The market for driver assistance systems based on millimeter-wave radar sensor technology is gaining momentum. In the near future, the full range of newly introduced car models will be equipped with radar based systems which leads to high volume production with low cost potential. This paper provides background and an overview of the state of the art of millimeter-wave technology for automotive radar applications, including two actual silicon based fully integrated radar chips. Several advanced packaging concepts and antenna systems are presented and discussed in detail. Finally measurement results of the fully integrated radar front ends are shown.

Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band

Model Predictive Control for Trajectory Tracking of Unmanned Aerial Vehicles Using ROS -- Design of Fuzzy Logic Controllers to ROS-based UAVs -- Flying Multiple UAVs Using ROS -- SkiROS -- A skill-based robot control architecture on top of ROS -- Control of Mobile Robots using ActionLib -- Parametric Identification of the Dynamics of Mobile Robots and Its Application for the Tuning of Controllers in ROS -- ROSLink: Bridging ROS with the Internet-of-Things for Cloud Robotics -- A ROS Package for Dynamic Bandwidth Management in Multi-Robot Systems -- An autonomous companion UAV for the SpaceBot Cup competition 2015. .

Robot Operating System (ROS) The Complete Reference (Volume 2)

The introduction of photonics in microwave systems is setting new paradigms in radar architectures, providing new features potentially improving the surveillance effectiveness. In particular, photonics is enabling a new generation of the multiband radars able to manage multiple coherent radar signals at different frequencies simultaneously, with high and frequency-independent quality, enabling multispectral imaging for advanced surveillance systems. In fact, thanks to its high stability and huge bandwidth, photonics matches the urgent requirements of the performance and flexibility of the next-generation software-defined radar architectures, and it guarantees system compactness, thanks to the use of a single shared transceiver for multiband operations and to the potentials for photonic integration, which also promises reduced power consumption. In this paper, we present the first field trial, in a maritime scenario, of a fully coherent multiband radar enabled by the use of photonics. The paper reviews the basic concepts exploited for the photonic generation and the detection of the radar signals, and describes the extension to the multiband operation. We present details on the implementation and testing of a dual-band coherent radar system, discussing the potentials for a software-defined radio approach. Moreover, the results obtained after a simple digital data fusion are discussed, highlighting the capability of the coherent photonics-based multiband radars in exploiting the extended observation bandwidth for improving the system detection resolution with minimum computational costs.

Photonics for Radars Operating on Multiple Coherent Bands

In this article, we consider two related aspects of radar resource management, scheduling and task prioritization. Two different methods of scheduling are examined and compared and their differences and similarities highlighted. The comparison suggests that prioritization of tasks plays a dominant role in determining performance. A prioritization scheme based on fuzzy logic is subsequently contrasted and compared with a hard logic approach as a basis for task prioritization. The setting of priorities is shown to be critically dependent on prior expert knowledge. By assessing the priorities of targets and sectors of surveillance according to a set of rules it is attempted to imitate the human decision-making process such that the resource manager can distribute the radar resources in a more effective way. Results suggest that the fuzzy approach is a valid means of evaluating the relative importance of the radar tasks; the resulting priorities have been adapted by the fuzzy logic prioritization method, according to how the radar system perceived the surrounding environment.

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Knowledge-based resource management for multifunction radar: a look at scheduling and task prioritization

This article discusses the benefits and challenges associated with the design of multi-function scalable phased arrays at millimeter wave frequencies. First, applications for phased arrays with tens to hundreds of elements are discussed. Existing solutions for scaling silicon-based phased arrays from microwave to terahertz frequencies are reviewed. The challenges and tradeoffs associated with multiple integration options for W-band phased arrays are analyzed, with special consideration given to packaging and antenna performance. Finally, a solution based on SiGe ICs and organic packages for a 64-element dual-polarized 94 GHz phased array is described, along with associated measurement results.

W-band scalable phased arrays for imaging and communications

This paper describes an active electronically scanned array (AESA) FMCW radar with eight transceivers. Each transceiver has its own direct digital synthesizer (DDS) for signal generation which enables digital beam forming on transmit as well as on receive. The coherent operation of the eight transceivers and the capability to perform digital beam forming on transmit and receive is demonstrated. Also the first results from radar measurements with suppression of sidelobe scatterers are shown.

Suppression of sidelobe scatterers in an AESA FMCW radar

In recent years, AI based on deep learning has achieved tremendous success in specialized tasks such as speech recognition, machine translation, and the detection of tumors in medical images. Despite these successes there are also some clear signs of the limitations of the current state-of-the-art in AI. For example, biases in AI-enabled face recognition and predictive policing have shown that prejudice in AI systems is a real problem that must be solved. In this position paper, we argue that current AI needs to be developed along four dimensions to become more generally applicable and trustworthy: environment, purpose, collaboration, and governance. Hybrid AI offers the potential for advancements along these four dimensions by combining two different paradigms in AI: knowledge-based reasoning and optimization, and data-driven machine learning

Hybrid AI: The Way Forward in AI by Developing Four Dimensions

False plots and plots with inaccurate range and Doppler estimates may severely degrade the performance of tracking algorithms in radar systems. This paper describes how a multiple hypothesis clustering technique can be applied to mitigate the problems involved in plot extraction. The measures of confidence for each hypothesis are based on fuzzy sets and possibility distributions of plot attributes such as the radial velocity and the radar cross section. Some results of radar experiments are shown.

Multiple hypothesis clustering in radar plot extraction

This chapter describes an alternative to the Bayesian approach to target classification that is based on possibility theory. A possibilistic classifier minimizes the maximum cost of the classification decision taking into account the a posteriori possibilities of the target classes given the measured target attributes. The advantage of a possibilistic classifier when compared with a Bayesian classifier is that it requires only an ordinal ranking of the costs associated with the classification decisions and the uncertainty about the target class. Owing to its qualitative character, a possibilistic classffier is less sensitive to inaccuracies in a priori knowledge than a Bayesian classifier at the expense of a degraded peformance in situations where accurate a priori knowledge is available. This robustness of the possibilistic classifier to inaccuracies in a priori knowledge is demonstrated in a case study where an average cost criterion is used to compare the peformance of a possibilistic and a Bayesian classifier. It is shown that when the characteristics of the measured target attributes deviate strongly from the expected characteristics, the possibilistic classifier provides a lower average cost than a Bayesian classifier.

Possibilistic approach to target classification

In the first part of the Special Issue on Cognitive Radar, articles were published covering high-level concepts and cognitive radar architectures as well as experimental results. The second part focuses on applications and case studies of cognitive radar principles in practice.

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Albert Huizing

Papers

Suppression of sidelobe scatterers in an AESA FMCW radar

Hybrid AI: The Way Forward in AI by Developing Four Dimensions

Multiple hypothesis clustering in radar plot extraction

Possibilistic approach to target classification

Cognitive Radar Special Issue—Part 2