With the increasing number of radar-equipped vehicles in dense traffic situations, millimeter wave radars are suffering from serious interference problems. Therefore, this article presents a novel bi-level <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> optimization based approach for reducing interferences between automotive radars for range, velocity and angle measurement. Firstly, sparse difference analysis between the interfering signal and the target signal is investigated. According to the analysis results, one bi-level based signal optimization model is further derived by using <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula>-norm penalized least squares. This bi-level optimization enables a trade-off between suppressing interference and preserving radar targets. Specifically, in the first <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> level, the interfering signal is first optimized as the “desired signal”, while the target is considered as “noise”. Meanwhile, sparse optimization is applied on the target signals in the frequency domain at the second <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> level. Finally, the iterated soft-thresholding algorithm is used to optimize the proposed model. In real road interference suppression experiments, the proposed method improves the signal to interference plus noise ratio (SINR) for the target from 5.06 dB to 19.26 dB in the range-Doppler domain and from 6.86 dB to 22.10 dB in the azimuth spatial domain.

Bi-Level l1 Optimization-Based Interference Reduction for Millimeter Wave Radars

This article discusses the effect of multipath in automotive radar imaging under different sensor configurations. The study is motivated by the fact that radar technologies are becoming indispensable in the automotive scenario. Many applications such as collision avoidance systems, assisted parking, and driving assistance systems take advantage of radar technologies to accomplish their task. However, one of the main concerns about automotive radars is the possibility of detecting false targets due to multiple signal reflections. In this article, we show how different sensor layouts experience multipath differently. In particular, we demonstrate that with multiple-input multiple-output (MIMO) radars, what really matters is the physical positions of the transmitting and receiving antennas. The monostatic/bistatic equivalent configurations cannot be used to design a system and to simulate an acquisition in the presence of a multipath. We also demonstrate how vehicle-based MIMO-synthetic aperture radar (MIMO-SAR) imaging can generate a bi-dimensional aperture which significantly reduces multipath effects in the focused image, avoiding the detection of false targets. All the theoretical analyses are supported by several simulations where different sensor layouts are tested, and the capability of MIMO-SAR to reject multipath is validated.

Multipath in Automotive MIMO SAR Imaging

Pressure sensing simulation using high resolution electronics is important in future robot and human-computer interactions. In this research, we prove that the two-dimensional distribution of strain can be mapped using an array of piezoelectric organic light-emitting diode (piezo-OLED) pressure sensors. Each dot is made up of a piezo-ZnO/TAZ OLED, whose emission intensity is determined by the local strain with piezoelectric photoelectric effect. Reading out the electroluminescence signal with a temporal resolution of 100 ms produces the pressure intensity. Individuality signature and artificial skin technology for optical MEMS and bioimaging can benefit from the device‘s ability to digitally image mechanical impulses via optical methods. 

Light Emission Enhancement of a ZnO/TAZ Heterostructure Piezo‐OLED by Means of a Piezo‐Phototronic Effect

In recent years, the tunability of solid-state light sources has become an ideal feature for the illumination and exhibition industries. However, the ability of spectral and intensity tunability independently is still crucial. In this paper, an integrated OLED device, serving as a conceivable solvent to illuminant utilized in the tunable luminous scheme, is put forward as the intelligent color displays. The manufactured OLED device consists of a forward and an inverted sub-cell, which allows the demand of different materials for device structure compared to devices composed of the same structure, thus realizing the requirements of high-efficiency, high-brightness, and easy-fabrication. Then, the working mechanism of this organic electroluminescent device is discussed and utilized with AC electrical signals to address each emitter cell independently for adjusting the corresponding color temperature and intensity. The functional structure using forward and inverse integration with AC-driven can be integrated into commercial power systems for various lighting situations, thus bringing potential development of future intelligent displays.

A color-tunable and high-effective organic light-emitting diode device with forward-inverse structure as intelligent lighting display

This paper considers spatial-domain detector design for mutual interference mitigation among automotive MIMO-FMCW radars. This detector design is based on our previously derived interference signal model that fully accounts for the time-frequency incoherence and the slow-time code incoherence between the victim and interfering radars. Compared with our previous spatial-domain detector in [1], the proposed detector further exploits the structural property of both transmit and receive steering vectors of the interference for stronger interference mitigation. Preliminary numerical results con- ﬁrm the performance of our proposed detector and show advantages over baseline detectors

Spatial-Domain Object Detection Under Mimo-Fmcw Automotive Radar Interference

Light-emitting diodes (LED) currently play a key role in solid-state illumination and displays. Using nanotechnology, more advanced and functional LEDs have been developed. Here, a violet-light-emi...

Realization of Nanostroke with a Violet-Light-EmittingDevice with High Monochromaticity

Recently, radar has been massively applied in driver assistance and autonomous driving system. However, radars in traffic may interfere each other, leading to decreased detection sensitivity or ghost targets. Over the past decades, there are multiple approaches proposed to solve this problem. In this work, we propose a sparsity-based method to mitigate the interference in the so-called time-frequency-slow-time (TFS) domain using order statistics for the first time. We evaluate and compare the performance of the proposed method with existing signal processing-based methods.

Time-frequency-sparsity-based interference mitigation using order statistics for automotive radar

With the massive application of automotive radars for driving assistance systems and autonomous driving system, mutual interferences between radars have become a key issue. Recently, different deep learning (DL)-based approaches have been proposed, which achieve superior performances compared with conventional signal processing-based methods. However, the existing DL-based interference mitigation techniques are supervised, i.e. paired co-registered datasets are required for training the model, which is not easy to acquire in real applications. In this work, we propose to utilize an unsupervised adversarial framework called cycleGAN for mitigating mutual interferences between automotive radars. Quantitative and qualitative comparisons with baseline methods demonstrate the superior performance of the proposed method.

Unsupervised Deep Interference Mitigation for Automotive Radar

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Realization of Nanostroke with a Violet-Light-EmittingDevice with High Monochromaticity

Time-frequency-sparsity-based interference mitigation using order statistics for automotive radar

Unsupervised Deep Interference Mitigation for Automotive Radar