The sparse microwave imaging combines the sparse signal processing theory with radar imaging to obtain new theory, new system, and new methodology of microwave imaging. In this paper, a brief review of fundamental issues in applying sparse signal processing to radar imaging is provided, including sparse representation, measurement matrix construction, unambiguity reconstruction, and so on. The developments of sparse signal processing in microwave imaging are discussed, and the initial airborne experiments on the prototype Synthetic Aperture Radar (SAR) framework with sparse constraints are introduced. The results demonstrate the feasibility and effectiveness of the principle and methodology of sparse microwave imaging. Besides, we also provide an overview of sparse signal processing in various radar applications, including  Tomographic SAR (TomoSAR), Inverse SAR (ISAR), Ground Penetrating Radar (GPR) as well.

Increasingly, detecting systems are facing the common problem of information processing owing to low SNR, low data rate, low resolution, and low information dimensions, and it is called the weak observation problem. This paper analyzes its origin and proposes the concept of information assembling by using the repetition and prediction properties of the object of interest. Then, the probability of the cloud inference method based on Bayesian theory is proposed to address a weak observation problem such as state estimation. Eventually several new requirements for sensor design, information processing, and system control are discussed, which are three crucial factors in information system design.

For ROtor Synthetic Aperture Radar (ROSAR) using the stepped-frequency waveform, an imaging method based on Range Migration Correction (RMC) for ROSAR is proposed. First, the signal model of ROSAR using stepped-frequency waveform is derived; Second, the range migration correction in frequency domain is proposed; Third, the high-resolution images are achieved by using the ω-k algorithm; Finally, the azimuth resolution performance is analyzed. Simulation results demonstrate the effectiveness and high resolution performance of the proposed method.

The Array 3D SAR has the ability of resolving imaging in three dimensions using the novel combination of linear array antennas and synthetic aperture. A practical array SAR system has the problem of multi-channel amplitude and phase errors, which can degrade the quality of the reconstruction image. The echo model with amplitude and phase errors is presented. The effect of each errors are analyzed and a calibration method based on Single Prominent Point Like Target Echo Data is proposed. For the application of 3D imaging, the range migration algorithm with calibration scheme is also proposed. Simulations and real data processing are performed to validate the proposed calibration scheme.

In image processing of high-resolution sliding spotlight SAR, it is important to know the Doppler rate with accuracy; however, traditional Doppler rate estimation algorithms are not very helpful because of the azimuth spectrum folding phenomenon. In this study, an algorithm that works on the transposition domain is proposed to solve this problem. Furthermore, the algorithm is also helpful in obtaining excellent focused images by embedding it in the two-step technique. Finally, the proposed algorithm is verified using computer simulations.

In the MOtion COmpensation (MOCO) approach to airborne repeat-pass interferometric Synthetic Aperture Radar (SAR) based on motion measurement data, the measurement inaccuracies of Inertial Measurement Unit/Global Positioning System (IMU/GPS) and the positioning errors of the target, which may contribute to the residual uncompensated motion errors, affect the imaging result and interferometric measurement. Considering the effects of the two types of error, this paper builds a mathematical model of residual motion errors in presence of squint, and analyzes the effects on the residual motion errors induced by the measurement inaccuracies of IMU/GPS and the positioning errors of the target. In particular, the effects of various measurement inaccuracies of IMU/GPS on interferometric SAR image quality, interferometric phase, and digital elevation model precision are disscussed. Moreover, the paper quantitatively researches the effects of residual motion errors on airborne repeat-pass interferometric SAR through theoretical and simulated analyses and provides theoretical bases for system design and signal processing.

The X-Estimator (XE) for the K-distribution shape parameter v based on the zlog(z) expectation can be computed without solving nonlinear equations; thus, it has high estimating efficiency. Nonetheless, its estimating accuracy is lower than many other moment estimators, and occasionally the XE results in odd values. To best use its advantages and avoid its disadvantages, on the basis of derivation and analysis of the XE bias using a set of Monte Carlo experiments, the X-Estimator with a Corrective term (XCE), which overcomes the above shortcomings of the XE, is discussed. Simulations show that the XE and XCE have the same high computational estimating efficiency, whereas the XCE significantly enhances the accuracy of the estimating shape parameter.

Target classification is particularly important in modern and future airborne radar. Nowadays, most investigations of radar target classification are based on wideband radar signals, which have higher requirements for SNR and radar systems, and are sensitive to the angles. Modern airborne radars require narrowband tracking and target classification; hence, an algorithm based on the narrowband fractal features and the amplitude modulation of a two-dimensional distribution is presented. Experimental data and Support Vector Machine (SVM) are used to verify the algorithm, and the classification results validate the proposed method, which show that jet aircrafts, propeller aircrafts, and helicopters can be classified with an average discrimination rate greater than 92%.

Density Tapered Arrays (DTA) with nonperiodic spaced elements have many interesting features when compared to traditional DTA in which antenna elements are located only at the intersections of periodic grid; however, a design method is yet to be fully develoed. A new approach is presented using Centroidal Voronoi Tessellations (CVT) to design DTA with nonperiodic element distributions proportional to the desired amplitude taper and with nonperiodic and ‘uniform’ properties. A 32.8λ diameter circular aperture array with 1656 elements is discussed and theoretical patterns are computed for density tapers modeled after −25 dB,−30 dB,−35 dB, and −40 dB circular Taylor distributions. The obtained sidelobe levels and aperture efficiency are better than heretofore reported.

The detection of multiple unresolved targets is critical in radar technology. To precisely detect multiple unresolved targets is a prerequisite and the basis of all other processes. For monostatic radar, the detection method of multiple unresolved targets based on the complex indicated angle difference technique is first analyzed and then extended to multistatic radar. The detector of multiple unresolved targets is designed and simulation tests are presented under several scenarios. Moreover, the effect on detection performance of SNR, sensor number, and the geometrical configuration of targets and sensors are analyzed. The results show that with the same SNR, the multistatic radar detector performs better than the monostatic radar detector.

A novel optimal power allocation algorithm for radar network systems is proposed for Low Probability of Intercept (LPI) technology in modern electronic warfare. The algorithm is based on the LPI optimization. First, the Schleher intercept factor for a radar network is derived, and then the Schleher intercept factor is minimized by optimizing the transmission power allocation among netted radars in the network to guarantee target-tracking performance. Furthermore, the Nonlinear Programming Genetic Algorithm (NPGA) is used to solve the resulting nonconvex, nonlinear, and constrained optimization problem. Numerical simulation results show the  effectiveness of the proposed algorithm.

The linear zone of the S-curve, which is formed with the digital beamforming (DBF) method, is extremely narrow for monopulse angle measurements with distributed Multiple-Input and Multiple-Output (MIMO) radar. Based on the transmitting-receiving beam pattern without gratelobe, the DBF method is proposed to form the ΣΔ-beam by using the absolute value of the echo signal. The ideal null depth can be achieved and the linear zone of the S-curve is expanded because the main lobe of the Δ-beam is enveloped by the Σ-beam. The monopulse angle for a target with high angle velocity was estimated. When the Signal-to-Noise Ratio (SNR) was higher than −15 dB , the accuracy of the proposed method was better than that of conventional methods. When the SNR was equal to −15 dB , the accuracy of the proposed method was similar to conventional methods that use coherent integration before taking the absolute value of the echo signal.

Digital Array Radar (DAR) has the ability of simultaneous multimode imaging and has many potential applications. This paper firstly introduces the basic hardware structure and the operation principle of DAR. Combined with the Digital BeamForming (DBF) technique, six operational modes that can be used in multimode Synthetic Aperture Radar (SAR) imaging are proposed, and the corresponding novel imaging modes are produced. Moreover, the design of the novel imaging modes is introduced in detail. The simulation results confirm the efficiency and precision of the imaging modes.

Radio Tomography Imaging (RTI) is an emerging technology with Wireless Sensor Network (WSN) widely applied to regional target monitoring and positioning. This study explores target imaging based on tomographic imaging mechanism and the loss of multiple links in the wireless network. For this purpose, Link Quality Indicator (LQI) data and the network structure are examined to derive the method of target image reconstruction using the Orthogonal Matching Pursuit (OMP) algorithm for compressed sensing. In addition, the proposed method is evaluated experimentally.