Sparse sensing of FMCWR with DFrFT basis and reconstruction with fewer samples

Pınar Özkan

Article Details

Sparse sensing of FMCWR with DFrFT basis and reconstruction with fewer samples

Authors:

Pınar Özkan

Publisher:

Pamukkale University

Main Topics:

Elektrik Mühendisliği (Diğer)

Journal:

Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi Vol.30 Issue 7, pp.906-911

Document Type:

Article

Publication Date:

12/2024

Document Language:

Turkish

Document ID (Org No.):

2001335276

pISSN:

2147-5881

Keywords:

Abstract:

 The concept of Compressive Sensing CS, which emerged in the 2000s, is about the existence of sparse states of signals in a transformation area other than the time axis or the axis they exist. Primary data organized in accordance with the purpose or secondary data collected in accordance with the purpose can be expressed sparsely in a size appropriate to the data structure. The CS method, which is applied to randomly collected data from the sparse form created as a result of the transformation, provides an advantage in cases where sample collection from the signal is difficult, incomplete and costly. When the total amount that can be taken is defined as 100, the reconstruction algorithm is applied to the amount taken below this amount and called the measurement, and a signal close to the original of the signal is obtained. This study is about CS method to Frequency Modulated Continuous Wave Radars FMCWR based on Discrete Fractional Fourier Transform DFrFT. The degree of FrFT is estimated with the first few scans of the FMCWR and the signal is randomly received in the amount of 25 in the next scans. In the reconstruction phase, the l1 norm of convex optimization is applied and its success is tested. When the most appropriate degree is determined as a result of degree estimation and the method is applied to 25 of the randomly collected signal; It can be seen that even whe n 75 of the signal is not received, very high performance is achieved in reconstructing the signal. This performance was compared with other fraction degrees and the results were expressed in a table.

 The concept of Compressive Sensing CS, which emerged in the 2000s, is about the existence of sparse states of signals in a transformation area other than the time axis or the axis they exist. Primary data organized in accordance with the purpose or secondary data collected in accordance with the purpose can be expressed sparsely in a size appropriate to the data structure. The CS method, which is applied to randomly collected data from the sparse form created as a result of the transformation, provides an advantage in cases where sample collection from the signal is difficult, incomplete and costly. When the total amount that can be taken is defined as 100, the reconstruction algorithm is applied to the amount taken below this amount and called the measurement, and a signal close to the original of the signal is obtained. This study is about CS method to Frequency Modulated Continuous Wave Radars FMCWR based on Discrete Fractional Fourier Transform DFrFT. The degree of FrFT is estimated with the first few scans of the FMCWR and the signal is randomly received in the amount of 25 in the next scans. In the reconstruction phase, the l1 norm of convex optimization is applied and its success is tested. When the most appropriate degree is determined as a result of degree estimation and the method is applied to 25 of the randomly collected signal; It can be seen that even whe n 75 of the signal is not received, very high performance is achieved in reconstructing the signal. This performance was compared with other fraction degrees and the results were expressed in a table.

 The concept of Compressive Sensing CS, which emerged in the 2000s, is about the existence of sparse states of signals in a transformation area other than the time axis or the axis they exist. Primary data organized in accordance with the purpose or secondary data collected in accordance with the purpose can be expressed sparsely in a size appropriate to the data structure. The CS method, which is applied to randomly collected data from the sparse form created as a result of the transformation, provides an advantage in cases where sample collection from the signal is difficult, incomplete and costly. When the total amount that can be taken is defined as 100, the reconstruction algorithm is applied to the amount taken below this amount and called the measurement, and a signal close to the original of the signal is obtained. This study is about CS method to Frequency Modulated Continuous Wave Radars FMCWR based on Discrete Fractional Fourier Transform DFrFT. The degree of FrFT is estimated with the first few scans of the FMCWR and the signal is randomly received in the amount of 25 in the next scans. In the reconstruction phase, the l1 norm of convex optimization is applied and its success is tested. When the most appropriate degree is determined as a result of degree estimation and the method is applied to 25 of the randomly collected signal; It can be seen that even whe n 75 of the signal is not received, very high performance is achieved in reconstructing the signal. This performance was compared with other fraction degrees and the results were expressed in a table.

 2000li yıllarda ortaya çıkan Sıkıştırmalı Algılama SA kavramı, sinyallerin zaman ekseni ya da var oldukları eksenden başka bir transformasyon alanında seyrek hallerinin var olması üzerinedir. Amaca uygun düzenlenen birincil veri ya da amaca uygun toplanan ikincil veri, veri yapısına uygun bir boyutta seyrek olarak ifade edilebilir. Transformasyon neticesinde oluşturulan seyrek formdan rasgele toplanan verilere uygulanan SA yöntemi, sinyalden örnek toplamanın zor, eksik ve maliyetli olduğu durumlarda avantaj sağlar. Toplamda alınabilecek miktar 100 olarak tanımlandığında bu miktarın daha altında alınan ve ölçüm olarak isimlendirilen miktara geri çatım algoritması uygulanır ve sinyalin aslına yakın bir sinyal elde edilir. Bu çalışma, Frekans Modülasyonlu Sürekli Dalga Radarlarına FMSDR, Ayrık Kesirli Fourier Dönüşüm AKFD Tabanlı SA yöntemi uygulanması üzerinedir. FMSDR nin ilk birkaç taramasında KFD derece kestirimi yapılarak, sonraki taramalarda sinyal rasgele olarak 25 miktarında alınmaktadır. Geri çatım aşamasında konveks optimizasyonun l1 normu uygulanmış ve başarısı test edilmiştir. Derece kestirimi neticesinde en uygun derece tespit edilip 25 miktarındaki rasgele toplanan sinyale yöntem uygulandığında; sinyalin 75i alınmadığında bile sinyalin geri çatımında oldukça yüksek başarım elde edildiği görülmektedir. Bu başarımın sonuçları diğer kesir dereceleri ile karşılaştırma yapılarak tablo halinde ifade edilmiştir.

Article Details

Sparse sensing of FMCWR with DFrFT basis and reconstruction with fewer samples

Authors:

Pınar Özkan

Publisher:

Pamukkale University

Main Topics:

Elektrik Mühendisliği (Diğer)

Journal:

Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi Vol.30 Issue 7, pp.906-911

Document Type:

Article

Publication Date:

12/2024

Document Language:

Turkish

Document ID (Org No.):

2001335276

pISSN:

2147-5881

FrFT

DFrFT

FMCWR

CSKFD

AKFD

FMSDR

SA

Issues

Article Details

Authors: Pınar Özkan

Publisher: Pamukkale University

Main Topics: Elektrik Mühendisliği (Diğer)

Journal: Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi Vol.30 Issue 7, pp.906-911

Document Type: Article

Publication Date: 12/2024

Document Language: Turkish

Document ID (Org No.): 2001335276

pISSN: 2147-5881

Issues

Authors:

Pınar Özkan

Publisher:

Pamukkale University

Main Topics:

Elektrik Mühendisliği (Diğer)

Journal:

Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi Vol.30 Issue 7, pp.906-911

Document Type:

Article

Publication Date:

12/2024

Document Language:

Turkish

Document ID (Org No.):

2001335276

pISSN:

2147-5881