CN111178232A - Method and system for determining source signal - Google Patents

Method and system for determining source signal Download PDF

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CN111178232A
CN111178232A CN201911368800.8A CN201911368800A CN111178232A CN 111178232 A CN111178232 A CN 111178232A CN 201911368800 A CN201911368800 A CN 201911368800A CN 111178232 A CN111178232 A CN 111178232A
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CN111178232B (en
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李卫民
徐高峰
刘国平
刘国
李邦超
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Shandong Zhongke Advanced Technology Research Institute Co ltd
Shenzhen Institute of Advanced Technology of CAS
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Abstract

The invention relates to a method and a system for determining a source signal. The method comprises the steps of obtaining an observation signal of a convolution blind signal; sampling an observation signal, and determining an initial particle set; according to the initial particle set, obtaining an updated particle set by adopting a pilotless Kalman particle filter algorithm; determining a particle risk function according to the updated particle set; determining a weight mapping function of the particle risk according to the particle risk function; determining a particle cost function according to the updated particle set and the weight mapping function; according to the weight mapping function and the particle cost function, fitting by adopting a least square method to obtain a predicted value of the extracted vector; obtaining a determined value of the extracted vector by adopting a weighted least square method according to the predicted value of the extracted vector; and determining a source signal according to the determined value of the extracted vector and the observation signal of the convolution blind signal. The method and the system solve the problem of low source signal determination accuracy caused by low efficiency of extracting the convolution blind signal in the prior art.

Description

Method and system for determining source signal
Technical Field
The present invention relates to the field of signal processing, and in particular, to a method and a system for determining a source signal.
Background
Blind signal extraction is a fundamental research problem, namely, under the condition that a source signal and a transmission channel are unknown, only an observation signal (mixed signal) is used for recovering the source signal or identifying the mixed channel. The extraction of blind signals has a wide range of applications, for example: the method comprises the following fields of medical signal extraction, weak signal detection, communication blind equalization, feature extraction and the like. Since the mixing process and the number of source signals are unknown, the solving process is complex and must depend on certain limiting conditions. The observation signal needs to be quantized before transmission, and various noises such as whitening, quantization, channel distortion and the like are inevitably introduced in the signal quantization processing process, so that the difficulty of extracting blind signals is increased to a great extent, and further, source signals cannot be accurately determined. The source signal cannot be accurately determined, resulting in distortion of the communication signal, the medical signal, and the like. The nonlinear filtering method based on the Bayesian framework is a recursion-update parameter estimation process, can process system parameters in real time, but has higher requirements on parameter setting and signal prior conditions, such as: the basis of bayesian recursion is that the statistical characteristics of noise are required to be known, and it is difficult to accurately model the noise in various applications, so the statistical characteristics of noise are the first problems to be solved by the nonlinear filtering method.
In view of the above problems, various solutions are proposed in the prior art, wherein patent CN201610150215.0 claims a PCMA signal single-channel blind separation method based on improved particle filtering. Performing framing processing on a single received PCMA signal, and determining the value range and distribution of parameters so as to initialize particles; updating to obtain new particles according to the importance sampling function and the particle orbit before the current moment; calculating the importance weight of the particles and setting a threshold value to discard some particles which have small contribution to posterior distribution, thereby dynamically adjusting the total number of the particles to simplify the calculation; after the estimated values of the parameters of the two uplink signals are obtained by using the linear minimum mean square error criterion, a signal separation method with known parameters is designed, in the recursive calculation process of the likelihood function, some tracks with poor quality are abandoned to further reduce the calculated amount, and finally, the symbol sequence estimated values of the two uplink signals are obtained by using the maximum likelihood criterion. Compared with the exhaustive method of the original algorithm for the state, the method can obviously reduce the calculated amount; but this method discards poor quality particles, which reduces the computational accuracy required for the implementation of the algorithm.
Patent CN201611165889.4 discloses a particle filtering method based on gaussian mixture model and variational bayes, which includes the following steps: 1) modeling observation noise by using a Gaussian mixture model, and initializing an initial state; 2) randomly generating N initial particles based on the probability density function of the initial state; 3) initializing a hyper-parameter of an unknown parameter of a Gaussian mixture model for observing noise; 4) generating sampling particles from the selected importance reference function; 5) measuring and updating, and calculating a particle weight according to the latest observed value and a particle weight iterative formula; 6) solving the distribution of unknown parameters in the Gaussian mixture model by using a variational Bayes method through a loop iteration method; 7) and normalizing the weight values of the particles, and resampling the particle set aiming at the problem of particle degradation. By the method, the filtering precision and the target state estimation performance are effectively improved.
In the blind signal extraction problem, the blind signal extraction process is the source signal extraction process because the transmission channel parameters are unknown. According to the mixing mode of the source signals, the method can be divided into a linear homeostatic mixing model, a linear convolution mixing model and a nonlinear mixing model. In an actual real environment, the transmission of signals is affected by time delay, reflection, attenuation and other factors, so that the signals obtained from the sensors or the signal arrays are signals obtained by convolution and mixing of source signals. Therefore, the solution process for researching the convolution mixed model is more practical.
In an actual application scenario, when source signal information is completely unknown, a mixed signal cannot be extracted or separated, and at present, extraction and restoration of a source signal from the mixed signal are mainly realized based on semi-blind (statistical characteristics of the source signal are known). When the source signal is known, the mixed channel is unknown and the mixing parameter is unknown, the source signal is extracted and restored, a mathematical model needs to be established on the statistical characteristics of the source signal to complete the estimation of the mixed channel parameter, and finally the source signal is effectively determined.
However, for the problem of extracting the convolution blind signal, the particle filtering method can handle the problem of strong nonlinearity, but the statistical characteristics of noise and source signals in a state measurement equation need to be known, and a general application scenario can not meet the condition limit; when the problem of extracting the convolution blind signal is researched, the observation signal needs to be quantized, the introduced quantization noise is difficult to express mathematically, and the whole system noise after the quantization noise, the channel noise, the observation noise and the like are mixed and superposed is more difficult to express by an accurate mathematical model. Therefore, the traditional particle filtering method is difficult to satisfy the complex convolution blind signal extraction task. Therefore, the prior art cannot accurately and quickly extract the convolution blind signal.
Disclosure of Invention
The invention aims to provide a method and a system for determining a source signal, which solve the problem of low source signal determination accuracy caused by low efficiency of extracting a convolution blind signal in the prior art.
In order to achieve the purpose, the invention provides the following scheme:
a method of determining a source signal, comprising:
acquiring an observation signal of a convolution blind signal;
sampling observation signals of the convolution blind signals to determine an initial particle set;
according to the initial particle set, obtaining an updated particle set by adopting a pilotless Kalman particle filter algorithm;
determining a particle risk function according to the updated particle set;
determining a weight mapping function of the particle risk according to the particle risk function;
determining a particle cost function according to the updated particle set and the weight mapping function;
according to the weight mapping function and the particle cost function, fitting by adopting a least square method to obtain a predicted value of an extracted vector;
determining the determined value of the extracted vector by adopting a weighted least square method according to the predicted value of the extracted vector;
acquiring a signal mapping relation; the signal mapping relationship is a mapping relationship among the observed signal, the determined value of the extraction vector and a source signal;
and determining the source signal by utilizing the mapping relation according to the determined value of the extracted vector and the observation signal of the convolution blind signal.
Optionally, the determining a particle cost function according to the updated particle set and the weight mapping function specifically includes:
resampling the updated particle set according to the weight mapping function, and determining a cost sample estimation set of particles;
determining the particle cost function from the set of cost sample estimates for the particle.
Optionally, the obtaining the signal mapping relationship further includes:
acquiring an observation signal of a known convolution signal;
determining a convolution mixing model according to an observation signal of a known convolution signal;
converting the convolutional hybrid model to a transient hybrid model;
and determining the signal mapping relation according to the instantaneous mixing model.
Optionally, the determining the value of the extracted vector by using a weighted least square method according to the predicted value of the extracted vector specifically includes:
determining a vector variance set according to the predicted value of the extracted vector;
and obtaining the determined value of the extracted vector by adopting a weighted least square method according to the predicted value of the extracted vector and the vector variance set.
A system for determining a source signal, comprising:
the first observation signal acquisition module is used for acquiring observation signals of the convolution blind signals;
an initial particle set determining module, configured to sample an observation signal of the convolution blind signal, and determine an initial particle set;
the particle set updating module is used for obtaining an updated particle set by adopting a pilotless Kalman particle filter algorithm according to the initial particle set;
a particle risk function determining module, configured to determine a particle risk function according to the updated particle set;
the weight mapping function determining module is used for determining a weight mapping function of the particle risk according to the particle risk function;
a particle cost function determining module, configured to determine a particle cost function according to the updated particle set and the weight mapping function;
the predicted value determining module is used for obtaining a predicted value of the extracted vector by adopting least square fitting according to the weight mapping function and the particle cost function;
the determination module of the extracted vector is used for determining the determination value of the extracted vector by adopting a weighted least square method according to the predicted value of the extracted vector;
the signal mapping relation acquisition module is used for acquiring a signal mapping relation; the signal mapping relationship is a mapping relationship among the observed signal, the determined value of the extraction vector and a source signal;
and the source signal determining module is used for determining the source signal by utilizing the mapping relation according to the determined value of the extraction vector and the observation signal of the convolution blind signal.
Optionally, the particle cost function determining module specifically includes:
a cost sample estimation set determining unit of the particle, configured to resample the updated particle set according to the weight mapping function, and determine a cost sample estimation set of the particle;
a particle cost function determination unit, configured to determine the particle cost function according to the cost sample estimation set of the particle.
Optionally, the method further includes:
the second observation signal acquisition module is used for acquiring an observation signal of a known convolution signal;
the convolution mixing model determining module is used for determining a convolution mixing model according to an observation signal of a known convolution signal;
an instantaneous mixture model determination module for converting the convolutional mixture model into an instantaneous mixture model;
and the signal mapping relation determining module is used for determining the signal mapping relation according to the instantaneous mixing model.
Optionally, the determining module for determining the determined value of the extracted vector specifically includes:
a vector variance set determination unit for determining a vector variance set according to the predicted value of the extracted vector;
and the determination unit of the extraction vector is used for determining the determination value of the extraction vector by adopting a weighted least square method according to the prediction value of the extraction vector and the vector variance set.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the method and the system for determining the source signal, provided by the invention, under the condition that the prior verification condition is unknown, the particle set obtained by sampling is updated by adopting a non-pilot Kalman particle filter algorithm, a particle risk function, a weight mapping function and a particle cost function are determined according to the updated particle set, the determination value of the extraction vector is further determined by adopting a least square method, and the extraction of the convolution blind signal is realized by the determination value of the extraction vector. By determining the determined value of the extracted vector, mathematical model modeling is not needed to be carried out on the introduced quantization noise, the defect that the noise signal is difficult to be modeled accurately is avoided, and the accuracy of determining the source signal is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a method for determining a source signal according to the present invention;
FIG. 2 is a schematic diagram of the signal convolution hybrid blind extraction principle of the present invention;
fig. 3 is a schematic structural diagram of a system for determining a source signal according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for determining a source signal, which solve the problem of low source signal determination accuracy caused by low efficiency of extracting a convolution blind signal in the prior art.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a schematic flow chart of a method for determining a source signal according to the present invention, and as shown in fig. 1, the method for determining a source signal according to the present invention includes:
s101, acquiring an observation signal of the convolution blind signal.
And S102, sampling the observation signals of the convolution blind signals, and determining an initial particle set.
For the convolutionThe observation signal of the blind signal is sampled in a bounded region covering the initial real value of the particles, and the particles are sampled to obtain NpAn initial particle
Figure BDA0002339138410000061
The cost value of each initial particle is
Figure BDA0002339138410000062
Initial particle cost sample set of
Figure BDA0002339138410000063
Wherein
Figure BDA0002339138410000064
Is the variance matrix of the initial particles.
And S103, obtaining an updated particle set by adopting a pilotless Kalman particle filter algorithm according to the initial particle set.
And (3) adopting a pilotless transformation to iteratively update all the particles at the time k:
Figure BDA0002339138410000071
Figure BDA0002339138410000072
Figure BDA0002339138410000073
wherein n iswIs the dimension of the vector to be integrated,
Figure BDA0002339138410000074
is an estimated value after no pilot transformation. In order to obtain updated particles with higher accuracy, kalman particle filter updating is performed on the particles at the time k, and the updating process is as follows:
Figure BDA0002339138410000075
Figure BDA0002339138410000076
Figure BDA0002339138410000077
Figure BDA0002339138410000078
Figure BDA0002339138410000079
Figure BDA00023391384100000710
and S104, determining a particle risk function according to the updated particle set.
The process noise variance during particle update can be assumed to define a particle risk function at time k, following a gaussian distribution, as:
Figure BDA00023391384100000711
Figure BDA00023391384100000712
is the particle cost value at time k,
Figure BDA00023391384100000713
and the lambda epsilon (0,1) is a particle weight forgetting factor and is used for adjusting the influence of the particle cost value at the previous moment on the current particle risk value, and when the lambda is 0, no influence is shown.
And S105, determining a weight mapping function of the particle risk according to the particle risk function.
Using formulas
Figure BDA00023391384100000714
The particle risk value is mapped to a resampling weight for the particle, i.e. a weight mapping function that determines the particle risk. Number of particles Npthe values of delta epsilon (0,1) and β > 1 are respectively mapping adjustment coefficients
Figure BDA00023391384100000715
In which is removed
Figure BDA00023391384100000716
The parameter delta is used for ensuring the diversity of the particles after resampling, and the parameter delta is used for ensuring that extreme conditions do not occur due to particle risk prediction values.
And S106, determining a particle cost function according to the updated particle set and the weight mapping function.
Resampling the updated particle set according to the weight mapping function, and determining a cost sample estimation set of particles;
resampling the updated particle set by using the weight mapping function of particle risk, and obtaining a cost sample estimation set of particles as
Figure BDA0002339138410000081
Determining the particle cost function from the set of cost sample estimates for the particle.
Particle set of k +1 time separation vectors
Figure BDA0002339138410000082
Is updated, wherein
Figure BDA0002339138410000083
Represents a mean value of
Figure BDA0002339138410000084
And variance of
Figure BDA0002339138410000085
A gaussian distribution of (a).
Using formulas
Figure BDA0002339138410000086
Determining the particle cost function.
Figure BDA0002339138410000087
Is that
Figure BDA0002339138410000088
An estimate of (d).
And S107, fitting by adopting a least square method according to the weight mapping function and the particle cost function to obtain a predicted value of the extracted vector.
And mapping the particle cost function into a posterior probability by using the weight mapping function.
Using formulas
Figure BDA0002339138410000089
And performing least square fitting on the posterior probability to obtain a predicted value of the extracted vector.
Figure BDA00023391384100000810
And extracting a predicted value of the vector for the k +1 moment.
And S108, determining the determined value of the extracted vector by adopting a weighted least square method according to the predicted value of the extracted vector.
And determining a vector variance set according to the predicted value of the extracted vector.
Let the extraction vector at time k be { w1,w2,…,wkThe vector is extracted with the increment of k +1
Figure BDA00023391384100000811
Determining a set of vector variances of
Figure BDA00023391384100000812
And obtaining the determined value of the extracted vector by adopting a weighted least square method according to the predicted value of the extracted vector and the vector variance set.
Using formulas
Figure BDA00023391384100000813
Determining a determined value of the extraction vector.
S109, acquiring a signal mapping relation; the signal mapping relationship is a mapping relationship between the observed signal, the determined value of the extraction vector, and a source signal.
The signal mapping relation is
Figure BDA0002339138410000091
Figure BDA0002339138410000092
The source signal is a signal that is,
Figure BDA0002339138410000093
extracting the determined value of the vector, Zk+1To observe the signal.
And S110, determining the source signal by utilizing the mapping relation according to the determined value of the extracted vector and the observation signal of the convolution blind signal.
Before the signal mapping relation is obtained, the signal mapping relation is determined by using the known convolution blind signals of the database. The determination process is as follows:
an observed signal of the known convolved signal is obtained.
A convolutional hybrid model is determined from the observed signal of the known convolutional signal.
Converting the convolutional hybrid model to a transient hybrid model.
And determining the signal mapping relation according to the instantaneous mixing model.
As shown in FIG. 2, N source signals are observed by M nodes (sensor array, signal receiving device) (M ≧ N). The observation equation (convolution mixture model) of the k-th observation node is as follows:
Figure BDA0002339138410000094
where K is a sequence of discrete time instants, sk＝[s1,k,s2,k,...,sN,k]TFor N observed source signals, vk＝[v1,k,v2,k,...,vM,k]TFor M noise vectors, xk＝[x1,k,x2,k,...,xM,k]TM observation column vector signals at the k time; h (l) ═ Hij(l)]M×N，l＝1,2,...,L，hij(l) Is the impulse response of L +1 order from the jth source signal to the ith observation node.
Quantization processing (uniform quantization method or weight method can be selected) is carried out on the obtained observation signal of the kth node, and quantization noise introduced in the quantization process is set as [ q ]1,k,q2,k,...,qM,k]TThe overall noise of the system can be expressed as uk＝[v1,k+q1,k,v2,k+q2,k,...,vM,k+qM,k]TThe quantized observed signal is:
Figure BDA0002339138410000095
wherein z iskIs a quantized observed signal. Assuming that the integer L ' satisfies ML ' being greater than or equal to N (L ' + L), the source signal, the observation signal and the noise are converted into:
Si,k＝[si,k,si,k-1,…,si,k-L-L'+1]T,i＝1,2,…,N，Zi,k＝[zi,k,zi,k-1,…,zi,k-L'+1]Ti-1, 2, …, M and Ui,k＝[ui,k,ui,k-1,…,ui,k-L'+1]T,i＝1,2,…,M。
Further, the converted source signal, observation signal and noise are transformed into a vector mode:
Figure BDA0002339138410000101
and
Figure BDA0002339138410000102
determining an ML '× N (L' + L) dimensional coefficient matrix A:
Figure BDA0002339138410000103
wherein A isijIs an L '× (L + L') dimensional matrix:
Figure BDA0002339138410000104
by the defined coefficient matrix A and matrix AijConverting the convolution mixture model to a transient mixture model:
Zk＝ASk+Uk。
determining a value w of a time invariant extraction vector for determining an ML' dimensionTFurther using the formula
Figure BDA0002339138410000105
Determining a source signal
Figure BDA0002339138410000106
Obtaining a mapping equation of s for the source signalj,k＝fj(sj,k-1)。
Determine an observation equation for the node as
Figure BDA0002339138410000107
The extraction vector or the separation matrix of the instantaneous hybrid model has the characteristics of singleness and time invariance, and the state observation equation of the nonlinear filtering algorithm is further determined as follows:
Figure BDA0002339138410000108
corresponding to the method for determining a source signal provided by the present invention, the present invention further provides a system for determining a source signal, as shown in fig. 3, the system for determining a source signal provided by the present invention includes: a first observed signal obtaining module 301, an initial particle set determining module 302, a particle set updating module 303, a particle risk function determining module 304, a weight mapping function determining module 305, a particle cost function determining module 306, a predicted value of extracted vector determining module 307, a determined value of extracted vector determining module 308, a signal mapping relation obtaining module 309, and a source signal determining module 310.
The first observation signal obtaining module 301 is configured to obtain an observation signal of the convolved blind signal.
The initial particle set determining module 302 is configured to sample the observation signals of the convolution blind signals to determine an initial particle set.
The particle set updating module 303 is configured to obtain an updated particle set by using a pilotless kalman particle filter algorithm according to the initial particle set.
The particle risk function determination module 304 is configured to determine a particle risk function according to the updated set of particles.
The weight mapping function determining module 305 is configured to determine a weight mapping function of the particle risk according to the particle risk function.
The particle cost function determining module 306 is configured to determine a particle cost function according to the updated set of particles and the weight mapping function.
And the predicted value determining module 307 for extracting the vector is configured to obtain the predicted value of the extracted vector by adopting least square fitting according to the weight mapping function and the particle cost function.
The determination module 308 for determining the determination value of the extracted vector is configured to determine the determination value of the extracted vector by using a weighted least square method according to the prediction value of the extracted vector.
The signal mapping relation obtaining module 309 is configured to obtain a signal mapping relation; the signal mapping relationship is a mapping relationship between the observed signal, the determined value of the extraction vector, and a source signal.
The source signal determining module 310 is configured to determine the source signal according to the determined value of the extracted vector and the observed signal of the convolutional blind signal by using the mapping relationship.
The particle cost function determining module 306 specifically includes: a cost sample estimation set determination unit and a particle cost function determination unit of the child.
And the particle cost sample estimation set determining unit is used for resampling the updated particle set according to the weight mapping function and determining a particle cost sample estimation set.
The particle cost function determination unit is used for determining the particle cost function according to the cost sample estimation set of the particles.
The invention provides a source signal determining system, which further comprises: the device comprises a second observation signal acquisition module, a convolution mixed model determination module, an instantaneous mixed model determination module and a signal mapping relation determination module.
The second observation signal acquisition module is used for acquiring an observation signal of the known convolution signal.
The convolution mixture model determining module is used for determining a convolution mixture model according to an observation signal of a known convolution signal.
The instantaneous mixture model determination module is used for converting the convolution mixture model into an instantaneous mixture model.
And the signal mapping relation determining module is used for determining the signal mapping relation according to the instantaneous mixing model.
The determining module 308 for determining the value of the extracted vector specifically includes: a vector variance set determination unit and an extracted vector determination value determination unit.
The vector variance set determining unit is used for determining a vector variance set according to the predicted value of the extracted vector.
And the determination unit of the extraction vector is used for determining the determination value of the extraction vector by adopting a weighted least square method according to the prediction value of the extraction vector and the vector variance set.
The method and the system for determining the source signal provided by the invention are based on the Kalman particle filter theory, and carry out real-time estimation on the parameters of the mixed channel through iterative update of particles so as to reduce the dependence on the statistical characteristics of the source signal and better accord with the actual application scene. And estimating the mixed channel parameters by adopting a Kalman particle filtering method.
According to the method and the system for determining the source signal, provided by the invention, the particle filtering method is improved by utilizing the particle cost and the risk function, the iterative precision of the particles is further improved by a volume point transformation method, and the real-time extraction of the convolution blind signal is finally completed. When the condition that the prior knowledge of the mixed signal is insufficient is processed, the method has a good extraction effect, namely when unknown noise is introduced into a system and the noise cannot be described mathematically accurately, the source signal can be effectively determined by the method and the system for determining the source signal, and the authenticity and the accuracy of the source signal are ensured.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (8)

1. A method for determining a source signal, comprising:
acquiring an observation signal of a convolution blind signal;
sampling observation signals of the convolution blind signals to determine an initial particle set;
according to the initial particle set, obtaining an updated particle set by adopting a pilotless Kalman particle filter algorithm;
determining a particle risk function according to the updated particle set;
determining a weight mapping function of the particle risk according to the particle risk function;
determining a particle cost function according to the updated particle set and the weight mapping function;
according to the weight mapping function and the particle cost function, fitting by adopting a least square method to obtain a predicted value of an extracted vector;
determining the determined value of the extracted vector by adopting a weighted least square method according to the predicted value of the extracted vector;
acquiring a signal mapping relation; the signal mapping relationship is a mapping relationship among the observed signal, the determined value of the extraction vector and a source signal;
and determining the source signal by utilizing the mapping relation according to the determined value of the extracted vector and the observation signal of the convolution blind signal.
2. The method for determining a source signal according to claim 1, wherein the determining a particle cost function according to the updated particle set and the weight mapping function specifically includes:
resampling the updated particle set according to the weight mapping function, and determining a cost sample estimation set of particles;
determining the particle cost function from the set of cost sample estimates for the particle.
3. The method of claim 1, wherein the obtaining the signal mapping relationship further comprises:
acquiring an observation signal of a known convolution signal;
determining a convolution mixing model according to an observation signal of a known convolution signal;
converting the convolutional hybrid model to a transient hybrid model;
and determining the signal mapping relation according to the instantaneous mixing model.
4. The method according to claim 1, wherein the determining the determined value of the extracted vector by using a weighted least squares method according to the predicted value of the extracted vector specifically comprises:
determining a vector variance set according to the predicted value of the extracted vector;
and obtaining the determined value of the extracted vector by adopting a weighted least square method according to the predicted value of the extracted vector and the vector variance set.
5. A system for determining a source signal, comprising:
the first observation signal acquisition module is used for acquiring observation signals of the convolution blind signals;
an initial particle set determining module, configured to sample an observation signal of the convolution blind signal, and determine an initial particle set;
the particle set updating module is used for obtaining an updated particle set by adopting a pilotless Kalman particle filter algorithm according to the initial particle set;
a particle risk function determining module, configured to determine a particle risk function according to the updated particle set;
the weight mapping function determining module is used for determining a weight mapping function of the particle risk according to the particle risk function;
a particle cost function determining module, configured to determine a particle cost function according to the updated particle set and the weight mapping function;
the predicted value determining module is used for obtaining a predicted value of the extracted vector by adopting least square fitting according to the weight mapping function and the particle cost function;
the determination module of the extracted vector is used for determining the determination value of the extracted vector by adopting a weighted least square method according to the predicted value of the extracted vector;
the signal mapping relation acquisition module is used for acquiring a signal mapping relation; the signal mapping relationship is a mapping relationship among the observed signal, the determined value of the extraction vector and a source signal;
and the source signal determining module is used for determining the source signal by utilizing the mapping relation according to the determined value of the extraction vector and the observation signal of the convolution blind signal.
6. The system for determining a source signal according to claim 5, wherein the particle cost function determining module specifically includes:
a cost sample estimation set determining unit of the particle, configured to resample the updated particle set according to the weight mapping function, and determine a cost sample estimation set of the particle;
a particle cost function determination unit, configured to determine the particle cost function according to the cost sample estimation set of the particle.
7. The system for determining a source signal of claim 5, further comprising:
the second observation signal acquisition module is used for acquiring an observation signal of a known convolution signal;
the convolution mixing model determining module is used for determining a convolution mixing model according to an observation signal of a known convolution signal;
an instantaneous mixture model determination module for converting the convolutional mixture model into an instantaneous mixture model;
and the signal mapping relation determining module is used for determining the signal mapping relation according to the instantaneous mixing model.
8. The system for determining a source signal according to claim 5, wherein the module for determining the value of the extracted vector specifically comprises:
a vector variance set determination unit for determining a vector variance set according to the predicted value of the extracted vector;
and the determination unit of the extraction vector is used for determining the determination value of the extraction vector by adopting a weighted least square method according to the prediction value of the extraction vector and the vector variance set.
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