Reconstruction Filter Design for Stepped-Frequency Continuous Wave

Cui, Guolong; Kong, Lingjiang; Yang, Xiaobo

doi:10.1109/tsp.2012.2197206

Cited by 22 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The transmitter is located at (1.44m, 1.68m) and the receiver is located at (1.47m, 1.61m), respectively. The stepped frequency continuous waveform [28] is employed as the probing signal and the detailed signal parameters are given in TABLE 5.…”

Section: B Experimental Resultsmentioning

confidence: 99%

NLOS Target Localization Behind an L-Shaped Corner With an L-Band UWB Radar

et al. 2020

Self Cite

View full text Add to dashboard Cite

For the reason of target multipaths ambiguities, localization for hidden moving target due to lack of line of sight (LOS) path in urban environments is a hard task. To handle this problem we propose a localization algorithm based on the internal connection of these multipaths in this paper. First, we establish an electromagnetic (EM) propagation model based on the characters of the measured signal received by an L-band single-input single-output (SISO) ultra-wideband (UWB) radar. After that, the model-based target localization algorithm is proposed based on a times-of-arrival (TOAs) association method, which is able to match the measured TOAs with the corresponding EM propagation paths correctly. The proposed method can cope with the case while some missing detections of TOAs happen. Simulation and experimental results ultimately validate the robustness and the state-of-the-art performance of the proposed algorithm. INDEX TERMS NLOS target localization, corner environment, UWB radar, multipath exploitation. GUOLONG CUI (Senior Member, IEEE) received the B.S. degree in electronic information engineering and the M.S. and Ph.D. degrees in signal and information processing from the

show abstract

Section: B Experimental Resultsmentioning

confidence: 99%

NLOS Target Localization Behind an L-Shaped Corner With an L-Band UWB Radar

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The tilt angular of the array baseline is 45°. The radar antenna installed on the car forms a 1.80 m long synthetic aperture, and the interval between adjacent antennas is 0.10 m. The stepped frequency continuous waveform is employed [15], with the frequency starting from 1 GHz to 2 GHz with the frequency interval 10 MHz. The multi-viewpoint imaging results of direct path imaging dictionary and double reflection imaging dictionary are shown in Figs.…”

Section: Resultsmentioning

confidence: 99%

“…Based on the above analysis, both the estimated image derived from the direct path imaging dictionary and the estimated images produced by the double reflection imaging dictionaries contain the target image of interest. Moreover, the elements will form a highlight focused region located at the position of the true target, and the noise in these images will not overlap [15].…”

Section: Image Fusion Of Multiple Dictionariesmentioning

confidence: 99%

Building feature extraction method based on double reflection imaging dictionary

Deng

Liu

et al. 2019

J Wireless Com Network

View full text Add to dashboard Cite

In the technology of building feature extraction, multi-path virtual image appears in the imaging result because the imaging scene is composed of complex wall model and the observation distance of the through-wall radar is short. In order to solve this problem, this paper proposes a double reflection imaging dictionary algorithm to extract building scattering center. In this algorithm, the propagation models of direct path and double reflection path are established with the direct path imaging dictionary and the double reflection imaging dictionary, the corresponding optimization problems are constructed. Finally, the multi-perspective fusion imaging steps are taken to suppress the false image according to the non-correlation of false image from the different reflection path. Experimental results verify the effectiveness of the proposed algorithm.

show abstract

“…The transmitted signal of the UWB TWR in this paper is stepped frequency signal [32]. The stepped-frequency signal can be expressed as…”

Section: Signal Modelmentioning

confidence: 99%

Human Motion Serialization Recognition With Through-the-Wall Radar

Yang

Chen

Wang³

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Motivated by the intrinsic dynamics of physical motion as well as establishment of target motion model, this paper addresses the problem of human motion recognition with ultra wide band (UWB) through-the-wall radar (TWR) in a novel view of range profile serialization. Specifically, we first convert the original radar echoes into range profiles. Then, an auto-encoder network (AEN) with three dense layers is adopted to reduce the dimension and extract the features of each range profile. After that, a gated recurrent unit (GRU) network with two hidden layers is employed to deal with the features of each time-range slice and output the recognition results at each slice in real time. Finally, experimental data with respect to four different behind-wall human motions is collected by self-developed UWB TWR to validate the effectiveness of the proposed model. The results show that the proposed model can validly recognize the human motion serialization and achieve 93% recognition accuracy within the initial 20% duration of the activities (the average durations are 4s, 5.5s, 3s and 4.5s), which is of great significance for real-time human motion recognition. INDEX TERMS Human motion recognition, ultra wide band through-the-wall radar, auto-encoder network, gated recurrent unit.

show abstract

Reconstruction Filter Design for Stepped-Frequency Continuous Wave

Cited by 22 publications

References 8 publications

NLOS Target Localization Behind an L-Shaped Corner With an L-Band UWB Radar

NLOS Target Localization Behind an L-Shaped Corner With an L-Band UWB Radar

Building feature extraction method based on double reflection imaging dictionary

Human Motion Serialization Recognition With Through-the-Wall Radar

Contact Info

Product

Resources

About