We consider the problem of estimating the directions of multiple target sources by exploiting auxiliary sources, focusing on a single snapshot obtained by the distributed array with position errors and angular offsets of subarrays. Former calibration methods generally assume the directions of auxiliary sources are unknown, while prior knowledge of the auxiliary sources is usually available in practice. In order to quantify the effects of auxiliary sources and their prior information, we model the directions of auxiliary sources as Gaussian random variables and use their standard deviations to quantify the prior information. We derive the prior Cramér-Rao lower bound (CRB) of the direction estimations in the new model. Simulation results show that calibration with auxiliary sources performs better than self-calibration and the prior CRB is much lower than the existing counterparts assuming unknown auxiliary sources, implying much potential to improve the estimation performance by employing the prior information.

It is a fundamental problem to analyze the performance bound of multiple‐input multiple‐output dual‐functional radar‐communication systems. To this end, we derive a performance bound on the communication function under a constraint on radar performance. To facilitate the analysis, in this paper, we consider a simplified situation where there is only one downlink user and one radar target. We analyze the properties of the performance bound and the corresponding waveform design strategy to achieve the bound. When the downlink user and the radar target meet certain conditions, we obtain analytical expressions for the bound and the corresponding waveform design strategy. The results reveal a tradeoff between communication and radar performance, which is essentially caused by the energy sharing and allocation between radar and communication functions of the system.

Monocular visual odometry (VO) has extensive applications in mobile robots and computer vision. However, current applications of monocular VO systems in complex environments still have limitations. Accurate, robust, and easy-to-use VO is still an unsolved problem to some extent. In recent years, the single-chip millimeter-wave (mmWave) radar has been increasingly used in various types of mobile robots due to its advantages of small size, low cost, and robustness in harsh weather conditions. In this paper, we apply the mmWave radar to a VO system and propose a multi-stage visual-radar fusion odometry framework, MS-VRO. The framework is based on a typical monocular VO system. By merging mmWave radar data in different stages, the proposed odometry improves the accuracy, robustness, and generalization ability of VO. The framework contains a new visual-radar initialization method, a visual-radar joint optimization method, and a radar-aided visual feature selection and processing method that can remove dynamic object features and bad map points. Through these, the proposed method solves the problems of monocular VO, including scale ambiguity, scale drift, and performance degradation in dynamic environments. We build a dataset that can be used for research on visual-radar fusion odometry and test the proposed method on the new dataset and other public datasets. The result shows that the proposed odometry achieves significantly better performance than VO methods and is more accurate and robust compared to some typical visual-inertial odometry methods.

In this paper, we consider the design of a multiple-input multiple-output (MIMO) transmitter which simultaneously functions as a MIMO radar and a base station for downlink multiuser communications. In contrast to the previous designs which guarantee communication performance, we require the covariance of the transmit waveform to be equal to a given optimal covariance for MIMO radar, to guarantee the radar performance. With this constraint, we formulate and solve the signal-to-interference-plus-noise ratio (SINR) balancing problem for multiuser transmit beamforming via convex optimization. By numerical simulations, we first demonstrate the radar performance loss caused by previous designs, and then show the communication performance for our proposed design in terms of balanced SINR versus transmit signal-to-noise ratio.

In this paper, we consider the waveform design of a multiple-input multiple-output (MIMO) transmitter which simultaneously functions as a MIMO radar and a base station for downlink multiuser communications. In addition to a power constraint, we require the covariance of the transmit waveform be equal to a given optimal covariance for MIMO radar, to guarantee the radar performance. With this constraint, we formulate and solve the signal-to-interference-plus-noise ratio (SINR) balancing problem for multiuser transmit beamforming via convex optimization. Considering that the interference cannot be completely eliminated with this constraint, we introduce dirty paper coding (DPC) to further cancel the interference, and formulate the SINR balancing and sum rate maximization problem in the DPC regime. Although both of the two problems are non-convex, we show that they can be reformulated to convex optimizations via the Lagrange and downlink-uplink duality. In addition, we propose gradient projection based algorithms to solve the equivalent dual problem of SINR balancing, in both transmit beamforming and DPC regimes. The simulation results demonstrate significant performance improvement of DPC over transmit beamforming, and also indicate that the degrees of freedom for the communication transmitter is restricted by the rank of the covariance.

Person reidentification (ReID) systems play a key role in intelligent visual surveillance systems and have widespread applications, for example, in public security. Usually, person ReID systems can identify a person with cameras. In this article, we focus on the relatively unexplored area of using low-cost automotive radar for the person ReID problem. Unlike the radar-based person identification, person ReID has some characteristics, such as the uncooperative scenes and the long-term robustness. Therefore, we design a new deep learning network to extract spatiotemporal information from 4-D radar point clouds. We also build a data set of radar point clouds collected from the real-world person ReID scenarios. The evaluation result shows that our method achieves 91% CMC-1 accuracy on the ReID task. Besides, for the person identification task, our method also achieves accuracies of 98% and 91% for 15 and 40 individuals, respectively. In addition, we discuss the potential of using radar for person ReID problems and intuitively explain the new method’s performance. Finally, we analyze the robustness and the influence of different parameters on the method and the contributions of different modules to the network model. The results of our experiment indicate that radar-based ReID not only preserves privacy but also outperforms camera-based ReID in some cases, such as in low-light environments or with substantial clothing changes.

Millimeter-wave (mmWave) radar has been widely used in autonomous driving due to its good performance under harsh weather conditions. In recent years, with the development of mmWave radar hardware performance, radar point clouds, as an important data format of mmWave radar, have been widely used in high-level perception tasks of mobile robots and autonomous driving. However, at present, compared to LiDAR point clouds, in common application scenes of mobile robots, mmWave radar point clouds have shortcomings such as sparsity and containing many “ghost” targets. Therefore, in this article, we analyze the reasons that cause these problems and propose a new method for point cloud generation as well as a new evaluation metric. After building a new dataset and carrying out experiments in real-world scenes, our method shows better performance on the quality of radar point clouds compared to other methods. In addition, by evaluating the performance of applying the high-quality radar point clouds to object detection tasks as well as localization and mapping tasks, the result shows that radar point clouds generated using our method can significantly improve the environment perception ability of mobile robots.

Block-sparse signal recovery has drawn increasing attention in many areas of signal processing, where the goal is to recover a high-dimensional signal whose non-zero coefficients only arise in a few blocks from compressive measurements. However, most off-the-shelf data-driven reconstruction networks do not exploit the block-sparse structure. Thus, they suffer from deteriorating performance in block-sparse signal recovery. In this paper, we put forward a block-sparse reconstruction network named Ada-BlockLISTA based on the concept of deep unfolding. Our proposed network consists of a gradient descent step on every single block followed by a block-wise shrinkage step. We evaluate the performance of the proposed Ada-BlockLISTA network through simulations based on the signal model of two-dimensional (2D) harmonic retrieval problems.

Dual function radar and communications (DFRC) systems are the focus of growing research attention. The common DFRC setup considers simultaneous probing and information transmission to a remote receiver, typically involving complex radar-oriented waveforms, whose detection can induce a notable burden on the receiver. In many DFRC applications, the communication receivers are devices which are limited in terms of hardware, power, and memory resources. These receivers are required to extract the desired information from the received dual-function waveform, while operating with a given bit budget. In this paper, we design bit constrained communication receivers in dual-function systems, by considering hybrid analog/digital architectures and treating their operation as task-based quantization. We study two forms of analog processing in these hybrid receivers, allowing to combine inputs in different time instances and antennas or only in different antennas at the same time instance. Simulation results demonstrate that the proposed task-based quantization strategy outperforms receivers operating only in the digital domain with the same total number of quantization bits.

Multistatic radar system (MSRS) is considered an effective scheme to suppress mainlobe jamming, since it has higher spatial resolution enabling jamming cancellation from spatial domain. To develop electronic countermeasures against MSRS, a random array subset selection (RASS)jamming method is proposed in this paper. In the RASS jammer, elements of the array antenna are activated randomly, leading to stable mainlobe and random sidelobes, different from the traditional jammer that applies the complete antenna array enjoying constant mainlobe and sidelobes. We study the covariance matrix of jamming signals received by radars, and derive its rank, revealing that the covariance matrix is of full rank. We also calculate the output jamming to signal and noise ratio (JSNR) after the subspace-based jamming suppression methods used in MSRS under the proposed jamming method, which demonstrates that the full rank property invalidates such suppression methods. Numerical results verify our analytical deduction and exhibit the improved countermeasure performance of our proposed RASS jamming method compared to the traditional one.

Dual-function radar communication (DFRC) systems implement both sensing and communication using the same hardware. An emerging DFRC strategy embeds transmission of digital messages into agility-based radar schemes in the form of index modulation (IM). This approach provides the ability to communicate without entailing degradation in radar performance, at the cost of increased decoding complexity at the receiver side. In this work we propose schemes for reducing the decoding complexity associated with IM-based DFRC systems. We first focus on the receiver side, developing a sub-optimal low complexity scheme for recovering IM symbols embedded in radar waveforms. Then, we propose a method to modify the radar waveform to facilitate the recovery of the communicated bits with minimal effect on the radar performance. Our numerical results demonstrate that the proposed techniques allow the receiver to reliably recover the transmitted symbols with an affordable computational burden.

Modern radar systems are expected to operate reliably in congested environments under cost and power constraints. A recent technology for realizing such systems is frequency agile radar (FAR), which transmits narrowband pulses in a frequency hopping manner. To enhance the target recovery performance of FAR in complex electromagnetic environments, and particularly, its range-Doppler recovery performance, multi-Carrier AgilE phaSed Array Radar (CAESAR) was proposed. CAESAR extends FAR to multi-carrier waveforms while introducing the notion of spatial agility. In this paper, we theoretically analyze the range-Doppler recovery capabilities of CAESAR. Particularly, we derive conditions which guarantee accurate reconstruction of these range-Doppler parameters. These conditions indicate that by increasing the number of frequencies transmitted in each pulse, CAESAR improves performance over conventional FAR, especially in complex environments where some radar measurements are severely corrupted by interference.

Sub-Nyquist radars require fewer measurements, facilitating low-cost design, flexible resource allocation, etc. By applying compressed sensing (CS) method, such radars achieve close performance to traditional Nyquist radars. However in low signal-to-noise ratio (SNR) scenarios, detecting weak targets is challenging: low probability of detection and many spurious targets could occur in the recovery results of traditional CS method. To overcome this issue, we propose a weighted sparse recovery based track-before-detect (TBD) method for weak targets detection by accumulating multi-frame information. Particularly, tracking results of targets are utilized as prior knowledge to enhance the recovery accuracy, thus improving the detection performance. Numerical results show that our method improves the detection performance particularly and reduces the occurrence of spurious targets in low SNR situations compared with traditional CS method.

We propose tensor-based four-dimensional sub-Nyquist radar that samples in spectral, spatial, Doppler, and temporal domains at sub-Nyquist rates while simultaneously recovering the target's direction, Doppler velocity, and range without loss of native resolutions. We formulate the radar signal model wherein the received echo samples are represented by a partial third-order tensor. We then apply compressed sensing in the tensor domain and use our tensor-orthogonal matching pursuit (OMP) and tensor completion algorithms for signal recovery. Our numerical experiments demonstrate joint estimation of all three target parameters at the same native resolutions as a conventional radar but with reduced measurements. Furthermore, tensor completion methods show enhanced performance in off-grid target recovery with respect to tensor-OMP.

Partitioning the antenna array into different subarrays is a flexible scheme in the joint radar and communication system. However, the traditional fixed partition of the antenna array cannot make full use of the complete aperture. In this paper, we propose a novel antenna partition scheme. In this scheme, the antenna is randomly and dynamically chosen as radar or communication unit. The dynamic randomness introduces extra channel capacity of the communication system, and enables the radar system approximately obtain the resolution and sidelobe level of a full antenna array simultaneously. The channel capacity, Cramér Rao Bound and the ambiguity function are theoretically analyzed. Pareto Front is used to demonstrate the performance improvement of the proposed system over the traditional fixed partition system.

This letter addresses the detection of a subspace distributed target signal obscured by disturbance. The disturbance consists of a clutter component with an unknown subspace structure and a white noise component with unknown noise power. A detection strategy is proposed based on the volume cross-correlation function, which provides a metric that measures the linear (in) dependency between two subspaces. Simulation results indicate that the proposed detector can achieve better performance than several peer methods, without resorting to secondary data and a priori knowledge about the clutter subspace including its rank.

This paper concerns sparse Bayesian learning (SBL) problem for group sparse signals. Group sparsity means that the signal components can be divided into groups, and the entries in one group are simultaneously zero or nonzero. In SBL, each group is controlled by a hyper-parameter. The marginal likelihood maximization (MLM) problem is to maximize the marginal likelihood of a given hyper-parameter by fixing all others. The main contribution of this paper is to solve the MLM problem by finding roots of a polynomial. Hence the global minimum of the marginal likelihood can be found efficiently. Furthermore, most large matrix inverses involved in MLM are replaced with the singular value decompositions of much smaller matrices, which substantially reduces the computational complexity. The proposed method is significantly different from the popular expectation maximization techniques in the literature where multiple iterations are required for MLM and the convergence to global optimum of marginal likelihood is not guaranteed.

This letter considers the problem of observation strategy design for compressed sensing. An adaptive method, based on Cramer-Rao bound minimization, is proposed to design the sensing matrix. Simulation results demonstrate that the adaptively constructed sensing matrix can lead to much lower recovery errors than those of traditional Gaussian matrices and some existing adaptive approaches.