Millimeter-wave MMICs and applications

The recent research results of monolithic millimeter-wave (MMW) integrated circuits (MMICs) implemented in CMOS technology at National Taiwan University (NTU) are presented in this paper. Using standard MS/RF CMOS processes, many impressive results were demonstrated. Both the MMIC designs and measured performances will be described. Significant impacts are expected due to the development for future research works in the related areas.

Millimeter wave (MMW) radar systems are now demonstrating the ability to image airport environments in fog. The authors discuss the use of MMW radar technology to provide pilots with enhanced vision and enhanced situation awareness. One of the first steps in providing an autonomous aircraft is the Enhanced Vision System (EVS). This system consists of a heads-up display (HUD) and an imaging MMW radar that provides the pilot with runway imagery on the HUD. Hughes Aircraft Co. has developed a MMW radar designed specifically for the EVS application. This radar was designed to provide the range, resolution, accuracy, update rate, and safety monitoring functions required to meet the demanding Category III FAA certification requirements. The Hughes MMW radar comprises three subsystems: the processor, transceiver and antenna. The radar is currently undergoing testing. To test the radar's ability to compensate for aircraft motion and provide real-time imagery, a dynamic platform was used to simulate aircraft motion. >

One of the main advantages of millimeter wave (MMW) imaging radar systems result from the fact that their imaging performance does nearly not depend on atmospheric affects such as fog, rain and snow. That is the reason that MMW radar seems to be one of the most promising sensors for enhanced vision systems (EVS), which can aid the pilot during approach, landing and taxiing, especially under bad weather conditions. Compared to other imaging devices, MMW radar system deliver a lower image resolution and update rate, and have a worse signal to noise ratio. Moreover, the commonly proposed method of the perspective view projection in EVS applications results in some imaging errors and artifacts. These sensor specific effects should be taken into account during the presently conducted EVS research and development. To get the opportunity of studying imaging radar systems in ground based research environments, we have developed a new type of a MMW radar sensor simulator. Our approach is based on detailed terrain and/or airport databases, as they are available for normal visual simulations or VR applications. We have augmented these databases with some specific attributes which describe object surface properties with respect to MMW. Our approach benefits from the state of the art of high speed computer graphics hard- and software. It is implemented in C/C++ and uses the Open GL graphic standard and the SGI Performer database handler. It runs on every SGI graphic workstation, and achieves an image update rate of about 20 Hz, which is more than actual available radar systems deliver. One of the main advantages of our approach is, that it can be integrated easily in emergent multisensor based enhanced vision systems and it is a useful tool for EVS research and development.

Advanced packaging concepts for highly integrated encapsulated millimeter-wave modules are demonstrated. Millimeter-wave monolithic integrated circuits (MMICs) are flip-chip mounted on top of a low-temperature co-fired ceramic (LTCC) package. This type of "smart package" can integrate various passive functions as well as compact interconnections between MMIC and vertical feedthroughs. The bottom side of this package is connected via a standard ball grid array (BGA) to a low-cost laminate printed circuit board serving as a motherboard for multiple LTCC modules. Fabricated passive LTCC test structures fully validate the approach for millimeter-wave applications. LTCC-/BGA-based packages feature good performance up to approximately 30 GHz. Alternatively to BGA technology, anisotropic conductive pastes are applied as well. They have proven to be suitable at least up to 40 GHz.

In this paper, we present the development of advanced W-band and G-band millimeter-wave monolithic integrated circuits (MMICs) and modules for use in a high-resolution radar system operating at 210 GHz. A W-band frequency multiplier by six as well as a subharmonically pumped 210 GHz dual-gate field-effect transistor (FET) mixer and a 105 GHz power amplifier circuit have been successfully realized using our 0.1 mum InAlAs/InGaAs based depletion-type metamorphic high electron mobility transistor (mHEMT) technology in combination with grounded coplanar circuit topology (GCPW). Additionally, a 210 GHz low-noise amplifier MMIC was fabricated using our advanced 0.05 mum mHEMT technology. To package the circuits, a set of waveguide-to-microstrip transitions has been realized on 50 mum thick quartz substrates, covering the frequency range between 75 and 220 GHz. The presented millimeter-wave components were developed for use in a novel 210 GHz radar demonstrator COBRA-210, which delivers an instantaneous bandwidth of 8 GHz and an outstanding spatial resolution of 1.8 cm.

Millimeter Wave (MMW) radars are currently used as range measuring devices in applications such as automotive driving aids (Langer and Jochem, 1996), (Rohling and Mende, 1996), the mapping of mines (Brooker et al., 2005) and autonomous field robotics (Brooker, 2001), (Langer, 1996). This recent interest is largely due to the advantages MMW radars offer over other range measuring sensors, as their performance is less affected by dust, fog, rain or snow and ambient lighting conditions. MMW radars can provide received signal strength values, at all discrete range intervals, within the working range of the radar (Clark and Durrant-Whyte, 1997), (Scheding et al., 2002). The received power versus range spectra hence contain useful range to target information, but are also corrupted by noise. User defined stochastic algorithms can then be implemented, which exploit this rich data to improve object detection and mapping performance. This is in contrast to many other range measuring devices which typically internally threshold received signals, to provide single hard decisions only, on the estimated range to objects (Mullane, 2007). This paper addresses the issues of predicting the power-range spectra from MMW radars which use the Frequency Modulated Continuous Wave (FMCW) range estimation technique. This is important for two reasons. First, in automotive and autonomous robotic applications, such sensors are used in conjunction with vehicle navigation and map state estimation filters. This is so that (typically uncertain) vehicle motion knowledge can be optimally fused with the noisy sensor information, to infer estimates of the state of interest (typically, the vehicle's pose (position and orientation) and/or information of the surrounding object locations). Hence, it is essential that predicted power versus range spectra can be computed, to apply a Bayesian recursive estimation framework, based on previous measurements, and uncertain vehicle motion information. Second, it is extremely useful to be able to simulate MMW radar data, given certain environmental configurations. This aids the development of reliable object detection algorithms, based on theoretical sensor and noise models, which can then be applied more effectively to real MMW radar data.

This paper presents a framework for detecting the obstacles from a rotor-wing UAV by fusing the information from millimeter-wave radar and monocular camera. In order to implement the angle measurement function of ranging radar, we designed a dual radar network system to locate the target position, and through calibration we achieved correspondence between target orientation and image area. In order to deal with the complex movements of UAV (such as yaw and pitch), this paper proposes a robust algorithm, which can calculate the accurate height of target position points in the image as well as the bottom boundary of the target area in the image, thus avoiding over segmentation of the target. Finally, the target is segmented from the background of the partition after fusion through the threshold algorithm according to the mutational gray value of the bottom boundary of the target position. Through extensive experiments, the effectiveness and efficiency of proposed algorithm have been proved and the experimental results show that the proposed method is novel, simple and feasible.

Environmental perception is a key technology for Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles, and thus a crucial prerequisite for traffic situation assessment and reasonable decision making. Sensor fusion can make full use of the characteristics of multi-sensors to achieve complementary advantages to improve the target recognition precision in various weather conditions. In this paper, a robust target detection algorithm based on millimeter wave (MMW) radar and camera fusion is proposed. Firstly, the images captured by the camera with low visibility in foggy weather are defogged. Then, the effective target is filtered by MMW radar, and mapped to the camera image to obtain the corresponding regions of interest (ROIs). Finally, the method of weighted is used to combine camera vision network recognition results with the radar target estimation results, and obtain the final result of the ROIs. The simulation results indicates that the detection precision through fusion of MMW radar and camera significantly outperforms that of single sensor.

This paper deals with the problem that how to use the distance information of millimeter wave (MMW) radar in the target-tracking of forward looking infrared (FLIR) image sequences. Many conventional image based trackers use timing updating to update template, but how to confirm an appropriate updating period is a difficult problem. However, the presence of noise and image magnification as the camera approaches the target may cause a tracked template to drift. In a MMW/FLIR dual-mode seeking guidance system, MMW radar can provide the precise distance between the missile and the target. The distance information can be used to direct how to update template. Firstly, a distance updating strategy is confirmed after image blowup's influence on pertinence is analyzed. Secondly, magnified template is used to direct template updating after images are magnified by distance information. Lastly, an adaptive template updating algorithm using distance information is presented. Results acquired for real FLIR images obtained from an aircraft are presented. Experiments results show that the higher tracking accuracy and the stronger stability are obtained in the algorithm proposed than those in other methods.

This thesis presents the development and the additive manufacturing of smart antennas and frequency selective surfaces (FSS) for microwave, millimetre wave, and low-Terahertz applications. Numerous fabrications techniques such as etching, low-cost inkjet-printing, Fused Filament Fabrication (FFF) and Aerosol Jet Printing are employed to fabricate the antennas and FSS. Firstly, frequency reconfigurable antennas with close-coupled biasing technique are developed using double-sided etching on a thin mylar substrate for smart current and voltage sensing applications. The frequency tunable antenna also acts as a novel current sensing antenna in a smart sensing system that can sense the alternating current passing through a wire. The design is followed by introducing a low-cost inkjet-printed industry ready solution for frequency reconfigurable antennas where a single antenna aperture solution is utilised to demonstrate frequency reconfigurability in both switching and tuning configurations. Development of Frequency Selective Surfaces using low-cost printing machine is demonstrated in which FSS structures are inkjet-printed as wallpaper posters for Radio Frequency (RF) shielding and signal enhancements for 4G and 5G applications. A novel approach for fully 3D printing an FSS structure is also demonstrated using a low-cost open-source printer, which was modified to print the filament and the conductive inks simultaneously. Widespread investigation of industry-grade Aerosol Jet printing is utilised for additive manufacturing of bandstop and bandpass FSS designs for RF shielding and signal filtering. The bandstop designs are developed for microwave and millimetre-wave applications. The bandpass slot FSS designs are developed for millimetre wave and low-Terahertz applications for futuristic Beyond 5G and 6G systems.

This paper summarizes a research effort to explore the use of neural networks for fusing information from multiple sensors in an automatic target recognition (ATR) scenario. Data from two sensors, forward-looking infrared (FLIR) and millimeter wave (MMW) radar, were collected on three ground targets: tank, truck, and armored personnel carrier (APC). Two different types of classifiers were developed and compared: a conventional statistical quadratic and a neural network consisting of a multilayer perceptron with a backpropagation of error training algorithm. Single sensor classifiers were developed for both FLIR and MMW radar data. In addition, multisensor classifiers were developed using merged FLIR and MMW radar features. The experimental results indicate that a backpropagation neural network (BPN) outperformed the conventional quadratic classifier. Neural networks may provide better solutions to the varying clutter problem found in ATR environments because neural networks can describe both linear and nonlinear boundaries required for complex decision surfaces.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In this paper, we focus on exploiting multipath returns to locate the Non-Line-of Sight (NLOS) target behind corner with a synthetic bistatic MilliMeter Wave (MMW) radar. The designed synthetic bistatic MMW radar uses Lidar to obtain the two-Dimensional (2-D) scenario layout, and forms a virtual bistatic through the movement of MMW radar. With the designed system, a novel location method is proposed combining the Times of Arrivel (TOAs). According to real data experiments, the proposed approach has a better positioning accuracy than the method based on Direction of Arrival (DOA) estimation does.

Research in moving object tracking has shown significant progress towards application in recent years. However, single sensors suffer from illumination variations for the vision sensor and low directional resolution for the radar. In this paper, we propose a moving object tracking method that fuses radar and vision data. First, false radar objects are filtered out. Second, an adaptive background subtraction method is used to detect candidate regions in images. Finally, moving objects are determined when the effective radar objects are in the candidate regions.

Despite the rising interest in the human skin signature over the millimetre wave band there is relatively little information about the human skin reflectance and the dielectric properties of the human skin, and how these varying with locations and between gender, and hydration level of the skin. This paper has investigated the reflectance of the human skin over the frequency band 80-100 GHz, and comparing the reflectance of the human skin with the reflectance of porcine skin samples under normal and wet skin conditions. For a sample of 60 healthy participants (36 males and 24 females) the mean reflectance of the skin over all measurement locations was found to be ~0.606 with a standard deviation of ~0.086. The skin regions of the palm of the hand, the outer wrist and the dorsal forearm skin had reflectances 0.068, 0.058 and 0.0677 lower than the skin regions of the back of the hand, the inner wrist and the volar forearms respectively. Reflectance measurements of human skin under normal and wet state on the palm of the hand and the back of the hand regions indicate that the mean differences in the reflectance before and after the application of water is ~0.15 and ~0.075 respectively. A comparison in reflectance between human skin and porcine skin samples indicates similar trends in signatures between ex-vivo porcine skin samples and human skin. During the cycle of life, human skin is affected by many factors such as the age, the environment, the interaction with different types of radiation, genetic defects, dehydration, and accidents. These factors might cause diseases, temporal skin conditions, and permanent disorders. In response to this, the skin presents signatures, which can be measured using non-contact millimetre wave sensors that could quantify the degree of the damage. These unique findings enable millimetre wave radiometry to be used for detecting human skin signatures and anomalies under different conditions by identify unusually high or low levels of reflectance in tens of seconds.

Sensor fusion of millimeter-wave radar and a camera is beneficial for advanced driver assistance functions such as obstacle avoidance and stopg thereafter, we display them as an image sequence using calibration results. We demonstrate the validity of the proposed methods through experiments using sensors that are mounted on a vehicle.