Low-cost radar-based target identification prototype using an expert system

Abstract

Smart and green cities are hot topics in current research because people are becoming more conscious about their impact on the environment and the sustainability of their cities as the population increases. Many researchers are searching for mechanisms that can reduce power consumption and pollution in the city environment. This paper addresses the issue of public lighting and how it can be improved in order to achieve a more energy efficient city. This work is focused on making the process of turning the streetlights on and off more intelligent so that they consume less power and cause less light pollution. The proposed solution is comprised of a radar device and an expert system implemented on a low-cost platform based on a DSP. By analyzing the radar echo in both the frequency and time domains, the system is able to detect and identify objects moving in front of it. This information is used to decide whether or not the streetlight should be turned on. Experimental results show that the proposed system can provide hit rates over 80%, promising a good performance. In addition, the proposed solution could be useful in kind of other applications such as intelligent security and surveillance systems and home automation.

On the adaptability of ensemble methods for distributed classification systems: A comparative analysis

“…The first one consists of detecting and identifying cars and pedestrians using low-cost radars. 24 This is a realistic application where the position of the radar device is so critical, therefore collaboration among devices can be a solution to play down this problem. For this application, only a small set of samples is available since samples were taken in a complex environment.…”

“…In spite of the sensor having some limitations, the obtained results were acceptable. 24 However, the radar position was so critical. In order to minimize this influence, a combination of several radars working together improve the results.…”

“…Then, the first 128 points of the fast Fourier transform (FFT) of the 512 points signals are introduced as inputs to the first-stage classifiers. In this case, instead of using a classification tree, 24,26 an ANN is used to provide the results of each radar device for this first stage.…”

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

“…In order to develop the proposed system, a low-cost hardware platform was implemented [ 8 ]. This is based on a DSP (Digital Signal Processor) which is in charge of analyzing the radar signal and implementing the expert system that deals with the radar information to identify the detected object.…”

“…Our proposal implies to obtain information from the radar echo, in both time and frequency domains, in order to distinguish vehicles and pedestrian from other kind of objects. An expert system based on a classification tree [ 8 ] is developed to meet those requirements. Moreover, sensor cooperation is a support not only to guarantee high reliability but also to estimate the movement tracking of the detected object considering the devices which can detect that object.…”