*Article* **People Walking Classification Using Automotive Radar**

### **Linda Senigagliesi \*, Gianluca Ciattaglia, Adelmo De Santis and Ennio Gambi**

Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Italy; g.ciattaglia@pm.univpm.it (G.C.); adelmo.desantis@univpm.it (A.D.S.); e.gambi@univpm.it (E.G.)

**\*** Correspondence: l.senigagliesi@staff.univpm.it

Received: 24 February 2020; Accepted: 28 March 2020; Published: 30 March 2020

**Abstract:** Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of radars to discriminate different types of people's movements in a real context. To this end, we exploit two different maps obtained from radar, that is, a spectrogram and a range-Doppler map. Through the application of dimensionality reduction methods, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the use of machine learning techniques we prove that is possible to classify with a very good precision people's way of walking even employing commercial devices specifically designed for other purposes.

**Keywords:** automotive radar; machine learning; walking analysis
