FPGA-Based Wigner–Hough Transform System for Detection and Parameter Extraction of LPI Radar LFMCW Signals

Guner, Kani K.; Gulum, Taylan O.; Erkmen, Burcu

doi:10.1109/tim.2021.3060584

Cited by 20 publications

(31 citation statements)

References 25 publications

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“…Em [6], [7], [8], a extração com a acurácia de até 10%, dos parâmetros baseada em processamento de imagem de formas de onda determinísticas foram alcançadas para SNR's até -8, -9 e -6 dB, respectivamente. Desta forma, os resultados das metodologias (A) e (B) para extração autônoma dos parâmetros da forma de onda radar FM Noise podem ser considerados promissores.…”

Section: Resultsunclassified

“…A necessidade de interpretação humana dos resultados obtidos pela ATF limita a operação de receptores de GE em tempo não-real. Assim, foram propostos em [6], [7], [8] métodos de extração autônoma, baseados em técnicas de processamento de imagem. Tais trabalhos consideraram apenas formas de onda determinísticas com modulação linear em frequência ou modulações que se aproximam dessa variação linear.…”

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Extração de Parâmetros Intrapulso e Interpulso de um Radar com Modulação Aleatória em Frequência

Barbosa¹,

Apolinario²,

Pralon³

et al. 2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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mais usuais, desafiando as defesas a intensificar seus métodos de proteção. Os radares de ruído, particularmente, ganharam notoriedade devido à maior dificuldade dos sistemas eletrônicos inteligentes em não somente interceptá-los, como também corretamente identificá-los com métodos convencionais. Nesse contexto, o presente trabalho propõe um método para extração de parâmetros de um sinal radar com modulação aleatória em frequência baseado em uma análise marginal de sua característica em tempo-frequência. O método proposto é comparado com uma análise feita a partir de técnicas de processamento de imagem, comumente utilizadas para identificação de sinais determinísticos.Palavras-Chave-radar LPI, MAGE, sinais aleatórios, análise tempo-frequência.

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Section: Resultsunclassified

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Extração de Parâmetros Intrapulso e Interpulso de um Radar com Modulação Aleatória em Frequência

Barbosa¹,

Apolinario²,

Pralon³

et al. 2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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“…Related scholars have proposed new ideas for applying vector quantization technology to English speech coding and recognition [15]. Hidden Markov model theory has become a research hotspot and an important theoretical basis for English speech processing technology [16]. Among them, the most representative is the Sphinx system, which is a nonspecific continuous English speech recognition system built using vector quantization and hidden Markov models.…”

Section: Related Workmentioning

confidence: 99%

Design and Implementation of Embedded Real-Time English Speech Recognition System Based on Big Data Analysis

Jin

Tsai

2021

Mathematical Problems in Engineering

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This article uses Field Programmable Gate Array (FPGA) as a carrier and uses IP core to form a System on Programmable Chip (SOPC) English speech recognition system. The SOPC system uses a modular hardware system design method. Except for the independent development of the hardware acceleration module and its control module, the other modules are implemented by software or IP provided by Xilinx development tools. Hardware acceleration IP adopts a top-down design method, provides parallel operation of multiple operation components, and uses pipeline technology, which speeds up data operation, so that only one operation cycle is required to obtain an operation result. In terms of recognition algorithm, a more effective training algorithm is proposed, Genetic Continuous Hidden Markov Model (GA_CHMM), which uses genetic algorithm to directly train CHMM model. It is to find the optimal model by encoding the parameter values of the CHMM and performing operations such as selection, crossover, and mutation according to the fitness function. The optimal parameter value after decoding corresponds to the CHMM model, and then the English speech recognition is performed through the CHMM algorithm. This algorithm can save a lot of training time, thereby improving the recognition rate and speed. This paper studies the optimization of embedded system software. By studying the fixed-point software algorithm and the optimization of system storage space, the real-time response speed of the system has been reduced from about 10 seconds to an average of 220 milliseconds. Through the optimization of the CHMM algorithm, the real-time performance of the system is improved again, and the average time to complete the recognition is significantly shortened. At the same time, the system can achieve a recognition rate of over 90% when the English speech vocabulary is less than 200.

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“…In different scenarios, the signals that need to be processed are different, and the desired results are different. Signal processing needs to be redesigned [ 18 ]. At the same time, most of the multi-channel signal processing designed by FPGA are simple channel superpositions, as shown in Figure 1 , which wastes a lot of resources [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Resource-Saving Customizable Pipeline Network Architecture for Multi-Signal Processing in Edge Devices

Song

Qie

Hao

et al. 2022

Sensors

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With the development of the information age, the importance of edge computing has been highlighted in industrial site monitoring, health management, and fault diagnosis. Among them, the processing and computing of signals in edge scenarios is the cornerstone of realizing these scenarios. While the performance of edge devices has been dramatically improved, the demand for signal processing in the edge side has also ushered in explosive growth. However, the deployment of traditional serial or parallel signal processing architectures on edge devices has problems such as poor flexibility, low efficiency, and low resource utilization, making edge devices unable to exert their maximum performance. Therefore, this paper proposes a resource-saving customizable pipeline network architecture with a space-optimized resource allocation method and a coordinate addressing method for irregular topology. This architecture significantly improves the flexibility of multi-signal processing in edge devices, improves resource utilization, and further increases the performance potential of edge devices. Finally, we designed a comparative experiment to prove that the resource-saving and customizable pipeline network architecture can significantly reduce resource consumption under the premise of meeting real-time processing requirements.

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FPGA-Based Wigner–Hough Transform System for Detection and Parameter Extraction of LPI Radar LFMCW Signals

Cited by 20 publications

References 25 publications

Extração de Parâmetros Intrapulso e Interpulso de um Radar com Modulação Aleatória em Frequência

Extração de Parâmetros Intrapulso e Interpulso de um Radar com Modulação Aleatória em Frequência

Design and Implementation of Embedded Real-Time English Speech Recognition System Based on Big Data Analysis

Resource-Saving Customizable Pipeline Network Architecture for Multi-Signal Processing in Edge Devices

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