The radar platform developed in B105 Electronic Systems Lab contains a microcontroller which process the I and Q signals adapted from the radar transceiver in order to obtain targets information -speed and distance-. The microcontroller used is a low-power STM32L496 that has a DSP module and enough RAM to perform processing tasks. It runs at 48 MHz and has low-power mode, which allows using our platform in battery-powered Wireless Sensor Networks applications.
The software developed in the microcontroller uses the YetiOS operating system which has also been developed in B105 Electronic Systems Lab and is based on well-known FreeRTOS. The architecture of the radar processing module is composed by two tasks:
Acquisition and Generation Task. This task is responsible of taking samples from the ADC and generating signals using the DAC synchronously. Both acquisition and generation is done using DMA, so other tasks -such as processing one- could run while taking samples.
Processing Task. This task provides the processed information -speed and distance of targets- to the final user. The acquired signal is filtered so the information in undesired frequency bands is eliminated. Besides, a Fast Fourier Transform (FFT) is performed in order to obtain the signals in the frequency domain. Then an OS-CFAR algorithm is applied to select the frequency peaks corresponding to targets, and the targets are selected based on signal levels and SNR ratio.
We have tested the complete radar system in real scenarios and we can process each 128 samples signal in 15 ms. That means that our radar sensor provides distance and speed information with a rate higher to 60 samples per second.
Finally, we have developed an user interface which allows us testing different configuration and the behaviour of the radar sensor on different scenarios.
Low-cost radar transceivers such as RFbeam ones allows using radar sensors in several applications where cost is an important constraint. However they need a hardware platform to work properly. Therefore, in B105 Electronic Systems Lab we have designed and implemented a hardware platform that allows obtaining using radar sensors in Doppler operating mode and FMCW operating mode.
The platform developed is low-sized and resource-constraint which allows using it in Wireless Sensor Networks applications in battery powered nodes. The hardware modules of the designed system are described below:
Power Source. Probably one of the most importan parts of the system as it must provide power to the radar transceiver and to the analog adaptation modules. The power source must provide 12 V, 5 V and 3.3 V for proper radar operation, and these sources must be highly noiseless to enchance radar performance.
Radar Transceiver. The main component of the radar sensor is the transceiver which sends and receive radar signals. K-LC5 and K-LC6 radar transceivers from RFbeam may be used, providing I and Q IF signals, and a VCO pin for FMCW operation.
Signal Adaptation Module. Signal adaptation is necessary to process radar I and Q signals and obtain information from them. An amplification stage, a low-pass filter and a high-pass filter are used in this module. Besides, a single-ended to differential stage is also used to improve signal acquisition.
Signal Acquisition. An ADC is used to digitalize the analog signals so they can be processed. The ADC used can be sigma-delta or SAR, with the higher resolution possible (12 to 16 bits), and with speeds from 10 KHz to 1 MHz. In our platform, the acquisition is done by the main microcontroller.
Signal Generation. A DAC is used to generate the signals to modulate the radar transceiver through its VCO pin. Besides, an adaptation stage is implemented to provide adequated modulation signals to the radar transceivers. The DAC used in our platform is integrated in the main microcontroller.
Processing Unit. A microcontroller is needed to process the acquired signals and obtain information from them. In our design a low-power STM32L496 microcontroller is used.
Radar technology is a well-known field used since 1940s. This technology has been traditionally applied in military and aerospace fields while it has not been highly exploited in civil applications. However, in the last years, radar transceivers cost-reduction and miniaturization have allowed its application in other fields such as traffic and vehicular safety.
These low-cost radar sensors uses the Doppler effect to obtain information about obstacles or targets in its range. The radar transmits a signal and the frequency shift of the returned signal provides the velocity of the moving targets. There are two main operating modes for these radar sensors:
Unmodulated Doppler radar. This operating mode is the most commonly used. The hardware and processing software needed is quite simple which allows using these sensors in size-constraint and resource-contraint devices. However, they only provide velocity information of moving objects in its range. That means that static objects are missed, the distance of the objects cannot be obtained, and two objects moving at the same velocity will be detected as one.
Frequency Modulated Continuous Wave (FMCW) radar. This operating mode is used to obtain the distance of static and moving objects. The radar signal is frequency modulated -usually with a frequency ramp- to allow obtaining distances and velocities from the returning signal frequency shift. Thereby, it is necessary to generate a signal to realize the frequency modulation which increases the hardware complexity. Besides, the software processing is harder as there are much more information to process and there are more noise sources from unwanted environment targets.
In B105 Electronic Systems Lab we have developed a full radar system that can operate in both modes and includes all the hardware and the software necessary. This radar system is being used for traffic safety and traffic monitoring applications in several research projects.
En el proyecto All in One el objetivo fundamental es recoger datos de tráfico para, mediante diferentes métodos, monitorizar el tráfico y realizar un conteo de vehículos.
Para realizar las primeras pruebas nos pusimos en contacto con nuestros compañeros de Aceinsa que nos facilitaron varios puntos clave de la ciudad de Majadahonda como posibles lugares para realizar las pruebas. Gracias a su colaboración, y a la del ayuntamiento de Majadahonda, hemos podido realizar las mismas y tener de forma permanente una caja con alimentación que nos servirá para las pruebas futuras.
El objetivo de estos tests ha consistido en la toma de, aproximadamente, una hora de medidas acompañadas de la correspondiente filmación de vídeo para el cotejo de los datos recogidos a posteriori.
Esperamos que, como resultado de estas pruebas, seamos capaces de realizar una calibración más apropiada de los cabezales radar utilizados en el sistema y que la detección y conteo de vehículos aumente en fiabilidad.
Traffic information has multiplied by three its volume market in the last five years. It is also expected that will continue growing greatly in the next years.
However, there are some fields still to be studied and probably exploited regarding traffic information. Such is the case of the integration of both the number of cars and their proper identification.
That is the starting point for the “All in One” project whose objectives are to create an integral traffic monitorization platform with low cost hardware and extended information.
The hardware platform developed will have one radar device for counting vehicles and a Bluetooth interface for identifying them. With this data available and using data integration techniques it will be possible to achieve a level of traffic information yet unknown.
Our group, the B105 Electronic Systems Lab is the one in charge of designing and developing the low-cost radar system. This system includes the electronic for adapting and handling the RF signals as well as the processing modules and digital filters for those signals.
In this project, there are other research groups and some companies involved. As research groups, we are working together with i3-UPM and CEI. The companies we are collaborating with are ACEINSA, KINEO and IPS. This consortium will allow to achieve the project objectives by integrating some partners with expertise in each of the modules of the whole system.