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Automotive perception involves understanding the external driving environment as well as the internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving high levels of safety and autonomy in driving. This paper provides an overview of different sensor modalities like cameras, radars, and LiDARs used commonly for perception, along with the associated data processing techniques. Critical aspects in perception are considered, like architectures for processing data from single or multiple sensor modalities, sensor data processing algorithms and the role of machine learning techniques, methodologies for validating the performance of perception systems, and safety. The technical challenges for each aspect are analyzed, emphasizing machine learning approaches given their potential impact on improving perception. Finally, future research opportunities in automotive perception for their wider deployment are outlined.

Vibration measurement is a crucial task in industrial systems, where vibration characteristics reflect health conditions and indicate anomalies of the devices. Previous approaches either work in an intrusive manner or fail to capture the micrometer-level vibrations. In this work, we propose mmVib, a practical approach to measure micrometer-level vibrations with mmWave radar. First, we derive a metric called Vibration Signal-to-Noise Ratio (VSNR) that highlights the directions of reducing measurement errors of tiny vibrations. Then, we introduce the design of mmVib based on the concept of Multi-Signal Consolidation (MSC) for the error reduction and multi-object measurement. We implement a prototype of mmVib, and the experiments show that it achieves 3.946% relative amplitude error and 0.02487% relative frequency error in median. Typically, the average amplitude error is only 3.174um when measuring the 100um-amplitude vibration at around 5 meters. Compared to two existing mmWave-based approaches, mmVib reduces the 80th-percentile amplitude error by 69.21% and 97.99% respectively.

An active transmitarray antenna is presented for Ka-band monopulse tracking radar in the 34-36-GHz range. A novel contribution is the application of commercial SiGe-based 5G beamforming chips in a large-scale naval radar array, demonstrating how developments in Ka-band communications can benefit radar technology. Three arrays of 0.77 m diameter each with 24368, 19424, and 7824 radiating elements are presented to compare amplitude-tapered, space-tapered, and hybrid-tapered designs. All designs achieve a sub-1° half-power beamwidth (HPBW), peak sidelobe levels (SLL) below -26 dB, and effective isotropic radiated power (EIRP) of 100 dBm at broadside with a ±60° grating-lobe-free scan range. Furthermore, a small-scale demonstrator is realized and presented that consists of an eight-layer printed circuit board (PCB) with 8× 8 patch antennas on one side and a 4× 4 open-ended waveguide (OEWG) feed-array with beamforming chips and integrated liquid cooling channels on the other side. After over-the-air calibration, the realized array achieves a peak EIRP of 56 dBm, a 12° HPBW, and the ability to scan to ±60° in the H-plane with cos θ scan loss and no grating lobes. E-plane scanning is achieved up to ±45° with θ 1.1 scan loss.

Gesture Recognition which is an inevitable part of human computer interaction is an ever evolving active research area. This has been in use under varied applications like smart home systems, sign language recognition, augment reality and in device controls. The acquisition of hand gestures are usually through optical sensors, in this work a radar based hand gesture data set is used for classifying the gestures. Micro doppler signatures were used as the input to the model. Radar dataset has fewer data samples compared to optical based data sets. The proposed work uses separable convolutional neural networks model which does depth wise convolution followed by point wise convolution to reduce overfitting effect of the training data. The proposed model was built in such a way that the model is capable of classifying any unseen data without exactly mimicking the training samples. The proposed model has achieved 94.56% as the testing accuracy which is certainly better than the previous work on this Dop Net data set. Moreover the model has also minimized the computational hours of the model using separable convolutions.

Nyquist Folding Receiver (NYFR) is a perceptron structure that realizes a low probability of intercept (LPI) signal analog to information. Aiming at the problem of LPI radar signal receiving, the time domain, frequency domain, and time-frequency domain problems of signals intercepted by NYFR structure are studied. Combined with the time-frequency analysis (TFA) method, a radar recognition scheme based on deep learning (DL) is introduced, which can reliably classify common LPI radar signals. First, the structure of NYFR and its characteristics in the time domain, frequency domain, and time and frequency domain are analyzed. Then, the received signal is then converted into a time-frequency image (TFI). Finally, four kinds of DL algorithms are used to classify LPI radar signals. Simulation results demonstrate the correctness of the NYFR structure, and the effectiveness of the proposed recognition method is verified by comparison experiments.

International audience; In this paper, a novel method to improve the readability of chipless tags is presented. When the EM wave falls upon the tag's surface at normal incidence or with a known incidence, the proposed approach permits to obtain a higher signal-to-noise ratio (SNR) of resonators whose orientation with respect to the normal to the plane in which the tag is positioned is unknown and thus, to increase the reading distance of the tag. It introduces a technique based on the detection of this tag orientation in relation to the antennas; a projection of the signals will allow to correct this misalignment in order to always be able to decode the tag identifier on a signal that corresponds to an ideal alignment between the tag and the antennas. With this principle, it is also possible to separate the resonance mode of the resonator that is connected to the identifier from other parasitic resonance modes that may appear due to a possible misalignment between the tag and the antenna. This principle is applied to loop resonators commonly used in chipless RFID which can present strong parasitic modes of resonance. Also, this method makes the reading orientation invariant, allowing to read the resonator whatever its orientation with respect to the normal to the plane in which the tag is positioned. Likewise, the proposed method can be also used to sense this resonator orientation. This method requires a dual-polarization antenna and a 2-ports vector network analyzer (VNA). Results are validated in simulation and with real-environment measurements for a single resonator as well as for multiple resonators, as it is classically used in the chipless RFID technology. Measurements in a harsh environment, as well as comparisons with classical techniques such as time gating, are presented. A study on the improvment of the read range is achieved to highligth the potential of the proposed technique. A reading distance of more than 80cm was thus obtained, which represents 20cm more than with the classic use of a post-processor based on time gatting. Finally, the principle of the measurement is generalized by applying it to a tag with a ground plane and considering that the antenna is not necessarily placed in normal incidence with respect to the plane of the tag.

The autonomous navigation of UAS requires, among others, detect-and-avoid capability as a prerequisite for enabling wide-ranging applications, including the transportation of goods and people. This article presents the design, implementation, and experimental results of a UAS-borne radar system detecting drones. The applications of the proposed system include not only detect-and-avoid systems for safe and autonomous navigation of unmanned aircraft systems but also airborne surveillance of malicious drones in controlled or restricted airspace for mitigating security and privacy threats. The system performance in terms of maximum detection range is evaluated through field tests. The experimental results show that the proposed UAS-borne radar can detect a DJI Phantom 4 and a DJI Matrice 600 Pro at a maximum distance of 440 and 500 meters, respectively. The article also provides insights into the system implementation and integration aspects, discusses future research direction, and stresses the need for standardization efforts to benchmark the required performance levels for UAS-borne radars.

Stealth aircraft use Stealth Technology in order to evade recognition by utilizing mix of highlights with the goal that Aircraft can invade with RADAR and furthermore decrease the deceivability in “radio frequency (RF) range”, sound and infrared visual. During the most recent decades, stealth technology has demonstrated to be one of the best methodologies to the extent the undertaking to escape radar systems is concerned. Stealth intends to evade the recognition or attempt to cover up, for planes, avoiding radar implied stealth. The idea utilized in the stealth innovation isn’t so difficult. It utilizes the guideline to retain and mirror the radar waves. Airplane redirects the radar waves in different ways and limits the radar waves which return back to radar. Another marvel which permits the air ship maintain completely the incoming radar wavelengths to inhabit the electromagnetic reincorporated energy in various way. The structure and material used to manufacture air ship choose the degree of stealth accomplish via air ship. The thought for the radar radio wire to release Radio energy that is then returned through any item that occurs to recognize. The time taken for the reflection to come is measure by the radar radio wire and it can tell how far off an object is.