## 1 Introduction  
The world sinks in waste--the ability of humankind to reduce the pollution of the world with industrial exhaust gases and household waste can determine the ability of humans to end the Anthropocene, i.e., the ongoing extinction initiated by humans [1]. One of the least visible and therefore one of the most dangerous pollutants is plastic. Plastics can be found on the ocean floor [2] and in marine animals [3]; microplastics pollute drinking water supplies [4] and even unborn babies [5,6].  
In order to reduce plastic pollution is the recycling of plastic waste. In the process of recycling, the most important step is the sorting of waste, since there is a lot of different types of plastic that have to be sent to different recycling companies. One way to sort plastic and waste in general is the application of machine vision [7,8]. Such systems are rather expensive, since the hardware used for the processing of visual information is intensely computing oriented. A result of such computing is the determination of markers that are compared to the markers of known products (not materials, not types of plastic) that contain a certain type of plastic. Systems based on machine vision have two major disadvantages: (1) they can only sort known products, if there is no information in the marker database on a new type of waste, it can not be classified; (2) if a known product is packed in a visually similar but different type of plastic packaging it can be classified incorrectly if the machine vision does not determine the type of plastic from the triangle marker on the package; this is a serious problem for pressed waste because the marker can be damaged on the way to the sorting site.  
The study described in this article contributes to the methods applicable in waste sorting systems that exploit the multistatic ultra-wideband (UWB) impulse radar (IR) enforced with a deep learning approach in combination with conventional signal pre/postprocessing methods. The project was based on two facts: (1) the propagation of electromagnetic (EM) waves does not depend on the color of the waste, but on the form and the material only; (2) the reflected or propagated-through-material EM wave contains information about properties of the material that in general is very hard to decode (deconvolute using multiple kernels) using exact signal processing methods; instead, artificial neuron networks (NNs) can be trained to recognize the material under analysis, since NNs have proven their value in different classification tasks [9; 10; 11], and the theory is well developed [12; 13; 14].  
The idea to combine radar and NN is not new, and it has been previously conducted for different needs: human activity recognition [15], gesture recognition [16], healthcare [17; 18], object classification [19], and localization [20]. Nevertheless, this study is unique since, at the time of writing this article, only a few publications exist that are devoted to the determination of material properties by means of UWB radar and NNs [21; 22; 23; 24]. The drawbacks of the referenced system are that objects under analysis have to be in contact with the radar [22]; it has only been validated using simulated data [23; 24]; and it can only determine irregularities in the radiated objects [21], and not exact material properties.  
This study investigated the hypothesis that multistatic radar architecture in combination with the use of NN-based signal processing can provide an opportunity to determine different properties of the radiated object: relative dielectric permittivity, loss tangent, material thickness, and object type (glass bottle, plastic bottle, metallic can) and shape (whole or smashed object).