Airborne array interferometric SAR (array-InSAR) tomography is an effective method for obtaining three-dimensional point clouds on the ground. However, the quality of point clouds is limited by multiple factors, such as phase noise, reconstruction algorithm, and so on. In this letter, the problem of point cloud stratification is discussed. In order to solve this problem, the authors propose a novel method. Firstly, the interference phase between multi-channel images are filtered to suppress the phase noise. Then, an elevation reconstruction algorithm based on off-grid target modeling and group sparsity is carried out, which can solve the mismatch problem of the target. In the final part, the effectiveness of the proposed method is validated using airborne array-InSAR data from the Sichuan province, China.

Synthetic aperture passive positioning (SAPP) has gradually become a research hotspot in the field of single-station passive positioning. Except for the acoustic band, almost all research in the microwave band only focuses on the spaceborne scenarios. In this paper, we attempt to extend SAPP to more scenarios, such as airborne and vehicle-mounted applications, and introduce the motion error problem. The difficulty is that, different from the problem of SAR motion compensation, the target area of passive positioning is unknown, and the compensation matrix cannot be calculated directly. We propose an iterative motion compensation (IMC) algorithm to solve it. Both simulation and vehicle experiment results verify the effectiveness of the IMC algorithm. The results show that IMC improves the positioning accuracy by two orders of magnitude in the presence of motion error and exhibits strong convergence.

Special attention has been devoted to joint wireless communication and radar sensing concepts over the past decades. Considerable confusion arose, however, with regard to waveform designs. In this article, various joint wireless communication and radar sensing waveform schemes are reviewed and classified into two categories. Disadvantages and limitations of each category are clarified theoretically. It is shown that waveforms designed merely in conventional time and frequency domains cannot fulfill the conflict requirements posed by radar and wireless communication. As a solution to this fundamental challenge, the multidimensional waveform concepts, along with a generalized correlation model, are proposed for joint wireless communication and radar sensing. To showcase our current research advances in this emerging area, several concrete waveforms designed in multiple dimensions are introduced. Theoretical analysis is validated by simulations and outfield demonstration experiments. It is shown that the suggested waveforms can simultaneously achieve superior detection and communication performances with shared spectrum resources.

Through-wall moving target tracking is an essential part of applications with the purpose of sensing moving human in the sealed building. However, the major challenge stems from the multipath effect in the actual environment, which creates errors in the range delay of moving target. In this letter, an effective through-wall moving target tracking algorithm in multipath is proposed. This method based on the correlation of human motion can restore and estimate the range delay of the moving target in the strong multipath interference. Both simulation and experiment results indicate that the proposed method can not only suppress multipath but also smooth the trajectory.

In the above article[1], the Acknowledgment section was published incorrectly. It should be as follows:

Foreign object debris (FOD) radar systems are often used to detect foreign materials that appear on the pavement that can pose as a threat to aircraft and personnel. Scan-mode millimeter-wave (MMW) radar with a fixed installation position is widely used as a leading solution. Small target detection is a puzzling challenge that requires a breakthrough for practical application. The conventional matched filter (MF) process along the range would encounter frequency leakage when strong scattering targets generate high sidelobe and mutual shield interference to object detection. The compressed sensing (CS) approach used for detection achieves efficiency by suppressing sidelobes and preventing small targets from being submerged by larger targets. The compressed imaging is introduced to retrieve the image scene with higher resolution and a small detectable object, such as 2-cm-diameter metal ball that equals −35-dBsm radar cross section (RCS). The long stripes scattering phenomenon can be reduced, which is beneficial to further detection processing. Furthermore, we demonstrate the concept of the MMW radar detection approach based on compressed imaging with the actual data. It validates the improved efficiency in at least two conditions: a small interval between multiple targets and strong scattering coverage of small targets. Nevertheless, we highlight some previously unrealized benefits of the FOD radar system application. There is no prior detection approach based on compressed imaging to enhance the performance of an FOD radar, as we present in this study.

Synthetic aperture radar (SAR) systems have extensively contributed to diverse scientific applications in the recent past decades. For the next generation of top-level SAR systems, the multimodal operation of wide swath imaging, sliding spotlight, and highly sensitive ground moving target indication (GMTI) is becoming increasingly crucial. Following this concept, in this article, a novel airborne multiple-input–multiple-output (MIMO) SAR system, along with the space–time coding (STC) waveform scheme and the phased array, is developed to realize the multimodal operation. In particular, the airborne MIMO SAR system consists of two transmitters and four receivers. The signals of the multiple modes, which overlap in the frequency domain, are interpulse modulated and transmitted by different subarrays concomitantly. The superposed echoes are separated by range-Doppler filtering. Based on the separated echoes, we can obtain eight equivalent transceiver channels and, as a result, enhance the signal-to-noise ratio (SNR) for airborne multimodal operation. In the future, the STC waveform scheme and flight experiments of this novel airborne MIMO SAR system will be further investigated to reduce the pulse repeat frequency (PRF) for spaceborne multimodal operation.

Foreign object debris (FOD) radar systems are often utilized to detect any foreign material on the pavement that may injure or threaten aircraft and personnel. The normal matched filter process along range would encounter frequency leakage when there are strong scattering targets. It causes low imaging quality and interference to object detection. With the compressed sensing (CS) technique implement, it does not require a Fourier transform or Nyquist sample rate. The compressed imaging is introduced to retrieve the image scene with better resolution and a reduced amount of collected samples. The long stripes scattering phenomenon could be alleviated, which is beneficial to further processing. Besides, we demonstrate this concept of FMCW imaging using compressed sensing theory with real data. Still, we point out some of its benefits with FOD radar system application never before realized. To the best of our knowledge, there is no prior compressed imaging approach to improve the range process of a FOD radar, as we present in this paper.

Radar SLAM has attracted wide attention due to its all-day and all-weather working characteristics in the last decade. The existing radar SLAM systems mainly adopt mechanically pivoting radar with simple principle and high resolution, but this kind of radar has disadvantages such as low frame rate, distortion of the radar image, and high cost. Although array snapshot radar has the advantages of high frame rate and low cost, its low azimuth resolution, multipath reflection, and angular glint limit its application in SLAM. This paper proposes a SLAM system developed on array snapshot radar. The system realizes angular super-resolution radar imaging through compressed sensing, which effectively solves the problems of poor azimuth resolution and multipath reflection of array snapshot radar. We also propose the corresponding point cloud extraction method and scan matching method, this method performs a centroid iterative closest point algorithm between the submaps, thereby effectively improving the interference of noise and angular glint. Experimental results show that our proposed array snapshot radar SLAM system can reduce the mean absolute trajectory error by more than 3 times compared with the existing system, and can show accuracy and robustness in various environments.

Through-the-wall (TTW) imaging is an important class of applications with the goal of sensing the environment inside the unknown area. In the computerized tomography (CT)-mode TTW imaging method, the layout of the area is reconstructed with the received signal strength (RSS). However, the multipath included in the RSS is difficult to mitigate, resulting in blurring, wall missing, or ghosts. In this letter, a novel imaging method is proposed. First, the received signal is processed by pulse compression. The multipath signals are separated from the directly transmitted wave (DTW), and then, the time delay of the latter one is easily estimated. Second, an improved total variation regularization algorithm is used to reconstruct the layout by assuming that there are only horizontal and vertical walls in the area. The validity of the proposed method is verified by the experimental results, where a clearer image could be obtained compared with the one generated by the traditional RSS method.

Array Tomographic Synthetic Aperture Radar (TomoSAR) is an advanced technique with elevation resolution through acquiring images from different angles. Due to the special single-pass mode, there is no temporal decorrelation caused by revisit time between multiple images. Therefore, the accuracy of the elevation reconstruction of the observed scene can be improved, especially in mountain area. In this paper, a framework to achieve 3D reconstruction in mountain area for Array TomoSAR is proposed. Furthermore, the complex overlays and terrain continuity are fully considered. To validate the effectiveness of the proposed framework, the experimental results are compared to SRTM-1 data obtained by Shuttle Radar Topography Mission (SRTM). Also, the original 2D image with scattering intensity is utilized.

The capability and of many high resolution sensors have been develop to targets detection task. The millimeter-wave (MMW) radar can use several GHz bandwidth with centimeter range resolution, narrow beam or moving platform to obtain high angular resolution. It has been researched in automobile radar, obstacle detection system, micro aerial vehicles detection and tracking, etc. Feasible and high performance detection strategy is particularly important to the extension of MMW radar application. Constant False Alarm Rate (CFAR) technique is widely used in target detection from radar signal. Conventional CFAR processors meet with problems mainly from two aspects, which are clutter edge effect and target masking effect in multi-target situations. A novel method of greatest of cell averaging and secondary detection (GOSD) CFAR is proposed in this paper. The GO-CFAR can avoid false alarm at clutter edges but results in strong target masking effect. While the SD-CFAR can solve the target masking effect well with detection probability increasing and no false alarm adding. Applied to real MMW radar data obtained by AIRCAS ground based radar device in an actual experiment in Shanghai. The results is demonstrated to be of great potential in alleviating the masking effect and reveal its performance advantages.

It has become a field of intensive research to exploit SAR tomography to reconstruct a 3-D model of the buildings. However, in multiple-input multiple-output (MIMO) SAR tomography, the double-bounce reflections of the building corner will cause symmetric virtual scatterers, affecting both the scattering coefficient and structure of the 3-D model. To solve this problem, the building corner reflection is discussed and compressive sensing (CS)-based corner reflection suppression (CSCRS) is proposed. In the final part of this letter, the effectiveness of the proposed method is validated using array InSAR data. It is found that the proposed method leads to considerable improvements with regard to suppression ratio and reconstruction accuracy.

Special attention has been devoted to joint wireless communication and radar sensing systems in recent years. However, since wireless communication and radar have conflict requirements in terms of waveforms, transceiver developments and signal processing algorithms, realization of this system concept is still a great challenge. In this paper, we introduce airborne multiple-input multiple-output (MIMO) radar, along with the space time coding (STC) waveform scheme, for the implementation of joint wireless communication and synthetic aperture radar (SAR) imaging. The proposed system, which simultaneously transmits mutually orthogonal waveforms with reconfigurable channels, can acquire adequate degrees of freedom. Thereby, it becomes a feasible method to perform both data transmission and SAR imaging at the same time without intra-modal interference. Theoretical analysis is validated by experimental flight results. Through our analysis, we aim to open up a new perspective of using MIMO radar to realize joint wireless communication and SAR imaging.

Recent advances in very high-resolution tomographic synthetic aperture radar (SAR) inversion using multiple data stacks from different viewing angles enable us to reconstruct the reflectivity function along the elevation direction by means of spectral analysis for every azimuth-range pixel. They can be potentially used for facade reconstruction and deformation monitoring in an urban area. When SAR tomographic processing is used to form 3-D point clouds, some points that we simply omitted in the traditional 3-D reconstruction are difficult to understand. It comes to our attention that this special detail which seems violated to a common practice is due to the mechanism of multipath (MP) scattering. Dominated by SAR tomography process, MP scattering reveals many interesting phenomena in 3-D point clouds. In this paper, a theoretical model of the MP scattering of the structure of dihedral corner which often appears in the urban area is given. There are two groups of points distributed symmetrically and compactly around the base angle of the building. The positions of these two groups of the points can be predicted by our model. Corresponding experiments are carried out to show the validity. The position is relative to the height of the building which indicates a direct utilization of our model to estimate the height of the building directly without 3-D reconstruction.

Special attention has been devoted to joint wireless communication and radar sensing systems in recent years. However, since communication and radar have conflict requirements in terms of waveforms, transceiver developments, and signal processing algorithms, realization of this system concept is still a great challenge. In this paper, we introduce an airborne multi-input multi-output (MIMO) radar, along with the modified orthogonal frequency-division multiplexing (OFDM) and space-time coding (STC) waveform schemes, for the implementation of the joint wireless communication and synthetic aperture radar (SAR) imaging. The proposed system, which simultaneously transmits multidimensional waveforms with reconfigurable channels, can acquire adequate degrees of freedom. Thereby, it becomes a feasible method to perform both data transmission and high-resolution SAR imaging at the same time without intramodal interference. Theoretical analysis is validated by laboratory and flight experiments. Through our analysis, we aim to open up a new perspective of using MIMO radar to realize joint wireless communication and SAR imaging.

Multiple-input multiple-output (MIMO) array synthetic aperture radar (SAR) with array antennas distributed along the cross-track direction can obtain 3-D scene information of the surveillance region. However, the cross-track resolution is unacceptable due to the length limitation of the MIMO antenna array. The superresolution algorithms within the framework of compressive sensing (CS) have been introduced to recover the cross-track signal because of its inherent spatial sparsity. The existing sparse recovery algorithms for 3-D SAR are attempted to find the sparse solution in the complex domain directly, which requires a very high computational complexity. To overcome this problem, a new fast 3-D imaging algorithm based on real-valued sparse representation is proposed in this paper. In this new algorithm, unitary transformation can be employed to transform the sparse signal recovery model of uniform/nonuniform MIMO array SAR from the complex domain to the real domain. Thus, a real-valued reweighted 12,1-norm minimization model is established. In addition, a modification of the fast iterative shrinkage-thresholding algorithm (FISTA) is used to reconstruct the 3-D image for further improving the computational efficiency. Moreover, the theoretical analysis of computational complexity of the proposed algorithm is derived when compared with an existing complex domain algorithm. Finally, numerical simulations and MIMO array SAR real experimental results are illustrated to validate that the proposed algorithm can reduce the computational complexity significantly in terms of CPU time while still maintaining the inherent advantages of superresolution and robustness against the noise.

Synthetic aperture radar (SAR) systems in forthcoming surveillance and reconnaissance tasks have to meet increasingly severe demands. The next generation of toplevel SAR systems will comprise high resolution wide swath (HRWS) imaging capability, highly sensitive ground moving target indication (GMTI) and a multitude of sophisticated operational modes. Multi-input multi-output (MIMO) radar systems, which can acquire more degrees of freedom, offer new potential solutions to future missions. In this paper, we introduce a novel airborne MIMO-SAR system built in IECAS. The proposed system contains a reconfigurable phased array, two transmit channels, and four receive ones. Thereby, it can realize 0.3m resolution for 30km swath, multimodal operation of wide swath imaging, spotlighting, ground-moving target indication (GMTI) et al. The system design and the orthogonal waveform scheme are detailed, along with the flight experimental results.

Attention has been devoted to multi-input-multi-output (MIMO) synthetic aperture radar (SAR) systems in recent years. The applications of MIMO-SAR systems that involve high-resolution wide-swath remote sensing, 3-D imaging, and multi-baseline interferometry are seriously limited by the ambiguous energy introduced by the imperfect orthogonal waveforms. The ambiguous energy degrades SAR image severely, because it can be accumulated from closely spaced scatters. In order to suppress the ambiguous energy, the space-time coding (STC) scheme using the Alamouti technique has been proposed recently. However, this scheme is based on the assumption that the channel responses are constant within the consecutive transmissions. For the time-variant channel responses caused by motion errors or other factors, there will be residual ambiguous energy and “ghost targets.” In this letter, a novel STC scheme suitable for MIMO-SAR systems is proposed. The performance of the proposed scheme is immune to the time-variant channel responses. Moreover, it can get more spatial diversities to enhance the MIMO SAR imaging applications. The proposed scheme is validated by numerical simulation results.

Attention has been devoted to multi-input multioutput (MIMO) synthetic aperture radar (SAR) systems in recent years. The applications of MIMO-SAR systems which involve high-resolution wide-swath remote sensing, 3-D imaging, and multibaseline interferometry are seriously limited to the orthogonal waveforms. This restriction is mainly caused by the imperfect orthogonal waveform-introduced ambiguous energy. For a single point target, the impact of the ambiguous energy can be neglected. However, when it comes to the spatially distributed scattering scenarios, the ambiguous energy from closely spaced scatterers, which would be accumulated, degrades the SAR image seriously. In order to suppress the ambiguous energy, a novel energy cancellation scheme using the Sequence CLEAN technique in the range direction is proposed in this letter. By employing the proposed novel scheme on the raw data of MIMO-SAR systems, the ambiguous energy from both point and distributed targets can be suppressed. The proposed scheme is validated by simulations and practical experiments.

Attention has been devoted to multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) systems in recent years. The applications of MIMO SAR systems, which involve high-resolution wide-swath remote sensing, 3-D imaging, and multibaseline interferometry, are seriously limited by the available sets of orthogonal waveforms. Although orthogonal frequency-division multiplexing (OFDM) chirp waveforms are proposed to avoid intrapulse interferences, this waveform scheme has not been investigated for practical implementation. In this paper, challenges in implementing the OFDM chirp waveforms on practical systems are analyzed and solved. First, the small extra carrier frequency between the mutually orthogonal waveforms, which renders the OFDM chirp waveforms not strictly on common spectral support, is avoided by improving the modulation of the OFDM chirp waveform. Second, the tedious demodulation, which is realized by circular-shift addition in the time domain and subcarrier extraction in the frequency domain, is improved. Third, the radar systematic error and the Doppler shift, which introduce bandwidth leakage and degrade the waveform orthogonality significantly, are compensated. Finally, taking all these challenges into consideration, a novel signal processing algorithm along with a MIMO SAR system model is proposed. Theoretical analysis is validated by simulations and systematic calibration measurements based on a C-band system.

A multi-frequency polarimetric SAR named “IECAS-SAR” has been developed in Institute of Electronics, Chinese Academy of Sciences (IECAS). It is composed of four subsystems, namely, P-, L-, C- and X-band SAR sensors. It would play the role of a powerful remote sensing instrument and support scientific researches on the mechanism of scattering. The first-stage test flights have been flown, validating the SAR sensors and getting valuable results. An overview of this system and processing results are presented in the paper.

Motion compensation (MoCo) is pivotal in the processing of airborne synthetic aperture radar (SAR) data. Motion errors are space variant according to the data acquisition geometry, which can be split into range-variant and azimuth-variant components. In the processing of wide-beam SAR data, the azimuth variance must be considered. Several approaches have been proposed, among which the subaperture algorithm based on frequency division (FD) is a good choice if motion errors involve high-frequency components. However, there are two drawbacks to using this algorithm in conditions where the magnitude of motion errors is large, i.e., the invalidation of the time-frequency relation in an FD with too many subapertures and paired echoes caused by periodic discontinuities in the azimuth phase history. Theoretical analysis and simulations are presented to demonstrate these two drawbacks. Improvements are then made on the traditional algorithm: implementing the FD after the subaperture MoCo instead of before it and adopting a nonuniform FD scheme rather than a uniform one. Finally, the validity of the improved algorithm is further demonstrated by experimental results with real data.

In order to reduce the complex image registration process for the airborne dual-antenna interferometric SAR (InSAR) system, a new auto-registration imaging algorithm is presented in the paper, which implements the registration step directly at the SAR raw data processing stage based on the EOK algorithm and the ECS algorithm. For the mismatch caused by the path difference of two antennas, it realizes the precision registration of interferometric image in the range by means of high degree polynomial approximation using scaling principle and shifting compensation in course of the imaging procedure. Finally, through the processing of the real interferometric data, better interferograms are obtained and the validity of the algorithm proposed in the paper is proved.

This paper designs a multi-input multi-output (MIMO) synthetic aperture radar (SAR) system using digital implemented orthogonal frequency division multiplexing (OFDM) waveforms, which can not only avoid ambiguous energy caused by the nonideal orthogonality of current-taken MIMO SAR waveforms, but also reduce the pulse repetition frequency (PRF) by a factor of 2Nl-1, where l N denotes the number of subapertures. One linearly frequency modulated (LFM) signal sampled in frequency domain is modulated into the odd subcarrier frequencies of OFDM waveforms, and another as the weights of even subcarrier frequencies. In order to avoid aliasing in time domain caused by extracting the weights of subcarrier frequencies, the sliding discrete Fourier transform (SDFT) is used for demodulation. The procedure of MIMO-OFDM SAR imaging is detailed. Theoretical analysis and simulation results illustrate the feasibility of this system.

The P-band ultra wideband synthetic aperture radar shares the band with other services, as for example TV broadcast and telecommunications transmitter stations, means distortion by radio frequency interference (RFI) to the SAR. It's well known the performance of UWB-SAR is degraded due to the existence of radio frequency interference. This paper describes the problems of RFI in InSAR, and analyses the effect of RFI to interferometric phase based on simulation.

Spaceborne INSAR (Interferometric Synthetic Aperture Radar) is an important remote sensing tool for topographic mapping, while there are inevitably shadow areas in Radar images. Shadow area has no radar echo signal or low coherence SNR (signal noise ratio). In this paper, Shadow area percentage estimation according to Radar look angle is proposed. Digital elevation model of natural terrains and ascending and descending orbits are used in simulation. This estimation method is suitable for incidence angle considerations in the phase of INSAR system design.

In this paper, first compared different receive channel schemes for InSAR application, then the main principal and technique of three practical internal calibration schemes are analyzed and compared, especially the famous SIR-C and X-SAR internal calibration system. Based on these different schemes, a new internal calibration scheme using microwave over fiber link is subscribed.