CN113866768A - Time sequence interference radar phase optimization estimation method - Google Patents
Time sequence interference radar phase optimization estimation method Download PDFInfo
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- CN113866768A CN113866768A CN202111455165.4A CN202111455165A CN113866768A CN 113866768 A CN113866768 A CN 113866768A CN 202111455165 A CN202111455165 A CN 202111455165A CN 113866768 A CN113866768 A CN 113866768A
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
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Abstract
The invention discloses a time sequence interference radar phase optimization estimation method, which comprises the following steps: acquiring an observation vector transmitted by a time sequence interference radar; the observation vector is used for representing a scattering point time dimension observation value transmitted by the time sequence interference radar; obtaining a phase likelihood function corresponding to the observation vector according to the observation vector; and carrying out maximum likelihood estimation and optimization processing on the phase likelihood function to obtain a target estimation phase. According to the embodiment of the invention, the phase quality of the scatterer in the time sequence interference radar can be improved by carrying out maximum likelihood estimation and optimization processing on the phase of the observation vector transmitted by the time sequence interference radar.
Description
Technical Field
The invention relates to the technical field of interference radars, in particular to a time sequence interference radar phase optimization estimation method.
Background
Due to the rapid development of satellite radar technology in recent years, the synthetic aperture radar interferometry (InSAR) technology has been moved from the research and development stage to practical engineering applications. The technique uses multiple synthetic aperture radar images to generate a map of the earth's surface deformation from the phase differences of echoes received by a satellite or aircraft. The technology has the characteristics of all weather and all day long, can penetrate through cloud layer dense fog smoke dust and can obtain ground surface deformation information in a large area, and is particularly suitable for areas with difficult imaging of the traditional optical sensor, and the monitoring precision can reach millimeter level. Based on these advantages, the InSAR technology has played a great role in the fields of emergency and daily monitoring.
In sequential synthetic aperture radar interferometric InSAR processing, sequential interferometric radars typically involve two types of point targets. The prior art has poor effect of optimizing and estimating the phase of a part of point targets in the time sequence interference radar.
Thus, there is still a need for improvement and development of the prior art.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method for optimizing and estimating a phase of a time-series interference radar, aiming at solving the problem of poor effect of optimizing and estimating the phase of a part of point targets in the time-series interference radar in the prior art.
The technical scheme adopted by the invention for solving the problems is as follows:
in a first aspect, an embodiment of the present invention provides a method for estimating phase optimization of a time-series interference radar, where the method includes:
acquiring an observation vector transmitted by a time sequence interference radar; the observation vector is used for representing a scattering point time dimension observation value transmitted by the time sequence interference radar;
obtaining a phase likelihood function corresponding to the observation vector according to the observation vector;
and carrying out maximum likelihood estimation and optimization processing on the phase likelihood function to obtain a target estimation phase.
In one implementation, the obtaining, according to the observation vector, a phase likelihood function corresponding to the observation vector includes:
acquiring a real number covariance matrix; the real covariance matrix is used for representing a scattering point real covariance value transmitted by the time sequence interference radar;
performing conditional probability operation on the observation vector and the real number covariance matrix to obtain a conditional probability function;
and carrying out logarithmic operation on the conditional probability function to obtain a phase likelihood function corresponding to the observation vector.
In one implementation, the performing maximum likelihood estimation and optimization on the phase likelihood function to obtain a target estimated phase includes:
carrying out derivation operation on the phase likelihood function to obtain a first derivative function;
solving the first derivative function after zero setting to obtain a maximum likelihood estimation value corresponding to the real covariance matrix;
and optimizing the phase likelihood function based on the maximum likelihood estimation value to obtain a target estimation phase.
In one implementation, the optimizing the phase likelihood function based on the maximum likelihood estimation value to obtain the target estimated phase includes:
substituting the maximum likelihood estimation value into the phase likelihood function to obtain an updated phase likelihood function;
and optimizing the updated phase likelihood function to obtain the target estimated phase.
In one implementation, the updated phase likelihood function includes a first sub-term, a second sub-term, and a third sub-term; the optimizing the updated phase likelihood function to obtain the target estimated phase includes:
calculating the minimum value of the second sub-item of the updated phase likelihood function based on a preset optimization algorithm;
and substituting the minimum value into the updated phase likelihood function to obtain the target estimated phase.
In one implementation, the optimizing the updated phase likelihood function to obtain the target estimated phase further includes:
acquiring a preset sample complex covariance matrix;
solving the absolute value of the sample complex covariance matrix to obtain the modulus of the sample complex covariance matrix;
substituting the modulus of the sample complex covariance matrix as a real covariance matrix into a preset first formula for calculation to obtain a first approximate phase;
and carrying out iterative computation on the first approximate phase based on the updated phase likelihood function and a preset first formula to obtain a target estimated phase.
In one implementation, the iteratively calculating the first approximate phase based on the updated phase likelihood function and a preset first formula to obtain a target estimated phase includes:
substituting the first approximate phase into a preset second formula to obtain an approximate maximum likelihood estimation value;
substituting the approximate maximum likelihood estimation value serving as a real covariance matrix into a preset first formula for calculation to obtain a second approximate phase;
substituting the second approximate phase into the second sub-term to obtain a second sub-term value;
and repeatedly executing the step of substituting the first approximate phase into a preset second formula to obtain an approximate maximum likelihood estimation value, stopping iteration when the second subentry value is smaller than a preset threshold value, and substituting the second subentry value when the iteration is stopped into an updated phase likelihood function to obtain a target estimated phase.
In one implementation, the optimizing the updated phase likelihood function to obtain the target estimated phase further includes:
carrying out derivation operation on the updated phase likelihood function to obtain a second derivative function;
and solving the second derivative function after the zero setting to estimate the phase by the target.
In a second aspect, an embodiment of the present invention further provides a time-series interferometric radar phase optimization estimation apparatus, where the apparatus includes:
the observation vector acquisition module is used for acquiring an observation vector transmitted by the time sequence interference radar; the observation vector is used for representing a scattering point time dimension observation value transmitted by the time sequence interference radar;
the phase likelihood function determining module is used for obtaining a phase likelihood function corresponding to the observation vector according to the observation vector;
and the target estimation phase determining module is used for carrying out maximum likelihood estimation and optimization processing on the phase likelihood function to obtain a target estimation phase.
In a third aspect, an embodiment of the present invention further provides an intelligent terminal, including a memory, and one or more programs, where the one or more programs are stored in the memory, and configured to be executed by one or more processors includes a processor configured to execute the method for estimating phase optimization of time-series interferometric radar according to any one of the above items.
In a fourth aspect, embodiments of the present invention further provide a non-transitory computer-readable storage medium, where instructions of the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the time-series interference radar phase optimization estimation method according to any one of the above.
The invention has the beneficial effects that: firstly, acquiring an observation vector transmitted by a time sequence interference radar; the observation vector is used for representing a scattering point time dimension observation value transmitted by the time sequence interference radar; then according to the observation vector, obtaining a phase likelihood function corresponding to the observation vector; finally, carrying out maximum likelihood estimation and optimization processing on the phase likelihood function to obtain a target estimation phase; therefore, the phase quality of the point target in the time sequence interference radar can be improved by carrying out maximum likelihood estimation and optimization processing on the phase of the observation vector transmitted by the time sequence interference radar in the embodiment of the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for phase optimization estimation of a time-series interference radar according to an embodiment of the present invention.
Fig. 2 is a schematic block diagram of a time-series interferometric radar phase optimization estimation apparatus according to an embodiment of the present invention.
Fig. 3 is a schematic block diagram of an internal structure of an intelligent terminal according to an embodiment of the present invention.
Detailed Description
The invention discloses a time sequence interference radar phase optimization estimation method, which is further described in detail below by referring to the attached drawings and embodiments in order to make the purpose, technical scheme and effect of the invention clearer and clearer. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Since in the prior art, two types of point targets are typically involved in the time series InSAR process. One is a Permanent Scatterer (PS) that can have a stable phase, and the other is a Distributed Scatterer (DS) that has a relatively poor phase quality. The PS is easy to process due to good phase quality, and the time sequence InSAR algorithm for processing the PS point is abundant. The DS point has poor phase quality and great processing challenge, and cannot be processed together with the PS point.
In order to solve the problems in the prior art, the embodiment provides a phase optimization estimation method for a time-series interference radar, which can improve the phase quality of scatterers in the time-series interference radar by performing maximum likelihood estimation and optimization processing on the phase of an observation vector transmitted by the time-series interference radar. In specific implementation, firstly, acquiring an observation vector transmitted by a time sequence interference radar; the observation vector is used for representing a scattering point time dimension observation value transmitted by the time sequence interference radar; then according to the observation vector, obtaining a phase likelihood function corresponding to the observation vector; and finally, carrying out maximum likelihood estimation and optimization processing on the phase likelihood function to obtain a target estimation phase. Exemplary method
The embodiment provides a phase optimization estimation method for a time-series interferometric radar, which can be applied to an intelligent terminal of the interferometric radar. As shown in fig. 1 in detail, the method includes:
s100, acquiring an observation vector transmitted by a time sequence interference radar; the observation vector is used for representing a scattering point time dimension observation value transmitted by the time sequence interference radar;
in particular, observation vectors
Is one
The vector value of the dimension, wherein,
the data are transmitted by the time-series interference radar and mainly comprise data of scattering points in a time dimension. Phase information can be extracted from the observation vector, so that the obtained observation vector is prepared for obtaining the target estimated phase subsequently.
After the observation vector is obtained, the following steps can be performed as shown in fig. 1: s200, obtaining a phase likelihood function corresponding to the observation vector according to the observation vector;
correspondingly, the step of obtaining the phase likelihood function corresponding to the observation vector according to the observation vector includes the following steps:
s201, acquiring a real number covariance matrix; the real covariance matrix is used for representing a scattering point real covariance value transmitted by the time sequence interference radar;
s202, performing conditional probability operation on the observation vector and the real covariance matrix to obtain a conditional probability function;
s203, carrying out logarithm operation on the conditional probability function to obtain a phase likelihood function corresponding to the observation vector.
In particular, a real covariance matrix
The method is characterized in that a real covariance matrix of scattering points transmitted by a time sequence interference radar gives phases of the scattering points under the assumption of complex circle Gaussian distribution
(in this embodiment, a closed phase) and a real covariance matrix G, performing conditional probability operation on the observation vector and the real covariance matrix to obtain a conditional probability function; the formula of the conditional probability function is as follows:
wherein the content of the first and second substances,
in the form of a matrix of a closed vector of the scattering points in time dimension,
(ii) a Complex covariance matrix
The relationship to the closed-phase and real covariance matrices is as follows:
wherein the content of the first and second substances,
is a scattering point complex covariance matrix.
The method for calculating the target estimated phase in the prior art is as follows: suppose a real covariance matrix
Known as closed phase
The maximum likelihood estimation of (c) can be achieved by maximizing the following expression:
wherein the content of the first and second substances,
is a time-dimensional closed phase vector of the scattering points,
an estimate of a random vector is characterized.
Is a sample complex covariance matrix given by the following equation:
wherein the content of the first and second substances,
is a set of adjacent homogenous points and is,
number of homogenous spots.
Due to real covariance matrix
Not in fact known, existing methods use the modulus of a sample complex covariance matrix
To replace the real covariance matrix
The phase optimization is performed according to equation (3). The significant drawback of the prior art method is that if the sample complex covariance matrix deviates too far from the true value, the estimation effect is greatly affected and the optimization effect is poor.
The invention adopts a new phase estimation method to avoid using a sample complex covariance matrix
To replace the real covariance matrix
And realizing accurate maximum likelihood estimation. Therefore, after obtaining the conditional probability function, performing a logarithm operation on the conditional probability function to obtain a phase likelihood function corresponding to the observation vector, where the phase likelihood function is as follows:
after obtaining the phase likelihood function, the following steps can be performed as shown in fig. 1: s300, carrying out maximum likelihood estimation and optimization processing on the phase likelihood function to obtain a target estimation phase. Correspondingly, the maximum likelihood estimation and optimization processing of the phase likelihood function to obtain the target estimated phase includes the following steps:
s301, carrying out derivation operation on the phase likelihood function to obtain a first derivative function;
s302, solving the first derivative function after zero setting to obtain a maximum likelihood estimation value corresponding to the real number covariance matrix;
and S303, optimizing the phase likelihood function based on the maximum likelihood estimation value to obtain a target estimation phase.
Specifically, predefined ones are obtained first
And
（6）
converting the likelihood function to the following formula:
wherein tr is a matrix trace, a derivation operation is performed on the phase likelihood function of the formula (7) to obtain a first derivative function, and then the first derivative function after being zeroed is solved to obtain a maximum likelihood estimation value corresponding to the real number covariance matrix; in the present embodiment, the pair of likelihood functions
Taking the derivative and let it be 0, the following equation is obtained:
solving the above equation can obtain the maximum likelihood estimation value corresponding to the real covariance matrix
：
And then, optimizing the phase likelihood function based on the maximum likelihood estimation value to obtain a target estimation phase. Correspondingly, the optimizing the phase likelihood function based on the maximum likelihood estimation value to obtain the target estimation phase includes the following steps: substituting the maximum likelihood estimation value into the phase likelihood function to obtain an updated phase likelihood function; and optimizing the updated phase likelihood function to obtain the target estimated phase.
Specifically, the maximum likelihood estimation value is substituted into the phase likelihood function, and the order is given
Obtaining an updated phase likelihood function:
wherein p is a p-dimensional identity matrix. The formula (10) is an innovative key point of the invention, because a phase optimization estimation mathematical formula of the distributed scattering points is established, an accurate maximum likelihood estimation value is obtained, and the assumed conditions in the existing method are removed, thereby overcoming the influence on the phase estimation result caused by too large deviation between the assumption and the actual in the existing method. And after the phase likelihood function is obtained, optimizing the updated phase likelihood function to obtain the target estimation phase.
In one implementation, the updated phase likelihood function includes a first sub-term, a second sub-term, and a third sub-term; the optimizing the updated phase likelihood function to obtain the target estimated phase comprises the following steps: calculating the minimum value of the second sub-item of the updated phase likelihood function based on a preset optimization algorithm; and substituting the minimum value into the updated phase likelihood function to obtain the target estimated phase.
Specifically, after the updated phase likelihood function is maximized, the target estimated phase (i.e. the maximum likelihood estimation value of the closed phase) can be obtained, and the first sub-term of the phase likelihood function is
Second sonThe item is
The third sub-item is
To maximize the updated phase likelihood function, a second sub-term is sought
In this embodiment, the preset optimization algorithm is a Newton-Raphson method, a simulated annealing method, and a search for the minimum value of
Is a minimum value of
Substituting the minimum value of the target phase into the updated phase likelihood function to obtain the target estimated phase. In practice, because the minimum value falls into a local optimal solution, the existing method and other phase optimization methods can be used to obtain a plurality of minimum values, the minimum values are iterated, the minimum value during convergence is taken as a final minimum value, and the final minimum value is substituted into the updated phase likelihood function to obtain the target estimated phase.
In one implementation, the optimizing the updated phase likelihood function to obtain the target estimated phase further includes: acquiring a preset sample complex covariance matrix; solving the absolute value of the sample complex covariance matrix to obtain the modulus of the sample complex covariance matrix; substituting the modulus of the sample complex covariance matrix as a real covariance matrix into a preset first formula for calculation to obtain a first approximate phase; and carrying out iterative computation on the first approximate phase based on the updated phase likelihood function and a preset first formula to obtain a target estimated phase.
Specifically, a preset sample complex covariance matrix is obtained
Solving the absolute value of the sample complex covariance matrix to obtain the modulus of the sample complex covariance matrix, wherein the absolute value is obtained by a formula (4); then substituting the modulus of the sample complex covariance matrix as a real covariance matrix into a preset first formula for calculation to obtain a first approximate phase; in this embodiment, the first formula is formula (3), based on existing methods, using the norm of the sample complex covariance matrix
To replace the real covariance matrix
The initial phase value, i.e., the first approximate phase, is estimated by equation (3). Substituting the first approximate phase into a preset second formula to obtain an approximate maximum likelihood estimation value; wherein the second formula is formula (9), and the approximate maximum likelihood estimation value is used as a real covariance matrix
Substituting the approximate maximum likelihood estimation value into a preset first formula for calculation, namely using the approximate maximum likelihood estimation value as a real covariance matrix
Substituting the formula (3) to obtain a second approximate phase; substituting the second approximate phase into the second sub-term to obtain a second sub-term value; and repeatedly executing the step of substituting the first approximate phase into a preset second formula to obtain an approximate maximum likelihood estimation value, stopping iteration when the iteration times exceed a certain number (such as 5000 times) or the second subentry value is smaller than a preset threshold (such as 0.001), and substituting the second subentry value when the iteration is stopped into an updated phase likelihood function to obtain a target estimation phase.
In one implementation, the optimizing the updated phase likelihood function to obtain the target estimated phase further includes: carrying out derivation operation on the updated phase likelihood function to obtain a second derivative function; and solving the second derivative function after the zero setting to estimate the phase by the target.
Specifically, a derivative operation is performed on the updated phase likelihood function to obtain a second derivative function; after the second derivative function is set to zero, the following expression is obtained:
wherein the content of the first and second substances,
is the real part of the complex number,
the imaginary part of the complex number. Solving the above expression can obtain the value of W, and
，
from this, can obtain
To do so
So that a closed phase can be obtained
I.e. the target estimated phase.
The three methods are used for optimizing the updated phase likelihood function and reliably and accurately solving the phase of the DS point, so that the obtained target estimation phase has high calculation efficiency and calculation accuracy.
In another implementation, the three methods for optimizing the updated phase likelihood function to obtain the target estimated phase may be used in a cross-combination manner, that is, the target estimated phase obtained by the first method may be used as an input of the second method or an input of the third method, an output of the second method may be used as an input of the first method or an input of the third method, and an output of the third method may be used as an input of the first method or an input of the second method. The method of the invention can be extended to other types of radar scatterers.
Exemplary device
As shown in fig. 1, the embodiment of the present invention provides a time-series interference radar phase optimization estimation apparatus, which includes an observation vector acquisition module 401, a phase likelihood function determination module 402, and a target estimation phase determination module 403, wherein,
an observation vector obtaining module 401, configured to obtain an observation vector transmitted by the time-series interference radar; the observation vector is used for representing a scattering point time dimension observation value transmitted by the time sequence interference radar;
a phase likelihood function determining module 402, configured to obtain, according to the observation vector, a phase likelihood function corresponding to the observation vector;
and a target estimated phase determining module 403, configured to perform maximum likelihood estimation and optimization processing on the phase likelihood function to obtain a target estimated phase.
Based on the above embodiment, the present invention further provides an intelligent terminal, and a schematic block diagram thereof may be as shown in fig. 2. The intelligent terminal comprises a processor, a memory, a network interface, a display screen and a temperature sensor which are connected through a system bus. Wherein, the processor of the intelligent terminal is used for providing calculation and control capability. The memory of the intelligent terminal comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the intelligent terminal is used for being connected and communicated with an external terminal through a network. The computer program is executed by a processor to implement a time-series interferometric radar phase optimization estimation method. The display screen of the intelligent terminal can be a liquid crystal display screen or an electronic ink display screen, and the temperature sensor of the intelligent terminal is arranged inside the intelligent terminal in advance and used for detecting the operating temperature of internal equipment.
It will be understood by those skilled in the art that the schematic diagram in fig. 3 is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation to the intelligent terminal to which the solution of the present invention is applied, and a specific intelligent terminal may include more or less components than those shown in the figure, or combine some components, or have different arrangements of components.
In one embodiment, an intelligent terminal is provided that includes a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: acquiring an observation vector transmitted by a time sequence interference radar; the observation vector is used for representing a scattering point time dimension observation value transmitted by the time sequence interference radar;
obtaining a phase likelihood function corresponding to the observation vector according to the observation vector;
and carrying out maximum likelihood estimation and optimization processing on the phase likelihood function to obtain a target estimation phase.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
In summary, the present invention discloses a method for estimating phase optimization of a time-series interference radar, the method includes: acquiring an observation vector transmitted by a time sequence interference radar; the observation vector is used for representing a scattering point time dimension observation value transmitted by the time sequence interference radar; obtaining a phase likelihood function corresponding to the observation vector according to the observation vector; and carrying out maximum likelihood estimation and optimization processing on the phase likelihood function to obtain a target estimation phase. According to the embodiment of the invention, the phase quality of the scatterer in the time sequence interference radar can be improved by carrying out maximum likelihood estimation and optimization processing on the phase of the observation vector transmitted by the time sequence interference radar.
Based on the above embodiments, the present invention discloses a method for estimating phase optimization of a time-series interference radar, and it should be understood that the application of the present invention is not limited to the above examples, and it is obvious to those skilled in the art that modifications and variations can be made in the light of the above description, and all such modifications and variations are intended to fall within the scope of the appended claims.
Claims (10)
1. A method for phase-optimized estimation of a time-series interferometric radar, the method comprising:
acquiring an observation vector transmitted by a time sequence interference radar; the observation vector is used for representing a scattering point time dimension observation value transmitted by the time sequence interference radar;
obtaining a phase likelihood function corresponding to the observation vector according to the observation vector;
and carrying out maximum likelihood estimation and optimization processing on the phase likelihood function to obtain a target estimation phase.
2. The method according to claim 1, wherein the obtaining a phase likelihood function corresponding to the observation vector according to the observation vector comprises:
acquiring a real number covariance matrix; the real covariance matrix is used for representing a scattering point real covariance value transmitted by the time sequence interference radar;
performing conditional probability operation on the observation vector and the real number covariance matrix to obtain a conditional probability function;
and carrying out logarithmic operation on the conditional probability function to obtain a phase likelihood function corresponding to the observation vector.
3. The time-series interference radar phase optimization estimation method according to claim 1, wherein the performing maximum likelihood estimation and optimization processing on the phase likelihood function to obtain the target estimation phase comprises:
carrying out derivation operation on the phase likelihood function to obtain a first derivative function;
solving the first derivative function after zero setting to obtain a maximum likelihood estimation value corresponding to a real number covariance matrix;
and optimizing the phase likelihood function based on the maximum likelihood estimation value to obtain a target estimation phase.
4. The time-series interference radar phase optimization estimation method according to claim 3, wherein the optimizing the phase likelihood function based on the maximum likelihood estimation value to obtain the target estimation phase comprises:
substituting the maximum likelihood estimation value into the phase likelihood function to obtain an updated phase likelihood function;
and optimizing the updated phase likelihood function to obtain the target estimated phase.
5. The time-series interference radar phase optimization estimation method of claim 4, wherein the updated phase likelihood function includes a first sub-term, a second sub-term, and a third sub-term; the optimizing the updated phase likelihood function to obtain the target estimated phase includes:
calculating the minimum value of the second sub-item of the updated phase likelihood function based on a preset optimization algorithm;
and substituting the minimum value into the updated phase likelihood function to obtain the target estimated phase.
6. The method according to claim 5, wherein the optimizing the updated phase likelihood function to obtain the target estimated phase further comprises:
acquiring a preset sample complex covariance matrix;
solving the absolute value of the sample complex covariance matrix to obtain the modulus of the sample complex covariance matrix;
substituting the modulus of the sample complex covariance matrix as a real covariance matrix into a preset first formula for calculation to obtain a first approximate phase;
and carrying out iterative computation on the first approximate phase based on the updated phase likelihood function and a preset first formula to obtain a target estimated phase.
7. The time-series interference radar phase optimization estimation method according to claim 6, wherein the iteratively calculating the first approximate phase based on the updated phase likelihood function and a preset first formula to obtain a target estimation phase comprises:
substituting the first approximate phase into a preset second formula to obtain an approximate maximum likelihood estimation value;
substituting the approximate maximum likelihood estimation value serving as a real covariance matrix into a preset first formula for calculation to obtain a second approximate phase;
substituting the second approximate phase into the second sub-term to obtain a second sub-term value;
and repeatedly executing the step of substituting the first approximate phase into a preset second formula to obtain an approximate maximum likelihood estimation value, stopping iteration when the second subentry value is smaller than a preset threshold value, and substituting the second subentry value when the iteration is stopped into an updated phase likelihood function to obtain a target estimated phase.
8. The method according to claim 4, wherein the optimizing the updated phase likelihood function to obtain the target estimated phase further comprises:
carrying out derivation operation on the updated phase likelihood function to obtain a second derivative function;
and solving the second derivative function after the zero setting to estimate the phase by the target.
9. An intelligent terminal comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory, and wherein the one or more programs being configured to be executed by the one or more processors comprises instructions for performing the method of any of claims 1-8.
10. A non-transitory computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method of any of claims 1-8.
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