JP2708109B2 - Underwater object depth detection method and depth detection device - Google Patents

Description

Description: TECHNICAL FIELD The present invention relates to a method and an apparatus for detecting and measuring the position of an underwater target using ultrasonic waves.

[Conventional technology]

The conventional device measures the azimuth and depression angle of a target by giving directivity both horizontally and vertically by means of an array of receivers and phasing means as described in JP-A-61-110074. is there. Also, as described in Japanese Patent Application Laid-Open No. 62-217173, a plurality of receivers are arranged vertically and the depth and horizontal distance of an unknown sound source are obtained by using a sound ray diagram.

[Problems to be solved by the invention]

In the above-mentioned conventional technique, in order to obtain the depth of the target, in any case, the vertical dimension of the receiver is increased, or a plurality of the receivers are arranged in the vertical direction so that the vertical Sex needs to be sharpened. In other words, in the case of detecting the depth of an underwater object in addition to the azimuth and the distance in detecting the position of the underwater object, the size of the apparatus has to be conventionally increased due to the necessity of using the directivity in both horizontal and vertical directions.

An object of the present invention is to provide a method and an apparatus for obtaining the depth of an underwater object and, consequently, a depression angle without particularly needing directivity in a vertical direction for depth detection in position detection of an underwater object. I do. The object of the present invention is to avoid an increase in the size of the device.

[Means for solving the problem]

In order to achieve the above object, the depth detection method of the present invention includes:
The orientation of the underwater object on the horizontal plane and the direct distance to the object are measured many times, using these measurement data and the position data of the mother ship at each measurement, calculating the observation matrix, calculating the Kalman gain, The present invention is characterized in that a calculation process for updating the state vector and updating the covariance matrix based on this is performed.

Further, in order to achieve the above object, the depth finder of the present invention is a means for power-amplifying a pulse-modulated signal from a transmitter, converting the signal into an acoustic signal via a transmitter / receiver, and radiating the signal into water, Means for receiving the reflected signal by the transmitter / receiver, means for amplifying and phasing the received signal, and means for converting it to azimuth data by an azimuth calculator; Means for converting the distance data to direct distance data, means for calculating the position of the mother ship using the data of the speed and direction of travel of the mother ship by the position calculator for the ship, and the above-mentioned bearing data, direct distance data, and position data for the mother ship. It is characterized by comprising arithmetic processing means for inputting to the Kalman filter to obtain the output of the position component of the underwater object.

[Action]

In the following description, it is considered that the operation will be effectively clarified by describing the embodiment including the mathematical treatment. However, the operation will be described particularly in the outline of the depth detection method of the present invention.

Position coordinates of the desired underwater object (hereinafter referred to as target object)
x _m , y _m , and z _m are elements of the state vector. On the other hand, what is observed is the azimuth θ of the target object on the horizontal plane and the direct distance r to the target object, which are elements of the observation vector.
An observation matrix is used to give a relational expression between the observation vector and the state vector. Therefore, the depth detection method of the present invention is to observe an observation vector and estimate a state vector using an observation matrix.

If there is no error in the observation data, and even if there is an error, if the result obtained using this is within the required accuracy range, the above-mentioned results will be obtained from a small number of observations according to the number of unknowns. The state vector can be obtained from the relation.

The present invention does not directly measure the depression angle to the target object, and presumes that the observation of the azimuth and the direct distance of the target object inevitably includes an error, so that the position components of the above x _m , y _m , z _m Therefore, a large error must be tolerated in the initial state vector. That is, in this state, the degree of variation of the state vector error, that is, the covariance value is large. Therefore, the state vector is sequentially updated by measuring the azimuth and the direct distance of the target object many times. In this process, the covariance value gradually decreases, and finally, the state vector attempts to approach a certain convergence value.

For this purpose, first, an initial value of the state vector is assumed, and a covariance matrix that gives the covariance of the state vector is also assumed. With these values as initial values,
The correction according to the difference between the observation vector derived from the assumed state vector and the actually observed data is performed on the variance matrix together with the assumed state vector. This correction value is a value proportional to the Kalman gain. Therefore, the state vector and the covariance matrix are sequentially corrected and updated using the Kalman gain. Such updating is performed many times, that is, every time observation data is obtained. Here, the correction by the Kalman gain can finally converge the state vector or the covariance matrix to be corrected to a certain value if the error of the observation data is within a certain range.

In the above description, the position data of the mother ship is omitted for convenience of explanation, but it goes without saying that the position data of the mother ship is also used for the correction process performed each time observation data is obtained.

With the above method, the position of the target object can be estimated. In other words, it is possible to obtain the depth of the target object and the depression angle without particularly requiring the directivity in the vertical direction.

Kalman filter in the depth finder of the present invention,
Calculates observation matrix and Kalman gain based on input of heading data, direct distance data and mother ship position data, updates state vector and covariance matrix based on this, and outputs position component of target object Calculation processing means. Therefore, according to the depth detecting device of the present invention,
By utilizing the depth detection method of the present invention, it is possible to detect the depth of a target object without requiring directivity in the vertical direction. For this reason, in the device of the present invention, only the horizontal directivity of the transducer is required, so that the device is simple and it is possible to avoid an increase in size of the device regardless of depth detection.

〔Example〕

FIG. 1 shows an embodiment of the hardware configuration of the depth detecting device of the present invention. In FIG. 1, a signal from a transmitter 1 is pulse-modulated by a tone burst signal generator 2, power-amplified by a transmitter 3, and transmitted to a transmitter / receiver 5 via a transmission / reception switch 4, where it is converted into an acoustic signal and converted into an underwater signal. Is radiated. This sound is reflected by the target and received by the transducer 5 with a delay corresponding to the direct distance to the target. Here, the signal is converted into an electric signal again, and the signal is amplified by the receiver 6 via the transmission / reception switch 4 and sent to the phase adjuster 7. The phasing signal is converted into azimuth data by the azimuth calculator 8.
On the other hand, timing signals obtained from the tone burst generator 2 and the receiver 6 are sent to the direct distance calculator 9 where they are converted into direct distance data. In addition, data on the speed and traveling direction of the mother ship is sent from the speed detector 10 and the gyro 11 of the mother ship to the mother ship position calculator 12, where the position of the mother ship is calculated every moment. Three types of data (azimuth, direct distance and mother ship position)
Is input to the Kalman filter 13 to calculate an analysis value (x, y, and z components of the target position) by a method described below.

FIG. 2 shows a flowchart for calculating an analysis value by a Kalman filter, and also shows an arithmetic processing procedure in the depth detection method of the present invention. As shown in FIG. 2, the processing of the Kalman filter is divided into an initial processing and a normal processing. First, in the initial processing, the state vector X and the covariance matrix P are initialized.
The state vector X in the present invention consists of target position x, y and z each x _m indicates components, y _m and z _m become elements. The covariance matrix P represents the distribution of each element of the state vector, and is a 3 × 3 matrix. Normal processing is performed when observation data is obtained. First, an observation matrix H is calculated based on observation data, and then a Kalman gain K is calculated. The state vector X and the covariance matrix P are updated based on H and K.
The above normal processing is performed every time observation data is obtained,
X and P are successively improved, and finally, a state vector X with sufficient accuracy, that is, a target position is obtained. In general, the Kalman filter also requires another state transition matrix F. However, in the present invention, the state does not transition because the target is stationary. Therefore, F becomes a unit matrix and is unnecessary.

FIG. 3 is a diagram showing the positional relationship between the mother ship and the target.
Referring to FIG. 3, a mathematical model is constructed, and the Kalman filter processing will be described below. In the coordinate system in FIG. 3, the x-axis indicates the east-west direction, the y-axis indicates the north-south direction, and the z-axis indicates the depth direction.
The direction θ is measured clockwise with reference to the north. In the figure, the target position is (x _m , y _m , z _m ), which is the state vector X. Since what can be observed is the azimuth θ and the direct distance r of the target, this is set as the observation vector Y.

The position of the ship is (x ₀ ,
y ₀ , 0). The coordinates of the point directly above the target are (x _m , y _m , 0)
Therefore, the target azimuth θ seen from the mother ship is The direct distance r between the mother ship and the target is It is. Equations (3) and (4) are the state vectors X
And the observation vector Y.

The observation matrix H is Because Then, h ₁₁ , h ₁₂ , h ₁₃ , h ₂₁ , h ₂₂ and h ₁₁ are respectively The state transition matrix F is That is, it is a unit matrix and need not be considered.

The covariance matrix P is And p ₁₁ , p ₂₂ and p ₃₃ denote the variance of x _m , y _m and z _m respectively. Other factors indicate the covariance between x _m , y _m and z _m . Therefore, p ₁₂ = p ₂₁ , p ₁₃ = p ₃₁ , p ₂₃ = p ₃₂ and this matrix is a symmetric matrix.

The estimation error estimation matrix R is It is. Here, σ ₁₁ ² is the estimated value of the observation azimuth θ, and σ ₂₂ ² is the estimated value of the variance of the observation direct distance r.

When the observation data is input, the observation matrix H is calculated according to the equations (7) to (12). Next, the Kalman gain K is calculated by the equation (16).

K = PH ^T [HPH ^T + R] ⁻¹ (16) where H ^T represents the transposed matrix of H. This Kalman gain K
Is used to update the state vector X according to equation (17).

Here, _Xn is a state vector before update, and _Xn + ₁ is a state vector after update. Θ (X _n ) and r (X _n ) are the estimated azimuth and estimated direct distance obtained using the state vector X _n before updating.

 The updating of the covariance matrix P is based on the equation (18).

_{P n + 1 = P n -KHP} n ... (18) is where P _n is the pre-update the covariance matrix P _n + ₁ is the covariance matrix of the updated.

The above processing is as follows when compared with the normal processing in the flowchart of FIG. H in the flowchart
Is the calculation of the expression (6), that is, specifically, the calculation of the expressions (7) to (12). Similarly, the calculation of K is
6) This is the calculation of the formula, which can be obtained by multiplication, addition and matrix operation of the matrix. The update of X is the first (1
7) According to the formula. Here, θ and r are the observation data itself, and θ (X _n ) is obtained by Expression (3), and r (X _n ) is obtained by Expression (4). Finally, the updating of P is expressed by equation (18), which can be calculated by matrix multiplication and subtraction.

If the above method is carried out, the target depth can be obtained only from the bearing data, the direct distance data obtained from the transducer, and the mother ship position data obtained from the mother ship. Also, the mother ship need only move over the surface of the water and does not need to move vertically.

An embodiment according to the present invention will be described below with reference to the flowchart in FIG. 2, the mathematical model in FIG. 3, and the experimental results in FIG.
In FIG. 3, the mother ship makes a circular motion starting from the origin and observes the azimuth θ and the direct distance r every 10 seconds. The observation data contains an error, and the error is assumed to be normally distributed. θ
Error is mean 0 standard deviation 0.002 radians (about 0.11 degrees)
The error of r was a standard deviation of 0.02 m from the average value 0. As the initial value of the state vector and the _{x m = r 0 sinθ 0 y} m = r 0 cosθ 0 z m = -10m. Here, θ ₀ and r ₀ are initial observation values. Depth z
Since there is no valid value for _m , only the true value (−5
0m). Observation error estimation matrix R
Σ ₁₁ = 0.2 radian (about 11 degrees) and σ ₂₂ = 2 m. The initial value of the covariance matrix is 100 for both p ₁₁ , p ₂₂ and p ₃₃
0 was set. This is because the error distribution of the target position is initially (About 31.6m). The initial values of the other elements of the covariance matrix were set to 0.

When the experiment was carried out under the above conditions, the results shown in FIG. 4 were obtained. True value of the target position _{x m = 1000m, y m =} 500m, z
_{While m} = −50 _m , values close to the correct answer are obtained immediately for x _m and y _{m, and} for z _m that cannot be directly observed, after about 100 seconds, which is the eleventh measurement. There is a tendency toward the true value, and after 150 seconds, it almost converges to the true value, and this is maintained thereafter.

〔The invention's effect〕

According to the present invention, the target depth can be obtained without directly measuring the vertical angle (depression angle) of the target. Since it is not necessary for the transducer to have vertical directivity, the vertical dimension of the transducer can be reduced. Further, the depth of the target and the depression angle can also be measured by a conventional transducer capable of measuring only the azimuth and the direct distance. Originally, the directivity in the vertical direction is broadened so as not to be easily affected by the motion of the mother ship, but the present invention meets this requirement.

[Brief description of the drawings]

FIG. 1 is a diagram showing an embodiment of a hardware configuration of a depth finder according to the present invention, and FIG. 2 is a flowchart for calculating an analysis value by a Kalman filter, and illustrates an arithmetic processing procedure in a depth finder according to the present invention. FIG. 3 is a mathematical model diagram for explaining the present invention, and FIG. 4 is a table showing experimental results according to the embodiment of the present invention. Description of symbols 1 ... Transmitter 2 ... Tone burst signal generator 3 ... Transmitter 4 ... Transceiver switch 5 ... Transmitter / receiver 6 ... Receiver 7 ... Phase adjuster 8 ... Direction Computing unit 9: Direct distance computing unit, 10: Speed detector 11: Gyro, 12: Mother ship position computing unit 13: Kalman filter 14: Initial setting of X, 15: Initial setting of P 16 ... Calculation of H, 17 Calculation of K 18 Update of X 19 Update of P

Claims (2)

(57) [Claims] 1. A method for detecting the depth of an underwater object by measuring from an observation point on a mother ship and performing arithmetic processing based on the measurement data, wherein the measurement includes: an orientation of the underwater object on a horizontal plane; Many times to measure the distance to
Using the position data of the mother ship at the time of each measurement together with these measurement data, the calculation processing of the observation matrix, the calculation of the Kalman gain, the update of the state vector based on this, and the update of the covariance matrix are performed. Method of detecting the depth of a moving underwater object. 2. An apparatus for radiating an acoustic signal to an underwater object, analyzing a reflected signal from the underwater object and detecting the depth of the underwater object, power-amplifies a pulse-modulated signal from a transmitter, and
Means for converting into an acoustic signal through a transducer and radiating it into water, means for receiving the reflected signal from an underwater object by the transducer, amplifying and phasing the received signal, and azimuth calculation by a azimuth calculator. Means for converting the data into data, means for converting the pulse modulation output and the timing signal obtained from the reception output into direct distance data by a direct distance calculator, and the position of the mother ship using data on the speed and traveling direction of the mother ship An underwater object comprising: means for calculating a position of the mother ship by an arithmetic unit; and arithmetic processing means for inputting the azimuth data, the direct distance data, and the position data of the mother ship to a Kalman filter to obtain an output of a position component of the underwater object. Depth detector.