Human motion detection, classification, and perceiving, the dynamics of moving objects in the environment, are crucial in many applications. Many sensors have been used to this detection; the radar represents one of the promising sensors. Kalman filter (KF) and convolutional neural network (CNN) represents a powerful tool for estimation and classification respectively. In this paper, a combination between the KF and CNN have been proposed to detect and classify human behavior. This proposal presents two important points, the precise features map from the combination of Kalman Filter and CNN, as well as the use of the radar, which is working under all circumstances and does not break the privacy. Twenty different experiments with three scenarios for different motion with and without glass wall have been studied, and they are classified. The results show that the overperform of the proposed algorithm and the classification accuracy can reach 98.7%. This advancement of the proposed algorithm depends on the efficient Wigner-Ville short time Fourier transform (STFT) which is used as a feature extractor and make Range-Doppler (RD) map.

This paper presents the results of accompanying classification of tracking for different classes of targets such as a car (moving non-rigid target), moving people (slow moving non-rigid target). The Data are collected using frequency modulation continuous wave (FMCW)radar, while different neural algorithms are considered for classification of targets recorded. A Doppler and Micro Doppler have been used as target features, while short time Fourier transform (STFT) has been used as a feature extraction algorithm. A novel combination between Kalman filter and tree-structured, as well as self-organizing map (SOM) neural network has been proposed as an estimator and classifier, respectively. The results show that the proposed approach is overperformed to the conventional classifier SVM by 1.6% and the gained classification is 92.6%, while the reduction error due to using Kalman filter is 95%.

Recently the Discrete-Wavelet-Transform (DWT) has been represented as signal processing powerful tool to separate the signal into its band frequency components. In this paper, improvement of the steganography techniques by hiding the required message into the suitable frequency band is presented. The results show that the increase of the message length will reduce the Peak Signal to Noise Ratio (PSNR), while the PSNR increases with the increasing the DWT levels. It should be noted that the PSNR reduction was from -13.8278 to -17.77208 when increasing the message length from 161 to 505 characters. In this context, the PSNR is increased from -13.8278 to 7.0554 and from -17.7208 to 1.7901 when the DWT increased from level (1) to level (2).

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