The automated detection of people having a fall is particularly important for the elderly in indoor care situations. Privacy concerns, and regulations that prohibit cameras in indoor environments, mean that optical sensing must give way, at least in some situations, to less explicit sensing such as radar. Currently, fall detection using radar Doppler signatures has limitations. We demonstrate a radar-based technique that detects breathing and other movements seamlessly, and can detect a fall after it has happened, i.e., even when the person is static. Using a low-cost, ceiling-mounted radar at low microwave frequencies (sub-6 GHz), our results show it is possible to remotely localize a person within a few cm. The sensor system includes a small neural network model that can distinguish a person from other moving objects in an indoor environment. This can reduce false alarms of the fall detection and hence improve system reliability in real-world deployment. In our experiments, the neural network differentiates a person from a pet (an example of a complex moving entity) with an accuracy exceeding 95%. This sensor system demonstration paves a way forward for general indoor fall detection, extending to the well-being of our elderly through real-time, ongoing monitoring of their breathing and other activities of daily living.

Deep Learning Radar Design for Breathing and Fall Detection

Autonomous vehicles (AVs) rely on various types of sensor technologies to perceive the environment and to make logical decisions based on the gathered information similar to humans. Under ideal operating conditions, the perception systems (sensors onboard AVs) provide enough information to enable autonomous transportation and mobility. In practice, there are still several challenges that can impede the AV sensors’ operability and, in turn, degrade their performance under more realistic conditions that actually occur in the physical world. This paper specifically addresses the effects of different weather conditions (precipitation, fog, lightning, etc.) on the perception systems of AVs. In this work, the most common types of AV sensors and communication modules are included, namely: RADAR, LiDAR, ultrasonic, camera, and global navigation satellite system (GNSS). A comprehensive overview of their physical fundamentals, electromagnetic spectrum, and principle of operation is used to quantify the effects of various weather conditions on the performance of the selected AV sensors. This quantification will lead to several advantages in the simulation world by creating more realistic scenarios and by properly fusing responses from AV sensors in any object identification model used in AVs in the physical world. Moreover, it will assist in selecting the appropriate fading or attenuation models to be used in any X-in-the-loop (XIL, e.g., hardware-in-the-loop, software-in-the-loop, etc.) type of experiments to test and validate the manner AVs perceive the surrounding environment under certain conditions.

An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions.

Vital signs are inarguably accepted as important key constituents to improve the health condition. Worldwide medical institutions and clinical observations have emphasized the need for continuous monitoring of vital signs such as heart rate (HR) and respiration rate (RR) for better health management. Radars are investigated as one of the potential technologies for non-contact continuous monitoring of vital signs. This paper provides a comprehensive technological review on the current state-of-art of non-contact vital sign (NCVS) measurements using radar. We highlight the need to move towards higher frequency for high accuracy in a multi-resident environment and analyze the implications of mmWave exposure on human health and the effect of environmental attenuation. Significant challenges associated with hardware and signal processing algorithm are discussed in detail. Finally, we conclude the review with future directions and challenges associated with the detection of vital signs in a multi-resident indoor environment.

Multi-Resident Non-Contact Vital Sign Monitoring Using Radar: A Review

In this paper, we propose a novel 77 GHz automotive radar sensor, and demonstrate its cyberattack resilience using real measurements. The proposed system is built upon a standard Frequency Modulated Continuous Wave (FMCW) radar RF-front end, and the novelty is in the DSP algorithm used at the firmware level. All attack scenarios are based on real radar signals generated by Texas Instruments AWR series 77 GHz radars, and all measurements are done using the same radar family. For sensor networks, including interconnected autonomous vehicles sharing radar measurements, cyberattacks at the network/communication layer is a known critical problem, and has been addressed by several different researchers. What is addressed in this paper is cyberattacks at the physical layer, that is, adversarial agents generating 77 GHz electromagnetic waves which may cause a false target detection, false distance/velocity estimation, or not detecting an existing target. The main algorithm proposed in this paper is not a predictive filtering based cyberattack detection scheme where an “unusual” difference between measured and predicted values triggers an alarm. The core idea is based on a kind of physical challenge-response authentication, and its integration into the radar DSP firmware.

https://www.mdpi.com/2079-9292/9/4/573/pdf

Design of a Cyberattack Resilient 77 GHz Automotive Radar Sensor

Frequency-modulated continuous wave (FMCW) radars are an important class of radar systems, and they are quite popular because of their simpler architecture and lower cost. A fundamental problem in FMCW radars is the nonlinearity of the voltage-controlled oscillator (VCO), which results in a range of measurement errors, problems in multitarget detection, and degradation in synthetic aperture radar (SAR) images. In this paper, we first introduce a novel upsampling theory, then propose new algorithms to improve range accuracy and multitarget detection capability. These improvements are demonstrated both by simulations and actual lab experiments on a 2.4 GHz radar system. There are several techniques reported in the literature for VCO nonlinearity correction, but what makes the proposed approach different is that we focus on real-time processing on low-cost hardware and optimize the design subject to this constraint. We first developed an optimal upsampling theory which is based on almost-causal finite impulse response (FIR) filters. Compared to the sinc-based noncausal interpolation-based upsamplers, the proposed approach is based on using interpolation filters with few number of coefficients. Furthermore, interpolators are trained for a specific class of signals rather than a highly general signal set. Therefore, the proposed approach can be implemented on lower-cost hardware and perform quite well compared to more expensive systems.

https://www.mdpi.com/2079-9292/8/11/1290/pdf

A Novel Nonlinearity Correction Algorithm for FMCW Radar Systems for Optimal Range Accuracy and Improved Multitarget Detection Capability

For an autonomous vehicle running at highway speeds, processing and response time of radar sensors are of extreme importance. In this paper, we present hardware accelerators for MIT Coffee Can Radar systems [1] using commercial off-the-shelf FPGA/SoC boards [5], [6]. There are multiple advantages of this proposed system architecture, but the ability to process/average more chirp cycles without slowing down the radar sensor output rate is probably the most important one. Averaging over multiple chirps is a known technique which can be used to reduce measurement errors, suppress noise, and improve multi-target detection performance. However, to be able to do more averaging without slowing down the radar sensor output rate, the frequency of the ramp signal driving the VCO must be increased. This will result increased frequency IF signals, and hence will require faster ADC converters. On a multitasking system, this will also increase the size of the data transferred between kernel and user spaces, and will also require handling/averaging over larger arrays stored in the dynamic RAM. These are all known performance bottlenecks both for delay as well as for throughput [7]. We propose the use of a Zynq SoC based system for implementing the signal processing chain. Basically, there will be an application specific hardware accelerator which does averaging of the received signal over multiple chirps inside the FPGA fabric using static block RAMs with deterministic timing. This greatly reduces the computational load on the ARM side of the Zynq, and AXI bus data traffic. Overall, the proposed system will have reduced delays, improved performance, and better real-time characteristics. A preliminary version of the hardware accelerator is synthesized for an entry level Zynq board [5] for I MSPS ADC speed, 100MHz AXI bus speed, and without any pipelining technique. Total resource usage on the FPGA fabric was less than 10%, worst setup slack was 1.090 ns, and worst hold slack was 0.088 ns. All of these show the feasibility of the proposed proposed hardware accelerator.

Design of an FPGA/SoC Hardware Accelerator for MIT Coffee Can Radar Systems

Autonomous Vehicle (AV) technology has the potential to provide enormous value to society. Thus, there is an urgent need to build comprehensive testing technology to prove efficacy under all environmental conditions of interest. In this paper, we will present the first step towards developing a comprehensive testbed for autonomous vehicles (AV) sensors testing and verification with a focus on the radar systems in the presence of rain. We will demonstrate the response of the radar systems onboard AVs based on multiple simulated scenarios. The simulated framework involves radars, target objects, and a stationary vehicle at specific environmental conditions using Matlab.

Marius Brinkmann

Papers

A Novel Nonlinearity Correction Algorithm for FMCW Radar Systems for Optimal Range Accuracy and Improved Multitarget Detection Capability

Design of an FPGA/SoC Hardware Accelerator for MIT Coffee Can Radar Systems

Development of Sensors Testbed for Autonomous Vehicles