Yichang Chen

Aim at the problem that traditional inter‐pulse frequency‐agile radar has difficulty balancing moving target indication (MTI) processing and coherent integration due to random echo phase variations, a transmit‐receive joint compensation coherent integration method for pulse‐group frequency‐agile radar is proposed. This method uses the same frequency within a pulse group and performs cancellation within the group to be compatible with MTI clutter suppression MTI processing. At the transmitter side, a frequency–pulse repetition interval (PRI) joint agility strategy is used to compensate the velocity–frequency coupled phase term, while at the receiver side, a minimum energy entropy search method is used to accurately compensate the range–frequency coupled phase term. Simulation results show that after compensation with the proposed method, the radar can achieve effective coherent integration while maintaining the frequency agility anti‐jamming advantage. Moreover, under the condition of a detection probability of 0.8, the signal‐to‐noise ratio required by the proposed method is only about 1 dB lower than that of the non‐frequency‐agile data.

In accord with imaging mechanism, by tackling the integrated quantum circuit without multiple preparations and measurements, a quantum algorithm with remarkable speedup for synthetic aperture radar (SAR) imaging is proposed to decrease the data storage and time consuming in this letter. First, the quantum algorithms for 2-D matched filtering and range cell migration correction (RCMC) are presented. Then, the relevant quantum circuits are designed and bonded together as an integrated quantum circuit for SAR imaging. It would be further applied to wide-swath imaging and sparse-driven radar imaging to form the corresponding quantum algorithm from quantum data to quantum data. The polynomial speedup in computational complexity is analyzed theoretically, and the needed quantum data are reduced exponentially compared to classical data. Experimental results obtained by computer simulation of quantum algorithm demonstrate that the proposed method can obtain the high-resolution SAR image and decrease at least $10^{2}$ orders in complexity.

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting condi-tions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed.

As a classical sparse reconstruction algorithm, iterative soft thresholding (IST) is often used in SAR sparse imaging applications. However, for moving targets, the radar echo contains an unknown phase error, so it is difficult to directly apply the IST algorithm to reconstruct the SAR moving target image. In this article, a parametric IST (P-IST) algorithm is proposed for refocusing of moving targets in synthetic aperture radar (SAR) images. In this algorithm, the echo phase error is modeled as a function of the phase compensation factor, and in the iteration process, a phase compensation factor optimization selection step based on residual energy minimization is added. Compared with the existing algorithms, the proposed P-IST algorithm can obtain the global optimal solution and has better efficiency and robustness. Both simulated data and measured data are used to validate the effectiveness of the proposed algorithm.

As a special micro-motion feature of rotor target, rotational angular velocity can provide a discriminant basis for target classification and recognition. In this paper, the authors focus on an efficient rotational angular velocity estimation method of the rotor target is based on the combination of the time–frequency analysis algorithm and Hough transform. In order to avoid the problems of low time–frequency resolution and cross-term interference in short-time Fourier transform and Wigner–Ville distribution algorithm, a modified short-time fractional Fourier transform (M-STFRFT) is proposed to obtain the time-FRFT domain (FRFD)-frequency spectrum with the highest time–FRFD–frequency resolution. In particular, an orthogonal matching pursuit (OMP)-based algorithm is proposed to reduce the computational complexity when estimating the matched transform order in the proposed M-STFRFT algorithm. Firstly, partial transform order candidates are selected randomly from the complete candidates. Then, a partial entropy vector corresponding to partial transform order candidates is calculated from the FRFT results and utilized to reconstruct the complete entropy vector via the OMP algorithm, and the matched transform order can be estimated by searching minimum entropy. Based on the estimated matched transform order, STFRFT is performed to obtain the time–FRFD–frequency spectrum. Moreover, Hough transform is employed to obtain the energy accumulation spectrum, and the micro-Doppler parameter of rotational angular velocity can be estimated by searching the peak value from the energy accumulation spectrum. Both simulated data and measured data collected by frequency modulated continuous wave radar validate the effectiveness of the proposed algorithm.

As a special feature of rotor target, rotational angular velocity can provide important information for target classification and recognition. In this paper, a rotational angular velocity estimation method is proposed based on complex empirical mode decomposition (CEMD) and three-dimensional orthogonal matching pursuit (3D-OMP)-based parametric sparse representation (PSR) technique. Firstly, the translational radial velocity is estimated from radar echo via Fourier transform, and then the translational compensation is implemented according to the estimated velocity. Secondly, in order to eliminate the influence of the target main body echo on micro-Doppler parameter estimation, CEMD algorithm is adopted for target echo separation. Then, the separated signal of rotor echo is formulated as a jointly sparse signal through a three-dimensional parametric dictionary matrix, which converts the rotational angular velocity estimation into a problem of dynamic representation of jointly sparse signals. Finally, in order to reduce the computational complexity of the estimation method, we propose a 3D-OMP-based parametric sparse representation algorithm to achieve the sparse solution, and the rotational angular velocity can be estimated by minimizing the reconstruction error. The experimental results verify the effectiveness of the proposed algorithm.

A modified signal processing and target fusion detection method based on the dual platform cooperative detection model is proposed in this paper. In this model, a single transmitter and dual receiver radar system is adopted, which can form a single radar and bistatic radar system, respectively. Clutter suppression is achieved by an adaptive moving target indicator (AMTI). By combining the AMTI technology and the traditional radar signal processing technology (i.e., pulse compression and coherent accumulation processing), the SNR is improved, and false targets generated by direct wave are suppressed. The decision matrix is obtained by cell averaging constant false alarm (CA-CFAR) and order statistics constant false alarm (OS-CFAR) processing. Then, the echo signals processed in the two receivers are fused by the AND-like fusion rule and OR-like fusion rule, and the detection probability after fusion detection in different cases is analyzed. Finally, the performance of the proposed method is quantitatively analyzed. Experimental results based on simulated data demonstrate that: (1) The bistatic radar system with a split transceiver has a larger detection distance than the single radar system, but the influence of clutter is greater; (2) the direct wave can be eliminated effectively, and no false target can be formed after suppression; (3) the detection probability of the bistatic radar system with split transceivers is higher than that of the single radar system; and (4) the detection probability of signal fusion detection based on two receivers is higher than that of the bistatic radar system and single radar system.

For a conventional narrow-band radar system, the detectable information of the target is limited, and it is difficult for the radar to accurately identify the target type. In particular, the classification probability will further decrease when part of the echo data is missed. By extracting the target features in time and frequency domains from multi-wave gates sparse echo data, this paper presents a classification algorithm in conventional narrow-band radar to identify three different types of aircraft target, i.e., helicopter, propeller and jet. Firstly, the classical sparse reconstruction algorithm is utilized to reconstruct the target frequency spectrum with single-wave gate sparse echo data. Then, the micro-Doppler effect caused by rotating parts of different targets is analyzed, and the micro-Doppler based features, such as amplitude deviation coefficient, time domain waveform entropy and frequency domain waveform entropy, are extracted from reconstructed echo data to identify targets. Thirdly, the target features extracted from multi-wave gates reconstructed echo data are weighted and fused to improve the accuracy of classification. Finally, the fused feature vectors are fed into a support vector machine (SVM) model for classification. By contrast with the conventional algorithm of aircraft target classification, the proposed algorithm can effectively process sparse echo data and achieve higher classification probability via weighted features fusion of multi-wave gates echo data. The experiments on synthetic data are carried out to validate the effectiveness of the proposed algorithm.

For radar imaging of multiple targets in different beams, it is necessary for the radar network to coordinate the imaging task among various radars to achieve the best multi-target imaging performance under limited system resources. This paper proposes a task allocation optimization method of inverse synthetic aperture radar (ISAR) imaging for multiple targets. The imaging resolution is an important indicator of the imaging quality. Therefore, the relationship between the imaging resolution and task time is analyzed at first. Thereafter, the task allocation problem is converted into a time resource optimization problem of the radar network. Combining with the sparse aperture ISAR imaging algorithm, the task allocation optimization model of the radar network is constructed, and the corresponding algorithm for solving the model is proposed. The proposed method can effectively implement the multi-target ISAR imaging task allocation for the radar network. Moreover, it can greatly improve the resource utilization of the radar network, while meeting the imaging resolution requirements. Finally, some experiments have been conducted to verify the effectiveness of the proposed method.