WO2013073399A1

WO2013073399A1 - Moving object position detection device and moving object position detection method

Info

Publication number: WO2013073399A1
Application number: PCT/JP2012/078623
Authority: WO
Inventors: 充伸神沼; 小野　順貴; 安藤　繁
Original assignee: 日産自動車株式会社; 国立大学法人東京大学
Priority date: 2011-11-14
Filing date: 2012-11-05
Publication date: 2013-05-23
Also published as: JPWO2013073399A1; JP5757595B2

Abstract

The invention is provided with: a sound wave input unit (10) that inputs a sound wave that contains moving-object-generated sound generated by steady-state noise and by the travel of a moving object existing in the surroundings; a steady-state assessment unit (22) that assesses whether the sound wave is steady-state; a covariance processing unit (23) that calculates a covariance matrix element based on the sound pressure of the sound wave, the time differential of the sound pressure, and the space differential of the sound pressure, said covariance matrix element being calculated as a first parameter for the sound wave when said sound wave is assessed as not steady-state by the steady-state assessment unit (22), or being calculated as a second parameter for the sound wave when said sound wave is assessed as steady-state by the steady state assessment unit (22); a moving object parameter computation unit (24) that subtracts the second parameter from the first parameter, thereby calculating as a third parameter, the covariance matrix element based on the sound pressure of the moving-object-generated sound, the time differential of the sound pressure, and the space differential of the sound pressure; and a direction detection unit (25) that detects the direction of the moving body from the third parameter.

Description

Other moving body position detecting device and other moving body position detecting method

The present invention relates to an other moving body position detecting device and an other moving body position detecting method for detecting the position of another moving body with respect to the own moving body by reducing the influence of noise.

A method called the spatio-temporal gradient method has been proposed as a method for detecting the direction of a wave source from the wavefront at the observation point using a mathematical expression expressing the wavefront at the observation point for a wave coming from one wave source. (Refer nonpatent literature 1). In the spatiotemporal gradient method, when a sound wave radiated from a point sound source is observed, the sound pressure (amplitude) of the observation point at any time, the temporal differentiation of the sound pressure, and the spatial differentiation of the sound pressure are used to The direction to the wave source and the distance between the observation point and the wave source are calculated.

Sound waves are weak to the influence of disturbance, and when there is a disturbance, it is difficult to calculate much of the position information of a desired wave source included in an arbitrary time frequency section. Examples of such disturbances include additive noise typified by waves generated by a moving body equipped with a device for detecting the direction of the wave source and waves reflected by other objects. For example, as described in Non-Patent Document 1, the position detection result of the wave source in a frequency band including noise is not localized in a desired direction, and the detection accuracy of the direction of the wave source is lowered.

In view of the above problems, an object of the present invention is to provide an other moving body position detecting device and an other moving body position detecting method capable of detecting the position of another moving body with respect to the own moving body by reducing the influence of noise. .

A mobile body position detection device according to a first aspect of the present invention is another mobile body position detection device mounted on a mobile body, and includes a traveling state detection unit, a sound wave input unit, a steady state determination unit, The gist is to include a distributed processing unit, a target parameter calculation unit, and a direction detection unit. The traveling state detection unit detects the traveling state of the mobile body. The sound wave input unit inputs a sound wave including stationary noise and other moving body generated sound generated by the traveling of another moving body existing around. The steady state determination unit determines whether or not the sound wave input by the sound wave input unit is in a steady state. The covariance processing unit uses the sound pressure of the sound wave, the temporal differentiation of the sound pressure, and the element of the covariance matrix based on the spatial differentiation of the sound pressure as the first parameter for the sound wave that the steady state determination unit has determined to be not in the steady state. Is calculated as the second parameter for the sound wave determined to be in the steady state. The target parameter calculation unit subtracts the second parameter corresponding to the traveling state detected by the traveling state detection unit from the first parameter, so that the sound pressure of the other moving body generated sound, the time differentiation of the sound pressure, the space of the sound pressure The element of the covariance matrix based on the differentiation is calculated as the third parameter. The direction detection unit detects the direction of the other moving body relative to the own moving body from the third parameter.

The other moving body position detecting method according to the second aspect of the present invention is an other moving body position detecting method for detecting the position of the other moving body with respect to the own moving body, and detects the traveling state of the own moving body. , Inputting sound waves including stationary noise and other moving body generated sound generated by the traveling of the other moving objects existing in the surroundings, determining whether or not the input sound waves are in a steady state, The elements of the covariance matrix based on the sound pressure, the temporal differentiation of the sound pressure, and the spatial differentiation of the sound pressure are the first parameter for the sound wave determined not to be in the steady state and the second parameter for the sound wave determined to be in the steady state. The covariance matrix based on the sound pressure of the other mobile body generated sound, the temporal differentiation of the sound pressure, and the spatial differentiation of the sound pressure by calculating and subtracting the second parameter corresponding to the running state from the first parameter The element of the third parameter Calculated as data, and summarized in that to detect the direction with respect to the own mobile body of said other mobile from said third parameter.

FIG. 1 is a block diagram illustrating a basic configuration of another mobile body position detection apparatus according to the first embodiment of the present invention. FIG. 2 is a block diagram for explaining a sound wave input unit provided in the other moving body position detection apparatus according to the first embodiment of the present invention. FIG. 3 is a schematic diagram for explaining how the other moving body position detection device according to the first embodiment of the present invention inputs sound waves. FIG. 4 is a flowchart for explaining the other moving body position detection method according to the first embodiment of the present invention. FIG. 5 is an example illustrating the processing steps of the other moving body position detection apparatus according to the first embodiment of the present invention. FIG. 6 shows the result of detecting the direction of the other moving body by the spatiotemporal gradient method when the frequency is 2 kHz. FIG. 7 shows the result of detecting the direction of the other moving body by the spatiotemporal gradient method when the frequency is 3 kHz. FIG. 8 shows the result of detecting the direction of the other moving body by the spatiotemporal gradient method when the frequency is 4 kHz. FIG. 9 shows the result of detecting the direction of the other moving body by the spatiotemporal gradient method when the frequency is 5 kHz. FIG. 10 shows the result of detecting the direction of the other moving body by the spatiotemporal gradient method when the frequency is 6 kHz. FIG. 11 shows the result of detecting the direction of the other moving body by the spatiotemporal gradient method when the frequency is 7 kHz. FIG. 12 shows a target direction detection result when the other moving body position detection method according to the first embodiment of the present invention is applied in the case of a frequency of 2 kHz. FIG. 13 shows a target direction detection result when the other moving body position detection method according to the first embodiment of the present invention is applied to a frequency of 3 kHz. FIG. 14 shows a target direction detection result when the other moving body position detection method according to the first embodiment of the present invention is applied to a frequency of 4 kHz. FIG. 15 shows a target direction detection result when the other moving body position detection method according to the first embodiment of the present invention is applied to a frequency of 5 kHz. FIG. 16 shows a target direction detection result when the other moving body position detection method according to the first embodiment of the present invention is applied to a frequency of 6 kHz. FIG. 17 shows a target direction detection result when the other moving body position detection method according to the first embodiment of the present invention is applied to a frequency of 7 kHz. FIG. 18 is a block diagram illustrating a basic configuration of the other moving body position detection apparatus according to the second embodiment of the present invention. FIG. 19 is a flowchart for explaining the other moving body position detection method according to the second embodiment of the present invention.

Next, first and second embodiments of the present invention will be described with reference to the drawings. In the following description of the drawings, the same or similar parts are denoted by the same or similar reference numerals. However, the embodiment described below exemplifies an apparatus and a method for embodying the technical idea of the present invention, and the technical idea of the present invention is an apparatus exemplified in the following embodiment. It is not specific to the method. The technical idea of the present invention can be variously modified within the technical scope described in the claims.

(First embodiment)
The other moving body position detection apparatus according to the first embodiment of the present invention is mounted on a moving body (self-moving body) such as a vehicle, and is generated from a target existing around the moving body (other movement). Body) Based on the generated sound, the relative position of the object is detected. The object to be detected is a moving body, for example, a vehicle approaching the moving body.

As shown in FIG. 1, the other moving body position detection device according to the first exemplary embodiment inputs a surrounding sound wave, converts the input sound wave into a discrete signal, and outputs the sound wave input unit 10. The processing unit 20 for processing various calculations performed by the other mobile object position detection apparatus according to the embodiment, the storage unit 30 for storing various data including program files, and the like according to the first embodiment And a traveling state detection unit 40 that detects a state (running state) of the moving body on which the moving body position detection device is mounted.

As shown in FIG. 2, the sound wave input unit 10 indicates the sound wave detected by the sensor units 11-1 and 11-n (n: positive integer) that detects the sound wave and the sensor units 11-1 and 11-n. An amplifier 12 for amplifying the signal and an AD converter 13 for A / D converting and discretizing the signal amplified by the amplifier 12 are provided. The sensor units 11-1 and 11-n are composed of acoustic devices such as a microphone and a hydrophone, for example. As shown in FIG. 3, a plurality of sensor units 11-1 and 11-n are provided in order to obtain a gradient of sound pressure of sound waves to be detected (spatial differential of sound pressure). Considering the azimuth angle θ indicating the direction of the sound source as shown in FIG. 3, the azimuth angle θ can be expressed as θ = sin ⁻¹ (n _x ). As long as the gradient of sound pressure can be obtained by signal processing, the number of sensor units 11-1 and 11-n need not be plural, and may be single.

The sound wave input by the sound wave input unit 10 includes stationary noise and target generated sound generated from a target existing around. The sound wave input unit 10 outputs a sound wave signal, which is an electric signal indicating the input sound wave, to the processing unit 20.

The processing unit 20 includes a sound wave signal processing unit 21 that processes a sound wave signal output from the sound wave input unit 10 and a sound wave input by the sound wave input unit 10 based on the sound wave signal processed by the sound wave signal processing unit 21. A steady state determination unit 22 that determines whether or not a state is present, and a sound pressure of a sound wave input by the sound wave input unit 10, a time derivative of the sound pressure, and an element of a covariance matrix based on a spatial differential of the sound pressure, A covariance processing unit 23 that calculates a first parameter for a sound wave that is determined not to be in a steady state and a second parameter for a sound wave that is determined to be in a steady state, and subtracts the second parameter from the first parameter The target parameter calculation unit 24 that calculates the third parameter of the covariance matrix element based on the sound pressure of the target generated sound, the temporal differentiation of the sound pressure, and the spatial differentiation of the sound pressure, and the third parameter Includes a direction detection unit 25 to detect the direction of the target from the over data, a storage processing unit 26, a reading processing unit 27. The processing unit 20 includes, for example, an arithmetic processing device such as a CPU (Central Processing Unit) and an MPU (Micro Processing Unit).

The first parameter calculated by the covariance processing unit 23 is a parameter based on a sound wave including a stationary noise such as a sound generated from the moving body itself and a non-stationary target generated sound. The second parameter is a parameter based on stationary noise such as sound generated from the moving body itself.

The storage unit 30 is a covariance matrix storage unit 31 that stores the second parameter calculated by the covariance processing unit 23, and a mobile unit information storage that stores information about the mobile unit such as tire information when the mobile unit is a vehicle. Unit 32 and a map information storage unit 33 that stores an environment corresponding to the current position on the map. The storage unit 30 includes a storage device such as a semiconductor memory or a hard disk device, for example.

The traveling state detector 40 includes a moving state detector 41 that detects the moving state of the moving body, a moving environment detector 42 that detects the environment around the moving body, and a position detector 43 that detects the position of the moving body. Is provided. The moving state detection unit 41 is connected to, for example, a CAN (Controller Area Network) or the like, and detects information indicating the moving state of the moving body such as a speed, an engine speed, and a gear ratio when the moving body is an automobile, for example. . The moving environment detection unit 42 detects information indicating the environment, such as a wall and an overhead around the moving body. The position detection unit 43 includes, for example, a global positioning system (GPS) receiver. For example, the moving environment detection unit 42 detects information related to the environment around the moving body by referring to the map information storage unit 33 using the current position information of the moving body detected by the position detection unit 32.

The storage processing unit 26 of the processing unit 20 stores the second parameter calculated by the covariance processing unit 23 in the covariance matrix storage unit 31. The second parameter stored in the covariance storage unit 31 may be calculated and stored at any time during the movement of the moving body, and a plurality of types of second parameters corresponding to the state of the moving body are stored in advance. You may do it. The read processing unit 27 reads the second parameter stored in the covariance matrix storage unit 31. The target parameter calculation unit 24 calculates the third parameter using the second parameter read from the covariance matrix storage unit 31 by the read processing unit 27.

The processing unit 20, the storage unit 30, and the traveling state detection unit 40 shown in FIG. 1 are each displayed as a logical structure, and the respective units constituting the processing unit 20, the storage unit 30, and the traveling state detection unit 40 are the same. It may be configured by an arithmetic processing unit that is hardware, or may be configured by separate hardware.

<The principle of the spatiotemporal gradient method>
Assuming that the sound pressure is f (t) and the time derivative of the sound pressure is f _t (t), the spatial differentials f _x (t), f _y (t), and f _z (t) of the sound pressure are expressed by the following equations ( It is expressed as 1) to (3).

n _x , n _y , and n _z are elements of a unit vector in the target direction that becomes a sound source from the center of the sensor unit 11 that becomes an observation point. R represents the distance from the observation point to the sound source, and C represents the speed of sound as a function of temperature. In Expressions (1) to (3), if the sound source is one point sound source, the sound pressure and the temporal differentiation of the sound pressure and the spatial differentiation of the sound pressure are in a dependency relationship. Hereinafter, the equations (1) to (3) are referred to as “wave source constrained partial differential equations”.

The position of the sound source can be calculated by acquiring the sound pressure at the observation point at any time, the time differential of the sound pressure, the spatial differential of the sound pressure, and applying the wave source-constrained partial differential equation. For example, an input time-domain sound wave signal is decomposed into frames by applying a window function with an appropriate time interval. Thereafter, the direction of the sound source is estimated using a sound wave signal converted into the frequency domain by short-time Fourier transform.

If the sound pressure at an arbitrary frequency is F (ω) and the time derivative of sound pressure is F _t (ω), the spatial differentials of sound pressure F _x (ω), F _y (ω), and F _z (ω) are The following expressions (4) to (6) are expressed.

By applying the least-squares method independently for each direction with respect to Expressions (4) to (6), the elements in the three-axis direction of the vector can be obtained. Here, if excluding the position of the horizontal plane of the target, it may be n _x is an element of the vector _n, n _y, any one of the n _x Motomare.
Hereinafter will be described a solution of n _x as an example. Equation (4) is modified to obtain _nx and distance R that minimize the square error of equation (7).

S, S _tt , S _xx , S _t , S _x , and S _xt are determined as shown in equations (8) to (13).

At this time, the elements of the vector n and the distance R can be obtained from the equations (14) and (15).

In equations (8) to (13), the sum is calculated for all the obtained frequency bands by the short-time Fourier transform, and the only direction estimation result is calculated for an arbitrary time interval. . As for the results of the equations (14) and (15), it is possible to obtain the direction estimation result for each frequency by calculating the equations (8) to (13) for each frequency instead of the sum.

<Basic principle of other moving body position detection device>
Hereinafter, the basic principle of the other moving body position detection apparatus according to the first embodiment will be described. The other mobile object position detection apparatus according to the first embodiment uses an algorithm for removing additive noise in the process of performing the spatiotemporal gradient method for a short time frame.
With respect to Expression (7), the result calculated for the time-frequency frame of the short-time Fourier transform using the window function can be expressed as Expression (16).

Expression (16) is not only a function of the frequency ω but also depends on the time interval in which the integration is performed. That is, it can be regarded as an expression in the time-frequency domain of the wave source-constrained partial differential equation. Similarly, the parameters S, S _tt , S _xx , S _t , S _x , S _xt calculated by the equations (8) to (13) are expressed by the equation (17) for the frame τ set in an arbitrary section. It can be expressed as shown in Formula (22).

At this time, it is assumed that the sum is taken within a close local time frequency region. The parameters S, S _tt , S _xx , S _t , S _x , and S _xt can be obtained for each frequency (that is, for each frequency bin that is half the FFT length used for Fourier transform). The parameter _{_{S, S tt, S xx,}} S t, S x, S xt sets further shortened divided time may be short time obtained for each frame and, in the overall time-frequency short time frame On the other hand, one value may be calculated as an average value.
Here, the sound pressure, the temporal differentiation of the sound pressure, and the spatial differentiation of the sound pressure can be expressed by the following vectors.

Using Expression (23), the parameters S, S _tt , S _xx , S _t , S _x , and S _xt related to the spatiotemporal gradient can be expressed as elements of the covariance matrix as shown in Expression (24). .

In addition, regarding the subscript on the upper right of the character, h indicates Hermitian transposition and * indicates conjugate transposition. Those with "~" above the parameter are complex numbers, and those without are only the real part of the complex conjugate product in the parameters.

When detecting a wave (sound wave) generated from a target approaching a moving body, it is detected as a signal in which a sound wave radiated from the target and a steady sound wave generated from the own moving body or the like are added. The signal detected at this time is a signal obtained by adding the sound pressure component F (τ, ω) coming from the target and the noise component U (τ, ω). The short-time correlation at this time can be expressed as shown in Equation (25).

Here, if the sound pressure component and the noise component are uncorrelated, it can be regarded as shown in Expression (26) by selecting a sufficient time frequency local region and taking the sum.

Thus, equation (25) can be expressed for equation (27).

Similarly, when the results of parameters relating to other spatiotemporal gradients are shown, since the correlation terms between the signal from the object and the noise signal can all be regarded as approximately 0, the equations (28) to (32) are obtained.

The other moving body position detection apparatus according to the first embodiment uses the parameters shown in the equations (27) to (32) as the first parameters S, S _tt , S _xx , S _t , S _x , S _xt. . The first parameters S, S _tt , S _xx , S _t , S _x , and S _xt are additively obtained by a signal based on sound waves generated from the object and a signal based on stationary noise in the spatiotemporal gradient covariance region. It means that they are superimposed.

Assuming that the second parameter of stationary noise is known, the second parameters N, N _tt , N _xx , N _t , N _x , and N _xt can be expressed as Equations (33) to (38). it can.

At this time, the arithmetic operation in the covariance region can be expressed as in Expression (39) to Expression (44).

Expressions (39) to (44) mean that the third parameter excluding noise can be estimated.

If the noise is stationary, the second parameter based on the noise signal is considered to have a substantially constant value. Therefore, by estimating the noise signal for each moving state in advance, the sound source whose position changes with time. On the other hand, the direction and distance of the sound source can be detected from the covariance matrix obtained for each time interval. Alternatively, the third parameter may be calculated using the second parameter calculated in the frame τ−1 immediately before the frame τ in which the first parameter is calculated and mainly in which noise can be observed. good.

These series of noise component removal algorithms, which are the basic principle of the other mobile object position detection method according to the first embodiment, are called a covariance subtraction method. In this method, the noise signal only needs to be stationary and does not necessarily satisfy the source-constrained partial differential equation. In other words, it may be effective against diffuse noise or noise that is not a point source such as that generated from a large area.

<Other moving body position detection method>
With reference to the flowchart of FIG. 4, an example of the other moving body position detection method according to the first embodiment of the present invention will be described.

First, in step S10, the other moving body position detection apparatus according to the first embodiment is initialized, and in step S20, the sound wave signal processing unit 21 converts the sound wave input by the sound wave input unit 10 into a discrete sound wave signal. Are input from the sound wave input unit 10 and processed.

As the signal processing in step S20, the sound wave signal processing unit 21 detects, for example, in the sensor units 11-1 and 11-n included in the sound wave input unit 10, as shown in steps S110-1 and S110-n in FIG. The signal processing is performed on the sound wave. For example, the sound wave signal processing unit 21 performs a short-time Fourier transform on a frame decomposed every short time by applying a window function such as a Hann window to the sound wave signal, and performs frequency domain signals (as a whole) for each frame. To time frequency domain signal).

Next, in step S311 in FIG. 4, the steady state determination unit 22 determines whether or not the sound wave input by the sound wave input unit 10 is in a steady state. The steady state determination unit 22 acquires, for example, the sound pressure of the sound wave input by the sound wave input unit 10, the gradient of the sound pressure, the sound pressure for each frequency, the frequency gradient, and the like, and sets a predetermined threshold for these variances. Thus, the continuity may be measured to determine whether or not the steady state is reached.

In addition, the steady state determination unit 22 estimates the presence or absence of an approaching vehicle by setting a predetermined threshold with respect to the magnitude of the variance of the direction detection result by the direction detection unit 25, and determines whether or not the vehicle is in a steady state. May be. Alternatively, a threshold may be set for the addition average of the direction detection results to determine whether or not the steady state is reached.

When it is determined in step S311 that the steady state determination unit 22 is in the steady state, in step S312, the covariance processing unit 23 performs the second processing based on the steady noise as shown in equations (33) to (38). parameter _{_{N, N tt, N xx,}} N t, N x, to compute the N _xt.

In step S313, the storage processing unit 26 stores the second parameter calculated by the covariance processing unit 23 in the covariance matrix storage unit 31. For example, the storage processing unit 26 stores the second parameter calculated from the immediately preceding frame to be calculated.

Further, the storage processing unit 26 may store the second parameter calculated using the time frame τ in which the signal is stationary noise (there is no target approaching the surroundings). In addition, the storage processing unit 26 may store the second parameter corresponding to all noise patterns corresponding to the state of the moving body such as speed, gear ratio, tire information, and moving environment. Alternatively, the map information is stored in advance in the map information storage unit 33 as the position of the wall, the elevated, etc., and based on the noise signal including the own vehicle running noise and the reflected sound measured in the environment corresponding to the stored wall. The calculated second parameter may be stored. At this time, it is effective to use a map position uniquely determined by GPS or the like as a label.

When the stationary determination unit 22 determines that the steady state is not in the steady state in step S311, the covariance processing unit 23 determines that the steady noise and the unsteady state are present in step S314 as shown in the equations (27) to (32). First parameters S, S _tt , S _xx , S _t , S _x , S _xt are calculated based on the sound wave composed of the target generated sound.

The calculation of the first parameter and the second parameter by the covariance processing unit 23 is performed based on the sound pressure (step S121) and the sound pressure from the sound wave signal processed by the sound wave signal processing unit 21, as shown in step S120 of FIG. This is performed after calculating time differentiation (step S122) and spatial differentiation of sound pressure (step S123).

Next, in step S315 of FIG. 4, the read processing unit 27 reads the second parameter from the covariance matrix storage unit 31.

Further, the read processing unit 27 may read the second parameter stored in the covariance matrix storage unit 31 according to the state of the moving body detected by the traveling state detection unit 40.

Next, in step S40, the target parameter calculation unit 24 calculates the first parameter based on the target generated sound from the first and second parameters calculated by the covariance processing unit 23 as shown in the equations (39) to (44). Three parameters S ^C , S ^C _tt , S ^C _xx , S ^C _t , S ^C _x , S ^C _xt are calculated.

Next, in step S50, the direction detection unit 25 substitutes the third parameter calculated by the target parameter calculation unit 24 into the formulas (14) and (15) to calculate the target direction and the target. The distance is detected.

<Verification of direction detection results>
The above-mentioned covariance subtraction method was applied to data measured while driving on a public road of a demonstration vehicle equipped with another moving body position detection device. The distance between the microphones serving as the sensor units is 2 cm. The sampling frequency is 44.1 kHz, the frame length of the Fourier transform is 48 points, the frame shift is 24 points, and the parameters related to the covariance matrix (the first parameter and the third parameter) are obtained by averaging over 50 adjacent frames. . In addition, the surrounding environment of other vehicles during recording was confirmed by photographing the surroundings with a video camera at the same time.

FIGS. 6 to 11 show direction detection results when a normal spatiotemporal gradient method is applied for each frequency of 2 to 7 kHz. 12 to 17 show direction detection results when processing by the covariance subtraction method is added. N _x the vertical axis represents the x component of the unit vector in the sound source direction, showing 1 backward, -1 forward. The horizontal axis indicates the time when data was measured. The transition from the rear to the front of the plot indicates that another vehicle overtakes the adjacent lane.

Comparing FIG. 6 to FIG. 11 with FIG. 12 to FIG. 17, according to the measurement results using the spatiotemporal gradient method, the plots are concentrated around n _x = 0 in the vicinity of 25 to 28 seconds. . These displays are considered to be the influence of the noise of the own vehicle because there is no vehicle around. On the other hand, according to the estimation result using the covariance subtraction method, it can be seen that the azimuth estimation result in the same time zone has a large variance and the influence of noise is removed. From this, it became clear that the influence of stationary additive noise such as own vehicle noise is reduced by the covariance subtraction method.

As described above, the position detection method according to the first embodiment observes the magnitude of the variance of the direction detection result, and if the variance is larger than the predetermined threshold, there is no vehicle around. It can be used to determine the existence of nearby objects that are considered. Therefore, the direction detection result by the direction detection unit 25 may be used to determine whether or not the steady state determination unit 22 is in a steady state.

Also, with regard to the range of angles (unit vector elements) where the direction can be detected, the detection result using the covariance subtraction method is wider than the detection result using only the spatiotemporal gradient method. Therefore, it can be seen that by using the covariance subtraction method, the influence of additive noise is reduced, the dynamic range of detection is improved, and as a result, the angle at which direction detection is possible increases.

The other mobile object position detection apparatus according to the first embodiment can detect the target position with high accuracy by reducing the influence of noise by subtracting additive noise in the region of the covariance matrix.

Moreover, according to the other moving body position detection apparatus which concerns on 1st Embodiment, the influence of noise can be reduced appropriately according to the state of a moving body by providing the traveling state detection part 40. FIG.

Moreover, according to the other moving body position detection apparatus according to the first embodiment, by including the moving state detection unit 41, it is possible to appropriately reduce the influence of noise according to the moving affective body of the moving body.

Further, according to the other moving body position detection device according to the first embodiment, by including the moving environment detection unit 42, it is possible to appropriately reduce the influence of noise according to the environment around the moving body.

Moreover, according to the other moving body position detection apparatus according to the first embodiment, by including the position detection unit 43, it is possible to appropriately reduce the influence of noise according to the position of the moving body.

Moreover, according to the other moving body position detection apparatus which concerns on 1st Embodiment, the sound wave signal processing part 21 can change the signal processing method of a sound wave signal by converting a sound wave signal into a frequency domain. .

Further, according to the other moving body position detecting apparatus according to the first embodiment, the second parameter calculated by the sound wave signal input at a time before the sound wave signal used for calculating the first parameter is used. By calculating the third parameter, the third parameter can be calculated using the second parameter stored in advance.

(Second Embodiment)
As shown in FIG. 18, in the other moving body position detection device according to the second exemplary embodiment of the present invention, the storage unit 30 includes a sonic wave signal storage unit 34, and the storage processing unit 26 and the read processing unit 27 include sonic waves. The second embodiment differs from the first embodiment in that an operation is performed on the signal storage unit 34. Other configurations that are not described in the second embodiment are substantially the same as those in the first embodiment, and thus redundant description is omitted.

In the first embodiment, the storage processing unit 26 stores the second parameter calculated by the covariance processing unit 23 in the covariance matrix storage unit 31, and the readout processing unit 27 performs covariance for the third parameter calculation. The case where the second parameter is read from the matrix storage unit 31 has been described. Hereinafter, as the other moving body position detection method according to the second embodiment, the storage processing unit 26 stores a sound wave signal based on stationary noise in the sound wave signal storage unit 34 using the flowchart of FIG. A case where the covariance processing unit 23 calculates the first and second parameters after the reading processing unit 27 reads out the sound wave signal from the sound wave signal storage unit 34 will be described. The processes other than step S32 shown in FIG. 19 are substantially the same as the processes other than step S31 shown in FIG.

In step S321, the steady state determination unit 22 determines whether the sound wave input by the sound wave input unit 10 is in a steady state. The steady state determination unit 22 acquires, for example, the sound pressure of the sound wave input by the sound wave input unit 10, the gradient of the sound pressure, the sound pressure for each frequency, the frequency gradient, and the like, and sets a predetermined threshold for these variances. Thus, the continuity may be measured to determine whether or not the steady state is reached.

In addition, the presence or absence of an approaching vehicle may be estimated by setting a predetermined threshold for the magnitude of the variance of the direction detection result by the direction detection unit 25. Or you may make it take the addition average of a direction detection result.

When it is determined in step S321 that the steady state determination unit 22 is in the steady state, in step S322, the storage processing unit 26 stores the sound wave signal of the frame determined to be in the steady state in the sound wave signal storage unit 34.

Further, the storage processing unit 26 may store the sound wave signal of the frame immediately before the frame determined to be in the steady state. In addition, the storage processing unit 26 may store types of sound wave signals corresponding to all noise patterns corresponding to the state of the moving body such as speed, gear ratio, tire information, and moving environment. Alternatively, as the map information, the position of a wall, an overpass or the like is stored in advance in the map information storage unit 33, and a sound wave signal indicating own vehicle running noise and reflected sound measured in an environment suitable for the stored wall is stored. You may make it leave. At this time, it is effective to use a map position uniquely determined by GPS or the like as a label.

When it is determined in step S321 that the steady state determination unit 22 is not in a steady state, the read processing unit 27 reads the sound wave signal from the sound wave signal storage unit 34 in step S323. The read processing unit 27 may read the sound wave signal stored in the sound wave signal storage unit 34 in accordance with the state of the moving body detected by the traveling state detection unit 40.

In step S324, the covariance processing unit 23 includes a stationary noise, the first parameter S based on sound waves comprising a non-stationary object generated sound, S _tt, S _xx, _S _{t, S} x, and S _xt calculate. Further, the covariance processing unit 23 calculates the second parameters N, N _tt , N _xx , N _t , N _x , N _xt based on the sound wave signal read by the reading processing unit 27 in step S323.

The other mobile object position detection apparatus according to the second embodiment can detect the target position with high accuracy by reducing the influence of noise by subtracting additive noise in the region of the covariance matrix.

Moreover, according to the other moving body position detection apparatus which concerns on 2nd Embodiment, the influence of noise can be reduced appropriately according to the state of a moving body by providing the traveling state detection part 40. FIG.

Also, according to the other moving body position detection apparatus according to the second embodiment, by including the moving state detection unit 41, it is possible to appropriately reduce the influence of noise according to the moving affective body of the moving body.

Also, according to the other moving body position detection apparatus according to the second embodiment, by including the moving environment detection unit 42, it is possible to appropriately reduce the influence of noise according to the environment around the moving body.

Moreover, according to the other moving body position detection apparatus according to the second embodiment, by including the position detection unit 43, it is possible to appropriately reduce the influence of noise according to the position of the moving body.

Moreover, according to the other moving body position detection apparatus which concerns on 2nd Embodiment, the sound wave signal processing part 21 can change the signal processing method of a sound wave signal by converting a sound wave signal into a frequency domain. .

Further, according to the other moving body position detection apparatus according to the second embodiment, the second parameter calculated by the sound wave signal input at a time before the sound wave signal used for the calculation of the first parameter is used. By calculating the third parameter, the third parameter can be calculated using the second parameter stored in advance.

The entire contents of Japanese Patent Application No. 2011-248370 (application date: November 14, 2011) are incorporated herein by reference.

(Other embodiments)
As described above, the first and second embodiments have been described. However, it should not be understood that the description and drawings constituting a part of this disclosure limit the present invention. From this disclosure, various alternative embodiments, examples, and operational techniques will be apparent to those skilled in the art.

In the first and second embodiments already described, the moving body on which the other moving body position detecting device is mounted is not limited to a vehicle, but can be applied to a helicopter, a ship, a submarine, and the like.

In the first and second embodiments already described, the method of detecting a symmetric position using the parameters of the covariance matrix has been described. However, the parameters need not necessarily be calculated using a determinant. However, the calculation may be omitted to the extent that the direction detection result can be regarded as substantially equivalent.

In the first and second embodiments already described, the moving environment detection unit 42 detects information indicating the environment, such as walls and overheads around the moving body, based on the detection result of the direction detection unit 25. You may make it do. For example, when there is a wall parallel to the moving direction of the moving body, the direction detection result by the direction detecting unit 25 is localized in a direction (angle) within a predetermined range. By setting these data in the moving environment detection unit 42 in advance, information indicating the environment around the moving body can be detected from the direction detection result by the direction detection unit 25.

Thus, it goes without saying that the present invention includes various embodiments not described herein. Therefore, the technical scope of the present invention is defined only by the invention specifying matters according to the scope of claims reasonable from the above description.

According to the present invention, it is possible to provide another mobile body position detection apparatus that can detect the target position by reducing the influence of noise by subtracting additive noise in the region of the covariance matrix. Therefore, the present invention has industrial applicability.

DESCRIPTION OF SYMBOLS 10 ... Sound wave input part 11 ... Sensor part 12 ... Amplifier 13 ... AD converter 20 ... Processing part 21 ... Sound wave signal processing part 22 ... Steady state determination part 23 ... Covariance processing part 24 ... Target parameter calculation part 25 ... Direction detection part 26 ... Storage processing unit 27 ... Read processing unit 30 ... Storage unit 31 ... Covariance matrix storage unit 32 ... Position detection unit 32 ... Sound wave signal storage unit 33 ... Mobile body information storage unit 34 ... Map information storage unit 40 ... Running state detection unit 41 ... Movement state detection unit 42 ... Movement environment detection unit 43 ... Position detection unit

Claims

Another mobile body position detection device mounted on the mobile body,
A traveling state detector for detecting a traveling state of the mobile body;
A sound wave input unit for inputting sound waves including stationary noise and other moving body generated sound generated by traveling of other moving objects existing in the surroundings;
A steady state determination unit that determines whether or not the sound wave input by the sound wave input unit is in a steady state;
The sound pressure of the sound wave, the temporal differentiation of the sound pressure, and the element of the covariance matrix based on the spatial differentiation of the sound pressure are the first parameter for the sound wave that the steady state determination unit has determined not to be in a steady state, and the steady state determination unit A covariance processing unit that calculates a second parameter for the sound wave determined to be in a state;
By subtracting the second parameter corresponding to the traveling state detected by the traveling state detection unit from the first parameter, the sound pressure of the other moving body generated sound, the temporal differentiation of the sound pressure, and the spatial differentiation of the sound pressure. A target parameter calculation unit that calculates the element of the covariance matrix based on the third parameter;
A direction detecting unit that detects a direction of the self-moving body of the other moving body from the third parameter.
The other moving body position detecting device according to claim 1, wherein the traveling state detecting unit detects an environment around the moving body as a state of the moving body.
The mobile body is a vehicle, and the other mobile body is a vehicle.
The other moving body position according to claim 2, wherein the traveling state detecting unit detects any one of a speed of the moving body, an engine speed, and a gear ratio as the traveling state of the own moving body. Detection device.
A sound wave signal processing unit that converts a time domain signal indicating a sound wave input by the sound wave input unit into a frequency domain, and obtains a sound pressure for each frequency of the sound wave, a time derivative of the sound pressure, and a spatial derivative of the sound pressure. In addition,
The covariance processing unit calculates the first parameter and the second parameter based on the sound pressure for each frequency, the temporal differentiation of the sound pressure, and the spatial differentiation of the sound pressure. The other moving body position detection apparatus of any one of Claims 3-4.
The target parameter calculation unit calculates the third parameter using a second parameter calculated by the sound wave input at a time before the sound wave for calculating the first parameter. 5. The other moving body position detection device according to any one of 1 to 4.
An other moving body position detection method for detecting the position of another moving body with respect to the own moving body,
Detect to detect the traveling state of the moving body,
Input a sound wave including stationary noise and other moving body generated sound generated by the traveling of the other moving body existing in the surroundings,
Determine whether the input sound wave is in a steady state,
The sound pressure of the sound wave, the time differential of the sound pressure, and the element of the covariance matrix based on the spatial differential of the sound pressure are the first parameter for the sound wave determined not to be in the steady state, and the sound wave determined to be in the steady state. Calculated as two parameters,
By subtracting the second parameter in accordance with the running state from the first parameter, the elements of the covariance matrix based on the sound pressure of the other moving body generated sound, the temporal differentiation of the sound pressure, and the spatial differentiation of the sound pressure are obtained. Calculated as the third parameter,
A method of detecting the position of another moving body, wherein a direction of the other moving body relative to the own moving body is detected from the third parameter.