CN103490848A - Method and device for sparsity order estimation - Google Patents

Abstract

The invention provides a method and device for sparsity order estimation, and the method and device are suitable for the field of signal processing technologies. The method includes the steps that according to the calculated number of sampling points and a preset scheduling strategy, a sampling command is sent to multiple user terminal devices, the sampling points needed for the current iteration step are obtained, and according to the sampling points needed for the current iteration step, all sampling points collected by all iteration steps prior to the current iteration step and multiple observation vectors of a sparsity signal to be observed, an objective function is generated based on a L1/L2 mixed norm and a multi-step accumulation minimum mean square error; according to the objective function, the joint sparsity order estimation is performed on the sparsity signal to be observed, and a result of the joint sparsity order estimation is obtained; according to the result of the joint sparsity order estimation, termination judgment is performed on the joint sparsity order estimation. Under the practical application scene that the signal to noise ratio condition is poor and no prior information exists, the method and device can still effectively perform the accurate sparsity order estimation, and total sampling expenditure of a system is minimized.

Description

Method and device that a kind of degree of rarefication is estimated

Technical field

The invention belongs to the signal processing technology field, relate in particular to method and device that a kind of degree of rarefication is estimated.

Background technology

In the prior art, mostly suppose that the degree of rarefication (number of nonzero element in sparse coefficient vector) of signal is known in advance, and be chosen for the number of the sampled point of guaranteeing correctly to rebuild the required collection of original signal as prior information according to this degree of rarefication.Yet in actual applications, this prior information of the degree of rarefication of signal is likely limited.For example, at cognitive radio (Cognitive Radio, CR) broader frequency spectrum detects in application scenarios, because the degree of rarefication of CR user's frequency spectrum detection Nodes wideband received signal directly depends on authorized user (PU, Primary User) frequency spectrum takies situation, but due between PU system and CR system usually without direct interaction information, usually have to adopt the maximum statistic (being maximum degree of rarefication) of degree of rarefication to replace actual degree of rarefication to go to be defined as rebuilding the number of the required sampled point of original signal.For solving limited this problem of the above degree of rarefication prior information, at present, the concept and methodology of degree of rarefication estimation (SOE, Sparsity Order Estimation) has been proposed.

In the technical scheme that existing degree of rarefication is estimated, usually by unique user according to this limited prior information S of maximum degree of rarefication _maxdetermine the number of required sampled point, and to actual degree of rarefication S _nzcarry out the single step degree of rarefication of unique user and estimate, there is following problem in prior art:

1, signal to noise ratio is lower usually in actual applications, and under the low signal-to-noise ratio scene, the unique user degree of rarefication is estimated usually can can't correctly estimate the degree of rarefication of signal due to having a strong impact on of noise.

2, existing single step degree of rarefication method of estimation, need to be according to maximum degree of rarefication S _maxthe number of determining sampled point is with to actual degree of rarefication S _nzestimated, because S _nz<S _maxand the required sampling expense increasing function that is degree of rarefication, so by S _maxthe number of determined sampled point will be greater than the number of actual required sampled point, cause in the degree of rarefication estimation existing the waste of sampling expense.

3 and in real system during without any hypothesis prior information, the number that existing single step degree of rarefication method of estimation can't be determined required sampled point, so that actual degree of rarefication is correctly estimated, can't effectively carry out existing method.

To sum up, how realizing at low signal-to-noise ratio and still can carry out correctly degree of rarefication to sparse signal under without any prior information scene and estimate, reduce total sampling expense in system is problem demanding prompt solution during degree of rarefication is estimated simultaneously.

Summary of the invention

A kind of method that the purpose of the embodiment of the present invention is to provide degree of rarefication to estimate, be intended to solve at low signal-to-noise ratio and can't correctly carry out degree of rarefication estimation and the system larger problem of expense of always sampling to sparse signal under without any prior information scene.

To achieve these goals, the embodiment of the present invention provides following technical scheme:

The embodiment of the present invention is achieved in that a kind of method that degree of rarefication is estimated, described method comprises:

Calculate the number of the required sampled point of current iteration step;

Number and default scheduling strategy according to the sampled point of described calculating, send the sampling instruction to a plurality of terminal equipments, so that terminal equipment is according to described sampling instruction, carry out sampling to obtain sampled data, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix;

Receive the sampled data that each local terminal reports, as the required sampled point of current iteration step obtained;

The sampled point that before required sampled point, current iteration step, all iterative steps gather according to described current iteration step, and a plurality of observation vectors of sparse signal to be observed, generate the target function that mixes norm and the cumulative least mean-square error of multistep based on L1/L2;

According to described target function, sparse signal to be observed is carried out to the estimation of joint sparse degree, and obtain the result that the joint sparse degree is estimated, described result comprises: the positional information of the nonzero element that the degree of rarefication estimated value of sparse signal to be observed and described degree of rarefication estimated value are corresponding;

The result of estimating according to described joint sparse degree, estimate to stop judgement to described joint sparse degree.

A kind of method that the embodiment of the present invention also provides degree of rarefication to estimate, described method comprises:

The sampling instruction that receiving system sends, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix;

According to described sampling instruction, terminal equipment is carried out sampling to obtain sampled data;

By described sampled data reporting system.

The device that the embodiment of the present invention also provides a kind of degree of rarefication to estimate, described device comprises:

Computing unit, for calculating the number of the required sampled point of current iteration step;

Transmitting element, number and default scheduling strategy according to the sampled point of described calculating, send the sampling instruction to a plurality of terminal equipments, so that terminal equipment is according to described sampling instruction, carry out sampling to obtain sampled data, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix;

Receiving element, the sampled data reported for receiving each local terminal, as the required sampled point of current iteration step obtained;

Generation unit, for sampled point that before required sampled point, current iteration step according to described current iteration step, all iterative steps gather, and a plurality of observation vectors of sparse signal to be observed, generate the target function that mixes norm and the cumulative least mean-square error of multistep based on L1/L2;

Estimation unit, be used for according to described target function, sparse signal to be observed is carried out to the estimation of joint sparse degree, and obtain the result that the joint sparse degree is estimated, described result comprises: the positional information of the nonzero element that the degree of rarefication estimated value of sparse signal to be observed and described degree of rarefication estimated value are corresponding;

Decision unit, for the result of estimating according to described joint sparse degree, estimate to stop judgement to described joint sparse degree.

The device that the embodiment of the present invention also provides a kind of degree of rarefication to estimate, described device comprises:

Receiving element, the sampling instruction sent for receiving system, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix;

Sampling unit, for according to described sampling instruction, terminal equipment is carried out sampling to obtain sampled data;

Report unit, for by described sampled data reporting system.

The embodiment of the present invention compared with prior art, beneficial effect is: the number of calculating the required sampled point of current iteration step, number and default scheduling strategy according to the sampled point of described calculating, send the sampling instruction to a plurality of subscriber terminal equipments, obtain the required sampled point of current iteration step, according to the required sampled point of described current iteration step, the sampled point that before the current iteration step, all iterative steps gather, and a plurality of observation vectors of sparse signal to be observed, generation mixes the target function of norm and the cumulative least mean-square error of multistep based on L1/L2, according to described target function, sparse signal to be observed is carried out to the estimation of joint sparse degree, and obtain the result that the joint sparse degree is estimated, the result of estimating according to described joint sparse degree, estimate to stop judgement to described joint sparse degree.The embodiment of the present invention realizes the joint sparse of utilization system to a plurality of observation vectors of same signal to be observed, and the gain of the study in the multi-Step Iterations process, make under and the practical application scene without any prior information poor in the signal to noise ratio condition, still can effectively carry out accurately degree of rarefication and estimate, the simultaneous minimization system expense of always sampling.

The accompanying drawing explanation

In order to be illustrated more clearly in the technical scheme of the embodiment of the present invention, in below describing embodiment, the accompanying drawing of required use is briefly described, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain according to these accompanying drawings other accompanying drawing.

Fig. 1 is the flow chart of the realization of the method estimated of the degree of rarefication that provides of the embodiment of the present invention one;

Fig. 2 is the flow chart of the realization of the method estimated of the degree of rarefication that provides of the embodiment of the present invention two;

Fig. 3 is the flow chart of the realization of the method estimated of the degree of rarefication that provides of the embodiment of the present invention three;

Fig. 4 is the structure chart of the device estimated of the degree of rarefication that provides of the embodiment of the present invention four;

Fig. 5 is the structure chart of the device estimated of the degree of rarefication that provides of the embodiment of the present invention four;

Fig. 6 is the structure chart of the device estimated of the degree of rarefication that provides of the embodiment of the present invention six.

Embodiment

In order to make purpose of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.

The embodiment of the present invention provides a kind of degree of rarefication method of estimation, and described method comprises:

Calculate the number of the required sampled point of current iteration step;

Number and default scheduling strategy according to the sampled point of described calculating, send the sampling instruction to a plurality of terminal equipments, so that terminal equipment is according to described sampling instruction, carry out sampling to obtain sampled data, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix;

Receive the sampled data that each local terminal reports, as the required sampled point of current iteration step obtained;

The sampled point that before required sampled point, current iteration step, all iterative steps gather according to described current iteration step, and a plurality of observation vectors of sparse signal to be observed, generate the target function that mixes norm and the cumulative least mean-square error of multistep based on L1/L2;

According to described target function, sparse signal to be observed is carried out to the estimation of joint sparse degree, and obtain the result that the joint sparse degree is estimated, described result comprises: the positional information of the nonzero element that the degree of rarefication estimated value of sparse signal to be observed and described degree of rarefication estimated value are corresponding;

The result of estimating according to described joint sparse degree, estimate to stop judgement to described joint sparse degree.

The embodiment of the present invention also provides a kind of degree of rarefication method of estimation, and described method comprises:

The sampling instruction that receiving system sends, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix;

According to described sampling instruction, terminal equipment is carried out sampling to obtain sampled data;

By described sampled data reporting system.

The embodiment of the present invention also provides a kind of degree of rarefication estimation unit, and described device comprises:

Computing unit, for calculating the number of the required sampled point of current iteration step;

Transmitting element, number and default scheduling strategy according to the sampled point of described calculating, send the sampling instruction to a plurality of terminal equipments, so that terminal equipment is according to described sampling instruction, carry out sampling to obtain sampled data, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix;

Receiving element, the sampled data reported for receiving each local terminal, as the required sampled point of current iteration step obtained;

Generation unit, for sampled point that before required sampled point, current iteration step according to described current iteration step, all iterative steps gather, and a plurality of observation vectors of sparse signal to be observed, generate the target function that mixes norm and the cumulative least mean-square error of multistep based on L1/L2;

Estimation unit, be used for according to described target function, sparse signal to be observed is carried out to the estimation of joint sparse degree, and obtain the result that the joint sparse degree is estimated, described result comprises: the positional information of the nonzero element that the degree of rarefication estimated value of sparse signal to be observed and described degree of rarefication estimated value are corresponding;

Decision unit, for the result of estimating according to described joint sparse degree, estimate to stop judgement to described joint sparse degree.

The embodiment of the present invention also provides a kind of degree of rarefication estimation unit, and described device comprises:

Receiving element, the sampling instruction sent for receiving system, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix;

Sampling unit, for according to described sampling instruction, terminal equipment is carried out sampling to obtain sampled data;

Report unit, for by described sampled data reporting system.

Below in conjunction with specific embodiment, realization of the present invention is described in detail:

embodiment mono-

The realization flow figure of the method that the degree of rarefication that Fig. 1 shows the embodiment of the present invention one to be provided is estimated, details are as follows:

In S101, calculate the number of the required sampled point of current iteration step;

In S102, number and default scheduling strategy according to the sampled point of described calculating, send the sampling instruction to a plurality of terminal equipments, so that terminal equipment is according to described sampling instruction, carry out sampling to obtain sampled data, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix;

In S103, receive the sampled data that each local terminal reports, as the required sampled point of current iteration step obtained;

In the present embodiment, send the sampling instruction to a plurality of terminal equipments, receive the sampled data that each local terminal reports, realize combining and obtaining sampled point by a plurality of terminal equipments.

In S104, the sampled point that before required sampled point, current iteration step, all iterative steps gather according to described current iteration step, and a plurality of observation vectors of sparse signal to be observed, generate the target function that mixes norm and the cumulative least mean-square error of multistep based on L1/L2;

In S105, according to described target function, sparse signal to be observed is carried out to the estimation of joint sparse degree, and obtain the result that the joint sparse degree is estimated, described result comprises: the positional information of the nonzero element that the degree of rarefication estimated value of sparse signal to be observed and described degree of rarefication estimated value are corresponding;

In S106, the result of estimating according to described joint sparse degree, estimate to stop judgement to described joint sparse degree.

In the present embodiment, S106 is specially: the result of estimating according to described joint sparse degree judges whether described joint sparse degree estimated result meets the termination judgment condition, if, stop the joint sparse degree and estimate, and output degree of rarefication estimated result, if not, perform step S101.

In the present embodiment, calculate the number of the required sampled point of current iteration step, number and default scheduling strategy according to the sampled point of described calculating, send the sampling instruction to a plurality of subscriber terminal equipments, obtain the required sampled point of current iteration step, according to the required sampled point of described current iteration step, the sampled point that before the current iteration step, all iterative steps gather, and a plurality of observation vectors of sparse signal to be observed, generation mixes the target function of norm and the cumulative least mean-square error of multistep based on L1/L2, according to described target function, sparse signal to be observed is carried out to the estimation of joint sparse degree, and obtain the result that the joint sparse degree is estimated, the result of estimating according to described joint sparse degree, estimate to stop judgement to described joint sparse degree.The embodiment of the present invention realizes the joint sparse of utilization system to a plurality of observation vectors of same signal to be observed, and the gain of the study in the multi-Step Iterations process, make under and the practical application scene without any prior information poor in the signal to noise ratio condition, still can effectively carry out accurately degree of rarefication and estimate, the simultaneous minimization system expense of always sampling.

embodiment bis-

The system that embodiment of the present invention application scenarios can form for a plurality of LUTs corresponding to base station and base station, wherein hypothesis has J LUT, below the method degree of rarefication estimated by intrasystem base station side and subscriber terminal side describe, but the application scenarios of the method that embodiment of the present invention degree of rarefication is estimated is not as limit, the realization flow figure of the method that the degree of rarefication that Fig. 2 shows the embodiment of the present invention two to be provided is estimated, details are as follows:

In S201, parameter initialization;

In the present embodiment, initialized parameter comprises: the degree of rarefication estimated value of a upper iterative step the number M of the sampled point that before the current iteration step, all iterative steps gather _pt, base station initiated after, due to without any prior information, at first base station starts initialization from less initial value, thereby avoids initialization value to be greater than actual degree of rarefication and the unnecessary sampling expense that causes, for example can initialization

wherein, S wherein ₀for default degree of rarefication initial value, S ₀can be 0, described the position of corresponding nonzero element

initial value be empty set

?

m _pt=0.

In S202, calculate the number of the required sampled point of current iteration step;

In the present embodiment, S202 is specifically as follows: according to the degree of rarefication estimated value of a upper iterative step

the number M of the sampled point that before the current iteration step, all iterative steps gather _pt, default adjacent twice iterative step the number smallest incremental Δ of sampled point, calculate the number M of the required sampled point of current iteration step _r, specific as follows:

$M_r=\max \{M\left({\hat{S}}_p\right),M_{\mathrm{pt}}+\mathrm{\&Delta;}\}-M_{\mathrm{pt}}$

Wherein, max{, for getting maxima operation, M () estimates the number computing function of required sampled point for degree of rarefication,

wherein N is signal dimension,

for the operation that rounds up.

In the present embodiment, for adjacent twice iterative step, identical Δ can be set, for whole iterative process, use a Δ, also different Δs can be set, use a plurality of Δs for whole iterative process, for example, when iterative process starts, what the Δ value can be arranged is larger, and, when approaching estimated value, what the Δ value can be arranged is smaller.

In S203, number and default scheduling strategy according to the sampled point of described calculating, send the sampling instruction to a plurality of subscriber terminal equipments, so that terminal equipment is according to described sampling instruction, carry out sampling to obtain sampled data, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix;

In the present embodiment, each LUT equipment, according to described sampling instruction, is dispatched the sampling resource of self to obtain sampled data, and the resource of wherein sampling can be that software, hardware of LUT equipment etc. can be for the resource of being sampled.

In the present embodiment, default scheduling strategy can adopt: the first scheduling strategy: the number of the sampled point that all intrasystem local user's mean allocation system current iteration steps are required, for example, j local user will be assigned with and obtain

individual this walks the number of required sampled point, perhaps, the second scheduling strategy: according to intrasystem local user's performance, the number of required sampled point according to weight allocation system current iteration step, wherein, local user's performance can comprise: channel quality, disposal ability and battery power consumption etc. that the local user is corresponding, for example, can distribute j local user

individual this walks the number of required sampled point, wherein α _jbe that j local user assigns weight, when j local user's channel quality, disposal ability and battery power consumption are better, α _jlarger, on the contrary less, and meet ∑ _jα _j=1.

In the present embodiment, S203 is specifically as follows: base station sends the sampling instruction to each LUT equipment in system according to the number of the sampled point of described calculating and default scheduling strategy, so that in system, each LUT equipment starts to carry out the low speed sampling, the number of the sampled point obtained according to the needs that distribute, obtain local sampled data, and this sampled data is reported to base station.Because each LUT equipment in system only is responsible for local low speed sampling, and the sampled data reporting base station that will obtain through the low speed sampling, the number of by base station, carrying out sampled point calculates that renewal, scheduling of resource, degree of rarefication are estimated, the relatively operation such as judgement, therefore, can reduce computation complexity and the energy consumption of local node, and then increase the operation life-span of system.

Wherein, the sampling instruction can comprise: the number of each local user sampled point of required collection when carrying out the low speed sampling and each LUT equipment are for generating the random seed sequence of sampling matrix, the instruction that j the local user of take receives is example, and the instruction of its reception is: the number of j local user sampled point of required collection when carrying out the low speed sampling is M _{r, j}, and LUT equipment is for generating the random seed sequence of sampling matrix.

Understand for the ease of the user, LUT equipment is treated to observation signal and carry out the low speed sampling and describe, j the LUT equipment of take is example, and it is treated observation signal and carries out the sampled data that the low speed sampling obtains and be:

y _j＝Φ _jx+n _j,j=1,...,J,

Wherein, x is sparse signal to be observed, by the vector representation of N * 1, Φ _jfor the low speed sampling matrix, by a M _{r, j}the random matrix of * N means (M _{r, j}<<N), the low speed sampling matrix that can Gong select in actual applications comprises: Gauss's matrix, shellfish are made great efforts matrix, analog information transition matrix (Analog-to-Information Conversion, AIC) etc., the random seed sequence that matrix element can send according to base station related concrete distribution rule (for example generates according to a certain matrix-type, if select Gauss's matrix, to generate each element of low speed sampling matrix according to Gaussian Profile), n _jbe the noise at j LUT equipment place, by a M _{r, j}* 1 vector representation, y _jbe the local sampled data that j LUT equipment obtains, by a M _{r, j}* 1 vector representation also reports base station by each LUT equipment by this sampled data.

For sparse signal x to be observed, its coefficient vector corresponding on one group of orthogonal basis of a certain transformation space is sparse, and therefore, sparse signal x to be observed can be expressed as:

x=Ψθ

Wherein, the coefficient vector that θ is N * 1, and in this coefficient vector the number S of nonzero element be far smaller than the dimension N(S of signal vector<<N, be sparse property), wherein, the size of S is the degree of rarefication of signal, it is the unknown quantity that embodiment of the present invention technical scheme will be estimated, the rarefaction representation matrix that Ψ is N * N (its each column vector is called one group of orthogonal basis of transformation space), the type of rarefaction representation matrix depends on concrete practical application, for example: at cognitive radio (Cognitive Radio, CR) thus in system because the present frequency domain rarefaction representation of the sparse gonosome of signal matrix is a discrete Fourier transform (DFT) (Discrete Fourier Transform, DFT) matrix, be generally a wavelet transform matrix or discrete cosine transform (Discrete Cosine Transform in image applications, DCT) matrix, , x and θ are one to one, two kinds of expressions of same signal.

In S204, receive the sampled data that each local terminal reports, as the required sampled point of current iteration step obtained;

In the present embodiment, by base station, collecting the sampled data that each LUT reports, is whole sampled datas that system in the rational and efficient use multi-Step Iterations obtains, the M obtained in collection k step _rafter individual this step systematic sampling point, also retain all M that gather in all k-1 steps of k step simultaneously _ptindividual sampled point.

In S205, the sampled point that before required sampled point, current iteration step, all iterative steps gather according to described current iteration step, and a plurality of observation vectors of sparse signal to be observed, generate the target function that mixes norm and the cumulative least mean-square error of multistep based on L1/L2;

In the present embodiment, because the observed object of each LUT is same sparse signal, by fully utilizing the inherent joint sparse of a plurality of LUTs to a plurality of observation vectors of same sparse signal, and the cumulative property of the sampling in the multi-Step Iterations process, the target function of being set up based on L1/L2 mixing norm and the cumulative least mean-square error of multistep by base station side is as follows:

$\widehat{\mathrm{\&Theta;}}=[{\widehat{\mathrm{\&theta;}}}_1,...,{\widehat{\mathrm{\&theta;}}}_J]=\underset{\mathrm{\&Theta;}}{\arg \min }\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{\left(\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}{\left|{\mathrm{\&theta;}}_{n,j}\right|}^2\right)}^{1/2}+\mathrm{\&omega;}\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}{\left|\right|\left[\begin{array}{c}{Y}_{k-1,j}\\ y_{k,j}\end{array}\right]-\left[\begin{array}{c}{\mathrm{\&Omega;}}_{k-1,j}\\ {\mathrm{\&Phi;}}_{k,j}\end{array}\right]\mathrm{\&Psi;\&theta;}\left|\right|}_2^2---\left(1\right)$

Wherein, Y _{k-1, j}be the cumulative vector of all sampled points of gathering in all k-1 iterative steps before current the k time iterative step of j local user,

wherein T is matrix transpose operation, Ω _{k-1, j}be j local user cumulative matrix of sampling matrix used in all k-1 iterative steps before current the k time iterative step,

y _{k, j}be the sampling point vector that j local user gathers in current the k time iterative step, Φ _{k, j}it is j local user sampling matrix used in current the k time iterative step, ω is the weighted value of the sparse property of compromise and error, the signal vector to be observed that θ is N * 1, wherein, the dimension that N is signal vector, in this vector, the number of nonzero element is far smaller than the dimension (this signal to be observed has sparse property) of signal vector, the rarefaction representation matrix that Ψ is N * N, the number that J is local user's (terminal), θ _jfor each observation vector corresponding by signal θ to be observed, Θ each θ that serves as reasons _jdo the matrix that column vector forms, j ∈ [1 ..., J], θ _{n, j}for θ _jin n element, joint estimate for a plurality of observation vectors of sparse signal to be observed.

In S206, according to described target function, sparse signal to be observed is carried out to the estimation of joint sparse degree, and obtain the result that the joint sparse degree is estimated, described result comprises: the positional information of the nonzero element that the degree of rarefication estimated value of sparse signal to be observed and described degree of rarefication estimated value are corresponding;

In the present embodiment, according to target function (1), sparse signal to be observed is carried out to the estimation of joint sparse degree, and obtains the result that the joint sparse degree estimates and be specially:

${\hat{S}}_c=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}\left(\frac{1}{J}\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}\right|{\widehat{\mathrm{\&theta;}}}_j|\&GreaterEqual;\mathrm{\&lambda;})$

${\hat{S}\mathrm{upp}}_c=\left\{\right\{q_1,...,q_{{\hat{S}}_c}\left\}\right|(1/J){\mathrm{\&Sigma;}}_{j=1}^J{\widehat{\mathrm{\&theta;}}}_{q_i,j}\&GreaterEqual;\mathrm{\&lambda;},q_i\&Element;\{1,...,N\}\}$

Wherein,

for the degree of rarefication estimated value of current sparse signal to be observed, i.e. the number of nonzero element in sparse signal to be observed,

for described degree of rarefication estimated value

the positional information of corresponding nonzero element,

for the location index that individual nonzero element is corresponding, λ is default degree of rarefication decision threshold, usually can set according to nonzero element amplitude in sparse signal, for example, can be set as half of nonzero element average.

In S207, judge whether current degree of rarefication estimated result occurs extremely, if, carry out S203, to carry out a new joint sparse degree, estimate, if not, carry out S208.

In the present embodiment, by judging current degree of rarefication estimated result, whether occur extremely, if occur, extremely re-execute the sampling of current step, estimate to carry out new joint sparse degree once, improved the robustness that degree of rarefication is estimated.

In the present embodiment, when the degree of rarefication estimated value

the time, think that current degree of rarefication estimated result occurs abnormal, wherein η is that default degree of rarefication is estimated abnormal thresholding, can be set as β N, and 0<β<1, N is vectorial dimension, because signal is while having sparse property, in vector the number S of nonzero element be far smaller than the dimension N(S of signal vector<<N), when the degree of rarefication estimated value occurs the time deviated from the sparse property principle of signal, so be judged as estimated result, occur abnormal.

In S208, the result of estimating according to described joint sparse degree, judge whether described joint sparse degree estimated result meets the termination judgment condition, if, carry out S209, if not, carry out S202.

In S209, stop the joint sparse degree and estimate, and output degree of rarefication estimated result.

Optionally, S208 can one realize in the following ways:

Whether the positional information that judges the nonzero element that the positional information of the nonzero element that current degree of rarefication estimated value is corresponding is corresponding with last degree of rarefication estimated value is consistent,

wherein, for the positional information of nonzero element corresponding to last degree of rarefication estimated value,

positional information for nonzero element corresponding to current degree of rarefication estimated value.

If, stop the joint sparse degree and estimate, and output degree of rarefication estimated result;

If not, the number of calculating the required sampled point of current iteration step is carried out in redirect, to carry out a new joint sparse degree, estimates.

Optionally, S208 can also two realize in the following ways:

Judge whether current degree of rarefication estimated value equates with last degree of rarefication estimated value,

wherein,

for current degree of rarefication estimated value,

last degree of rarefication estimated value;

If unequal, the number of calculating the required sampled point of current iteration step is carried out in redirect, to carry out a new joint sparse degree, estimates;

If equate, judge that whether the positional information of the nonzero element that the positional information of the nonzero element that current degree of rarefication estimated value is corresponding is corresponding with last degree of rarefication estimated value is consistent,

If consistent, stop the joint sparse degree and estimate, and output degree of rarefication estimated result;

If inconsistent, the number of calculating the required sampled point of current iteration step is carried out in redirect, to carry out a new joint sparse degree, estimates.

Stop the judgement mode from top two kinds, judgment condition one " judges whether current degree of rarefication estimated value and last degree of rarefication estimated value equate " and is contained in judgment condition two " whether the positional information of the nonzero element that the positional information that judges the nonzero element that current degree of rarefication estimated value is corresponding and last degree of rarefication estimated value are corresponding is consistent ", while being judgment condition two establishment, judgment condition one must be set up, but when judgment condition one is set up, judgment condition two is not necessarily set up, it should be noted that, judgment condition one only compares two numerical value, and judgment condition two need compare two ordered series of numbers, so realize the judgement of condition two more complicated to the judgement of condition one than realizing, in actual applications, can be according to the hardware computational resource of real system with to the implementation of two kinds of judgements more than selecting factors such as algorithm execution efficiency requirement.

embodiment tri-

The realization flow figure of the method that the degree of rarefication that Fig. 3 shows the embodiment of the present invention three to be provided is estimated, details are as follows:

In S301, the sampling instruction that receiving system sends, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix.

In the present embodiment, the sampling instruction can comprise: the number of each LUT equipment sampled point of required collection when carrying out the low speed sampling and each LUT equipment are for generating the random seed sequence of sampling matrix, the instruction that j the local user of take receives is example, and the instruction of its reception is: the number of j local user sampled point of required collection when carrying out the low speed sampling is M _{r, j}, and LUT equipment is for generating the random seed sequence of sampling matrix.

In S302, according to described sampling instruction, terminal equipment is carried out sampling to obtain sampled data;

In the present embodiment, terminal equipment receives described sampling instruction, and, according to described sampling instruction, dispatch the sampling resource of self to obtain sampled data, the resource of wherein sampling can be that software, hardware of LUT equipment etc. can be for the resource of being sampled.

In the present embodiment, the execution of the terminal equipment of S302 is sampled similar with the implementation of the process of obtaining sampled data and the step S203 in above-described embodiment two, and details are referring to the description of above-described embodiment two.

In S303, by described sampled data reporting system.

In the present embodiment, each LUT equipment only is responsible for local low speed sampling, and the sampled data reporting system that will obtain through the low speed sampling, the number of by system, carrying out sampled point calculates that renewal, scheduling of resource, degree of rarefication are estimated, the relatively operation such as judgement, therefore, can reduce computation complexity and the energy consumption of local node, and then increase the operation life-span of system.

embodiment tetra-

The structure chart of the device that the degree of rarefication that Fig. 4 shows the embodiment of the present invention four to be provided is estimated, for convenience of explanation, only show the part relevant to the embodiment of the present invention.

Described device comprises: computing unit 41, transmitting element 42, receiving element 43, generation unit 44, estimation unit 45 and decision unit 46.

Computing unit 41, for calculating the number of the required sampled point of current iteration step;

Transmitting element 42, number and default scheduling strategy according to the sampled point of described calculating, send the sampling instruction to a plurality of subscriber terminal equipments, so that terminal equipment is according to described sampling instruction, carry out sampling to obtain sampled data, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix;

Receiving element 43, the sampled data reported for receiving each local terminal, as the required sampled point of current iteration step obtained;

Generation unit 44, for sampled point that before required sampled point, current iteration step according to described current iteration step, all iterative steps gather, and a plurality of observation vectors of sparse signal to be observed, generate the target function that mixes norm and the cumulative least mean-square error of multistep based on L1/L2;

Estimation unit 45, be used for according to described target function, sparse signal to be observed is carried out to the estimation of joint sparse degree, and obtain the result that the joint sparse degree is estimated, described result comprises: the positional information of the nonzero element that the degree of rarefication estimated value of sparse signal to be observed and described degree of rarefication estimated value are corresponding;

Decision unit 46, for the result of estimating according to described joint sparse degree, estimate to stop judgement to described joint sparse degree.

The degree of rarefication estimation unit that the embodiment of the present invention provides can be used in the embodiment of the method one of aforementioned correspondence, and details, referring to the description of above-described embodiment one, do not repeat them here.

embodiment five

The structure chart of the device that the degree of rarefication that Fig. 5 shows the embodiment of the present invention five to be provided is estimated, for convenience of explanation, only show the part relevant to the embodiment of the present invention.

Described device comprises: computing unit 51, transmitting element 52, receiving element 53, generation unit 54, estimation unit 55, abnormal deciding means 56 and decision unit 57.

Wherein, described default scheduling strategy is:

The first scheduling strategy: the number of the sampled point that all intrasystem local user's mean allocation system current iteration steps are required; Perhaps

The second scheduling strategy: according to intrasystem local user's performance, the number of required sampled point according to weight allocation system current iteration step.

Optionally, described computing unit 51, specifically for the degree of rarefication estimated value according to a upper iterative step

the number M of the sampled point that before the current iteration step, all iterative steps gather _pt, default adjacent twice iterative step the number smallest incremental Δ of sampled point, calculate the number M of the required sampled point of current iteration step _r, specific as follows:

$M_r=\max \{M\left({\hat{S}}_p\right),M_{\mathrm{pt}}+\mathrm{\&Delta;}\}-M_{\mathrm{pt}}$

Wherein, max{, for getting maxima operation, M () estimates the number computing function of required sampled point for degree of rarefication,

wherein N is signal dimension,

for the operation that rounds up.

Optionally, abnormal deciding means 56, for judging whether current degree of rarefication estimated result occurs extremely, when the described current degree of rarefication estimated result of judgement occurs when abnormal, start transmitting element 52, to carry out new joint sparse degree, estimate, when the described current degree of rarefication estimated result of judgement does not occur starting decision unit 57 when abnormal.

Optionally, whether the positional information of described decision unit 57 nonzero element corresponding with last degree of rarefication estimated value specifically for the positional information that judges the nonzero element that current degree of rarefication estimated value is corresponding is consistent, if, stopping the joint sparse degree estimates, and output degree of rarefication estimated result, if not, start computing unit 51, estimate to carry out a new joint sparse degree.

Optionally, described decision unit 57 is specifically for judging whether current degree of rarefication estimated value equates with last degree of rarefication estimated value;

If unequal, start computing unit 51, to carry out a new joint sparse degree, estimate;

If equate, judge that whether the positional information of the nonzero element that the positional information of the nonzero element that current degree of rarefication estimated value is corresponding is corresponding with last degree of rarefication estimated value is consistent;

If consistent, stop the joint sparse degree and estimate, and output degree of rarefication estimated result;

If inconsistent, start computing unit 51, to carry out a new joint sparse degree, estimate.

Optionally, the sampled point that before described generation unit 54 sampled point required according to described current iteration step, current iteration step, all iterative steps gather, and a plurality of observation vectors of sparse signal to be observed generate the target function that mixes norm and the cumulative least mean-square error of multistep based on L1/L2 and are specially:

$\widehat{\mathrm{\&Theta;}}=[{\widehat{\mathrm{\&theta;}}}_1,...,{\widehat{\mathrm{\&theta;}}}_J]=\underset{\mathrm{\&Theta;}}{\arg \min }\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{\left(\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}{\left|{\mathrm{\&theta;}}_{n,j}\right|}^2\right)}^{1/2}+\mathrm{\&omega;}\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}{\left|\right|\left[\begin{array}{c}{Y}_{k-1,j}\\ y_{k,j}\end{array}\right]-\left[\begin{array}{c}{\mathrm{\&Omega;}}_{k-1,j}\\ {\mathrm{\&Phi;}}_{k,j}\end{array}\right]\mathrm{\&Psi;\&theta;}\left|\right|}_2^2$

Wherein, Y _{k-1, j}be the cumulative vector of all sampled points of gathering in all k-1 iterative steps before current the k time iterative step of j local user,

wherein T is matrix transpose operation, Ω _{k-1, j}be j local user cumulative matrix of sampling matrix used in all k-1 iterative steps before current the k time iterative step,

y _{k, j}be the sampling point vector that j local user gathers in current the k time iterative step, Φ _{k, j}it is j local user sampling matrix used in current the k time iterative step, ω is the weighted value of the sparse property of compromise and error, in the signal vector to be observed that θ is N * 1 and this vector, the number of nonzero element is far smaller than the dimension N of signal vector, the rarefaction representation matrix that Ψ is N * N, the number that J is the local user, θ _jfor each observation vector corresponding by signal θ to be observed, Θ each θ that serves as reasons _jdo the matrix that column vector forms, j ∈ [1 ..., J], θ _n,jfor θ _jin n element,

joint estimate for a plurality of observation vectors of sparse signal to be observed;

Described estimation unit 55, according to described target function, carry out the estimation of joint sparse degree to sparse signal to be observed, and obtain the result that the joint sparse degree estimates and be specially:

${\hat{S}}_c=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}\left(\frac{1}{J}\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}\right|{\widehat{\mathrm{\&theta;}}}_j|\&GreaterEqual;\mathrm{\&lambda;})$

${\hat{S}\mathrm{upp}}_c=\left\{\right\{q_1,...,q_{{\hat{S}}_c}\left\}\right|(1/J){\mathrm{\&Sigma;}}_{j=1}^J{\widehat{\mathrm{\&theta;}}}_{q_i,j}\&GreaterEqual;\mathrm{\&lambda;},q_i\&Element;\{1,...,N\}\}$

Wherein,

for the degree of rarefication estimated value of current sparse signal to be observed,

for described degree of rarefication estimated value

the positional information of corresponding nonzero element, for

the location index that individual nonzero element is corresponding, λ is the degree of rarefication decision threshold.

The degree of rarefication estimation unit that the embodiment of the present invention provides can be used in the embodiment of the method two of aforementioned correspondence, and details, referring to the description of above-described embodiment two, do not repeat them here.

embodiment six

The structure chart of the device that the degree of rarefication that Fig. 6 shows the embodiment of the present invention six to be provided is estimated, for convenience of explanation, only show the part relevant to the embodiment of the present invention.

Described device comprises: receiving element 61, sampling unit 62 and report unit 63.

Receiving element 61, the sampling instruction sent for receiving system, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix;

Sampling unit 62, for according to described sampling instruction, terminal equipment is carried out sampling to obtain sampled data;

Report unit 63, for by described sampled data reporting system.

The degree of rarefication estimation unit that the embodiment of the present invention provides can be used in the embodiment of the method three of aforementioned correspondence, and details, referring to the description of above-described embodiment three, do not repeat them here.

It should be noted that in said apparatus embodiment, included unit is just divided according to function logic, but is not limited to above-mentioned division, as long as can realize corresponding function; In addition, the concrete title of each functional unit also, just for the ease of mutual differentiation, is not limited to protection scope of the present invention.

In addition, one of ordinary skill in the art will appreciate that all or part of step realized in the various embodiments described above method is to come the hardware that instruction is relevant to complete by program, corresponding program can be stored in a computer read/write memory medium, described storage medium, as ROM/RAM, disk or CD etc.

The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, all any modifications of doing within the spirit and principles in the present invention, be equal to and replace and improvement etc., within all should being included in protection scope of the present invention.

Claims (16)

1. the method that degree of rarefication is estimated, is characterized in that, described method comprises:

Calculate the number of the required sampled point of current iteration step;

Number and default scheduling strategy according to the sampled point of described calculating, send the sampling instruction to a plurality of terminal equipments, so that terminal equipment is according to described sampling instruction, carry out sampling to obtain sampled data, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix;

Receive the sampled data that each local terminal reports, as the required sampled point of current iteration step obtained;

The sampled point that before required sampled point, current iteration step, all iterative steps gather according to described current iteration step, and a plurality of observation vectors of sparse signal to be observed, generate the target function that mixes norm and the cumulative least mean-square error of multistep based on L1/L2;

According to described target function, sparse signal to be observed is carried out to the estimation of joint sparse degree, and obtain the result that the joint sparse degree is estimated, described result comprises: the positional information of the nonzero element that the degree of rarefication estimated value of sparse signal to be observed and described degree of rarefication estimated value are corresponding;

The result of estimating according to described joint sparse degree, estimate to stop judgement to described joint sparse degree.

2. the method for claim 1, is characterized in that, described default scheduling strategy is:

The first scheduling strategy: the number of the sampled point that all intrasystem local user's mean allocation system current iteration steps are required; Perhaps

The second scheduling strategy: according to intrasystem local user's performance, the number of required sampled point according to weight allocation system current iteration step.

3. the method for claim 1, is characterized in that, the number of the sampled point that described calculating current iteration step is required is specially:

Degree of rarefication estimated value according to a upper iterative step

the number M of the sampled point that before the current iteration step, all iterative steps gather _pt, default adjacent twice iterative step the number smallest incremental Δ of sampled point, calculate the number M of the required sampled point of current iteration step _r, specific as follows:

$M_r=\max \{M\left({\hat{S}}_p\right),M_{\mathrm{pt}}+\mathrm{\&Delta;}\}-M_{\mathrm{pt}}$

Wherein, max{, for getting maxima operation, M () estimates the number computing function of required sampled point for degree of rarefication,

wherein N is signal dimension,

for the operation that rounds up.

4. the method for claim 1, is characterized in that, the described result of estimating according to described joint sparse degree, and before described joint sparse degree is estimated to stop judgement, described method also comprises:

Judge whether current degree of rarefication estimated result occurs extremely;

When the described current degree of rarefication estimated result of judgement occurs when abnormal, redirect is carried out according to the number of the sampled point of described calculating and default scheduling strategy, send the sampling instruction to a plurality of subscriber terminal equipments, so that terminal equipment is according to described sampling instruction, carry out sampling to obtain sampled data, estimate to carry out a new joint sparse degree;

When the described current degree of rarefication estimated result of judgement does not occur when abnormal, carry out the result of estimating according to described joint sparse degree, described joint sparse degree is estimated to stop adjudicating.

5. the method for claim 1, is characterized in that, the described result of estimating according to described joint sparse degree is estimated to stop judgement to described joint sparse degree and is specially:

Whether the positional information that judges the nonzero element that the positional information of the nonzero element that current degree of rarefication estimated value is corresponding is corresponding with last degree of rarefication estimated value is consistent;

If, stop the joint sparse degree and estimate, and output degree of rarefication estimated result;

If not, the number of calculating the required sampled point of current iteration step is carried out in redirect, to carry out a new joint sparse degree, estimates.

6. the method for claim 1, is characterized in that, the described result of estimating according to described joint sparse degree is estimated to stop judgement to described joint sparse degree and is specially:

Judge whether current degree of rarefication estimated value equates with last degree of rarefication estimated value;

If unequal, the number of calculating the required sampled point of current iteration step is carried out in redirect, to carry out a new joint sparse degree, estimates;

If equate, judge that whether the positional information of the nonzero element that the positional information of the nonzero element that current degree of rarefication estimated value is corresponding is corresponding with last degree of rarefication estimated value is consistent;

If consistent, stop the joint sparse degree and estimate, and output degree of rarefication estimated result;

If inconsistent, the number of the required sampled point of current iteration step is calculated in redirect, to carry out a new joint sparse degree, estimates.

7. the method for claim 1, it is characterized in that, the sampled point that before the described sampled point required according to described current iteration step, current iteration step, all iterative steps gather, and a plurality of observation vectors of sparse signal to be observed generate the target function that mixes norm and the cumulative least mean-square error of multistep based on L1/L2 and are specially:

$\widehat{\mathrm{\&Theta;}}=[{\widehat{\mathrm{\&theta;}}}_1,...,{\widehat{\mathrm{\&theta;}}}_J]=\underset{\mathrm{\&Theta;}}{\arg \min }\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{\left(\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}{\left|{\mathrm{\&theta;}}_{n,j}\right|}^2\right)}^{1/2}+\mathrm{\&omega;}\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}{\left|\right|\left[\begin{array}{c}{Y}_{k-1,j}\\ y_{k,j}\end{array}\right]-\left[\begin{array}{c}{\mathrm{\&Omega;}}_{k-1,j}\\ {\mathrm{\&Phi;}}_{k,j}\end{array}\right]\mathrm{\&Psi;\&theta;}\left|\right|}_2^2$

Wherein, Y _{k-1, j}be the cumulative vector of all sampled points of gathering in all k-1 iterative steps before current the k time iterative step of j local user,

wherein T is matrix transpose operation, Ω _{k-1, j}be j local user cumulative matrix of sampling matrix used in all k-1 iterative steps before current the k time iterative step,

y _{k, j}be the sampling point vector that j local user gathers in current the k time iterative step, Φ _{k, j}it is j local user sampling matrix used in current the k time iterative step, ω is the weighted value of the sparse property of compromise and error, in the signal vector to be observed that θ is N * 1 and this vector, the number of nonzero element is far smaller than the dimension N of signal vector, the rarefaction representation matrix that Ψ is N * N, the number that J is the local user, θ _jfor each observation vector corresponding by signal θ to be observed, Θ each θ that serves as reasons _jdo the matrix that column vector forms, j ∈ [1 ..., J], θ _n,jfor θ _jin n element, joint estimate for a plurality of observation vectors of sparse signal to be observed;

Described according to described target function, sparse signal to be observed is carried out to the estimation of joint sparse degree, and obtain the result that the joint sparse degree estimates and be specially:

${\hat{S}}_c=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}\left(\frac{1}{J}\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}\right|{\widehat{\mathrm{\&theta;}}}_j|\&GreaterEqual;\mathrm{\&lambda;})$

${\hat{S}\mathrm{upp}}_c=\left\{\right\{q_1,...,q_{{\hat{S}}_c}\left\}\right|(1/J){\mathrm{\&Sigma;}}_{j=1}^J{\widehat{\mathrm{\&theta;}}}_{q_i,j}\&GreaterEqual;\mathrm{\&lambda;},q_i\&Element;\{1,...,N\}\}$

Wherein,

for the degree of rarefication estimated value of current sparse signal to be observed,

for described degree of rarefication estimated value

the positional information of corresponding nonzero element, for

the location index that individual nonzero element is corresponding, λ is the degree of rarefication decision threshold.

8. the method that degree of rarefication is estimated, is characterized in that, described method comprises:

The sampling instruction that receiving system sends, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix;

According to described sampling instruction, terminal equipment is carried out sampling to obtain sampled data;

By described sampled data reporting system.

9. the device that degree of rarefication is estimated, is characterized in that, described device comprises:

Computing unit, for calculating the number of the required sampled point of current iteration step;

Transmitting element, number and default scheduling strategy according to the sampled point of described calculating, send the sampling instruction to a plurality of terminal equipments, so that terminal equipment is according to described sampling instruction, carry out sampling to obtain sampled data, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix;

Receiving element, the sampled data reported for receiving each local terminal, as the required sampled point of current iteration step obtained;

Generation unit, for sampled point that before required sampled point, current iteration step according to described current iteration step, all iterative steps gather, and a plurality of observation vectors of sparse signal to be observed, generate the target function that mixes norm and the cumulative least mean-square error of multistep based on L1/L2;

Estimation unit, be used for according to described target function, sparse signal to be observed is carried out to the estimation of joint sparse degree, and obtain the result that the joint sparse degree is estimated, described result comprises: the positional information of the nonzero element that the degree of rarefication estimated value of sparse signal to be observed and described degree of rarefication estimated value are corresponding;

Decision unit, for the result of estimating according to described joint sparse degree, estimate to stop judgement to described joint sparse degree.

10. device as claimed in claim 9, is characterized in that, described default scheduling strategy is:

The first scheduling strategy: the number of the sampled point that all intrasystem local user's mean allocation system current iteration steps are required; Perhaps

The second scheduling strategy: according to intrasystem local user's performance, the number of required sampled point according to weight allocation system current iteration step.

11. device as claimed in claim 9, is characterized in that, described computing unit, specifically for the degree of rarefication estimated value according to a upper iterative step

the number M of the sampled point that before the current iteration step, all iterative steps gather _pt, default adjacent twice iterative step the number smallest incremental Δ of sampled point, calculate the number M of the required sampled point of current iteration step _r, specific as follows:

$M_r=\max \{M\left({\hat{S}}_p\right),M_{\mathrm{pt}}+\mathrm{\&Delta;}\}-M_{\mathrm{pt}}$

Wherein, max{, for getting maxima operation, M () estimates the number computing function of required sampled point for degree of rarefication,

wherein N is signal dimension, for the operation that rounds up.

12. device as claimed in claim 9, is characterized in that, device also comprises:

Abnormal deciding means, for judging whether current degree of rarefication estimated result occurs extremely, when the described current degree of rarefication estimated result of judgement occurs when abnormal, start transmitting element, to carry out a new joint sparse degree, estimate, when the described current degree of rarefication estimated result of judgement does not occur when abnormal, start decision unit.

13. device as claimed in claim 9, it is characterized in that, described decision unit, specifically for the positional information that judges the nonzero element that current degree of rarefication estimated value is corresponding, whether the positional information of the nonzero element corresponding with last degree of rarefication estimated value is consistent, if stop the joint sparse degree and estimate, and output degree of rarefication estimated result, if not, start computing unit, estimate to carry out a new joint sparse degree.

14. device as claimed in claim 9, is characterized in that, whether described decision unit equates with last degree of rarefication estimated value specifically for judging current degree of rarefication estimated value;

If unequal, start computing unit, to carry out a new joint sparse degree, estimate;

If equate, judge that whether the positional information of the nonzero element that the positional information of the nonzero element that current degree of rarefication estimated value is corresponding is corresponding with last degree of rarefication estimated value is consistent;

If consistent, stop the joint sparse degree and estimate, and output degree of rarefication estimated result;

If inconsistent, start computing unit, to carry out a new joint sparse degree, estimate.

15. device as claimed in claim 9, it is characterized in that, the sampled point that before the described generation unit sampled point required according to described current iteration step, current iteration step, all iterative steps gather, and a plurality of observation vectors of sparse signal to be observed generate the target function that mixes norm and the cumulative least mean-square error of multistep based on L1/L2 and are specially:

$\widehat{\mathrm{\&Theta;}}=[{\widehat{\mathrm{\&theta;}}}_1,...,{\widehat{\mathrm{\&theta;}}}_J]=\underset{\mathrm{\&Theta;}}{\arg \min }\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{\left(\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}{\left|{\mathrm{\&theta;}}_{n,j}\right|}^2\right)}^{1/2}+\mathrm{\&omega;}\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}{\left|\right|\left[\begin{array}{c}{Y}_{k-1,j}\\ y_{k,j}\end{array}\right]-\left[\begin{array}{c}{\mathrm{\&Omega;}}_{k-1,j}\\ {\mathrm{\&Phi;}}_{k,j}\end{array}\right]\mathrm{\&Psi;\&theta;}\left|\right|}_2^2$

Wherein, Y _{k-1, j}be the cumulative vector of all sampled points of gathering in all k-1 iterative steps before current the k time iterative step of j local user,

wherein T is matrix transpose operation, Ω _{k-1, j}be j local user cumulative matrix of sampling matrix used in all k-1 iterative steps before current the k time iterative step,

y _{k, j}be the sampling point vector that j local user gathers in current the k time iterative step, Φ _k,jit is j local user sampling matrix used in current the k time iterative step, ω is the weighted value of the sparse property of compromise and error, in the signal vector to be observed that θ is N * 1 and this vector, the number of nonzero element is far smaller than the dimension N of signal vector, the rarefaction representation matrix that Ψ is N * N, the number that J is the local user, θ _jfor each observation vector corresponding by signal θ to be observed, Θ each θ that serves as reasons _jdo the matrix that column vector forms, j ∈ [1 ..., J], θ _n,jfor θ _jin n element,

joint estimate for a plurality of observation vectors of sparse signal to be observed;

Described estimation unit, according to described target function, carry out the estimation of joint sparse degree to sparse signal to be observed, and obtain the result that the joint sparse degree estimates and be specially:

${\hat{S}}_c=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}\left(\frac{1}{J}\underset{j=1}{\overset{J}{\mathrm{\&Sigma;}}}\right|{\widehat{\mathrm{\&theta;}}}_j|\&GreaterEqual;\mathrm{\&lambda;})$

${\hat{S}\mathrm{upp}}_c=\left\{\right\{q_1,...,q_{{\hat{S}}_c}\left\}\right|(1/J){\mathrm{\&Sigma;}}_{j=1}^J{\widehat{\mathrm{\&theta;}}}_{q_i,j}\&GreaterEqual;\mathrm{\&lambda;},q_i\&Element;\{1,...,N\}\}$

Wherein, for the degree of rarefication estimated value of current sparse signal to be observed, for described degree of rarefication estimated value

the positional information of corresponding nonzero element,

for

the location index that individual nonzero element is corresponding, λ is the degree of rarefication decision threshold.

16. the device that degree of rarefication is estimated, is characterized in that, described device comprises:

Receiving element, the sampling instruction sent for receiving system, described sampling instruction comprises: the number of the sampled point that terminal equipment need to obtain and terminal equipment are for generating the random seed sequence of sampling matrix; Sampling unit, for according to described sampling instruction, terminal equipment is carried out sampling to obtain sampled data; Report unit, for by described sampled data reporting system.

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