CN114665991A - Short wave time delay estimation method, system, computer equipment and readable storage medium - Google Patents
Short wave time delay estimation method, system, computer equipment and readable storage medium Download PDFInfo
- Publication number
- CN114665991A CN114665991A CN202210559349.3A CN202210559349A CN114665991A CN 114665991 A CN114665991 A CN 114665991A CN 202210559349 A CN202210559349 A CN 202210559349A CN 114665991 A CN114665991 A CN 114665991A
- Authority
- CN
- China
- Prior art keywords
- delay estimation
- time delay
- short
- mean square
- short wave
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/391—Modelling the propagation channel
- H04B17/3912—Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
Landscapes
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
- Noise Elimination (AREA)
Abstract
The present application relates to the field of signal processing, and in particular, to a short-wave time delay estimation method, system, computer device, and readable storage medium, wherein the short-wave time delay estimation method includes: a parameter initialization step of initializing initial parameters of a filter, wherein the initial parameters comprise: weight coefficient vector W, number of iterationsk max And convergence factorμ(ii) a A signal modulus taking step for obtaining the received two short wave multipath signaly 1 、y 2 For two said short wave multipath signalsy 1 、y 2 Calculating the mean square error after modulus selection; and a time delay estimation result obtaining step, namely performing iterative updating on the weight coefficient vector by using the mean square error minimization as a criterion so as to obtain a time delay estimation result. By the method and the device, the interference of a channel gain function to signals is overcome, and more accurate time comparison of short wave signals is realizedAnd carrying out estimation.
Description
Technical Field
The present application relates to the field of signal processing, and in particular, to a short-wave delay estimation method, system, computer device, and readable storage medium.
Background
The time delay estimation algorithm is the key research content in the field of signal processing and is also the premise for realizing the short wave positioning technology based on the arrival time difference.
The traditional time delay estimation algorithm is developed on the basis of a double-element signal processing model, the main research points are mainly put on the aspects of a narrow-band signal time delay estimation algorithm, a multipath time delay estimation algorithm, a time delay estimation algorithm in a non-Gaussian noise environment and the like, and the main research problems are how to improve the time delay estimation precision and reduce the algorithm complexity under the complex additive interference noise. However, in short-wave signal delay estimation, due to the severe fading characteristics of the signal under the short-wave ionosphere channel, the noise superimposed in the signal includes both additive noise (such as high-gaussian noise or impulse noise) in the conventional sense and multiplicative noise interference, which makes the short-wave signal delay estimation problem extremely complicated.
Taking the classic short wave channel model Watterson channel as an example, the short wave channel gain function causes multiplicative interference to the short wave signal, the multiplicative interference conforms to complex Gaussian distribution with the mean value of zero, and the multiplicative interference in the traditional delay estimation model is a constant, so that the traditional delay estimation algorithm is influenced by the short wave channel gain function. Specifically, when a conventional LMS algorithm (Least Mean Square) is applied to short-wave signal delay estimation, we find that a Mean-Square error function in the algorithm only contains noise and does not contain any useful information due to the influence of a channel gain function, and thus the method cannot be applied to short-wave signal delay processing.
At present, no effective solution is provided for the influence of a channel gain function on a short-wave signal in the related technology.
Disclosure of Invention
The embodiment of the application provides a short-wave time delay estimation method, a short-wave time delay estimation system, computer equipment and a computer readable storage medium, so as to at least overcome the interference of a channel gain function on a signal.
In a first aspect, an embodiment of the present application provides a short-wave time delay estimation method, including:
a parameter initialization step of initializing initial parameters of a filter, wherein the initial parameters comprise: weight coefficient vector W, maximum number of iterationsk max And convergence factorμ；
A signal modulus taking step for obtaining the received two short wave multipath signaly 1 、y 2 As for twoShort wave multipath signaly 1 、y 2 Calculating the mean square error after modulus selection;
a time delay estimation result obtaining step, which is to carry out iterative update on the weight coefficient vector W by using the mean square error minimization as a criterion so as to obtain a time delay estimation result;
wherein, thek max Less than or equal to the signal length NR, and the initial value W (0) of the weight coefficient vector is of order 2M f And +1, and the value of the convergence factor is related to the convergence speed and the convergence stability.
In some of these embodiments, the mean square error is calculated according to the following model:
wherein the mean square error
Is a two said short wave multipath signaly 1 、y 2 Square of error of
In the expectation that the position of the target is not changed,
for representing two said short-wave multipath signalsy 1 、y 2 Is determined by the error function of (a),kis the number of iterations andk=2M f +1,2M f ,……,k max t is used to denote vector transposition, 2M f +1 is the order of the filter, which can be set by user, ŷ1Is shown as ŷ1(k)=[y 1 (k),y 1 (k-1),…,y 1 (k-2M f )]T。
In some embodiments, the delay estimation result is calculated according to the following model:
in some of these embodiments, when 0 <k≤k max Then, the weight coefficient vector W is represented as:
W(k)=W(k-1)+2μ(|y 2 (k-1)|-|ŷ1(k-1)|TW(k-1))ŷ1(k-1)|。
in some embodiments, the time delay estimation result obtaining step obtains the minimum value of the mean square error by using a steepest descent method, so as to obtain a statistically optimal filter in a sense, at this time, the filter converges, and calculates the iteration number corresponding to the maximum value of the model reading weight coefficient vector based on the time delay estimation result, so as to obtain the time delay estimation result.
In a second aspect, an embodiment of the present application provides a short-wave delay estimation system, including:
a parameter initialization module, configured to initialize initial parameters of a filter, where the initial parameters include: weight coefficient vector W, maximum number of iterationsk max And convergence factorμ；
A signal module for obtaining the received two-short wave multipath signaly 1 、y 2 For two said short wave multipath signalsy 1 、y 2 Calculating the mean square error after modulus selection;
the time delay estimation result acquisition module is used for carrying out iterative update on the weight coefficient vector by using the mean square error minimization as a criterion so as to acquire a time delay estimation result;
wherein, thek max Less than or equal to the signal length NR, and the initial value W (0) of the weight coefficient vector is of order 2M f And +1, and the value of the convergence factor is related to the convergence speed and the convergence stability.
In some of these embodiments, the mean square error is calculated according to the following model:
wherein the mean square error
Is a two said short wave multipath signaly 1 、y 2 Square of error of
In the expectation that the position of the target is not changed,
for representing two said short-wave multipath signalsy 1 、y 2 Is determined by the error function of (a),kis the number of iterations andk=2M f +1,2M f ,……,k max and T is used to denote vector transposition，2M f +1 is the order of the filter, which can be set by user, ŷ1Is shown as ŷ1(k)=[y 1 (k),y 1 (k-1),…,y 1 (k-2M f )]T。
In some embodiments, the delay estimation result obtaining module obtains the minimum value of the mean square error by using a steepest descent method, and calculates the iteration number corresponding to the maximum value of the model reading weight coefficient vector based on the delay estimation result, so as to obtain the delay estimation result.
In a third aspect, an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor, when executing the computer program, implements the short wave delay estimation method according to the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the short-wave time delay estimation method according to the first aspect.
Compared with the related art, in order to overcome the interference of a channel gain function to a signal, the embodiment of the application provides a LMS short-wave time delay estimation method, a system, computer equipment and a readable storage medium based on a module value aiming at a Watterson short-wave channel model, in order to improve the traditional LMS algorithm by using a signal module taking mode.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart of a short wave delay estimation method according to an embodiment of the present application;
fig. 2 is a block diagram of a short-wave delay estimation system according to an embodiment of the present application;
FIG. 3 is a graph of the variation of the filter weight coefficient vector of the prior art LMS algorithm;
fig. 4 is a graph of a filter weight coefficient vector variation according to an embodiment of the present application.
In the figure:
1. a parameter initialization module; 2. a signal modulus taking module; 3. and a time delay estimation result acquisition module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The use of the terms "including," "comprising," "having," and any variations thereof herein, is meant to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The adaptive filtering time delay estimation algorithm has the following characteristics: firstly, the self-adaptive filtering algorithm can finish the accurate estimation of the time delay without knowing any prior information in advance; secondly, the adaptive filter can continuously adjust the relevant parameters in the iterative process until approaching to a certain optimal criterion, thereby realizing the dynamic tracking of the changed input signals.
An embodiment of the present application provides a short wave time delay estimation method, and fig. 1 is a flowchart of the short wave time delay estimation method according to a first aspect of the embodiment of the present application, and as shown in fig. 1, the flowchart includes the following steps:
a parameter initialization step S1, initializing initial parameters of the filter, the initial parameters including: weight coefficient vector W, maximum number of iterationsk max And convergence factorμOptionally, the filter is an fir (finite Impulse response) filter;
a signal modulus step S2, acquiring the received two short wave multipath signaly 1 、y 2 Calculating the mean square error after the module of the two short wave multipath signals is obtained; in particular, said short-wave multipath signaly 1 、y 2 Signals of the same transmitting end received by two base stations which are relatively far away are as follows:
wherein, thek max Less than or equal to the signal length NR, and the initial value W (0) of the weight coefficient vector is of order 2M f A zero vector of +1, theM f The value of (2) can be flexibly set according to the required time delay estimation precision, for example and without limitation, in this embodimentM f +1 is set to 32, the value of the convergence factor is related to the convergence speed and the convergence stability,x(n)、x(n-τ 12 ) In order to be a short-wave signal,n=1,2,……，NR，C 1 (n)、C 2 (n) As a function of channel gain in the waterson channel model,τ 12 in order to provide a relative time delay of the signal,e 1 (n) Ande 2 (n) Respectively has a mean value of 0 and a variance ofηOf additive white Gaussian noise, theηThe value of (c) is related to the environment of the channel through which the particular signal travels, it being noted that,x(n)、e 1 (n) Ande 2 (n) Are not related to each other.
In some embodiments, in the signal modulus step S2, the mean square error is obtained by calculating an error function and an error square of the two short-wave multipath signals after modulus taking, and the specific solving process is sequentially as follows:
the error function is calculated according to the following model (2-3):
the square root of the error is obtained by calculation according to the following model (2-4):
the mean square error is calculated according to the following model (2-5):
wherein the mean square errorDifference (D)
Is a two said short wave multipath signaly 1 、y 2 Square of error of
And, T is used to denote vector transposition,
for representing two said short-wave multipath signalsy 1 、y 2 Is determined by the error function of (a),kis the number of iterations andk=2M f +1,2M f ,……,k max ，ŷ1is shown as ŷ1(k)=[y 1 (k),y 1 (k-1),…,y 1 (k-2M f )]T。
Will be provided withy 1 、y 2 When the expressions (2-1, 2-2) of (a) are substituted into the mean square error expression, the mean square error can be expressed as:
in the formula (2-6):
a delay estimation result obtaining step S3, iteratively updating the weight coefficient vector according to the criterion of minimizing the mean square error, so as to obtain a delay estimation result. As can be seen from the above expression (2-6) of the mean square error, the mean square error is a quadratic function of the weight coefficient vector W and is a parabolic curved surface having a unique lowest point after taking a modulus of a signal.
Based on this, in step S3, the steepest descent method is used to obtain the minimum value of the mean square error, so as to obtain a statistically optimal filter in the sense that the filter converges at this time, and meanwhile, in the steepest descent method, the weight coefficient vector W is updated through iteration. Then, step S3 calculates the number of iterations corresponding to the maximum value, i.e., the peak value, of the model read weight coefficient vector based on the delay estimation result, thereby obtaining the delay estimation result. Specifically, the time delay estimation result is obtained by calculation according to the following model (3-1):
specifically, the weight coefficient vector in the iterative process is represented as:
W(k+1)=W(k)-μ▽(k)（3-2）
that is, the weight coefficient vector W (W) for each iterationk+1 is the weight coefficient vector W (of the last iteration)k) And mean square error gradient (v)k) (v) product with the convergence factor, wherein: (vk) Is composed of
The result of the derivation of W; consider to
The derivation is too complex, and is not convenient to realize in practical application, and the embodiment adopts
To represent
Then, then▽(k) Can be expressed as:
the weight coefficient vector is then expressed as:
substituting the error function expression (2-3) into the weight coefficient vector expression (3-4) can obtain:
when 0 <k≤k max The weight coefficient vector W is then expressed as:
in order to verify the practical effects of the embodiments of the present application, referring to fig. 3 to 4, the present application performs simulation analysis on a conventional LMS algorithm and the present application under a short-wave channel, a signal modulation mode is BPSK modulation, a symbol rate is 2400Hz, a sampling rate is 9600Hz, doppler frequency shifts of two multipath signals are 1Hz and 2Hz respectively, doppler frequency spreads are 0.5Hz, a relative time delay is 2ms, the number of time delay points is 19, a signal-to-noise ratio is 10dB, and a data length is 1000, fig. 3 is a graph of a weight coefficient vector change curve of a filter of the conventional LMS algorithm in the background art, as shown in fig. 3, a multi-peak phenomenon occurs in a weight coefficient vector of the conventional LMS algorithm, each peak is not large, a maximum peak is easily affected, and a difference between a maximum peak position and a real time delay value is obvious. This indicates that the minimum mean square error criterion of the conventional LMS algorithm has failed due to the presence of the channel gain function. Fig. 4 is a variation graph of a weight coefficient vector of a filter according to an embodiment of the present invention, as shown in fig. 4, the weight coefficient vector of the embodiment of the present invention has only a single peak, and the peak position is exactly the true delay value, which means that the mean value of the channel gain in the mean square error function can be non-zero after the signal is modulo, so that the weight coefficient vector can be effectively updated in each iteration.
To sum up, the embodiment of the present application effectively avoids the influence of the zero-mean property of the channel gain on the mean square error function by using a signal modulus taking mode.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here.
The embodiment also provides a short wave time delay estimation system. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 2 is a block diagram of a short-wave delay estimation system according to an embodiment of the present application, and as shown in fig. 2, the system includes:
a parameter initialization module 1, configured to initialize initial parameters of a filter, where the initial parameters include: weight coefficient vector W, maximum number of iterationsk max And convergence factorμ；
A signal module 2 for obtaining the received two-short wave multipath signaly 1 、y 2 For two said short wave multipath signalsy 1 、y 2 Calculating the mean square error after modulus selection;
a delay estimation result obtaining module 3, configured to iteratively update the weight coefficient vector W according to a criterion of minimizing a mean square error, so as to obtain a delay estimation result, where,k max ≦ Signal Length NR.
Based on the above modules, the system is used for implementing the short wave time delay estimation method in the embodiment of the application, which has already been described and is not described again.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules may be located in different processors in any combination.
In addition, the short wave delay estimation method described in conjunction with fig. 1 may be implemented by a computer device. The computer device may include a processor and a memory storing computer program instructions.
In particular, the processor may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory may include, among other things, mass storage for data or instructions. By way of example, and not limitation, memory may include a Hard Disk Drive (Hard Disk Drive, abbreviated to HDD), a floppy Disk Drive, a Solid State Drive (SSD), flash memory, an optical Disk, a magneto-optical Disk, tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. The memory may include removable or non-removable (or fixed) media, where appropriate. The memory may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory is a Non-Volatile (Non-Volatile) memory. In particular embodiments, the Memory includes Read-Only Memory (ROM) and Random Access Memory (RAM). The ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), Electrically rewritable ROM (EAROM), or FLASH Memory (FLASH), or a combination of two or more of these, where appropriate. The RAM may be a Static Random-Access Memory (SRAM) or a Dynamic Random-Access Memory (DRAM), where the DRAM may be a Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), an Extended data output Dynamic Random-Access Memory (EDODRAM), a Synchronous Dynamic Random-Access Memory (SDRAM), and the like.
The memory may be used to store or cache various data files for processing and/or communication use, as well as possibly computer program instructions for execution by the processor.
The processor reads and executes the computer program instructions stored in the memory to implement any one of the short wave time delay estimation methods in the above embodiments.
In addition, in combination with the short-wave time delay estimation method in the foregoing embodiment, the embodiment of the present application may provide a computer-readable storage medium to implement. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the short wave delay estimation methods in the above embodiments.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A short-wave time delay estimation method is characterized by comprising the following steps:
a parameter initialization step of initializing initial parameters of a filter, wherein the initial parameters comprise: weight coefficient vector W, maximum number of iterationsk max And convergence factorμ；
A signal modulus taking step for obtaining the received two short wave multipath signaly 1 、y 2 For two said short wave multipath signalsy 1 、y 2 Calculating the mean square error after modulus selection;
a time delay estimation result obtaining step, which is to carry out iterative update on the weight coefficient vector W by using the mean square error minimization as a criterion so as to obtain a time delay estimation result, wherein the time delay estimation result is obtainedk max Less than or equal to the signal length NR.
2. The short wave time delay estimation method according to claim 1, wherein the mean square error is calculated according to the following model:
wherein the mean square error
Is twoThe short wave multipath signaly 1 、y 2 Square of error of
In the expectation of the above-mentioned method,
for representing two said short-wave multipath signalsy 1 、y 2 Is determined by the error function of (a),kis the number of iterations andk=2M f +1,2M f ,……,k max t is used to denote vector transposition, ŷ1Is shown as ŷ1(k)=[y 1 (k),y 1 (k-1),…,y 1 (k-2M f )]T，2M f +1 is the order of the filter, which can be set by user.
3. The short-wave time delay estimation method according to claim 2, wherein the time delay estimation result is obtained by calculation according to the following model:
4. the short wave time delay estimation method according to claim 3, wherein when 0 <, the time delay is less than 0 < >k≤k max Then, the weight coefficient vector W is represented as:
W(k)=W(k-1)+2μ(|y 2 (k-1)|-|ŷ1(k-1)|TW(k-1))ŷ1(k-1)|。
5. the short-wave time delay estimation method according to claim 3, wherein the time delay estimation result obtaining step obtains the minimum value of the mean square error by using a steepest descent method, and calculates the iteration number corresponding to the maximum value of the model reading weight coefficient vector based on the time delay estimation result, thereby obtaining the time delay estimation result.
6. A short wave delay estimation system, comprising:
a parameter initialization module, configured to initialize initial parameters of a filter, where the initial parameters include: weight coefficient vector W, maximum number of iterationsk max And convergence factorμ；
A signal module for obtaining the received two-short wave multipath signaly 1 、y 2 For two said short wave multipath signalsy 1 、y 2 Calculating the mean square error after modulus selection;
a delay estimation result obtaining module, configured to iteratively update the weight coefficient vector W according to a criterion of minimizing a mean square error, so as to obtain a delay estimation result, where the delay estimation result is obtainedk max ≦ Signal Length NR.
7. The short wave delay estimation system of claim 6, wherein: the mean square error is calculated according to the following model:
whereinThe mean square error
For two said short wave multipath signalsy 1 、y 2 Square of error of
In the expectation of the above-mentioned method,
for representing two said short-wave multipath signalsy 1 、y 2 Is determined by the error function of (a),kis the number of iterations andk=2M f +1,2M f ,……,k max t is used to denote vector transposition, ŷ1Is shown as ŷ1(k)=[y 1 (k),y 1 (k-1),…,y 1 (k-2M f )]T，2M f +1 is the order of the filter, which can be set by user.
8. The short wave delay estimation system of claim 7, wherein: and the time delay estimation result acquisition module acquires the minimum value of the mean square error by adopting a steepest descent method, and calculates the iteration times corresponding to the maximum value of the model reading weight coefficient vector based on the time delay estimation result so as to obtain the time delay estimation result.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the short wave delay estimation method according to any one of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the short wave delay estimation method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210559349.3A CN114665991B (en) | 2022-05-23 | 2022-05-23 | Short wave time delay estimation method, system, computer equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210559349.3A CN114665991B (en) | 2022-05-23 | 2022-05-23 | Short wave time delay estimation method, system, computer equipment and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114665991A true CN114665991A (en) | 2022-06-24 |
| CN114665991B CN114665991B (en) | 2022-08-09 |
Family
ID=82037801
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210559349.3A Active CN114665991B (en) | 2022-05-23 | 2022-05-23 | Short wave time delay estimation method, system, computer equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114665991B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115801145A (en) * | 2023-01-29 | 2023-03-14 | 清华大学 | Time delay estimation method and device for mixed signal and electronic equipment |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006114102A1 (en) * | 2005-04-26 | 2006-11-02 | Aalborg Universitet | Efficient initialization of iterative parameter estimation |
| EP1841065A1 (en) * | 2006-03-29 | 2007-10-03 | Mitel Networks Corporation | Modified least-mean-squares method with reduced computational complexity |
| CN101662433A (en) * | 2009-06-23 | 2010-03-03 | 中山大学 | Channel prediction method based on particle filtration correction |
| CN104316945A (en) * | 2014-11-13 | 2015-01-28 | 中国人民解放军总参谋部第六十三研究所 | Satellite interference source three-satellite positioning method based on high-order cumulants and unscented Kalman filtering |
| CN105891810A (en) * | 2016-05-25 | 2016-08-24 | 中国科学院声学研究所 | Fast adaptive joint time delay estimation method |
| CN109119061A (en) * | 2018-08-15 | 2019-01-01 | 西南交通大学 | A kind of active noise control method based on gradient matrix |
| CN109347458A (en) * | 2018-12-04 | 2019-02-15 | 钟祥博谦信息科技有限公司 | A kind of adaptive filter method |
| CN109799484A (en) * | 2019-01-31 | 2019-05-24 | 河海大学 | A kind of external radiation source radar system multipaths restraint method, system and storage medium |
| CN112871614A (en) * | 2021-01-12 | 2021-06-01 | 武汉大学 | MEMS ultrasonic transducer with high emission performance |
| CN113595528A (en) * | 2021-07-30 | 2021-11-02 | 重庆长安汽车股份有限公司 | Adaptive variable-step LMS filter based on power function and implementation method thereof |
-
2022
- 2022-05-23 CN CN202210559349.3A patent/CN114665991B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006114102A1 (en) * | 2005-04-26 | 2006-11-02 | Aalborg Universitet | Efficient initialization of iterative parameter estimation |
| EP1841065A1 (en) * | 2006-03-29 | 2007-10-03 | Mitel Networks Corporation | Modified least-mean-squares method with reduced computational complexity |
| CN101662433A (en) * | 2009-06-23 | 2010-03-03 | 中山大学 | Channel prediction method based on particle filtration correction |
| CN104316945A (en) * | 2014-11-13 | 2015-01-28 | 中国人民解放军总参谋部第六十三研究所 | Satellite interference source three-satellite positioning method based on high-order cumulants and unscented Kalman filtering |
| CN105891810A (en) * | 2016-05-25 | 2016-08-24 | 中国科学院声学研究所 | Fast adaptive joint time delay estimation method |
| CN109119061A (en) * | 2018-08-15 | 2019-01-01 | 西南交通大学 | A kind of active noise control method based on gradient matrix |
| CN109347458A (en) * | 2018-12-04 | 2019-02-15 | 钟祥博谦信息科技有限公司 | A kind of adaptive filter method |
| CN109799484A (en) * | 2019-01-31 | 2019-05-24 | 河海大学 | A kind of external radiation source radar system multipaths restraint method, system and storage medium |
| CN112871614A (en) * | 2021-01-12 | 2021-06-01 | 武汉大学 | MEMS ultrasonic transducer with high emission performance |
| CN113595528A (en) * | 2021-07-30 | 2021-11-02 | 重庆长安汽车股份有限公司 | Adaptive variable-step LMS filter based on power function and implementation method thereof |
Non-Patent Citations (2)
| Title |
|---|
| 李曼: "在Matlab中实现基于LMS算法语音信号去噪", 《CNKI》 * |
| 郭莹等: "改进的LMS自适应时延估计方法", 《沈阳工业大学学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115801145A (en) * | 2023-01-29 | 2023-03-14 | 清华大学 | Time delay estimation method and device for mixed signal and electronic equipment |
| CN115801145B (en) * | 2023-01-29 | 2023-05-12 | 清华大学 | Time delay estimation method and device for mixed signal and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114665991B (en) | 2022-08-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| TWI575900B (en) | Methods and devices for channel estimation and ofdm receiver | |
| JP5484682B2 (en) | WIRELESS COMMUNICATION DEVICE, EQUALIZER, EQUALIZER WEIGHT COEFFICIENT PROGRAM AND Equalizer Weight Coefficient Calculation Method | |
| CN114665991B (en) | Short wave time delay estimation method, system, computer equipment and readable storage medium | |
| CN111628946B (en) | Channel estimation method and receiving equipment | |
| CN113242191B (en) | Improved time sequence multiple sparse Bayesian learning underwater acoustic channel estimation method | |
| CN109729032B (en) | Method and device for correcting frequency offset estimation value and computer readable storage medium | |
| CN103873411B (en) | Method and device for maximum likelihood frequency offset estimation based on joint pilot frequency | |
| TWI728028B (en) | Chip, user equipment and method for enhanced channel estimation, manufacturing method of chip | |
| Cho et al. | Multiuser acoustic communications with mobile users | |
| CN114449584B (en) | Distributed computing unloading method and device based on deep reinforcement learning | |
| US7130342B2 (en) | Wireless receiver and method employing forward/backward recursive covariance based filter coefficient generation | |
| CN106685555B (en) | MIMO underwater acoustic system channel state information feedback method based on low-rank matrix recovery | |
| CN111726309B (en) | Channel estimation method for mobile orthogonal frequency division multiplexing system and estimation device thereof | |
| CN117544453A (en) | Method and device for stably estimating underwater acoustic channel under ice with anti-ice noise | |
| WO2017080359A1 (en) | Interference cancellation method and apparatus, and base station | |
| CN108259395B (en) | Channel estimation method and device | |
| WO2015101418A2 (en) | Methods and devices for doppler shift compensation in a mobile communication system | |
| CN115118556A (en) | Sparse channel estimation method, device and medium for OFDM underwater acoustic communication system | |
| WO2013013616A1 (en) | Data reconstruction method and device | |
| CN109861936B (en) | Method and device for thinning short wave channel and computer storage medium | |
| EP2756603A1 (en) | Channel estimation method, channel estimation apparatus and communication device for cdma systems | |
| CN108989261B (en) | Timing synchronization method, device and related equipment of communication system | |
| JP6373408B2 (en) | Channel equalization tracking apparatus, method and receiver | |
| CN113472704B (en) | HPLC channel estimation method and device based on weight selection iteration integral least square | |
| CN117014261B (en) | Dual-polarized channel estimation implementation method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |