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 PDF

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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
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information
moving object
trajectory
filtering
linear
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Korean (ko)
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최종수
김승균
정준호
하재현
박웅
민준기
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국방과학연구소
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    • 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

Abstract

The present invention relates to a technique for estimating a trajectory of a moving object. More specifically, the present invention relates to a method and an apparatus for estimating a trajectory of an object, capable of performing optimized smoothing filtering on position information of an object obtained on the basis of a beamforming technique in an acoustic signal provided through a microphone array.

Description

FIELD OF THE INVENTION The present invention relates to an object trajectory estimation method and apparatus using an optimal smoothing filter based on beamforming information,

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.

1. Korean Patent Publication No. 10-2009-0017435

1. Ha JH, et al., "Estimation of rocket noise arrival angle using acoustic beamforming technique" Proceedings of the Korean Society for Noise and Vibration Engineering Conference,.

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

Figure 112015059474672-pat00001
(Where "-" is a priori estimate, "+" is a posteriori estimate,
Figure 112015059474672-pat00002
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

Figure 112015059474672-pat00003

(Where a is a priori estimate, a is a posteriori estimate, S is an error covariance,

Figure 112015059474672-pat00004
), F is the discretization system state matrix, T is the transpose matrix,
Figure 112015059474672-pat00005
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 trajectory estimating apparatus 100 using an optimal smoothing filter based on beamforming information according to an embodiment of the present invention. Referring to FIG.
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 filtering unit 130 and the smoothing unit 140 shown in FIG.
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 trajectory estimating apparatus 100 using an optimal smoothing filter based on beamforming information according to an embodiment of the present invention. Referring to FIG. 1, a moving object trajectory estimating apparatus 100 includes an acquiring unit 110 for receiving an acoustic signal from a microphone array 1 to acquire beam forming position information of a moving object, A filtering unit 130 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 estimation value, A smoothing unit 140 for filtering and summing the trajectory estimates in a reverse direction, and an output unit 150 for outputting the last moving object trajectory information.

The acquisition unit 110 forms beamforming position information of the moving object using the acoustic signal acquired through the microphone array 1. [

The linear decision unit 120 determines whether the trajectory information for filtering is linear or non-linear through the trajectory of raw data obtained through the beamforming technique of the acquired moving object.

The filtering unit 130 performs filtering on the moving object obtained by the beamforming position information obtaining unit 110 using the linear or nonlinear extended Kalman filter 130a and the nonlinear extended Kalman filter 130b based on the linear or nonlinear information determined by the linear determining unit 120. [ And performs forward filtering on the beamforming position information of the moving object to generate a moving object trajectory estimate.

The smoothing unit 140 performs an inverse filtering and / or a weighting sum on the moving object trajectory estimation value obtained through the filtering unit 130 with the optimal smoothing technique to generate the final moving object trajectory information of the improved estimation.

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 =

Figure 112015059474672-pat00006

here,

Figure 112015059474672-pat00007
Is the derivative of the system state matrix, z is the system observation value measured by the sensor,
Figure 112015059474672-pat00008
Is the sampling time of a stem in which the kinematic model is exercising,
Figure 112015059474672-pat00009
Is a system state matrix,
Figure 112015059474672-pat00010
Is a system state matrix,
Figure 112015059474672-pat00011
Is an input matrix, and
Figure 112015059474672-pat00012
Is an observation matrix that tells the system ecological variables measured by the sensor.

Continuing with FIG. 1, the filtering unit 130 and the smoothing unit 140 perform a flow illustrated in FIG. 3 with an optimal smoothing scheme. That is, a forward filter process, an inverse filter process, and a weighted sum process are performed.

The output unit 150 outputs the final moving object locus information.

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 microphone array 1. The acquiring unit 110 generates and acquires beamforming position information of the moving object using the acoustic signal (step S210).

The linearity determination unit 120 determines the linear or nonlinear shape of the locus information from the obtained beamforming position information (step S220).

If it is determined in step S220 that the trajectory information is linear, the filtering unit 130 calculates a trajectory estimation value of the moving object (not shown) using the linear KalDor filter 130a (step S230).

Otherwise, if it is determined in step S220 that the trajectory information is non-linear, the filtering unit 130 calculates a trajectory estimation value of the moving object (not shown) using the non-linear Kalman filter 130b (step S230).

The smoothing unit 140 generates the final moving object trajectory information by back filtering and weighting the moving object trajectory estimation value, and outputs the final moving object trajectory information through the output unit 150 (steps S250 and S260).

FIG. 3 is a flowchart of an optimal smoothing filter algorithm of the filtering unit 130 and the smoothing unit 140 shown in FIG. Referring to FIG. 3, it includes a data acquisition step S310, a forward filtering step S320, a reverse filtering step S330, a weighted sum step S340, and the like.

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.

Figure 112015059474672-pat00013
(I. E., The object) is obtained through an acoustic sensor, the Kalman gain < RTI ID = 0.0 >
Figure 112015059474672-pat00014
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.

Figure 112015059474672-pat00015

Here, "-" is a priori estimate, "+" is a posteriori estimate,

Figure 112015059474672-pat00016
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

Figure 112015059474672-pat00017
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.

Figure 112015059474672-pat00018

Here, "-" is a priori esimate, "+" is a posteriori esimate, S is an error covariance

Figure 112015059474672-pat00019
), F is the discretization system state matrix, T is the transpose matrix,
Figure 112015059474672-pat00020
Represents the inverse Kalman gain.

Equation (3) can be transformed into a filtered position information value by the following equation.

Figure 112015059474672-pat00021

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

Figure 112015059474672-pat00022
Coupled in
Figure 112015059474672-pat00023
Lt; th > sampling,
Figure 112015059474672-pat00024
And the weighted sum of the forward-inverse filters performed up to the i-th sampling (step 240).

Figure 112015059474672-pat00025

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 Nth photosensors 410a to 410e for acquiring reference values of the model rocket locus, a model rocket 420 capable of flying together with noise, a microphone And an array (1).

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 model rocket trajectory 520 using the beamforming technique shows that the position information is scattered between 0.5 seconds and 1 second.

The model rocket flight trajectory 530 using the Kalman filter technique can be confirmed to improve the position information scattering section of the model rocket flight trajectory 520 using the beam forming technique. The model rocket flight trajectory 540 using the optimal smoothing filter technique compensates for the trajectory tracking performance degradation due to the measured value of the model rocket trajectory 530 using the Kalman filter technique.

1: microphone array
100: object trajectory estimation device
110:
120: linear judgment unit
130:
130a: Linear Dioxide Kalman Filter 130b: Nonlinear Extended Kalman Filter
140: Smoothing part
150:

Claims (9)

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
Outputting the final moving object locus information;
Wherein the object trajectory estimating method is based on an optimal smoothing filter based on beamforming information.
The method according to claim 1,
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.
3. The method of claim 2,
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 method of claim 3,
The first correction value may be expressed by Equation
Figure 112015059474672-pat00026
(Where "-" is a priori estimate, "+" is a posteriori estimate,
Figure 112015059474672-pat00027
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. 3. The method of claim 2,
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.
6. The method of claim 5,
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.
The method according to claim 6,
Wherein the second correction value is calculated using Equation
Figure 112015059474672-pat00028

(Where a is a priori estimate, a is a posteriori estimate, S is an error covariance,
Figure 112015059474672-pat00029
), F is the discretization system state matrix, T is the transpose matrix,
Figure 112015059474672-pat00030
Wherein the inverse Kalman gain is defined as the inverse Kalman gain.
An acquiring unit receiving an acoustic signal from a microphone array and acquiring 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;
And an object trajectory estimating unit that estimates an object trajectory based on the optimal smoothing filter based on the beamforming information.
9. The method of claim 8,
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.
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Cited By (2)

* Cited by examiner, † Cited by third party
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)

* Cited by examiner, † Cited by third party
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

Patent Citations (3)

* Cited by examiner, † Cited by third party
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)

* Cited by examiner, † Cited by third party
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)

* Cited by examiner, † Cited by third party
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

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