1. Introduction
Sampling is the process of representing a continuous-time signal from a discrete set of measurements, namely the samples. The classical sampling theory focuses mainly on samples that are taken from a signal at some specified instances. A typical example is the Whittaker–Shannon–Kotel’nikov sampling theorem [
1] which has been extended in various ways (see [
2,
3] and references therein).
A more general method of sampling is to consider a signal f in an arbitrary separable Hilbert space , and take measurements (i.e., generalized samples) as inner products of f with a set of vectors , which span a subspace called the sampling space. With these samples, we reconstruct f using a set of vectors , which span a subspace called the reconstruction space. Since any signal lying outside cannot be perfectly reconstructed, our goal is to obtain a meaningful approximation for each input signal of . A natural approach is to assume the ‘consistency’ which means that an input signal and its approximated signal both yield the same measurements; that is, they look the same to observers through acquisition devices.
The idea of consistent sampling was first introduced by Unser and Aldroubi [
4] in a shift-invariant subspace of
with single pre- and single post- filters. In [
5,
6,
7,
8], Eldar et al. studied the consistency in an abstract Hilbert space
with
, under which a unique consistent sampling operator exists. Later, Hirabayashi and Unser [
9] studied the consistent sampling in a finite-dimensional Hilbert space
where
is not necessarily
. Further, Arias and Conde [
10] extended the concept of consistency to ‘quasi-consistency’, which requires only that the samples of the approximated signal are as close as possible to the original samples in
sense. Kwon and Lee [
11] gave complete characterizations of the quasi-consistency and provided an iterative algorithm to compute the quasi-consistent approximations. Another related work is by Adcock, Hansen, and Poon [
12], who analyzed the optimality of consistent sampling using the finite section method [
13]. Recently, Arias and Gonzalez [
14] studied the problem of reconstructing a vector in a Hilbert space from its samples by means of a weighted least square approximation.
In this work, we study consistent or quasi-consistent approximations that have optimal properties, such as possessing the minimum norm or being closest to the original signal. We also provide an example to illustrate our results.
2. Preliminaries
For any countable index set I, let be the set of all complex-valued sequences with . The canonical basis of is given by , where for .
For any closed subspaces
and
of a separable Hilbert space
, we define the
sum of and by
which may not be closed if
is infinite-dimensional. If
, then
is also denoted by
and is referred to as the
direct sum of and . In particular, if
we say that
is the
(internal) direct sum of and [
15].
For any closed subspaces and of with , let be the oblique projection onto along defined by for , where and . In particular, is the orthogonal projection onto .
A sequence
in
is a
frame of if there are constants
, such that we have the following:
Let
and
be two closed subspaces of
. Given a frame
of
, a
dual frame of
is a frame
of
satisfying
When
, a frame
of
is called an
oblique dual frame of
on
if
or equivalently (cf. Lemma 3.1 in [
16])
For further details on oblique dual frames, see [
16,
17] and references therein.
For any two Hilbert spaces,
and
, let
denote the set of all bounded linear operators from
into
, and
. For any
, let
and
be the range and the kernel of
T, respectively. When
is closed,
denotes the Moore–Penrose pseudo-inverse of
T ([
18]).
3. Generalized Consistent Sampling
We consider a generalized consistent sampling problem in a separable Hilbert space
. Let
be a set of sampling vectors in
, which forms a frame of the sampling space
with synthesis operator
given by
for
. Similarly, let
be a set of reconstruction vectors in
, which forms a frame of the reconstruction space
with synthesis operator
given by
for
. For any signal
f in
, we take generalized samples
of
f and we seek its approximation
of
f. Specifically, we seek an operator
satisfying the following:
We call
satisfying (
1)–(
3) a
consistent sampling operator, and denote the set of all such operators by
. It follows from ([
11], Lemma 2.1) (see also [
18,
19]) that (
1) and (
2) hold if and only if
for some
. We call
Q a
consistent filter if
satisfies (3), and denote the set of all such filters by
. Then
and
Throughout the paper, we will always assume that is closed (equivalently, and is closed so that exists). Let .
Theorem 1. if and only if . In this case,where is a closed complementary subspace of in . Proof. See Theorem 3.1 in [
10] and Theorem 3.2 in [
11]. □
When , the consistent approximation of f, with some , can be expressed using oblique dual frames as follows:
Proposition 1. Assume that and let and a frame of L with synthesis operator U. Then and, moreover, we have the following.
- (a)
is an oblique dual frame of on (with synthesis operator ), where denotes the canonical basis for .
- (b)
is an oblique dual frame of on L (with synthesis operator ), where denotes the canonical basis for .
- (c)
For any ,where and have the minimum norm properties:
Proof. See Proposition 3.2 in [
7] and Proposition 5.1 in [
8]. □
A generalization of the consistency is the ‘quasi-consistency’ introduced by Arias and Conde [
10]. Recall that an operator
satisfies (
1) and (2) if and only if
for some
. An operator
with
is called a
quasi-consistent sampling operator if
is as small as possible for every
; that is, for all
and all
,
When
is a quasi-consistent sampling operator, we call
Q a
quasi-consistent filter. We denote by
the set of all quasi-consistent sampling operators and by
the set of all quasi-consistent filters, so that
. It is easily seen that
(
) if and only if
.
Note that
implies
, a situation that is not interesting. Therefore, we will assume that
. Then we have the following:
where
(see Theorem 5.1 in [
10], Proposition 4.2 in [
11]).
Proposition 2 (Theorem 4.10 in [
11]).
There exists a one-to-one correspondence between and , where . Now we consider the sets of consistent or quasi-consistent approximations of
f. For any
, we define
Clearly, we have
, and
. Note that
if and only if
, in which case,
.
Proposition 3. Let .
- (a)
If is nonempty, then it is a closed affine subspace of . Moreover, for any .
- (b)
The set is a closed affine subspaces of . Moreover, we have for any .
Proof. (a) It suffices to show that if , then . Assume that . Then so that . If , then since both g and belong in and . Then, , which shows that . Conversely, if for some , then and so that . Therefore, we conclude that .
Our first main result is the following.
Theorem 2. The following are equivalent.
- (a)
;
- (b)
.
- (c)
;
Moreover, if , then is either ∅ or , , and ; if , then .
Proof. (a) ⇒ (c): Let , where , , and . Then , which yields that .
(c) ⇒ (b): Assume that . Then and, therefore, .
(b) ⇒ (a): Assume that . Then since , we have . Therefore, .
Now, let , so that , , by (c). First, assume that . Then, . Note that for any with . Therefore, if is nonempty, i.e., , then ; if is empty, then by definition . Since is always nonempty, we have .
Finally, assume that
. Then
shows that
. Let
, which can be expressed as
with
, due to the fact that
. Then
yields
and, therefore, exists
containing
. Noticing that
belongs in
(see Proposition 2 and its proof given in [
11]), we write
and compute
. Therefore,
. Since
by definition, we conclude that
. This completes the proof. □
Let be the orthogonal complementary subspace of in . Note that if , then is the unique element of .
Proposition 4. We have and . If , then where .
Proof. See Proposition 3.8, Theorem 3.10, and Lemma 4.11 in [
11]. □
Among the quasi-consistent approximations of f, we can identify some special ones that have optimal properties.
Proof. Note that since
, every element
in
can be written as
for some
. Observe that
where equality is achieved if and only if
, i.e.,
. Since
, we obtain that
. Similarly, observe that
where equality is achieved if and only if
, i.e.,
. Therefore,
. □
Note that if , then the set coincides with by Theorem 2 and moreover, by Proposition 4. As a consequence, we obtain the following.
Corollary 1 (cf. Proposition 3.1 in [
10]).
Let . Then Remark 1. (i) It should be noted that for a generic . Theorem 3.2 in [10] asserts that if , then with , which is not exactly accurate. A correct statement is that if , then . (ii) It was shown in ([5], Theorem 1) that if is of finite dimension, then with . The authors of [10] noticed that this is true even if is infinite dimensional (see Theorem 3.2 in [10]), and showed that if , then (see Proposition 3.1 in [10]). Since by Proposition 4, this follows from the first part of Corollary 1. Note that we have replaced with in the original statements of [10], as discussed in (i).
Let us now illustrate our results with some examples. In the finite-dimensional case, we consider the band-limited sampling of time-limited vectors (cf. [
6,
11]).
Example 1. Let , J, K, and N be positive integers such that and , and let be the space of N-dimensional vectors with for all . Define the sampling vectors by for , and the reconstruction vectors by for . Then it is easily seen thatwhere denotes the N point DFT (discrete Fourier transform) of . Note that consists of time-limited sequences while consists of band-limited sequences. The synthesis operators of and of are given by and respectively. For any , its measurements are given bywhere denotes the residue modulo N. That is, the measurements are precisely the J low-pass DFT coefficients of the N point DFT of . Therefore, a consistent approximation of in has the same low-pass DFT coefficients as . Note that for and , we have the following:Therefore, the input–output cross-correlation matrix (or the generalized Gram matrix) is given bywhere , and . Note that always has full rank. In general, we have from ([11], Lemma 2.4) thatbut since B has full rank, the size of B immediately determines the injectivity/surjectivity of B and the corresponding conditions. We will focus on the under-determined case (), where the number of measurements is strictly less than the number of reconstruction vectors. In this case, B is surjective but not injective; correspondingly, we have and . Then, has infinite cardinality, so there exist infinitely many consistent sampling operators . Note that if we fix a subspace , then every element in can be exactly recovered from its measurements . Unfortunately, this does not apply to every element in . For a generic element in , we seek its (quasi-) consistent approximations in the subspace based on its measurements . In fact, all such approximations are collected in the sets , , , and . Since , we have from Theorem 2 thatandwhere . In particular, the set contains all possible candidates for consistent approximations of in . Among these candidates, Corollary 1 identifies those with optimal properties:The determined case () and over-determined case () are rather obvious, so we refer interested readers to ([11], Example 4.19) for further details. For an example in the infinite dimensional case, we consider complex exponential systems. For a discrete set , we define , which consists of complex exponential functions with frequencies from .
Example 2. It is well known that is an orthonormal basis for . Let be the sampling vectors and let be the reconstruction vectors, so thatFor any , its measurements are given bywhich are in fact the Fourier coefficients of f for . Clearly, these coefficients are not sufficient for the exact recovery of in general, and we aim to approximate f in the reconstruction space using the coefficients. That is, we seek some approximations of f in based on the measurements , namely the consistent approximations
, which produce the same measurements or the quasi-consistent approximations
, which minimize the measurement error in the sense of (
4)
. All such approximations are collected in the sets , , , and . Note thatSince , we have and, thus, for all . Moreover, Theorem 2 shows the following: if , then ;
if , then , and ;
if , in particular, if , then .
Further, Corollary 1 shows that if , thenwhere . This means that ifthenand therefore,