Forward Scattering Radar (FSR) is a special type of bistatic radar that can implement image detection, imaging, and identification using the forward scattering signals provided by the moving targets that cross the baseline between the transmitter and receiver. Because the forward scattering effect has a vital significance in increasing the targets' Radar Cross Section (RCS), FSR is quite advantageous for use in counter stealth detection. This paper first introduces the front line technology used in forward scattering RCS, FSR detection, and Shadow Inverse Synthetic Aperture Radar (SISAR) imaging and key problems such as the statistical characteristics of forward scattering clutter, accurate parameter estimation, and multitarget discrimination are then analyzed. Subsequently, the current research progress in FSR detection and SISAR imaging are described in detail, including the theories and experiments. In addition, with reference to the BeiDou navigation satellite, the results of forward scattering experiments in civil aircraft detection are shown. Finally, this paper considers future developments in FSR target detection and imaging and presents a new, promising technique for stealth target detection.

An (M, N)-coprime array comprises two well-organized subarrays: an M-element and an N-element. This sparse array configuration is capable of resolving a number of remote sources up to O(MN) solely with the use of an M + N - 1 sensors, which allows the identification of more targets with fewer transceivers while maintaining high resolution. In this way, the coprime array theory can significantly help to simplify the configuration of traditional transceiver systems. However, to date, the coprime array approaches reported in the literature rely strongly on far-field approximation, which is associated with significant error when dealing with the problem of short-range radar detection because the probed objects are nearby the sensors. To solve this problem, we extend the theory of the standard coprime array to short-range detection, whereby the probed object is located NOT far away from the sensors (either the transmitter or receiver). We demonstrate that the (M, N)-coprime array configuration can retrieve the object spectrum over [-2τk₀, 2τk₀] with a resolution of 4τk₀/MN, where k₀ denotes the free space wavenumber and τ is a scenario-dependent factor. As a consequence, the (M, N)-coprime array allows for the resolution of O(MN) objects nearby sensors, with a spatial resolution of λ/4τ. We also examined the performance of the coprime array with respect to the through-wall-imaging problem. Finally, we verified the usefulness of the coprime array for short-range radar detection with a selected number of numerical experiments.

In this study, we investigate the estimation of the Two-Dimensional (2D) Direction Of Arrival (DOA) in monostatic multiple-input-multiple-output radar with cross array and propose a novel, highly accurate DOA estimation method based on unitary transformation. First, we design a new unitary matrix using the central symmetry of a cross array at transmit and receive sites. Then, the rotational invariance relationships of these arrays with long and short baselines can be transformed into a real-value field via unitary transformation. In addition, non-ambiguous and highly accurate 2D DOA estimations can be obtained using a unitary dual-resolution ESPRIT algorithm. Simulations show that the proposed method can estimate 2D highly accurate spatial angles using automatic pairing without incurring the expense of array aperture and peak searching. Compared with traditional unitary transformation, the steering vectors of transmit and receive arrays can be transformed into real-value fields via the unitary matrix and the transformation method of our scheme, respectively. This effectively overcomes the problem of shift invariance factors in real-value fields that cannot be extracted using traditional algorithms. Therefore, the proposed method can absolutely compute eigenvalue decomposition and estimate parameters in a real-value field, resulting in lower computational complexity compared with traditional methods. Simulation results verify both the correctness of our theoretical analysis and the effectiveness of the proposed algorithm.

Terahertz holographic imaging has broad applications in the field of personal security verification, concealed weapon detection, and non-destructive testing. To suppress the range ambiguity, a fast sparse image reconstruction approach and imaging scheme is proposed for three-dimensional terahertz holography. The proposed algorithm establishes the terahertz imaging geometry and corresponding echo model. The range ambiguity is eliminated using the random step frequency method, and a frequency shift procedure is applied to recover the targets with a high computational efficiency. Simulation and experimental results verify the proposed algorithm.

In this study, a new type of Continuous Wave (CW) signal modulated by pseudocode family is designed, solving the problem of more quantity of warhead dynamic fragments, larger speed variation, larger distribution and more difficult resolution during the measurement of warhead dynamic fragments. This signal has thumbtack ambiguity function and multiresolution characteristics. It can meet the measurement needs very well. Herein, correlation properties and ambiguity function characteristics of this signal are analyzed. Moreover, the signal's limitations are reported. Recommendations pertaining to signal selection, number option, and usage are presented. The analysis results show that this signal can be used for dynamic fragments measurement of warhead. This signal is also of great importance to improve complex waveform design and radar performance.

Because a sparse array has the advantages of a simplified structure and reduced cost in a radar system, radar technology based on the sparse array has gained widespread attention. To take advantage of the sparse array, in this paper, we designed a Multi-Resolution Composite digital Array antenna (MRCA), and then used it in single-target and multi-target detection experiments. Using the same number of array elements, our experimental results demonstrate that the MRCA can obtain a narrower main lobe and a lower side lobe, enhances the direction of the array antenna, and improves the angular resolution compared with the uniform linear array.

While Wireless Fidelity (WiFi)-based passive radar can achieve high detection resolution in both the range and Doppler domain, it is difficult to extract the reference signal because of the complexities of its signal format and application scenarios. In this study, we analyze a typical application of WiFi-based passive radar and discuss different methods for reference signal extraction. Based on the format and features of WiFi signals, we propose a method for reference signal reconstruction, and analyze the influence of the reconstructed reference signal's performance on detection. The results show that higher reference SNRs generate lower decoding bit rate errors and better clutter suppression with the reconstructed reference signal. Moreover, we propose a method for removing irrelevant signals to avoid the impact on target detection of a non-direct path signal in the receiving signal. The experimental results validate the efficacy of the proposed signal processing method.

Passive radar experiences a significant problem called multipath clutter. The Batch version of the Extensive Cancellation Algorithm (ECA-B) is an efficient method for clutter mitigation. With the increase in signal bandwidth, a greater number of segments is required to cancel the clutter across the entire frequency range. This affects the processing rate, detrimentally weakening and modulating the signal from low-speed targets. Thus, this paper proposes a method that uses ECA-B to process both reference and echo signals in the frequency domain. This method not only reduces the amount of calculation required but also avoids weakening and modulating the target signal, which is spread across many segments. The simulated and experimental data results confirm the correctness and validity of the proposed method.

In order to determine single-observer passive coherent locations using illuminators of opportunity, we propose a jointing angle and Time Difference Of Arrival (TDOA) Weighted Least Squares (WLS) location method. First, we linearize the DOA and TDOA measurement equations. We establish the localization problem as the WLS optimization model by considering the errors in the location equations. Then, we iteratively solve the WLS optimization. Finally, we conduct a performance analysis of the proposed method. Simulation results show that, unlike the TDOA-only method, which needs at least three illuminators to locate a target, the jointing DOA and TDOA method requires only one illuminator. It also has a higher localization accuracy than the TDOA-only method when using the same number of illuminators. The proposed method yields a lower mean square error than the least squares algorithm, which makes it possible to approach the Cramér-Rao lower bound at a relatively high TDOA noise level. Moreover, on the basis of the geometric dilution of precision, we conclude that the positions of the target and illuminators are also important factors affecting the localization accuracy.

In this study, we focus on the ultra-long integration of orbital perturbations of geosynchronous Synthetic Aperture Radar (SAR) for imaging. By deriving mathematical expressions for the Doppler rate and quadratic phase from orbital elements perturbated by oblateness or the J2 term of the non-spherical gravitational force of the Earth, we analyze the impact on SAR data focusing. Based on our results, we conclude that the quadratic phase will exceed 45°, which is the defocusing threshold for imaging, after accumulation during a long integration time at the minute level. Because the potential for defocusing exists throughout nearly the entire satellite motion cycle, the SAR processor must carefully manage and compensate for the quadratic phase to avoid image degradations.

This study presents a new method of Synthetic Aperture Radar (SAR) image target recognition based on a convolutional neural network. First, we introduce a class separability measure into the cost function to improve this network's ability to distinguish between categories. Then, we extract SAR image features using the improved convolutional neural network and classify these features using a support vector machine. Experimental results using moving and stationary target acquisition and recognition SAR datasets prove the validity of this method.

The Doppler parameters of moving targets affect the conventional Synthetic Aperture Radar (SAR) imaging. In this study, the relation between the motion and Doppler parameters is established. With improved popular technology, a set of moving ship SAR imaging processes is proposed to obtain a focused and rightlocated image. Simulations and experimental data are used to verify the method.