LFM Signal Detection and Estimation Based on Deep Convolutional Neural Network

Chen, Xiaolong; Jiang, Qiaowen; Su, Ningyuan; Chen, Baoxin; Guan, Jian

doi:10.1109/apsipaasc47483.2019.9023016

Cited by 11 publications

(10 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From this work and the works in References [9][10][11], it can be inferred that is proportional to the input frequency ; hence, for high frequencies the size can be large if DL is used, and much larger for classical FE if the same accuracy as DL is required, calling for very complicated DFT implementation in the complex-valued domain.…”

Section: Impact On Iot Sensors and Sdrmentioning

confidence: 87%

“…Reference [11] presented an approach for estimating the frequency of linear frequency modulation (LFM) signals, a topic that has applications in radar and communication engineering. The approach utilized convolutional neural network (CNN), while this problem has been traditionally handled using time-frequency analysis [12], however, this approach requires significant computational cost as it involves two-dimensional transforms.…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…This work handles the problem of estimating the frequency of the single-tone model in Equation 3under Gaussian noise ( ). Our approach is to use a deep-learning (DL) neural network with multiple layers in this process, following recent works that confirmed the high accuracy of this approach [9][10][11]. Despite the pioneering role of these works, they haven't addressed many fundamental issues in DL frequency estimation.…”

Section: Problem Statementmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning versus Spectral Techniques for Frequency Estimation of Single-Tones: Reduced Complexity for SDR and IoT Sensor Communications

Almayyali¹,

Zm²

2021

Preprint

View full text Add to dashboard Cite

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents a comprehensive analysis of deep-learning (DL) approach for frequency estimation of single-tones. It is shown that DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques. The study is comprehensive, filling gaps of existing works, where it analyzes error under different signal-to-noise ratios, numbers of nodes, and numbers of input samples; also, under missing SNR information. It is found that DL-based FE is not significantly affected by SNR bias or number of nodes. DL-based approach can work properly using minimal number of input nodes N at which classical methods fail. It is possible for DL to use as little as two layers with two or three nodes each, with complexity of O{N} versus O{Nlog2 (N)} for DFT-based FE, noting that less N is required for DL. Hence, DL can significantly reduce FE complexity, memory, cost, and power consumption, making DL-based FE attractive for resource-limited systems like some IoT sensor applications. Also, reduced complexity opens the door for hardware-efficient implementation using short word-length (SWL) or time-efficient software-defined radio (SDR) communications.

show abstract

Section: Impact On Iot Sensors and Sdrmentioning

confidence: 87%

Section: Motivation and Related Workmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning versus Spectral Techniques for Frequency Estimation of Single-Tones: Reduced Complexity for SDR and IoT Sensor Communications

Almayyali¹,

Zm²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In [ 11 ], the authors presented an approach for estimating the frequency of linear frequency modulation (LFM) signals, a topic that has applications in radar and communication engineering. The approach utilized a convolutional neural network (CNN), while this problem was traditionally handled using time–frequency analysis [ 12 ]; however, this approach requires significant computational cost as it involves two-dimensional transforms.…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…This work focuses on the problem of estimating frequency

of the single-tone model in Equation (3) under Gaussian noise

. Our approach was to use a deep-learning (DL) neural network with multiple layers in this process, following recent works that confirmed the high accuracy of this approach [ 9 , 10 , 11 ]. Despite the pioneering role of these works, they did not address many fundamental issues in DL frequency estimation.…”

Section: Problem Statementmentioning

confidence: 99%

Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications

Almayyali

Hussain

2021

Sensors

View full text Add to dashboard Cite

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents comprehensive analysis of a deep-learning (DL) approach for frequency estimation of single tones. A DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques can. While filling the gap in the existing literature, the study is comprehensive, analyzing errors under different signal-to-noise ratios (SNRs), numbers of nodes, and numbers of input samples under missing SNR information. DL-based FE is not significantly affected by SNR bias or number of nodes. A DL-based approach can properly work using a minimal number of input nodes N at which classical methods fail. DL could use as few as two layers while having two or three nodes for each, with the complexity of O{N} compared with discrete Fourier transform (DFT)-based FE with O{Nlog2 (N)} complexity. Furthermore, less N is required for DL. Therefore, DL can significantly reduce FE complexity, memory cost, and power consumption, which is attractive for resource-limited systems such as some Internet of Things (IoT) sensor applications. Reduced complexity also opens the door for hardware-efficient implementation using short-word-length (SWL) or time-efficient software-defined radio (SDR) communications.

show abstract

Chirp Signal Denoising Based on Convolution Neural Network

Ben

Zheng

Wang

et al. 2021

Circuits Syst Signal Process

View full text Add to dashboard Cite

Many classical chirp signal processing algorithm may experience distinct performance decrease in noise circumstance. To address the problem, this paper proposes a deep learning based approach to filter noises in time domain.The proposed denoising convolutional neural network (DCNN) is trained to recover the original clean chirps from observation signals with noises. Following denosing, we employ two parameter estimation algorithm to DCNN output.Simulation result show that the proposed DCNN method improves the signal noise ratio (SNR) and parameter estimation accuracy to a great extent compared to the signals without denoising. And DCNN have a strong adaptability of low SNR input scenarios that never trained.

show abstract

LFM Signal Detection and Estimation Based on Deep Convolutional Neural Network

Cited by 11 publications

References 12 publications

Deep Learning versus Spectral Techniques for Frequency Estimation of Single-Tones: Reduced Complexity for SDR and IoT Sensor Communications

Deep Learning versus Spectral Techniques for Frequency Estimation of Single-Tones: Reduced Complexity for SDR and IoT Sensor Communications

Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications

Chirp Signal Denoising Based on Convolution Neural Network

Contact Info

Product

Resources

About