Automatic recognition of ground radar targets based on target RCS and short time spectrum variance

Liaqat, Sidrah; Khan, Shoab Ahmed; Ihsan, Mojeeb Bin; Asghar, Sidra; Ejaz, Aqib; Bhatti, Aamer Iqbal

doi:10.1109/inista.2011.5946127

Cited by 16 publications

(6 citation statements)

References 4 publications

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“…Consequently, we need to compute both transverse and longitudinal velocities, and with such additional features, we attempt to avoid misclassification. Another feature is the radar cross-section (RCS), which is the equivalent scattering surface of the target seen by the radar and is related to the amount of power that is reflected by the object [12,13].…”

Section: Pedestrian-vehicle Classification Reviewmentioning

confidence: 99%

Pedestrian and Multi-Class Vehicle Classification in Radar Systems Using Rulex Software on the Raspberry Pi

et al. 2020

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Nowadays, cities can be perceived as increasingly dangerous places. Usually, CCTV is one of the main technologies used in a modern security system. However, poor light situations or bad weather conditions (rain, fog, etc.) limit the detection capabilities of image-based systems. Microwave radar detection systems can be an answer to this limitation and take advantage of the results obtained by low-cost technologies for the automotive market. Transportation by car may be dangerous, and every year car accidents lead to the fatalities of many individuals. Humans require automated assistance when driving through detecting and correctly classifying approaching vehicles and, more importantly, pedestrians. In this paper, we present the application of machine learning to data collected by a 24 GHz short-range radar for urban classification. The training and testing take place on a Raspberry Pi as an edge computing node operating in a client/server arrangement. The software of choice is Rulex, a high-performance machine learning package controlled through a remote interface. Forecasts with a varying number of classes were performed with one, two, or three classes for vehicles and one for humans. Furthermore, we applied a single forecast for all four classes, as well as cascading forecasts in a tree-like structure while varying algorithms, cascading the block order, setting class weights, and varying the data splitting ratio for each forecast to improve prediction accuracy. In the experiments carried out for the validation of the presented approach, an accuracy of up to 100% for human classification and 96.67% for vehicles, in general, was obtained. Vehicle sub-classes were predicted with 90.63% accuracy for motorcycles and 77.34% accuracy for both cars and trucks.

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Section: Pedestrian-vehicle Classification Reviewmentioning

confidence: 99%

Pedestrian and Multi-Class Vehicle Classification in Radar Systems Using Rulex Software on the Raspberry Pi

et al. 2020

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“…Among the automotive sensors, radar exhibits advantages of robustness and reliability, especially under adverse weather conditions [3]. Self-driving cars can estimate the distance, relative speed, and angle of the detected target through radar, and, furthermore, classify the detected target by using its features such as radar cross-section (RCS) [4,5], phase information [6], and micro-Doppler signature [7][8][9]. In addition, the radar mounted on autonomous vehicles can recognize the driving environment [10,11].…”

Section: Introductionmentioning

confidence: 99%

YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems

Kim

Cho

Kim

et al. 2020

Sensors

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This paper proposes a method to simultaneously detect and classify objects by using a deep learning model, specifically you only look once (YOLO), with pre-processed automotive radar signals. In conventional methods, the detection and classification in automotive radar systems are conducted in two successive stages; however, in the proposed method, the two stages are combined into one. To verify the effectiveness of the proposed method, we applied it to the actual radar data measured using our automotive radar sensor. According to the results, our proposed method can simultaneously detect targets and classify them with over 90% accuracy. In addition, it shows better performance in terms of detection and classification, compared with conventional methods such as density-based spatial clustering of applications with noise or the support vector machine. Moreover, the proposed method especially exhibits better performance when detecting and classifying a vehicle with a long body.

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“…Because the RCS is an unique feature that reflects the inherent characteristics of targets, it can be used as a representative target classification criterion. Some research using the RCS as the target classification criterion was reported [8]. In [8], RCS‐based target classification with a pulse‐Doppler ground surveillance radar system was conducted.…”

Section: Introductionmentioning

confidence: 99%

“…Some research using the RCS as the target classification criterion was reported [8]. In [8], RCS‐based target classification with a pulse‐Doppler ground surveillance radar system was conducted. To classify targets based on RCS values with a 77‐GHz FMCW radar, we first estimate those of the targets.…”

Section: Introductionmentioning

confidence: 99%

Human–vehicle classification using feature‐based SVM in 77‐GHz automotive FMCW radar

Lee

Yoon

Lee

et al. 2017

IET Radar, Sonar & Navigation

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Automatic recognition of ground radar targets based on target RCS and short time spectrum variance

Cited by 16 publications

References 4 publications

Pedestrian and Multi-Class Vehicle Classification in Radar Systems Using Rulex Software on the Raspberry Pi

Pedestrian and Multi-Class Vehicle Classification in Radar Systems Using Rulex Software on the Raspberry Pi

YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems

Human–vehicle classification using feature‐based SVM in 77‐GHz automotive FMCW radar

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