In order to provide an alternative solution of human machine interfaces, this paper proposed to recognize 10 human hand gestures regularly used in the consumer electronics controlling scenarios based on a three-dimensional radar array. This radar array was composed of three low cost 24GHz K-band Doppler CW (Continuous Wave) miniature I/Q (In-phase and Quadrature) transceiver sensors perpendicularly mounted to each other. Temporal and spectral analysis was performed to extract magnitude and phase features from six channels of I/Q signals. Two classifiers were proposed to implement the recognition. Firstly, a decision tree classifier performed a fast responsive recognition by using the supervised thresholds. To improve the recognition robustness, this paper further studied the recognition using a two layer CNN (Convolutional Neural Network) classifier with the frequency spectra as the inputs. Finally, the paper demonstrated the experiments and analysed the performances of the radar array respectively. Results showed that the proposed system could reach a high recognition accurate rate higher than 92%.

The lateral velocity is of importance in cases like target identification and traffic management. Conventional Doppler methods are not capable of measuring lateral velocities since they quantify only the radial component. Based on the spectrogram characteristic of laterally moving targets, an algorithm based on fractional Fourier transform has been studied in the signal processing literature. The algorithm searches the peak position of the transformation, and calculates the lateral velocity from the peak position. The performance analysis of this algorithm is carried out in this paper, which shows that this algorithm approaches Cramer-Rao bound with reasonable computational complexity. Simulations are conducted at last to compare the analytical performance and the experimental result.

This paper proposes a multiple open-loop frequency estimation scheme based on differential detection for M-ary phase shift keying (MPSK), which accomplishes fast initial acquisition, precise frequency estimation and wide frequency coverage at the same time. The proposed scheme, which has a good trade-off between complexity and performance, operates as follows: 1) it consists of several frequency error detectors (FEDs) based on differential detection with different delays; 2) it precisely estimates frequency in a wide range (the same range of one symbol differential detection) by open-loop according to frequency errors detected by the FEDs. For real-time symbol-by-symbol operation in order to track fast time-varying frequency, it has a smaller complexity than the other frequency estimation schemes. It is confirmed by analysis, numerical calculation and computer simulation that the frequency estimation error of the proposed scheme is close to the Cramer-Rao lower bound (CRLB) (asymptotic degradation of the proposed scheme from the CRLB is about 0. 5 dB) while keeping a wide frequency coverage and this scheme can track fast time-varying frequency.

We have developed a Beam Space CMA (Constant Modulus Algorithm) Adaptive Array Antenna system (BSCMA adaptive array antenna) that may be suitable for mobile communications. In this paper, we present experimental results of interference cancellation characteristics using the developed system. The experiment was carried out in a large radio anechoic chamber, while desired and interference signals were transmitted to the system. We focused on the characteristics of capture, convergence and tracking in adaptive processing. The experimental results show excellent interference cancellation characteristics, and demonstrate that the BSCMA adaptive array antenna has a greater feasibility to be applied practically in mobile communications.