**5. Future directions**

Despite plentiful human target analysis researches have been done with all kinds of deep learning methods and the effect is considerable, there are still many challenges and opportunities. Next, a few future research considerations will be listed below.

#### **5.1. Distinguish radar images from natural images**

Among three forms of backscattered radar signals mentioned above, 2D domain radar signals such as time-Doppler maps and time-range maps are mostly used for recognition because they are represented in two dimensions and look more intuitive. Furthermore, these deep learning models are usually introduced from the field of computer vision. In CV area, the images are natural images but the radar images are not. This will lead a doubt that it is proper or not to treat radar 2D images as natural images completely. As a result, it is very urgent to create some techniques to distinguish more radar images with natural images.

#### **5.2. Notice phase information**

and performed better in many tasks. LSTM owns three special gates: input gate, output gate, and forget gate. By using these memory units especially the forget gate, LSTM can access a long-range context of the sequential data. Due to these advantages above, many human activity recognition systems adopted RNN and its variants. Zhi Zhou et al. adopted multimodal signals, including HRRPs and Doppler profiles, which are acquired by the terahertz radar system to recognize dynamic gestures, and the recognition rate reaches more than 91% [22].

Auto-encoder is a high-performance deep learning network suitable for dealing with one-dimensional data by extracting optimized deep features. It learns a deep feature representation of raw input via several rounds of encoding-decoding procedures. Auto-encoder applies the layer-wise greedy unsupervised pre-training principle so as to quickly obtain an efficient deep network.

The commonly used variants of auto-encoder are mainly the following kinds: (1) sparse autoencoder, which is able to rebuild the input data well, and (2) de-noising auto-encoder and contractive auto-encoder which can make the models more generic by adding noise or a well-

Auto-encoder is able to provide a powerful feature extraction approach for many tasks, which saves a lot of labor. In this way, auto-encoder can combine with whether conventional machine learning algorithm or other deep learning models and becomes a more robust one. Mehmet Saygin Seyfiolu et al. [33] used a convolutional auto-encoder architecture to discriminate 12 indoor activity classes involving aided and unaided human motions by recognizing different 2D Doppler maps, and Branka Jokanovic et al. [34] applied three stacked auto-encoders to extract deep features, respectively, and fuse the result together with a voting principle to

Despite plentiful human target analysis researches have been done with all kinds of deep learning methods and the effect is considerable, there are still many challenges and opportu-

Among three forms of backscattered radar signals mentioned above, 2D domain radar signals such as time-Doppler maps and time-range maps are mostly used for recognition because they are represented in two dimensions and look more intuitive. Furthermore, these deep learning models are usually introduced from the field of computer vision. In CV area, the images are natural images but the radar images are not. This will lead a doubt that it is proper or not to treat radar 2D images as natural images completely. As a result, it is very urgent to create some

nities. Next, a few future research considerations will be listed below.

techniques to distinguish more radar images with natural images.

**5.1. Distinguish radar images from natural images**

**4.3. Auto-encoder**

70 UWB Technology and its Applications

chosen penalty term.

classify activities.

**5. Future directions**

Common energy-based power spectrograms after FT or STFT always abandon the phase information in backscatter echoes. But phase is an important attribute of any signal and contains a wealth of information such as transmission duration and distance. Pavlo Molchanov et al. investigated frequency and phase coupling phenomena for radar backscattered signals and proposed novel bicoherence-based information features [31]. We think phrase information in radar backscattering signals should be considered more in future studies.

#### **5.3. Take orientation sensitivity into consideration**

Doppler shift is caused by the radial velocity of the moving target. The radial velocity changes with the position of the target and the radar because it is the component of the object's velocity. In other words, when the radar is above the pedestrian, the Doppler is partly induced by the motion vertical component such as arm and leg vertical motions. In this case, negative Doppler will appear. As a result, if the relative position is different, radar backscattered signals produced by one subject performing a specified activity will differ a lot. How to overcome the orientation sensitivity of radar-based HAR is one of the future research topics.

#### **5.4. Focus more on 1D and 3D domain radar echoes**

Through the investigation of the current research status, compared with the researches in 2D domain, there are few research results on 1D and 3D domains of human echo signals, but through the discussion in previous chapters, we have reason to believe that the two forms of echoes have enough development potential and explore space. Thus, more attention should be paid to this part of human target analysis field.
