High Resolution Radar Real-Time Signal and Information Processing

Radar is an electronic device that uses radio waves to determine the range, angle, or velocity of objects. Real-time signal and information processor is an important module for real-time positioning, imaging, detection and recognition of targets. With the development of ultra-wideband technology, synthetic aperture technology, signal and information processing technology, the radar coverage, detection accuracy and resolution have been greatly improved, especially in terms of one-dimensional(1D) high-resolution radar detection, tracking, recognition, and two-dimensional(2D) synthetic aperture radar imaging technology. Meanwhile, for the application of radar detection and remote sensing with high resolution and wide swath, the amount of data has been greatly increased. Therefore, the radar is required to have low-latency and real-time processing capability under the constraints of size, weight and power consumption. This paper systematically introduces the new technology of high resolution radar and real-time signal and information processing. The key problems and solutions are discussed, including the detection and tracking of 1D high-resolution radar, the accurate signal modeling and wide-swath imaging for geosynchronous orbit synthetic aperture radar, and real-time signal and information processing architecture and efficient algorithms. Finally, the latest research progress and representative results are presented, and the development trends are prospected.

Data association based on target signal classification information

In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information （TSCI） should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA （joint probabilistic data association）. The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.

A Vehicle Behavioral Analysis of the Signal Pre-warning Information Provided to the Driver

In this study, we analyze whether there is a change in driver actions because of voice navigation information provided by utilizing ITS （intelligent transport systems）. Specifically, when a vehicle approaches a signalized intersection, the driver is provided with the voice navigation information that the signal light will shortly change red. We focus on what is effective when the voice navigation information is provided. Even if the timing of the provision of voice information is delayed, we have evaluated whether the vehicle is able to stop safely. From the results of the analysis, by providing voice navigation information, we know that a vehicle will stop safely at a signalized intersection. Even if the information provided by voice navigation has been delayed, it could be shown to be safe compared with the case where no information was provided.

WiFi CSI Gesture Recognition Based on Parallel LSTM-FCN Deep Space-Time Neural Network

In this study,we developed a system based on deep space–time neural networks for gesture recognition.When users change or the number of gesture categories increases,the accuracy of gesture recognition decreases considerably because most gesture recognition systems cannot accommodate both user differentiation and gesture diversity.To overcome the limitations of existing methods,we designed a onedimensional parallel long short-term memory–fully convolutional network(LSTM–FCN)model to extract gesture features of different dimensions.LSTM can learn complex time dynamic information,whereas FCN can predict gestures efficiently by extracting the deep,abstract features of gestures in the spatial dimension.In the experiment,50 types of gestures of five users were collected and evaluated.The experimental results demonstrate the effectiveness of this system and robustness to various gestures and individual changes.Statistical analysis of the recognition results indicated that an average accuracy of approximately 98.9% was achieved.

Image Thresholding Using Two-Dimensional Tsallis Cross Entropy Based on Either Chaotic Particle Swarm Optimization or Decomposition

The segmentation effect of Tsallis entropy method is superior to that of Shannon entropy method, and the computation speed of two-dimensional Shannon cross entropy method can be further improved by optimization. The existing two-dimensional Tsallis cross entropy method is not the strict two-dimensional extension. Thus two new methods of image thresholding using two-dimensional Tsallis cross entropy based on either Chaotic Particle Swarm Optimization (CPSO) or decomposition are proposed. The former uses CPSO to find the optimal threshold. The recursive algorithm is adopted to avoid the repetitive computation of fitness function in iterative procedure. The computing speed is improved greatly. The latter converts the two-dimensional computation into two one-dimensional spaces, which makes the computational complexity further reduced from O(L2) to O(L). The experimental results show that, compared with the proposed recently two-dimensional Shannon or Tsallis cross entropy method, the two new methods can achieve superior segmentation results and reduce running time greatly.

Efficient practical codebook-precoding MIMO scheme based on signal space diversity

A novel practical codebook-precoding multiple-input multiple-output（MIMO） system based on signal space diversity（SSD） with the minimum mean squared error（MMSE）receiver is proposed.This scheme utilizes rotation modulation and space-time-frequency component interleaving.A novel precoding matrix selection criterion to maximize the average signal to interference plus noise ratio（SINR） is also put forward for the proposed scheme,which has a larger average mutual information（AMI）.Based on the AMI- maximization criterion,the optimal rotation angles for the proposed system are also investigated.The new scheme can make full use of space-time-frequency diversity and signal space diversity,and exhibit high spectral efficiency and high reliability in fading channels.Simulation results show that the proposed scheme greatly outperforms the conventional bit- interleaved coded modulation（BICM） MIMO-orthogonal frequency division multiplexing（OFDM） scheme without SSD,which is up to4.5 dB signal-to-noise ratio（SNR） gain.

Vibration time series analysis of bubbling and turbulent fluidization

A non-intrusive vibration monitoring technique was used to study the hydrodynamics of a gas-solid fluidized bed. Experiments were carried out in a 15 cm diameter fluidized bed using 226,470 and 700 um sand particles at various gas velocities, covering both bubbling and turbulent regimes. Auto correlation function, mutual information function, Hurst exponent analysis and power spectral density function were used to analyze the fluidized bed hydrodynamics near the transition point from bubbling to turbulent fluidization regimes. The first pass of the autocorrelation function from one half and the time delay at which it becomes zero, and also the first minimum of the mutual information, occur at a higher time delay in comparison to stochastic systems, and the values of time delays were maximum at the bubbling to turbulent transition gas velocity. The maximum value of Hurst exponent of macro structure occurred at the onset of regime transition from bubbling to turbulent. Further increase in gas velocity after that regime transition velocity causes a decrease in the Hurst exponent of macro structure because of breakage of large bubbles to small ones. The results showed these methods are capable of detecting the regime transition from bubbling to turbulent fluidization conditions using vibration signals.

Robust and Passive Motion Detection with COTS WiFi Devices

Device-free Passive（DfP） detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength（RSS）, most recent studies rely on finer-grained Channel State Information（CSI）. However, existing methods have some limitations, in that they are effective only in the Line-Of-Sight（LOS） or for more than one moving individual. In this paper, we analyze the human motion effect on CSI and propose a novel scheme for Robust Passive Motion Detection（R-PMD）. Since traditional low-pass filtering has a number of limitations with respect to data denoising, we adopt a novel Principal Component Analysis（PCA）-based filtering technique to capture the representative signals of human motion and extract the variance profile as the sensitive metric for human detection. In addition, existing schemes simply aggregate CSI values over all the antennas in MIMO systems. Instead, we investigate the sensing quality of each antenna and aggregate the best combination of antennas to achieve more accurate and robust detection. The R-PMD prototype uses off-the-shelf WiFi devices and the experimental results demonstrate that R-PMD achieves an average detection rate of 96.33% with a false alarm rate of 3.67%.