We are interested in the mobile target state detection (TSD) based on multi-input-multi-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) communication signals. Yet, communication-based TSD is of challenge, due to complex problem structures and communication symbol randomness. To address this challenge, we exploit structured features of low-speed and narrow-band systems to decouple the target state parameters and random communication symbols, and then use coherent detection to equalize random symbols. As such, a simplified sensing model holding an explicit space-time-frequency-domain correlation structure with respect to target direction angle, radial speed and relative distance, respectively, is obtained. Then, an efficient MIMO OFDM waveform-based TSD method extracting space-time-frequency correlation features is devised. It is verified by simulations that the proposed TSD method outperforms state-of-the-art baselines, due to the above problem-specific algorithm design. In addition, we establish the closed-form boundaries of the maximum detectable speed and maximum detectable range for MIMO OFDM-based TSD, which are essentially subject to the limited coherent time and bandwidth, respectively. The impact of system parameters (e.g., signal bandwidth, subcarrier spacing and carrier frequency) on the detection capability boundaries is analysed to gain insights into the fundamental limits of MIMO OFDM communication-based TSD. This work does not only build a technical foundation for sensing-assisted communication design, but also provide a unified framework for understanding the potentials of MIMO OFDM communication-based sensing.

This paper proposes an interleaved transceiver design method for a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system utilizing orthogonal frequency division multiplexing (OFDM). We consider a continuous transmission system and focus on transceiver design for alternate symbols to mitigate the interference to the radar from reflections of adjacent OFDM symbols. Constructive interference (CI) is incorporated into the optimization to improve communication performance, while the integrated mainlobe-tosidelobe ratio (IMSR) of the transmission beampattern ensures directivity. A time-domain radar receive filter is designed to reduce the range sidelobes and retain loss-in-processing gain, while also mitigating the interference to the radar and eliminating spurious peaks induced by distant targets. Given the high peak-to-average power ratio (PAPR) in OFDM systems, we constrain the power of each transmitted sample. The optimization problem is addressed using alternating optimization (AO), with the subproblem of transmitted waveform design being solved via successive convex approximation (SCA). Numerical simulations validate the effectiveness of our transceiver design in achieving desirable performance in both radar sensing and communication.

With the advancement of self-driving vehicles, autonomous driving systems depend on multimodal data to achieve a dynamic perception of the surrounding environment. Synthetic aperture radar (SAR) techniques can enhance azimuth resolution by utilizing the relative motion between the vehicle and targets, requiring a precise trajectory of the vehicle, normally without the assistance of automotive-grade navigation systems. In this case, data-driven autofocus-based algorithms are typically used to implement compensation for non-systematic motion errors. Despite demonstrating robust autofocus capabilities in numerous scenarios, their potential for application in automotive scenarios still needs to be exploited. This paper aims to provide a comprehensive automotive SAR imaging with autofocus workflow and to analyze the performance of autofocus algorithms based on phase gradient autofocus (PGA) in typical automotive scenarios. We rigorously derive the Omega-$\boldsymbol {K}$algorithm based on the system-grade waveform of frequency modulated continuous wave (FMCW) signals. Based on the analysis of motion error and phase error characteristics, a sub-aperture contrast-based weighted PGA (SAC-WPGA) method, a contrast-based selection strategy (CBSS), and a contrast-based WPGA kernel are proposed to improve the robustness of autofocus for automotive scenarios. In addition, we theoretically discuss the impact of the selection strategy, the PGA kernel, and the selection threshold in detail, highlighting the validity of the proposed method. Finally, we showcase the superiority of the proposed technique by employing experimental data in two typical automotive scenarios, i.e., a simple scenario with isolated dominant points and a complex scenario with strong clutter.

Due to the strong consistency with the synthetic aperture radar (SAR) system, deceptive jamming can be well integrated with SAR images and has high concealment. Therefore, deceptive jamming suppression in SAR is an urgent problem that needs to be solved. This article proposes a slow-time group phase coding (GPC) scheme for deceptive jamming suppression. Specifically, the proposed method can be divided into three steps: first, by using the slow-time GPC, the SAR transmitted signals are encoded separately in pulses and divided into two groups. Second, based on each group of signals, we propose a new optimization problem to reconstruct the SAR images and eliminate the unmatched deceptive jamming, i.e., the deceptive jamming combined with the second kind of GPC is unmatched with the first kind of GPC. Third, due to the design of GPC, each group of signals generates a SAR image where the scene stays almost the same, while the residual matched deceptive jamming is located at different azimuths. In this context, this difference is successfully used to eliminate the remaining deceptive jamming. Finally, the RADARSAT-1 and MiniSAR datasets are used to evaluate the effectiveness of the proposed method.

In order to play the key role of automotive millimeter wave radar in intelligent vehicle systems, direction-of-arrival (DOA) estimation is an essential problem to be solved. For practical intelligent driving applications, DOA estimation requires both real-time performance and high accuracy. Due to unique advantages, deep learning (DL) based methods have attracted more attention. Most of the existing DL-based methods require a large number of snapshots, but only a few snapshots can be guaranteed in practical applications. Moreover, they usually model DOA estimation as a multi-label classification task. The output represents the position of signal DOA on the discrete grid, and the resolution will be limited by the grid. In this paper, a new space-time Network (STNet) is proposed, which models DOA estimation as a regression task to achieve the effect of gridless estimation. We design a space correlation extraction module (SCEM) and a time correlation extraction module (TCEM), using the covariance matrix of the received signal and the original received signal as inputs respectively, treat them as different types of data. In these two modules, skip connection dense blocks (SCDBs) and long short-term memory (LSTM) networks are adopted to process two different forms of data. Through such processing, we retain sufficient information, obtain more features for the regression task, and ensure the estimation effect of using a small number of snapshots. The experimental results indicate that the STNet shows obvious performance gain in the case of small snapshots, achieves gridless estimation effect, and demonstrates excellent adaptability in situations where target DOAs are closely positioned.

This paper addresses joint design of the transmitting waveform and the receivers of a dual-function radar-communication (DFRC) system that enables both multiple-input multiple-output (MIMO) radar sensing and multi-user multiple-input single-output (MU-MISO) communications. The proposed approach incorporates the design of the communication receiving (Rx) coefficients, in addition to the radar Rx filters. We seek to maximize the minimum radar signal-to-interference-plus-noise ratio (SINR) over multiple targets, subject to per-antenna power constraints, peak-to-average-power ratio (PAPR) constraints and a communication SINR constraint for each user. A successive convex approximation algorithm is developed to find a good solution for the resultant nonconvex design problem. Numerical results show that by incorporating the communication Rx coefficients into the joint design, the radar and communication capabilities of the DFRC system can be significantly enhanced over the state-of-the-art designs.

Rotating Synthetic Aperture Radar (ROSAR) can generate a 360° image of its surrounding environment using the collected data from a single moving track. Due to its non-linear track, the Back-Projection Algorithm (BPA) is commonly used to generate SAR images in ROSAR. Despite its superior imaging performance, BPA suffers from high computation complexity, restricting its application in real-time systems. In this paper, we propose an efficient imaging method based on robust sparse array synthesis. It first conducts range-dimension matched filtering, followed by azimuth-dimension matched filtering using a selected sparse aperture and filtering weights. The aperture and weights are computed offline in advance to ensure robustness to array manifold errors induced by the imperfect radar rotation. We introduce robust constraints on the main-lobe and sidelobe levels of filter design. The resultant robust sparse array synthesis problem is a non-convex optimization problem with quadratic constraints. An algorithm based on feasible point pursuit and successive convex approximation is devised to solve the optimization problem. Extensive simulation study and experimental evaluations using a real-world hardware platform demonstrate that the proposed algorithm can achieve image quality comparable to that of BPA, but with a substantial reduction in computational time up to 90%.

As a wideband radar system, a synthetic aperture radar (SAR) may conflict with several electromagnetic systems, such as frequency modulation (FM), TV, and other communication systems. These signals, termed as radio frequency interference (RFI), may severely interfere SAR systems from generating a high-resolution image. Numerous previous researches focused on the RFI suppression problem, among which the semiparametric methods have been verified to have the state-of-the-art performance. However, most of the semiparametric methods are computationally expensive and can hardly be used on wide-swath SAR imaging processing. In this article, an efficient semiparametric algorithm is proposed to suppress RFIs via alternating projections. It has comparable performance as the other methods but significantly improves the computational efficiency a lot. It is able to remove both narrowband and wideband RFIs and can be used directly on the Level-1 SAR data. Finally, multiple real SAR data are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

Synthetic aperture radar (SAR), as a wideband radar system, may be subject to strong interferences with a variety of signals. The narrowband interference (NBI) exemplifies the most typical of its kind, such as the form of radio frequency interference (RFI). With the development of SAR imaging technology, the high-resolution wide-swath (HRWS) imagery technology now reaches its maturity to finally take shape in current SAR systems. To obtain HRWS images, the multichannel SAR (MC-SAR) system has been employed to tackle the contradictory requirements for both high resolution and low pulse repetition frequency (PRF). Previous interference methods focused on single-channel SAR systems and few research works for MC-SAR systems. In this article, we first derive a new interference-mitigation model for HRWS SAR systems and conclude that the low-rank property of the NBI is suitable for MC-SAR systems. Then we employ an image-domain sparse regularization to protect the real echoes of the SAR system and mitigate the NBIs by solving the low-rank recovery problems of NBIs. Also, the MC-SAR system errors are further taken into account as a measure for our method’s practical applicability. Finally, the real SAR data is used to demonstrate the effectiveness of the proposed method.

In this paper, a new sparse array structure, which further improves the degrees of freedom (DOFs) and enhanced the DOA estimation performance, for the fourth-order cumulant based direction of arrival (DOA) estimation is proposed. The new-formed array is hole-free and can achieve a large consecutive range in its fourth-order difference coarray. By analyzing its second-order sum coarray and fourth-order difference coarray, the closed form expression for the physical sensor locations and the corresponding virtual sensor configurations are derived. Compared with the existing fourth-order based sparse array structures, such as FLNA and SAFOE-NA, when the number of sensors is less than 23, the proposed sparse array can obtain longer consecutive virtual array, leading to more detected sources with a higher accuracy. Numerical simulations are performed to verify the superiorities of the proposed sparse array for fourth-order cumulant based DOA estimation.

The multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA) acting as the transmit (Tx) array, referred to as FDA-MIMO radar, is capable of providing additional degrees-of-freedom (DOFs) in range domain, and thereby offers the potential benefits in range-dependent interference mitigation and range ambiguity resolving. The existing FDA-MIMO radar literature either simply assumes that the Tx waveforms are mutually orthogonal or employs the code division multiple access (CDMA) waveforms to extract Tx DOFs. However, for real applications of the ground moving target indication (GMTI) using the space-time adaptive processing (STAP) technique, the CDMA waveforms are not preferable due to their poor ground clutter cancellation performance. To address this issue, a novel slow-time FDA-MIMO radar, which transmits slow-time phase-coded waveforms, is developed. As the slow-time FDA-MIMO radar emits highly correlated waveforms, it is expected to achieve excellent clutter cancellation performance. In addition, a new signal processing strategy is proposed, which is capable of extracting range-dependent Tx DOFs effectively. Numerical experiments are conducted to validate the effectiveness of the proposed radar framework for STAP applications.

Nowadays, narrowband interferences (NBIs) severely affect the imaging quality of synthetic aperture radar (SAR) systems. Fortunately, NBIs has nearly fixed frequencies along the azimuth time and they are demonstrated to be low rank in previous studies. All the NBI suppression methods are based on one-dimensional (1-D) and two-dimensional (2-D) domains to extract NBIs from the received signal. Actually, NBIs have a special low-rank property which can be employed in three-dimensional (3-D) domain for extra spacial degrees of freedom (DOFs). Hence in this paper, we propose a reweighted tensor nuclear norm minimization (RTNNM) algorithm to efficiently and effectively mitigate NBIs via three-mode tensor structure. The proposed method employs the special low-rank property of NBIs via multiple views in range-azimuth-space domain and deals with the drawback of the tensor nuclear norm minimization algorithm. The real X-band SAR data is employed to demonstrate the effectiveness and efficiency of the proposed method.

In synthetic aperture radar (SAR) system, the separation and reconstruction of useful signal from Narrow-band interference (NBI) and Wide-band interference (WBI) components is a challenging problem. In this paper, a novel time-varying interference suppression algorithm is proposed based on the signal synthesis from joint time-frequency (TF) distribution. This algorithm makes full use of two TF representations: Wigner distribution (WD) and cross WD (CWD). After cross-terms elimination, these two TF representations are equal or close to the sum of WDs or CWDs of individual signal components, respectively. Based on this property, interferences can be separated and reconstructed by matrix rearrangement and eigenvalue decomposition (EVD). Compared with the traditional SSM (TSSM), the proposed algorithm has two advantages: 1) it is more accurate, since it avoids the approximate interpolation to WD; 2) it is quite time-saving, due to its matrix obtained by fast Fourier transform (FFT) and matrix rearrangement instead of the discrete Fourier transform (DFT). Experimental results demonstrate the effectiveness of the proposed approach in terms of accuracy and computational complexity.