Journal of Applied Mathematics

Volume 2012, Article ID 428142, 11 pages

http://dx.doi.org/10.1155/2012/428142

## Parseval Relationship of Samples in the Fractional Fourier Transform Domain

School of Mathematics, Beijing Institute of Technology, Beijing 100081, China

Received 8 February 2012; Revised 13 April 2012; Accepted 8 May 2012

Academic Editor: Huijun Gao

Copyright © 2012 Bing-Zhao Li and Tian-Zhou Xu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper investigates the Parseval relationship of samples associated with the fractional Fourier transform. Firstly, the Parseval relationship for uniform samples of band-limited signal is obtained. Then, the relationship is extended to a general set of nonuniform samples of band-limited signal associated with the fractional Fourier transform. Finally, the two dimensional case is investigated in detail, it is also shown that the derived results can be regarded as the generalization of the classical ones in the Fourier domain to the fractional Fourier transform domain.

#### 1. Introduction

As a generalization of the classical Fourier transform, the fractional Fourier transform (FrFT) has received much attention in recent years [1–5]. It has been shown that the FrFT can be applied to various applications, including optics, radar and sonar, communication signals and underwater signal processing, and so forth, [1–5]. The relationship between the Fourier transform and the FrFT is derived in [6–8]. The discretization and fast computation of FrFT have been proposed by researchers from different perspectives [9–14]. The generalization of the sampling formulae in the traditional Fourier domain to the FrFT domain has been deduced in [7, 8] and [15, 16]. The properties and advantages of the FrFT in signal processing community have been discussed in [17, 18]. For further properties and applications of FrFT in optics and signal processing community, one can refer to [1, 2].

The well-known operations and relations (such as Hilbert transform [19], convolution and product operations [20, 21], uncertainty principle [22], and Poisson summation formula [23]) in traditional Fourier domain have been extended to the fractional Fourier domain by different authors. The spectral analysis and reconstruction for periodic nonuniform samples is investigated in [24], and the short-time FrFT and its applications are studied in [25]. Recently, Lima and Campello De Souza give the definition and properties of FrFT over finite fields [26], Irarrazaval et al. investigates the application of the FrFT in quadratic field magnetic resonance imaging [27]. The relationship between the FrFT and the fractional calculus operators is studied and given in [28]. But, so far none of the research papers throw light on the extension of the traditional Parseval's relationship for band-limited signals associated with the fractional Fourier domain. It is, therefore, worthwhile and interesting to investigate the extension of the Parseval's relationship of band-limited signals in the FrFT domain.

Parseval relationship plays an important role in the Fourier transform domain [29–31], it relates the energy (or power) in the uniformly spaced sample values of a band-limited signal and the energy in the corresponding analog signal. Based on the relationship between the Fourier transform and the FrFT, this paper investigates the generalization of the traditional Parseval relationship of the Fourier domain to the FrFT domain.

The paper is organized as follows: the preliminaries are presented in Section 2, the main results of the paper are obtained in Section 3, and the conclusion and future working directions are given in Section 4.

#### 2. Preliminaries

##### 2.1. The Fractional Fourier Transform

The ordinary Fourier transform plays an important role in modern signal processing community, little need be said of the importance and ubiquity of the ordinary Fourier transform, and frequency domain concepts and techniques in many diverse areas of science and engineering. The Fourier transform of a signal is defined as

The FrFT can be viewed as the generalization of the Fourier transform with an order parameter , and the FrFT of a signal is given by [1, 2] as where . The original signal can be derived by the inverse FrFT transform of as

It is easy to show that the FrFT reduces to the ordinary Fourier transform when . In order to obtain new results, this paper deals with the case of .

A signal is said to be band-limited with respect to in FrFT domain with order , if

For a signal bandlimited in the LCT domain, the following lemma reflects the relationship between the band-limited signals in Fourier domain and the FrFT domain.

Lemma 2.1. *Suppose that a signal is band-limited with respect to in FrFT domain with order , and let**
then the Fourier transform of signal can be represented by the FrFT of signal as
**
and is a band-limited signal in the ordinary Fourier transform domain. *

*Proof. *Performing the Fourier transform to (2.5), we obtain that
This proves the relationship between the Fourier transform of and the FrFT of . Because is band-limited with respect to in FrFT domain with order , so it is easy to show that signal is a band-limited in the ordinary Fourier transform domain.

From the definition of signal and , the relationship between the signal and can be derived as

##### 2.2. The Two Dimensional FrFT

In [32], the-two dimensional FrFT of a signal is defined as

where the FrFT kernel can be written as

The original signal can be recovered by a two-dimensional FrFT with backward angles as follows:

The definition of bandlimited two-dimensional signals can be similarly defined as the one-dimensional signal; following the prove of Lemma 2.1, the two-dimensional cases can be summarized as Lemma 2.2.

Lemma 2.2. *Suppose that a signal is band-limited with respect to in FrFT domain with order and , and let
**
then the Fourier transform of signal can be represented by the FrFT of signal as
**
and is a band-limited signal in the ordinary Fourier transform domain. *

*Proof. *Similar with the proof of Lemma 2.1, the results can derived easily.

From (2.12) and the definition of the two-dimensional FrFT, the relationship between the signal and is

##### 2.3. The Parseval Relationship

The Parseval's relation states that the energy in time domain is the same as the energy in frequency domain, which can be expressed as follows [29]: where and are Fourier transforms of and , respectively. This formula is called Parseval's relation and holds for all members of the Fourier transform family.

The FrFT can be regarded as the generalization of the Fourier transform, and the similar relation of (2.15) in the FrFT sense can be obtained as [1, 2] where and are FrFT of and with order , respectively. When , the relation of (2.16) can be written as

Equations (2.15)–(2.17) are the Parseval's relationship between the continuous signal and its fractional Fourier transform (or Fourier transform) and can be derived by the Parseval theorem for signals.

In practical situations, we often encounter the calculation of Parseval relations between the discrete signal and the analog signal. Marvasti and Chuande in [30], and Luthra in [31] investigate the Parseval relations of band-limited signal in the traditional Fourier transform domain. The Parseval relation for band-limited discrete uniformly sampled signal in the Fourier domain is [30, 31]

where is the ordinary Fourier transform of , and is the sampling interval that satisfies , and is band-limited to in the ordinary Fourier transform domain. Similarly, the Parseval relationship for bandlimited two-dimensional signal associated with the Fourier transform can be written as follow:

It is proved in [30] that if a set of samples is a sampling set, then the associated Parseval relation for the nonuniformly sampled signals can be written as

where is the low-pass filtered version of the nonuniformly samples, and . and are the corresponding Fourier transforms of and .

The objective of this paper is to obtain the corresponding Parseval relationship for a set of uniform and nonuniform samples of a band-limited signal in the FrFT domain. It is shown that the derived results can be seen as the generalization of the classical results in the Fourier domain.

#### 3. The Main Results

Suppose that a signal is band-limited to in the FrFT domain for order , and is the sampling interval that satisfies the uniform sampling theorem of signal in the FrFT domain [1, 2]; for example, . The objective of this section is to investigate the Parseval relationship for uniform and nonuniform samples of signal in the FrFT domain.

##### 3.1. The Parseval Relationship for Uniform Samples

Theorem 3.1. *Suppose that a signal is band-limited in the FrFT domain with order , then the Parseval relationship associated with the signal in the FrFT domain can be expressed as
**
where is sampling interval, and is the FrFT of signal with order .*

*Proof. *Let
then is a band-limited signal in the traditional Fourier domain. Applying (2.18) to signal , we obtain that
Substituting (2.8) into (3.3), we obtain that
It is easy to verify that, , and the magnitude of exponential function is
Substituting these results in (3.4), we obtain the final result as follows:

Equation (3.1) can be seen as the generalization of the Parseval relations for the uniformly sampled signals associated with the FrFT. The next subsection focus on the generalization of the Parseval relations for the nonuniform sampling sets in the FrFT domain.

##### 3.2. The Parseval Relationship of Nonuniform Samples

Suppose that a general nonuniform sampling set is obtained from the bandlimited signal in the FrFT domain. If this sampling set satisfies the condition proposed in [30], then the Parseval relationship for this nonuniform sampling set can be derived as the following Theorem 3.2.

Theorem 3.2. *The Parseval relationship of nonuniform samples can be written as
**
where , is the FrFT of , and is the Fourier transform of .*

*Proof. *Let
then is a bandlimited signal in the Fourier domain. Applying the classical Parseval relationship of (2.20) for the bandlimited signals in the Fourier domain to signal , we obtain
where is the signal obtained after low-pass filtering of the sampled signal
From the relationship between and , the following relations can be obtained:

From (3.11), the first part of (3.9) can be rewritten as
From Lemma 2.1, the following relationship holds for and :
Substitute (3.13) in to the final part of (3.9), we obtain that
The final result follows from (3.11) and (3.14).

##### 3.3. The Parseval Relationship for Two-Dimensional Case

Based on the definitions of two-dimensional FrFT and bandlimited signals, the Parseval relationship of the one-dimensional cases can be generalized to 2-D signals based on the Lemma 2.2. We would like to give the following Theorem 3.3.

Theorem 3.3. *Suppose that a signal is band-limited in the FrFT domain with order and , and then the Parseval relationship associated with the signal in the FrFT domain can be expressed as
**
where and are sampling interval, and is the two-dimensional FrFT of signal with order and .*

*Proof. *Similar with the proof of Theorem 3.1, let
Then, from Lemma 2.2 is a band-limited signal in the ordinary Fourier transform domain. By applying the classical two-dimensional Parseval relationship of (2.19) to signal , we can obtain the final result.

#### 4. Conclusions

Based on the relationship between the Fourier transform and the FrFT, this paper investigates the Parseval's relationship of sampled signals in the FrFT domain. We firstly investigate the Parseval relationship for the uniformly samples of bandlimited signal associated with the FrFT. Then, we extend this relationship to a general set of nonuniform samples of band-limited signal in the FrFT domain. Finally, we studied the Parseval relations for uniformly sampled bandlimited two-dimensional signals, and it is also shown that the derived results can be seen as the generalization of the classical results in the Fourier domain to the FrFT domain. Future works includes the derivation of the Parseval's relations in the linear canonical transform domain for one- and two-dimensional uniformly (nonuniformly) sampled signals, and the applications of the derived results in the sampling theories and other related areas.

#### Acknowledgments

The authors would like to thank the anonymous reviewers and the handing editor for their valuable comments and suggestions for the improvements of this manuscript. The authors also thank Dr. Hai Jin of Beijing Institute of Technology for the proofreading of the paper. This work was partially supported by the National Natural Science Foundation of China (no. 60901058 and no. 61171195) and Beijing Natural Science Foundation (no. 1102029).

#### References

- H. M. Ozaktas, M. A. Kutay, and Z. Zalevsky,
*The Fractional Fourier Transform with Applications in Optics and Signal Processing*, John Wiley & Sons, New York, NY, USA, 2001. - R. Tao, B. Deng, and Y. Wang,
*Fractional Fourier Transform and Its Applications*, Tsinghua University Press, Beijing, China, 2009. - R. Jacob, T. Thomas, and A. Unnikrishnan, “Applications of fractional Fourier transform in sonar signal processing,”
*IETE Journal of Research*, vol. 55, no. 1, pp. 16–27, 2009. View at Publisher · View at Google Scholar - D. Yang, D. Xiao, and L. Zhang, “The parameters estimation and the feature extraction of underwater transient signal,” in
*Proceedings of the IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC '11)*, pp. 1–4, October 2011. View at Publisher · View at Google Scholar - Y. Feng, R. Tao, and Y. Wang, “Modeling and characteristic analysis of underwater acoustic signal of the accelerating propeller,”
*Science China Information Sciences*, vol. 55, no. 2, pp. 270–280, 2012. View at Publisher · View at Google Scholar - A. W. Lohmann and B. H. Soffer, “Relationships between the Radon-Wigner and fractional Fourier transforms,”
*Journal of the Optical Society of America A*, vol. 11, no. 6, pp. 1798–1801, 1994. View at Publisher · View at Google Scholar - A. I. Zayed, “On the relationship between the Fourier and fractional Fourier transforms,”
*IEEE Signal Processing Letters*, vol. 3, no. 12, pp. 310–311, 1996. View at Publisher · View at Google Scholar - X.-G. Xia, “On bandlimited signals with fractional Fourier transform,”
*IEEE Signal Processing Letters*, vol. 3, no. 3, pp. 72–74, 1996. View at Publisher · View at Google Scholar - H. M. Ozaktas, O. Ankan, M. Alper Kutay, and G. Bozdagi, “Digital computation of the fractional Fourier transform,”
*IEEE Transactions on Signal Processing*, vol. 44, no. 9, pp. 2141–2150, 1996. View at Publisher · View at Google Scholar - S.-C. Pei, M.-H. Yeh, and C.-C. Tseng, “Discrete fractional Fourier transform based on orthogonal projections,”
*IEEE Transactions on Signal Processing*, vol. 47, no. 5, pp. 1335–1348, 1999. View at Publisher · View at Google Scholar - S.-C. Pei and J.-J. Ding, “Closed-form discrete fractional and affine Fourier transforms,”
*IEEE Transactions on Signal Processing*, vol. 48, no. 5, pp. 1338–1353, 2000. View at Publisher · View at Google Scholar - C. Candan, M. A. Kutay, and H. M. Ozaktas, “The discrete fractional Fourier transform,”
*IEEE Transactions on Signal Processing*, vol. 48, no. 5, pp. 1329–1337, 2000. View at Publisher · View at Google Scholar - A. Serbes and L. Durak-Ata, “The discrete fractional Fourier transform based on the DFT matrix,”
*Signal Processing*, vol. 91, no. 3, pp. 571–581, 2011. View at Publisher · View at Google Scholar - D. Y. Wei, Q. W. Ran, Y. M. Li, J. Ma, and L. Y. Tan, “Fractionalisation of an odd time odd frequency DFT matrix based on the eigenvectors of a novel nearly tridiagonal commuting matrix,”
*IET Signal Processing*, vol. 5, no. 2, pp. 150–156, 2011. View at Publisher · View at Google Scholar - T. Erseghe, P. Kraniauskas, and G. Cariolaro, “Unified fractional Fourier transform and sampling theorem,”
*IEEE Transactions on Signal Processing*, vol. 47, no. 12, pp. 3419–3423, 1999. View at Publisher · View at Google Scholar - D. Wei, Q. Ran, and Y. Li, “Generalized sampling expansion for bandlimited signals associated with the fractional Fourier transform,”
*IEEE Signal Processing Letters*, vol. 17, no. 6, pp. 595–598, 2010. View at Publisher · View at Google Scholar - R. Saxena and K. Singh, “Fractional Fourier transform: a novel tool for signal processing,”
*Journal of the Indian Institute of Science*, vol. 85, no. 1, pp. 11–26, 2005. View at Google Scholar - R. Tao, B. Deng, and Y. Wang, “Research progress of the fractional Fourier transform in signal processing,”
*Science in China Series F: Information Sciences*, vol. 49, no. 1, pp. 1–25, 2006. View at Publisher · View at Google Scholar - A. I. Zayed, “Hilbert transform associated with the fractional Fourier transform,”
*IEEE Signal Processing Letters*, vol. 5, no. 8, pp. 206–208, 1998. View at Publisher · View at Google Scholar - A. I. Zayed, “A convolution and product theorem for the fractional Fourier transform,”
*IEEE Signal Processing Letters*, vol. 5, no. 4, pp. 101–103, 1998. View at Publisher · View at Google Scholar - L. B. Almeida, “Product and convolution theorems for the fractional Fourier transform,”
*IEEE Signal Processing Letters*, vol. 4, no. 1, pp. 15–17, 1997. View at Google Scholar - S. Shinde and V. M. Gadre, “An uncertainty principle for real signals in the fractional Fourier transform domain,”
*IEEE Transactions on Signal Processing*, vol. 49, no. 11, pp. 2545–2548, 2001. View at Publisher · View at Google Scholar - B.-Z. Li, R. Tao, T.-Z. Xu, and Y. Wang, “The Poisson sum formulae associated with the fractional Fourier transform,”
*Signal Processing*, vol. 89, no. 5, pp. 851–856, 2009. View at Publisher · View at Google Scholar - R. Tao, B.-Z. Li, and Y. Wang, “Spectral analysis and reconstruction for periodic nonuniformly sampled signals in fractional Fourier domain,”
*IEEE Transactions on Signal Processing*, vol. 55, no. 7, part 1, pp. 3541–3547, 2007. View at Publisher · View at Google Scholar - R. Tao, Y.-L. Li, and Y. Wang, “Short-time fractional Fourier transform and its applications,”
*IEEE Transactions on Signal Processing*, vol. 58, no. 5, pp. 2568–2580, 2010. View at Publisher · View at Google Scholar - J. B. Lima and R. M. Campello De Souza, “The fractional Fourier transform over finite fields,”
*Signal Processing*, vol. 92, no. 2, pp. 465–476, 2012. View at Publisher · View at Google Scholar - P. Irarrazaval, C. Lizama, V. Parot, C. Sing-Long, and C. Tejos, “The fractional Fourier transform and quadratic field magnetic resonance imaging,”
*Computers & Mathematics with Applications*, vol. 62, no. 3, pp. 1576–1590, 2011. View at Publisher · View at Google Scholar - A. A. Kilbas, Yu. F. Luchko, H. Martínez, and J. J. Trujillo, “Fractional Fourier transform in the framework of fractional calculus operators,”
*Integral Transforms and Special Functions*, vol. 21, no. 9-10, pp. 779–795, 2010. View at Publisher · View at Google Scholar - A. V. Oppenheim and R. W. Schafer,
*Digital Signal Processing*, Prentice-Hall, Englewood Cliffs, NJ, USA, 1989. - F. A. Marvasti and L. Chuande, “Parseval relationship of nonuniform samples of one- and two-dimensional signals,”
*IEEE Transactions on Acoustics, Speech, and Signal Processing*, vol. 38, no. 6, pp. 1061–1063, 1990. View at Publisher · View at Google Scholar - A. Luthra, “Extension of Parsevals relation to nonuniform samples,”
*IEEE Transactions on Signal Processing*, vol. 36, no. 12, pp. 1909–1991, 1988. View at Google Scholar - S.-C. Pei and M.-H. Yeh, “Two dimensional discrete fractional Fourier transform,”
*Signal Processing*, vol. 67, no. 1, pp. 99–108, 1998. View at Google Scholar