KR101649198B1 - Method and Apparatus for estimating object trajectories using optimized smoothing filter based beamforming information - Google Patents
Method and Apparatus for estimating object trajectories using optimized smoothing filter based beamforming information Download PDFInfo
- Publication number
- KR101649198B1 KR101649198B1 KR1020150087332A KR20150087332A KR101649198B1 KR 101649198 B1 KR101649198 B1 KR 101649198B1 KR 1020150087332 A KR1020150087332 A KR 1020150087332A KR 20150087332 A KR20150087332 A KR 20150087332A KR 101649198 B1 KR101649198 B1 KR 101649198B1
- Authority
- KR
- South Korea
- Prior art keywords
- information
- moving object
- trajectory
- filtering
- linear
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000009499 grossing Methods 0.000 title claims abstract description 33
- 238000001914 filtration Methods 0.000 claims abstract description 62
- 239000011159 matrix material Substances 0.000 claims description 14
- 238000005259 measurement Methods 0.000 claims description 11
- 238000012937 correction Methods 0.000 claims description 10
- 238000005070 sampling Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 description 5
- 238000012360 testing method Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000015556 catabolic process Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 230000005236 sound signal Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
- G01S11/14—Systems for determining distance or velocity not using reflection or reradiation using ultrasonic, sonic, or infrasonic waves
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H11/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
- H04R1/00—Details of transducers, loudspeakers or microphones
- H04R1/20—Arrangements for obtaining desired frequency or directional characteristics
- H04R1/22—Arrangements for obtaining desired frequency or directional characteristics for obtaining desired frequency characteristic only
- H04R1/222—Arrangements for obtaining desired frequency or directional characteristics for obtaining desired frequency characteristic only for microphones
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Health & Medical Sciences (AREA)
- Otolaryngology (AREA)
- Acoustics & Sound (AREA)
- Signal Processing (AREA)
- Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
- Circuit For Audible Band Transducer (AREA)
Abstract
Description
More particularly, the present invention relates to an object trajectory estimation method for performing optimal smoothing filtering on location information of an object obtained based on a beam forming technique on an acoustic signal provided through a microphone array, Device.
Measurement data based filtering techniques using radar, image processing, and sound for moving object trajectory estimation are applied / studied in various fields.
In the case of object trajectory estimation using radar and image processing, the accuracy of the trajectory estimation is degraded due to the measurement noise and the data unmeasured interval according to the terrain condition.
In order to estimate the object trajectory by measuring the position information, the acoustic signal was measured by the microphone array and the beamforming technique was used. However, the trajectory estimation through such a beam forming technique is sensitive to the influences of the external environment requirements such as the flow environment and the interference signal, which causes the object trajectory estimation performance to deteriorate.
It is an object of the present invention to provide a method and apparatus for estimating an object trajectory using an optimal smoothing filter based on beamforming information, which improves trajectory estimation accuracy of a moving object.
The present invention provides an object trajectory estimation method using an optimal smoothing filter based on beamforming information for improving trajectory estimation accuracy of a moving object to achieve the above-described object.
The object trajectory estimation method includes:
The acquiring unit receiving acoustic signals from the microphone array to acquire beamforming position information of the moving object;
A determining step of determining linear or non-linearity of the locus information from the beam forming position information obtained by the linear determining unit;
A filtering step of performing forward filtering on the positional information of the moving object based on the linear or nonlinear information determined according to the filtering result of the filtering unit to calculate a moving object trajectory estimate;
A trajectory information generating step for generating a trajectory information of the final moving object by filtering and weighting the moving object trajectory estimation value in a reverse direction; And
And the output unit outputs the final moving object locus information.
The filtering may include performing forward filtering on the beamforming position information of the moving object according to the linear information using a linear KalDox filter; And performing forward filtering on the beamforming position information of the moving object according to the nonlinear information using the nonlinear extended Kalman filter.
In the forward filtering, a basic filtering operation is performed by selecting an initial value, a trajectory after one sampling of the beam forming position information of the moving object is predicted using the dynamic modeling information, And generating a filtered first correction value through a weighted sum between the current measured trajectories.
Further, the first correction value may be expressed by Equation
(Where "-" is a priori estimate, "+" is a posteriori estimate, Z k is a measurement value of the moving object, and H is a discrete system measurement matrix).Also, the locus information generation step may be performed using an optimal smoothing technique having dynamic modeling reflecting dynamic characteristics of the moving object.
The trajectory information generation step may include calculating a second correction value filtered through the backward filtering and the weighted sum on the moving object trajectory estimation value after performing the forward filtering.
Also, the second correction value may be expressed by Equation
(Where a is a priori estimate, a is a posteriori estimate, S is an error covariance,
), F is the discretization system state matrix, T is the transpose matrix, Represents an inverse Kalman gain).On the other hand, another embodiment of the present invention includes an acquisition unit that receives an acoustic signal from a microphone array and acquires beamforming position information of a moving object; A linear determination unit for determining linear or nonlinear shape of the locus information from the obtained beamforming position information; A filtering unit for performing forward filtering on the positional information of the moving object based on the determined linear or nonlinear information to calculate a moving object trajectory estimate; A smoothing unit for backward filtering and weighting the moving object trajectory estimate to generate final moving object trajectory information; And an output unit for outputting the final moving object locus information. The apparatus for estimating an object locus using an optimal smoothing filter based on beamforming information.
In this case, the filtering unit may include: a linear DFM filter for performing forward filtering on the beam forming position information of the moving object according to the linear information; And a nonlinear extended Kalman filter for performing forward filtering on the beamforming position information of the moving object according to the nonlinear information.
According to the present invention, it is possible to improve the accuracy of trajectory estimation by reducing the scattering of the position information by the measurement noise and the external environment factors in the trajectory estimation of the moving object by using the beam forming technique.
FIG. 1 is a block diagram of a moving object
FIG. 2 is a flowchart illustrating a process of estimating a trajectory of a moving object using an optimal smoothing filter based on beamforming information according to an embodiment of the present invention.
FIG. 3 is a flowchart of an optimal smoothing filter algorithm of the
4 is a conceptual diagram showing a model rocket experimental environment according to an embodiment of the present invention.
5 is a graph showing a time trajectory of a location based on a model rocket test data according to the model rocket test environment shown in FIG.
While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It is to be understood, however, that the invention is not to be limited to the specific embodiments, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
Like reference numerals are used for similar elements in describing each drawing.
The terms first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another.
For example, without departing from the scope of the present invention, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component. The term "and / or" includes any combination of a plurality of related listed items or any of a plurality of related listed items.
Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the contextual meaning of the related art and are to be interpreted as either ideal or overly formal in the sense of the present application Should not.
Hereinafter, a method and an apparatus for estimating an object trajectory using an optimal smoothing filter based on beamforming information according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.
FIG. 1 is a block diagram of a moving object
The
The
The
The
In general, the optimal smoothing technique includes dynamic modeling that reflects the dynamic characteristics of a moving object (i.e., object), and the dynamic modeling can be expressed as a state space equation as: < EMI ID =
here,
Is the derivative of the system state matrix, z is the system observation value measured by the sensor, Is the sampling time of a stem in which the kinematic model is exercising, Is a system state matrix, Is a system state matrix, Is an input matrix, and Is an observation matrix that tells the system ecological variables measured by the sensor.Continuing with FIG. 1, the
The
FIG. 2 is a flowchart illustrating a process of estimating a trajectory of a moving object using an optimal smoothing filter based on beamforming information according to an embodiment of the present invention. Referring to Figure 2,
And generates a sound signal for a moving object (not shown) using the
The
If it is determined in step S220 that the trajectory information is linear, the
Otherwise, if it is determined in step S220 that the trajectory information is non-linear, the
The smoothing
FIG. 3 is a flowchart of an optimal smoothing filter algorithm of the
The data acquisition step S310 is a process of acquiring data. Further, the beam forming position information of the moving object shown in FIG. 2 is obtained.
The forward filtering step S320 includes a step S321 of setting a state variable average and a covariance initial value, a step S322 of estimating an estimated value and an error covariance, a calculation of a Kalman gain, a measurement value and an error covariance S323), determining the sampling (S324), and the like.
Further, in the forward filter 220, a filtering basic operation is performed by selecting an initial value, and a trajectory after one sampling of the moving object (i.e., object) is predicted using the dynamic modeling information.
(I. E., The object) is obtained through an acoustic sensor, the Kalman gain < RTI ID = 0.0 > And then uses the weighted sum between the trajectory of the predicted moving object and the current measured value (i.e., the current measurement trajectory).The filtered correction value through the forward filtering step S320 may be expressed by the following equation.
Here, "-" is a priori estimate, "+" is a posteriori estimate,
Z k is a measure of the moving object, and H is a discrete system measurement matrix.The backward filtering step S330 includes a final value selection step S331, a measurement value and error covariance calculation step S332, a Kalman gain calculation and estimation value, an error covariance calculation step S333, and a sampling determination step S334.
In addition, the procedure of the forward filter 220 may be combined with the reverse filter 230
Th sampling. In the newly defined variable, the final value is selected rather than the initial value due to the characteristics of the backward filter. Then, the process of calculating the measured value and the error covariance is performed, and the Kalman gain calculation and the estimation of the error and covariance based on the dynamics are performed do.The filtered correction value through the backward filtering step S330 may be expressed by the following equation.
Here, "-" is a priori esimate, "+" is a posteriori esimate, S is an error covariance
), F is the discretization system state matrix, T is the transpose matrix, Represents the inverse Kalman gain.Equation (3) can be transformed into a filtered position information value by the following equation.
Where P -1 represents the reciprocal of the error covariance.
The value obtained through the filtering step S320 may be expressed by the following equation through a weighted sum step S40.
In other words, the last time of the data
Coupled in Lt; th > sampling, And the weighted sum of the forward-inverse filters performed up to the i-th sampling (step 240).
Where K k is the Kalman gain and P is the error covariance.
4 is a conceptual diagram showing a model rocket experimental environment according to an embodiment of the present invention. 4, the model rocket test environment includes first to
The distance between the model rocket flight trajectory and the microphone array (440), and the distance between the model rocket trajectory and the ground (450) are shown.
5 is a graph showing a time trajectory of a location based on a model rocket test data according to the model rocket test environment shown in FIG. Referring to FIG. 5, the
The model
1: microphone array
100: object trajectory estimation device
110:
120: linear judgment unit
130:
130a: Linear
140: Smoothing part
150:
Claims (9)
A determining step of determining linear or non-linearity of the locus information from the beam forming position information obtained by the linear determining unit;
A filtering step of performing forward filtering on the positional information of the moving object based on the linear or nonlinear information determined according to the filtering result of the filtering unit to calculate a moving object trajectory estimate;
A trajectory information generating step for generating a trajectory information of the final moving object by filtering and weighting the moving object trajectory estimation value in a reverse direction; And
Outputting the final moving object locus information;
Wherein the object trajectory estimating method is based on an optimal smoothing filter based on beamforming information.
Wherein the filtering comprises performing forward filtering on the beamforming position information of the moving object according to linear information using a linear KalDic oxide filter; And performing forward filtering on the beamforming position information of the moving object according to the nonlinear information using the nonlinear extended Kalman filter.
The forward filtering is performed by performing a filtering basic operation by selecting an initial value, predicting a trajectory after one sampling of the beam forming position information of the moving object by using the dynamic modeling information, calculating a trajectory of the predicted moving object, And generating a filtered first correction value through a weighted sum between the measured trajectories.
The first correction value may be expressed by Equation (Where "-" is a priori estimate, "+" is a posteriori estimate, Z k is a measurement value of the moving object, and H is a discrete system measurement matrix. The method of claim 1, wherein the step of estimating an object trajectory using the optimal smoothing filter is based on beamforming information.
Wherein the locus information generation step is performed using an optimal smoothing technique having dynamic modeling reflecting the dynamic characteristics of the moving object, wherein the locus information generation step uses an optimal smoothing filter based on the beamforming information.
Wherein the trajectory information generation step calculates a second correction value filtered through the backward filtering and the weighted sum on the moving object trajectory estimation value after performing the forward filtering, Method of estimating trajectory.
Wherein the second correction value is calculated using Equation
(Where a is a priori estimate, a is a posteriori estimate, S is an error covariance, ), F is the discretization system state matrix, T is the transpose matrix, Wherein the inverse Kalman gain is defined as the inverse Kalman gain.
A linear determination unit for determining linear or nonlinear shape of the locus information from the obtained beamforming position information;
A filtering unit for performing forward filtering on the positional information of the moving object based on the determined linear or nonlinear information to calculate a moving object trajectory estimate;
A smoothing unit for backward filtering and weighting the moving object trajectory estimate to generate final moving object trajectory information; And
An output unit for outputting the final moving object locus information;
And an object trajectory estimating unit that estimates an object trajectory based on the optimal smoothing filter based on the beamforming information.
Wherein the filtering unit comprises: a linear discrete Kalman filter for performing forward filtering on the beam forming position information of the moving object according to the linear information; And
And a nonlinear extended Kalman filter for performing forward filtering on the beamforming position information of the moving object according to the nonlinear information based on the nonlinear extended Kalman filter.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
KR1020150087332A KR101649198B1 (en) | 2015-06-19 | 2015-06-19 | Method and Apparatus for estimating object trajectories using optimized smoothing filter based beamforming information |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
KR1020150087332A KR101649198B1 (en) | 2015-06-19 | 2015-06-19 | Method and Apparatus for estimating object trajectories using optimized smoothing filter based beamforming information |
Publications (1)
Publication Number | Publication Date |
---|---|
KR101649198B1 true KR101649198B1 (en) | 2016-08-18 |
Family
ID=56874617
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
KR1020150087332A KR101649198B1 (en) | 2015-06-19 | 2015-06-19 | Method and Apparatus for estimating object trajectories using optimized smoothing filter based beamforming information |
Country Status (1)
Country | Link |
---|---|
KR (1) | KR101649198B1 (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR101878617B1 (en) * | 2017-12-19 | 2018-07-13 | 부산대학교 산학협력단 | Method and system for processing traictory data |
US11658712B1 (en) | 2022-03-31 | 2023-05-23 | lERUS Technologies, Inc. | Computer implemented method for reducing adaptive beamforming computation using a Kalman filter |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR20040079085A (en) * | 2003-03-06 | 2004-09-14 | 삼성전자주식회사 | Microphone array structure, method and apparatus for beamforming with constant directivity and method and apparatus for estimating direction of arrival, employing the same |
KR20060050991A (en) * | 2004-09-03 | 2006-05-19 | 하만 베커 오토모티브 시스템즈 게엠베하 | Speech signal processing with combined noise reduction and echo compensation |
KR20090017435A (en) | 2007-08-13 | 2009-02-18 | 하만 베커 오토모티브 시스템즈 게엠베하 | Noise reduction by combined beamforming and post-filtering |
-
2015
- 2015-06-19 KR KR1020150087332A patent/KR101649198B1/en active IP Right Grant
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR20040079085A (en) * | 2003-03-06 | 2004-09-14 | 삼성전자주식회사 | Microphone array structure, method and apparatus for beamforming with constant directivity and method and apparatus for estimating direction of arrival, employing the same |
KR20060050991A (en) * | 2004-09-03 | 2006-05-19 | 하만 베커 오토모티브 시스템즈 게엠베하 | Speech signal processing with combined noise reduction and echo compensation |
KR20090017435A (en) | 2007-08-13 | 2009-02-18 | 하만 베커 오토모티브 시스템즈 게엠베하 | Noise reduction by combined beamforming and post-filtering |
Non-Patent Citations (3)
Title |
---|
1. 하재현 외, "음향 빔형성 기법을 이용한 로켓소음 도래각 추정 연구"한국소음진동공학회 2012년도 춘계학술대회 논문집. |
고영주 외 4명, 마이크로폰 어레이를 이용한 모형로켓의 이동 궤도 추적, 한국항공우주학회 2014년도 춘계학술대회, 2014.4, 405-408 * |
박수홍 외 1명, 확장 칼만필터와 스무딩 필터를 이용한 위성의 궤도결정, 한국항공우주학회, 한국항공우주학회지 제18권 제4호, 1990.12, 76-86 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR101878617B1 (en) * | 2017-12-19 | 2018-07-13 | 부산대학교 산학협력단 | Method and system for processing traictory data |
US11658712B1 (en) | 2022-03-31 | 2023-05-23 | lERUS Technologies, Inc. | Computer implemented method for reducing adaptive beamforming computation using a Kalman filter |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN106950562B (en) | State fusion target tracking method based on predicted value measurement conversion | |
KR101413229B1 (en) | DOA estimation Device and Method | |
KR101529516B1 (en) | Sound sourcelocalization device and sound sourcelocalization method | |
JP4709117B2 (en) | Radar device and angle measuring device | |
JP7049085B2 (en) | Systems and methods for parallel processing and pipeline processing of variable blind separation filters | |
RU2015112126A (en) | DEVICE AND METHOD FOR PROVIDING AN INFORMED EVALUATION OF PROBABILITY AND PRESENCE OF MULTI-CHANNEL SPEECH | |
CN110286357B (en) | Target motion positioning method based on underwater sound detection | |
JP2011013189A (en) | Positioning device and program | |
CA2920519A1 (en) | Angles-only initial orbit determination (iod) | |
KR101649198B1 (en) | Method and Apparatus for estimating object trajectories using optimized smoothing filter based beamforming information | |
JP5046793B2 (en) | Wind measuring device | |
JP2005108246A (en) | Method and device for estimating position of unmanned mobile body by use of sensor fusing, and computer-readable storage medium recording program | |
US20160054435A1 (en) | Method and apparatus of adaptive beamforming | |
CN111678513A (en) | Ultra-wideband/inertial navigation tight coupling indoor positioning device and system | |
JP5361008B2 (en) | Object tracking method in three-dimensional space using acoustic sensor based on particle filter | |
US10820152B2 (en) | Device diversity correction method for RSS-based precise location tracking | |
CN104407366A (en) | Pseudo-range smooth processing method | |
CN116929343A (en) | Pose estimation method, related equipment and storage medium | |
US20230109019A1 (en) | Pipelined cognitive signal processor | |
JP4882544B2 (en) | TRACKING PROCESSING DEVICE, ITS METHOD, AND PROGRAM | |
JP2007086039A (en) | Method and device for analyzing motion of target object | |
JP2014157110A (en) | Signal processing device, radar device and signal processing method | |
JP2006250693A (en) | Target body motion method and device for analyzing | |
JP2020046774A (en) | Signal processor, distance measuring device and distance measuring method | |
CN111121827B (en) | TMR magnetic encoder system based on Kalman filtering |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
E701 | Decision to grant or registration of patent right | ||
GRNT | Written decision to grant |