2016/3

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Array Pattern Synthesis Using a Digital Position Shift Method
(Společnost pro radioelektronické inženýrství, 2016-09) Han, Chuang; Wang, Ling
Considering all possible steering directions for beam scanning, a digital position shift method (DPSM) is presented to minimize the Peak Sidelobe Level (PSL) by searching the best position solution for every sensor and calculating the pattern with position offset factor. For the truly minimum PSL, digital position shift with optimal amplitude (DPSOA) is considered simultaneously for beam scanning. For searching the best solution to the two methods, constrained conditions for position shift range and amplitude range are described. The method of feedback particle swarm optimization (FPSO) is presented to obtain a large searching space and fast convergence in local space with refined solution. Numerical examples show that the optimized results by DPSM and DPSOA in all steering directions can be used in beam scanning for its digital realization. When compared with the other techniques published in the literature, especially the steering direction close to endfire direction, this method has lower PSL when the main beam width is maintained.
Strong Spurious Noise Suppression for an FMCW SAR
(Společnost pro radioelektronické inženýrství, 2016-09) Tian, Haishan; Chang, Wenge; Li, Xiangyang; Liu, Zhaohe
To meet the miniature requirement, a frequency modulated continuous wave synthetic aperture radar (FMCW SAR) puts tight constraint on the compactness, which causes the interference of narrow band noise. The aim of this study is to suppress the strong noise for an FMCW SAR. First, the quantitative analysis of the noise is performed. It is found that a strong spurious noise of the analog-to-digital converter (ADC) is introduced from interferences and significantly affects the image quality; the other noise components are sufficiently small, thus having ignorable influences. Then, a Fast Fourier Transform (FFT) based method of noise suppression is proposed to eliminate the ADC strong spurious noise, adopting an ADC and a field programmable gate array (FPGA). Finally, using the real Ku-band FMCW SAR data, the level of the noise components is measured and the effectiveness of the proposed noise suppression method is validated. The results show that the measured noise level coincides with the theoretical noise level, and the proposed noise suppression method effectively eliminates the ADC strong spurious noise.
Time of Arrival Complementing Method for Cooperative Localization of a Target by Two-Node UWB Sensor Network
(Společnost pro radioelektronické inženýrství, 2016-09) Svecova, Maria; Kocur, Dusan
Recently, the detection, localization and tracking of moving persons in emergency situations using ultra-wideband (UWB) sensors have attracted the attention of researchers and final users as well. Experiences with single UWB sensors in real applications have shown that their reliability and accuracy in person detection and localization may be considerably reduced. In contrast, the improved performance of a UWB sensor-based localization system can be provided by a UWB sensor network, which benefits from cooperation among spatially distributed sensor nodes. This cooperation extends the coverage of the monitored area and improves detection capability and localization performance, especially in the case of complex environments and multiple targets. In this paper, we will introduce a new approach to cooperative localization of a target, referred to as the time of arrival complementing method (TOACOM). TOACOM, developed for a two-node UWB sensor network, is based on the time of arrival (TOA) complementing and combining algorithms in combination with the conventional direct calculation method (DC). Its properties will be analyzed for through-the-wall single moving person localization. The obtained results will show the superior performance of TOACOM as compared with person localization by a single UWB sensor, or by a two-node sensor network. In the conclusion, we will outline that the presented version of TOACOM can be further modified for a multiple target scenario and an N-node sensor network.
Beam Footprint Detection and Tracking for Non-cooperative Bistatic SAR
(Společnost pro radioelektronické inženýrství, 2016-09) Yan, Feifei; Chang, Wenge; Li, Xiangyang; Zhang, Qilei
In non-cooperative bistatic synthetic aperture radar (SAR), the position of transmitter’s beam footprint should be detected and tracked in real-time to perform beam synchronization. Theoretical analysis shows that signal-to-noise ratio (SNR) of the reflected echoes from the observational scene is too low to apply the conventional detection and tracking method. According to the cross correlation and Doppler frequency information of the backscattering echo, a beam footprint detection and tracking method is proposed in this paper. This method can realize accumulation of signal energy, therefore enormously improve the performance of beam footprint detection and tracking. Meanwhile, vehicle-based bistatic SAR experiment and airborne bistatic SAR experiment are performed to evaluate the performance of the proposed beam footprint detection and tracking method. Experimental results show that the proposed method performs well for real-time transmitter beam footprint detection and tracking.
Optimization Methods in Emotion Recognition System
(Společnost pro radioelektronické inženýrství, 2016-09) Povoda, Lukas; Burget, Radim; Masek, Jan; Uher, Vaclav; Dutta, Malay Kishore
Emotions play big role in our everyday communication and contain important information. This work describes a novel method of automatic emotion recognition from textual data. The method is based on well-known data mining techniques, novel approach based on parallel run of SVM (Support Vector Machine) classifiers, text preprocessing and 3 optimization methods: sequential elimination of attributes, parameter optimization based on token groups, and method of extending train data sets during practical testing and production release final tuning. We outperformed current state of the art methods and the results were validated on bigger data sets (3346 manually labelled samples) which is less prone to overfitting when compared to related works. The accuracy achieved in this work is 86.89% for recognition of 5 emotional classes. The experiments were performed in the real world helpdesk environment, was processing Czech language but the proposed methodology is general and can be applied to many different languages.

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