Colocated multiple-input multiple-output (MIMO) technology has been widely used in automotive radars as it provides accurate angular estimation of the objects with a relatively small number of transmitting and receiving antennas. Since the Direction Of Departure (DOD) and the Direction Of Arrival (DOA) of line-of-sight targets coincide, MIMO signal processing allows for the formation of a larger virtual array for angle finding. However, multiple paths impinging the receiver is a major limiting factor, in that radar signals may bounce off obstacles, creating echoes for which the DOD does not equal the DOA. Thus, in complex scenarios with multiple scatterers, the direct paths of the intended targets may be corrupted by indirect paths from other objects, which leads to inaccurate angle estimation or ghost targets. In this paper, we focus on detecting the presence of ghosts due to multipath by regarding it as the problem of deciding between a composite hypothesis, ${\cal H}_{0}$ say, that the observations only contain an unknown number of direct paths sharing the same (unknown) DOD's and DOA's, and a composite alternative, ${\cal H}_{1}$ say, that the observations also contain an unknown number of indirect paths, for which DOD's and DOA's do not coincide. We exploit the Generalized Likelihood Ratio Test (GLRT) philosophy to determine the detector structure, offering closed-form expressions for theoretical detection performance, and a convex waveform optimization approach to improve detection performance. In practical scenarios, the unknown parameters of GLRT philosophy are replaced by carefully designed estimators. The angles of both the active direct paths and of the multi-paths are indeed estimated through a sparsity-enforced Compressed Sensing (CS) approach with Levenberg-Marquardt (LM) optimization to estimate the angular parameters in the continuous domain. Simulation and experimental results are finally offered in order to validate the proposed solution.

In radar automatic target recognition (RATR), high-resolution range profile (HRRP) has received intensive attention due to its low computational cost. As HRRP is sensitive to the aspect of the target, a training set covering sufficient aspects is essential to the success of an RATR model, which is however intractable in complex environment with noncooperative targets. In this article, an aspect-directed attention generative adversarial network is proposed to generate multiview HRRPs using real samples from few aspects. The key is that the HRRPs from the similar targets share the same aspect variation pattern. Hence, an HRRP is decomposed into its identity and aspect features via an aspect-directed disentangled representation network with self-attention modules. In the training stage, the decomposition network and the aspect variation pattern are learned from full aspect samples of cooperative targets. When generation, the desired multiview HRRPs of the noncooperative target are synthesized by its identity features extracted from few aspect samples and the learned aspect variation pattern. Three types of experiments on the simulated and measured datasets demonstrate the generation performances of our method. First, the generated HRRPs are visually compared with the truth. Second, the similarity of the scattering center power and handcrafted feature distributions are quantitatively evaluated. Finally, recognition experiments verify the feasibility of data augmentation with the generated HRRPs. Extensive results show the superior performance of our method over other state-of-the-art methods.

A single-layer novel windmill element is designed to broaden the bandwidth of reflectarray antenna in this paper. To obtain the enough phase range, a windmill ring patch and a circular ring patch are utilized to realize the multi-resonant state. As a result, a linear phase shift curve which covers 473.6° phase range is achieved by optimizing the parameters. Based on that, a large proportion of phase shift curve keeps parallel from 27.5 GHz to 42.5 GHz, it indicates the proposed element features broadband property. To this end, a single-layer offset-fed prototype with a square aperture of 80mm×80mm which is composed of 400 elements is designed, measured, and tested at Ka-band. Through detailed measurements, a peak gain of main beam is 27.86 dBi with an aperture efficiency of 51.7% has achieved at 37 GHz. The sidelobe level and cross-polarization are also lower than -17 dB and -25 dB, respectively. More importantly, the degradation of the peak gain from 27.5GHz to 40 GHz (35.71%) is less than 3 dB. The proposed antenna which features wide bandwidth with good radiation performance and simple structure will be suitable for 5G millimeter-wave communication.

With the continuous development of synthetic aperture radar (SAR) systems, multipolarization information has been increasingly applied to numerous fields, and automatic target recognition (ATR) in polarimetric SAR (POLSAR) has been recognized as vital problem. The SAR recognition methods can primarily fall into handcrafted feature-based algorithms and deep learning algorithms. The former exhibits excellent interpretability but insufficient generalization; the latter achieves stronger representational ability but relies on a considerable number of samples. To solve above problems, a feature fusion framework is proposed in this article based on monogenic signal and complex-valued nonlocal network (CVNLNet) for POLSAR target recognition. The proposed feature fusion framework effectively uses the complementarity of handcrafted features and deep features, while making up for the disadvantages of single feature-based methods. First, a Mono-BOVW model is proposed based on monogenic signal and bag-of-visual-words (BOVW) model to extract handcrafted features, which can more fully mine the information covered in POLSAR data in multiscale space. Moreover, CVNLNet is built for deep feature extraction to use both the amplitude and phase covered in POLSAR data. Next, a kernel discrimination correlation analysis algorithm is proposed to jointly analyze and transform the two features, so as to remove redundant information while retaining effective and discriminative information. Experiments on the MSTAR dataset and the GOTCHA dataset show that the proposed framework has superior performance on single polarimetric and fully polarimetric datasets.

Doppler division multiple access (DDMA) waveform shows great orthogonality for multiple-input multiple-output (MIMO) radar. However, it leads to a serious velocity ambiguous problem and hinders its application in automotive radar. In this work, a maximum unambiguous velocity extension approach is developed for DDMA MIMO radar by using a global ergodic optimization phase modulation during transmission. To be specific, we take the cumulative sum of the peak amplitude difference of global as the optimization cost function to obtain modulation codes. Furthermore, a matched filter phase demodulation carried out at the receiver to determine the results of angle measurement and velocity expansion. Finally, the simulation analysis and real-data experiments demonstrate our approach effectively improves velocity ambiguity resolving capabilities of DDMA waveform and suitable for massive MIMO array in nowadays automotive radar.

Low-altitude platforms, such as uncrewed aerial vehicles (UAVs), are increasingly employed for real-time sensing in edge-enabled Internet of Things (IoT) networks, where radar-based perception is critical for situational awareness. Multiple-input multiple-output (MIMO) radar systems offer high angular resolution and spatial diversity, making them well-suited for such applications. To fully exploit the benefits of MIMO, efficient waveform designs that support simultaneous multichannel transmission are essential. Doppler division multiple access (DDMA) is a promising waveform strategy that achieves transmit orthogonality through Doppler-domain encoding, offering strong inter-channel orthogonality and supporting low-complexity implementation. However, in multitarget environments, DDMA-MIMO radar suffers from mutual shadowing effects that degrade detection performance. To address this challenge, this article proposes a double-threshold detection framework tailored for DDMA-MIMO radar in edge sensing systems. The method employs a double-threshold strategy: an adaptive first threshold based on the Akaike information criterion (AIC) selects candidate targets, and a second threshold derived via maximum likelihood estimation produces the final detections. Theoretical analysis and simulation results demonstrate that the proposed double-threshold framework consistently outperforms conventional approaches, providing robust detection and effective false alarm regulation.