCW Mosaic mode SAR imaging algorithm based on modified polar formation algorithm (PFA), which considers the radar motion during the signal transmission and performs the SAR imaging operation for spotlight mode in each Mosaic unit. The simulation results of point targets verify the effectiveness and feasibility of the proposed method, which may provide a valuable reference for the application of Mosaic system to miniaturized platforms.

Compared with a monostatic inverse synthetic aperture radar (ISAR) imaging system, a bistatic ISAR (Bi-ISAR) system offers more comprehensive target information, along with higher system security and resistance to interference. However, the inherent geometric configuration of Bi-ISAR will introduce the linear-geometry distortion (LGD), causing the target to appear sheared in shape and impacting subsequent target recognition. To deal with this issue, in this paper, a novel deep learning-based algorithm is proposed to realize the LGD correction. In the proposed algorithm, a neural network called DoubleUNet is employed for the accurate semantic segmentation of target's ISAR image. Then the initial target ISAR image is transformed into two-dimensional point clouds based on the estimated radar parameters. Finally, the linear coupling relationship between azimuth and range dimensions is removed, beneficial to the target ISAR shape restoration and subsequent recognition. Simulation experiments validate the proposed algorithm.

In this paper, a method aimed at removing the linearly continuous false alarm caused by the artificial building with unignored elevation in a multichannel synthetic aperture radar (SAR) system with ground moving target indication (GMTI) mode is proposed. In the proposed method, the Hough transform is firstly utilized to obtain the potential false alarms during the iteration procedure. After that, the linearly continuous false alarms are finally obtained and removed based on the phase similarity characteristic of those building scatters. Real-measured SAR data processing results are presented to verity the effectiveness and feasibility of the proposed method.

In this paper, the influence of different receiving array element weighting manners on the space-time adaptive processing (STAP) technique is analyzed. Firstly, the element-based echo signal in a space-borne early warning radar (SBEWR) system is established, and then the STAP procedure by utilizing two alternative receiving array element weighting models are introduced. After that, the antenna patterns with respect to two weighting manners are discussed and the target output signal-to-clutter-plus-noise ratio (SCNR) after STAP are evaluated. The numerical results show that the subarray element weighting manner obtains a better STAP performance without channel mismatch compensation while a slightly inferior STAP performance with channel mismatch compensation compared with the whole antenna weighting manner.

For maneuvering targets with weak surface reflections, the conventional ISAR imaging algorithms based on the motion parameter estimation for each scatterer may have a poor performance under low signal-to-noise ratio (SNR) scenario. To tackle this problem, a novel ISAR imaging algorithm is proposed in this letter. The effects of translational motion, the high-order migration through resolution cells (MTRC), and the nonstationary phase distribution are simultaneously eliminated by constructing the compensation function and utilizing the time polynomial rescaling-nonuniform fast Fourier transform (TPRS-NUFFT). Then, the particle swarm optimization (PSO) algorithm is applied to accomplish the motion parameter estimation. Finally, a high-resolution ISAR image can be obtained after motion compensation. Both simulation and real-measured data processing results demonstrate that the proposed algorithm can obtain focused ISAR image and improve the SNR by 29.8 dB.

The rapid development of the modern electronic counter-countermeasures (ECCMs) has made the conventional inverse synthetic aperture radar (ISAR) associated with the regular signal waveforms more vulnerable and unreliable. To deal with this issue, the complex waveform designment with staggered pulse sampling is developed in an ISAR system in this letter. In the proposed method, the generalized time-scaled transform (GTST) is adopted to effectively accomplish the irregular signal reconstruction and linear phase decoupling. After that, a set of matched filtering functions are established and interspersed in the imaging processes to accomplish the subsequent motion compensation, target imaging, and range and cross-range scaling. Finally, a satisfied ISAR imagery corresponding to the real size of a moving target can be recovered. The simulation and real-measured radar data processing results are applied to demonstrate the effectiveness of the proposed method.

In inverse synthetic aperture radar (ISAR) imaging for a uniformly moving rigid-body target, a finely focused ISAR image can be obtained by using the conventional range-Doppler algorithm. However, the ISAR image quality may significantly deteriorate when the time-vary Doppler phases in virtue of target maneuvering motions are present, such as an airplane with nonuniformly rotation and a ship with fluctuation. This has become a challenging task, especially under nonhigh signal-to-noise ratio (SNR) environment. In this article, a novel ISAR imaging algorithm for a maneuvering target with moderate reflection intensity is proposed. After motion compensation, the radar echo signal in a range cell is modeled as a multicomponent cubic phase signal (CPS), in which the chirp rate and the quadratic chirp rate are two important physical quantities that may determine the target ISAR focusing quality. Based on a symmetrical instantaneous autocorrelation function, the received CPSs are transformed into the time and lag-time plane, and then a 2-D coherent integration can be realized after the generalized time-scaled transform and 1-D maximization. This forms a high-quality ISAR image. The effectiveness and superiority of the proposed algorithm are validated by the ISAR imaging results of simulated and real measured data.

High speed, high maneuverability, and weak space-moving targets (SMTs) are major threats for space-borne radar (SBR) systems. First, the severe range migration (RM) and Doppler extension are induced by complex relative motion between the radar platform and non-cooperative moving targets, making moving target detection (MTD), and parameter estimation particularly difficult. Apart from this challenge, Doppler ambiguity and Doppler aliasing arise from the limited pulse repetition frequency (PRF) of a SBR system to ensure an adequate coverage rate, which may make the existing MTD algorithms deteriorate dramatically. To address these issues, we focus on the detection of high speed and high maneuverability targets based on the modified keystone transform matched filtering (MKTMF), whose Doppler frequency exceeds PRF as well as spans multiple PRFs. The proposed method is suitable for the weak MTD under a low signal-to-noise ratio (SNR) case since the nonlinear operation is not involved. Finally, some numerical results and real data results are provided to validate the superiority of the proposed method.

Narrow-band interference (NBI) is a major threat for synthetic aperture radar (SAR) system, which degrades the imaging quality severely. In this paper, a new type of narrow-band interference (NBI) is investigated, i.e., periodicity transmitted NBI (PT -NBI). Compared with the conventional NBI, PT -NBI is intermittently emerged in one azimuth pulse, which shows periodic property in the range time domain and broadband characteristic in the range frequency domain, resulting in the severe performance degradation by using the existing NBI suppression methods. The distinctions and characteristics between the NBI and PT -NBI in different transform domains are analyzed firstly. On this basis, a PT -NBI suppression method is proposed by applying the joint time domain detection and Eigensubspace (ESP) filtering in this paper, which can effectively realize the interference suppression for PT - NBI.

The detection and parameters estimation of linear frequency-modulated (LFM) signal are important for modern radar applications, but they are also challenged by the fact that echo signal is often of low signal-to-noise ratio (SNR) due to reasons of long imaging distance and/or limited transmitted power, and the target of small size and/or hidden characteristics. To enhance the SNR, in our previous work, a novel coherently integrated cubic phase function (CICPF) was recently developed for the parameters estimation of the multicomponent LFM signal. In the CICPF, the auto-terms are coherently integrated to enhance the performance in the case of low SNR and also to suppress the cross-terms and spurious peaks. In this paper, as an extension of our previous work, the theoretical performance analyses including several important properties and the fast implementation are provided. Furthermore, the asymptotic mean squared error of a CICPF-based estimator as well as the output SNR of a CICPF-based detector are theoretically derived in closed-forms. From the performance point of view, the proposed CICPF attains the Cramer-Rao bound at low input SNR. The complexity analysis also indicates that the CICPF with the nonuniform fast Fourier transform is computationally efficient without needing the interpolation operation and parameter search. Numerical studies of the CICPF confirm the theoretical analysis and demonstrate superior performance of the proposed approach compared with other state-of-the-art approaches, especially under the low-SNR condition. Finally, the proposed CICPF is applied for the ground moving target imaging in synthetic aperture radar. Results using simulated and experimental data demonstrate that it provides an effective means to obtain well-focused image for ground moving targets.