CN117348087A - Underwater multi-target tracking method - Google Patents
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
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Abstract
The invention relates to an underwater multi-target tracking method, which comprises the following steps: establishing a three-dimensional space coordinate system, acquiring coordinates of a sensor, monitoring the volume of water, and measuring the coordinate Z of the water surface on the Z axis 2 Coordinate Z of water bottom on Z axis 3 Clutter density λ; the method comprises the steps that a sensor periodically acquires measurement information, wherein the measurement information comprises an azimuth angle, a pitch angle and time delay between the sensor and a direct wave, a set of measurement information in an ith frame is marked as Z (i), and the number of measurement information of a single target acquired by the sensor is L; randomly generating K virtual targets c in a monitored water volume K ＝[c 1 ,c 2 ,...,c K ]And according to the prior formula, the possibility that one measurement information comes from a certain propagation path is expressed; establishing an underwater target model according to the log-likelihood ratio; and obtaining the number of the actual targets and the optimal coordinates of each actual target according to the underwater target model. The underwater multi-target positioning and tracking device can efficiently position and track underwater multi-targets.
Description
Technical Field
The invention belongs to the technical field of underwater detection, and particularly relates to an underwater multi-target tracking method.
Background
With the development of technology, the underwater activities of using the unmanned or unmanned underwater vehicle are more and more frequent, so that the monitoring of the underwater vehicle is particularly important.
When targets such as unmanned underwater vehicles are tracked and positioned under water, the conditions of missed detection and false detection are often caused by noise interference and multipath effects existing under water.
Therefore, accurate tracking and positioning of underwater multi-target under noise and multipath environment is a technical problem to be solved.
Disclosure of Invention
In view of the above, the present invention provides an underwater multi-target tracking method capable of efficiently locating and tracking underwater multi-targets.
The technical scheme adopted by the invention is as follows:
an underwater multi-target tracking method, comprising the steps of:
s100, establishing a three-dimensional space coordinate system, and acquiring coordinates (x 0 ，y 0 ，z 0 ) Monitoring the volume V of the water body and the coordinate Z of the water surface on the Z axis 2 Coordinate Z of water bottom on Z axis 3 Clutter density λ;
s200, periodically acquiring measurement information by a sensor, wherein the measurement information comprises an azimuth angle theta and a pitch angleAnd time delay tau between the sensor and the direct wave, wherein the set of measurement information in the ith frame is marked as Z (i), the quantity of measurement information obtained by the sensor by a single target is L, i is any positive integer, and the jth measurement information in the ith frame is marked as Z j (i) The total number of measurement information in the i-th frame is m i ；
S300, randomly generating K virtual targets c in the monitored water body volume K ＝[c 1 ,c 2 ,...,c K ]And expressing that one measurement information comes from a certain propagation path according to a priori formulaThe energy, the formula is:
wherein,target c k Generating detection probability of measurement information pi through path l 00 The probability of the measurement information from clutter; pi kl Generating metrology information for path l from target c k Probability of (2);
s400, establishing an underwater target model according to the log likelihood ratio, wherein the underwater target model is expressed as:
wherein p is l (z j (i)|c k )＝N[z j (i)；h l (c k ,c s ,i)]Representing object c k Generating measurement z through path l j (i) Likelihood functions of (2); c s Representing the coordinates (x 0 ，y0，z 0 )，c k Representing the coordinates (x) k ，y k ，z k )，h l (. Cndot.) represents a model of the measurement information corresponding to the first path;
s500, obtaining the number of the actual targets and the optimal coordinates of each actual target according to the underwater target model.
Preferably, the measurement information l=3 of the single target is a direct wave path, a water surface reflected wave path and a water bottom reflected wave path respectively;
wherein,
the measurement information corresponding to the direct wave path is expressed as
The measurement information corresponding to the water surface reflected wave path is expressed as
The measurement information corresponding to the underwater reflected wave path is expressed as
Preferably, the method comprises the steps of,
τ 1 (c k )＝0
where c is the speed of sound under water.
Preferably, in step S100, the azimuth variance μ, the pitch variance ω and the delay variance v are also obtained,
the sensor is a vector hydrophone,
the azimuth variance and the time delay variance are directly obtained from the sensor;
the pitch angle variance omega of the direct wave path Direct to Satisfy the following requirements
ω Direct to ～GMM＝0.6*N(0,1^2)+0.4*N(0,4^2)，
The pitch angle variance omega of the water surface reflected wave path and the water bottom reflected wave path Multipath Satisfy the following requirements
ω Multipath ～GMM＝0.4*N(0,1^2)+0.6*N(0,4^2)；
The measurement information corresponding to the direct wave path is expressed as
The measurement information corresponding to the water surface reflected wave path is expressed as
The measurement information corresponding to the underwater reflected wave path is expressed as
Preferably, in step S200, the sensor continuously acquires Nw frame data;
in step S400, an underwater target model is built according to the log-likelihood ratio, expressed as:
the invention has the beneficial effects that:
according to the invention, the actual measurement information is acquired through the sensor, then K virtual targets are randomly generated, each virtual target can generate L virtual measurement information, the virtual measurement information is compared with the actual measurement information, so that the detection probability and clutter probability of each virtual measurement information are obtained, and an underwater target model is built according to the detection probability and clutter probability, so that the number of underwater targets and the optimal coordinates of each target can be obtained, and the positioning and tracking of underwater multiple targets in a noise environment are realized.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent from the following description of embodiments of the present invention with reference to the accompanying drawings, in which:
FIG. 1 is a schematic illustration of an actual target reaching a sensor through three propagation paths;
FIG. 2 is a schematic diagram of three measurement information acquired by a sensor within a frame;
fig. 3 is a schematic view of the state obtained from the underwater target model.
Detailed Description
The present invention is described below based on examples, but the present invention is not limited to only these examples. In the following detailed description of the present invention, certain specific details are set forth in order to avoid obscuring the present invention, and in order to avoid obscuring the present invention, well-known methods, procedures, flows, and components are not presented in detail.
Moreover, those of ordinary skill in the art will appreciate that the drawings are provided herein for illustrative purposes and that the drawings are not necessarily drawn to scale.
Unless the context clearly requires otherwise, throughout the description and the claims, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, it is the meaning of "including but not limited to".
In the description of the present invention, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more.
Referring to fig. 1-3, the invention provides an underwater multi-target tracking method for positioning and tracking an underwater vehicle, comprising the following steps:
s100, establishing a three-dimensional space coordinate system, and acquiring coordinates (x 0 ，y 0 ，z 0 ) Monitoring the volume V of the water body and the coordinate Z of the water surface on the Z axis 2 Coordinate Z of water bottom on Z axis 3 Clutter density λ;
s200, periodically acquiring measurement information by a sensor, wherein the measurement information comprises an azimuth angle theta and a pitch angleAnd time delay tau between the sensor and the direct wave, wherein the set of measurement information in the ith frame is marked as Z (i), the quantity of measurement information obtained by the sensor by a single target is L, i is any positive integer, and the jth measurement information in the ith frame is marked as Z j (i) The total number of measurement information in the i-th frame is m i ；
S300, randomly generating K virtual targets c in the monitored water body volume K ＝[c 1 ,c 2 ,...,c K ]And expressing the likelihood that a piece of measurement information comes from a certain propagation path according to a priori formula, wherein the formula is as follows:
wherein,target c k Generating detection probability of measurement information pi through path l 00 The probability of the measurement information from clutter; pi kl Generating metrology information for path l from target c k Probability of (2);
s400, establishing an underwater target model according to the log likelihood ratio, wherein the underwater target model is expressed as:
wherein p is l (z j (i)|c k )＝N[z j (i)；h l (c k ,c s ,i)]Representing object c k Generating measurement z through path l j (i) Likelihood functions of (2); c s Representing the coordinates (x 0 ，y 0 ，z 0 )，c k Representing the coordinates (x) k ，y k ，z k ) Hl (·) represents the measurement model corresponding to the first path;
s500, obtaining the number of the actual targets and the optimal coordinates of each actual target according to the underwater target model.
For step S100, the sensor is first set to a fixed position under water, and a three-dimensional space coordinate system is established, where the water body monitored by the sensor and the sensor are both located in the three-dimensional space coordinate system, that is, the position of the sensor can be represented by coordinates, and any point in the monitored water body can also be represented by coordinates.
The "monitoring water body" refers to an effective range which can be acquired by the sensor, and the specific value of the monitoring water body volume V depends on the position of the sensor in the water body, the depth of the water body and the performance of the sensor.
In the three-dimensional space coordinate system, an X axis and a Y axis are mutually perpendicular and are both in a horizontal direction, and a Z axis is in a vertical direction.
The water surface and the water bottom of the water body are assumed to be plane and parallel to the horizontal plane, so that the height of the water surface and the height of the water bottom can be expressed on the Z axis,thus, the coordinate Z of the water surface on the Z axis can be determined 2 Coordinate Z of water bottom on Z axis 3 ，Z 2 And Z 3 Is a specific value and can be obtained according to the monitoring water body.
Clutter density λ can be obtained by pre-monitoring the water body, which is a fixed value.
In step S200, the sensor periodically acquires measurement information, for example, the sensor acquires measurement information once every second, and the measurement information acquired by the sensor is referred to as actual measurement information in order to distinguish between measurement information generated by a virtual target.
Referring to fig. 1, an acoustic wave emitted from an object (actual object) propagates in water (may be sea water) to a sensor and is detected by the sensor, so that the foregoing measurement information is obtained, and when the acoustic wave propagates in water, the acoustic wave directly reaches the sensor in an almost straight line manner by the object (corresponding to a direct wave path), the acoustic wave reaches the sensor after being reflected by the water surface (for example, sea surface), and the acoustic wave reaches the sensor after being reflected by the water bottom (for example, sea bottom).
The direct wave path is shortest, so that the energy intensity detected by the sensor is the largest, and the time point detected by the sensor is the earliest; the water surface reflected wave path and the water bottom reflected wave path are longer, compared with the direct wave path, the time point detected by the sensor is closer to the time point of detecting the direct wave path, and the detected energy intensity is lower than that of detecting the direct wave path; therefore, the actual measurement information of the direct wave path can be distinguished according to the characteristics.
For both the actual measurement information of the water surface reflected wave path and the actual measurement information of the water bottom reflected wave path, the water surface reflected wave path is inclined downward and the water bottom reflected wave path is inclined upward at the position of the sensor, so that the pitch angle in the actual measurement information can be calculatedThe actual measurement information of the water surface reflected wave path is distinguished from the actual measurement information of the water bottom reflected wave path.
As shown in FIG. 2, when a target exists under water, the actual measurement information of a frame obtained by the sensor has three continuous waves, the first wave is the earliest in time point and has the greatest energy intensity, the first wave is the actual measurement information of the direct wave path, the second two measurement information are the actual measurement information of the reflected wave path of the water surface, the other is the actual measurement information of the reflected wave path of the water bottom, and the two measurement information can be distinguished by the pitch angle in the actual measurement informationTo realize the method.
Each actual measurement information comprises an azimuth angle theta and a pitch angleAnd a time delay tau between the water surface reflected wave path and the direct wave, wherein the azimuth angle and the pitch angle can be directly measured, and as for the time delay, the actual measurement information of the direct wave path, the actual measurement information of the water surface reflected wave path and the actual measurement information of the water bottom reflected wave path can be distinguished, so that the time delay between the water surface reflected wave path and the direct wave is 0 for the actual measurement information of the direct wave path, and the time delay between the water surface reflected wave path and the water bottom reflected wave path is the time difference between the corresponding time point of the actual measurement information and the time point of the actual measurement information of the direct wave path respectively.
For the sensor to acquire a frame of actual measurement information, the total number of measurement information acquired in the frame is 100, at this time m i 100, each actual measurement information is given a specific number, for example, the 1 st actual measurement information, the 2 nd actual measurement information … … the 100 th actual measurement information, etc. (the number corresponds to j); as mentioned above, in each frame, theoretically, the sensor can obtain three pieces of actual measurement information (i.e. l=3) transmitted by each actual target, and some of the 100 pieces of actual measurement informationThe remaining part is clutter generated according to the actual target. However, the prior art does not effectively distinguish between the two, resulting in an inability to effectively locate and track the actual target (the actual target may also be multiple).
In order to solve the problem, in step S300 of the present invention, K virtual targets are randomly generated within the range of the monitored water body, so that the coordinates of each virtual target in the three-dimensional space coordinate system are also clear, for each virtual target, the sensor can theoretically obtain three pieces of virtual measurement information (corresponding to three propagation paths) corresponding to the virtual target, each piece of the foregoing actual measurement information is compared with the virtual measurement information, and it is determined whether three pieces of actual measurement information are simultaneously close to all three pieces of virtual measurement information of a certain virtual target, if so, it is indicated that the three pieces of actual measurement information are present corresponding to the three pieces of actual measurement information, and the actual target is located near the coordinates of the virtual target (particularly, the virtual target close to the three pieces of virtual measurement information).
The above process can be expressed by the following formula:
wherein,target c k Generating detection probability of measurement information pi through path l 00 The probability of the measurement information from clutter; pi kl Generating metrology information for path l from target c k Is a probability of (2).
In step S400, an underwater target model is built,
wherein p is l (z j (i)|c k )＝N[z j (i)；h l (c k ,c s ,i)]Representing object c k Generating measurement z through path l j (i) Likelihood functions of (2); c s Representing the coordinates (x 0 ，y 0 ，z 0 )，c k Representing the coordinates (x) k ，y k ，z k )，h l (.) represents a model of the metrology information corresponding to the first path (i.e., virtual metrology information corresponding to each virtual target is generated).
Thus, the underwater target model corresponding to each frame can be built according to the actual measurement information acquired by the sensor in the frame.
In step S500, the number of actual targets and the optimal coordinates of each actual target are obtained according to the underwater target model.
As shown in fig. 3, which is a schematic view of the underwater target model, three obvious protrusions can be clearly seen, so that the number of actual targets can be determined to be three, and the coordinates corresponding to the positions of the three protrusions are the preferred coordinates of the three actual targets, thereby realizing the positioning and tracking of the underwater targets.
Of course, the positioning is only described for measurement information of one frame acquired by the sensor, that is, the positioning of the target is only performed for the frame time, and the coordinates of the actual target are preferred, so that the continuous coordinates of the actual target can be obtained by processing the measurement information of each frame acquired by the sensor according to steps S100-S500, so that the actual target can be tracked in real time, and the moving track of the actual target can be drawn, so that the advancing direction and advancing position of the target can be pre-determined.
The measurement information L=3 of the single target is a direct wave path, a water surface reflected wave path and a water bottom reflected wave path respectively;
wherein, for each virtual target,
the measurement information corresponding to the direct wave path is expressed as
The measurement information corresponding to the water surface reflected wave path is expressed as
The measurement information corresponding to the underwater reflected wave path is expressed as
Thus, corresponding virtual measurement information can be generated for each virtual target.
In particular, the method comprises the steps of,
τ 1 (c k )＝0
where c is the speed of sound under water, for example in seawater at 25 ℃, c=1531 m/s.
Due to the coordinates c of the sensor s (x 0 ，y 0 ，z 0 ) Coordinates c of virtual target k (x k ，y k ，z k ) Are known, so that each virtual metrology information generated by each virtual target can be obtained.
In step S100, the azimuth variance μ, the pitch variance ω and the delay variance v are also obtained,
the sensor is a vector hydrophone,
the azimuth variance and the time delay variance are directly obtained from the sensor;
the pitch angle variance conforms to the gaussian mixture distribution, and therefore:
the pitch angle variance omega of the direct wave path Direct to Satisfy the following requirements
ω Direct to ～GMM＝0.6*N(0,1^2)+0.4*N(0,4^2)，
The pitch angle variance omega of the water surface reflected wave path and the water bottom reflected wave path Multipath Satisfy the following requirements
ω Multipath ～GMM＝0.4*N(0,1^2)+0.6*N(0,4^2)；
The measurement information corresponding to the direct wave path is expressed as
The measurement information corresponding to the water surface reflected wave path is expressed as
The measurement information corresponding to the underwater reflected wave path is expressed as
The azimuth angle variance mu, the pitch angle variance omega and the time delay variance v are taken into consideration, so that the accuracy of positioning an actual target can be enhanced.
In step S200, the sensor continuously acquires Nw frame data;
in step S400, an underwater target model is built according to the log-likelihood ratio, expressed as:
nw is preferably 3-5, and in practical application, the period of acquiring the actual measurement information of the sensor is controlled within 2S, for example, 1S is acquired once, and the model is more accurate by constructing continuous Nw frame data into the target model, so that the obtained preferred coordinates are closer to the actual coordinates.
It will be understood that the above-described embodiments are merely illustrative and not restrictive, and that all obvious or equivalent modifications and substitutions to the details given above may be made by those skilled in the art without departing from the underlying principles of the invention, are intended to be included within the scope of the appended claims.
Claims (5)
1. An underwater multi-target tracking method is characterized by comprising the following steps:
s100, establishing a three-dimensional space coordinate system, and acquiring coordinates (x 0 ，y 0 ，z 0 ) Monitoring the volume V of the water body and the coordinate Z of the water surface on the Z axis 2 Coordinate Z of water bottom on Z axis 3 Clutter density λ;
s200, periodically acquiring measurement information by a sensor, wherein the measurement information comprises an azimuth angle theta and a pitch angleAnd time delay tau between the sensor and the direct wave, wherein the set of measurement information in the ith frame is marked as Z (i), the quantity of measurement information obtained by the sensor by a single target is L, i is any positive integer, and the jth measurement information in the ith frame is marked as Z j (i) The total number of measurement information in the i-th frame is m i ；
S300, randomly generating K virtual targets c in the monitored water body volume K ＝[c 1 ,c 2 ,...,c K ]And expressing the likelihood that a piece of measurement information comes from a certain propagation path according to a priori formula, wherein the formula is as follows:
wherein,target c k Generating detection probability of measurement information pi through path l 00 The probability of the measurement information from clutter; pi kl Generating metrology information for path l from target c k Probability of (2);
s400, establishing an underwater target model according to the log likelihood ratio, wherein the underwater target model is expressed as:
wherein p is l (z j (i)|c k )＝N[z j (i)；h l (c k ,c s ,i)]Representing object c k Generating measurement z through path l j (i) Likelihood functions of (2); c s Representing the coordinates (x 0 ，y 0 ，z 0 )，c k Representing the coordinates (x) k ，y k ，z k )，h l (. Cndot.) represents a model of the measurement information corresponding to the first path;
s500, obtaining the number of the actual targets and the optimal coordinates of each actual target according to the underwater target model.
2. The underwater multi-target tracking method according to claim 1, wherein the measurement information l=3 of the single target is a direct wave path, a water surface reflected wave path, and a water bottom reflected wave path, respectively;
wherein,
the measurement information corresponding to the direct wave path is expressed as
The measurement information corresponding to the water surface reflected wave path is expressed as
The measurement information corresponding to the underwater reflected wave path is expressed as
3. An underwater multi-target tracking method as in claim 2, wherein,
τ 1 (c k )＝0
where c is the speed of sound under water.
4. An underwater multi-target tracking method as in claim 3, characterized in that in step S100, the azimuth variance μ, the pitch variance ω and the delay variance v are also obtained,
the sensor is a vector hydrophone,
the azimuth variance and the time delay variance are directly obtained from the sensor;
the pitch angle variance omega of the direct wave path Direct to Satisfy the following requirements
ω Direct to ～GMM＝0.6*N(0,1^2)+0.4*N(0,4^2)，
The pitch angle variance omega of the water surface reflected wave path and the water bottom reflected wave path Multipath Satisfy the following requirements
ω Multipath ～GMM＝0.4*N(0,1^2)+0.6*N(0,4^2)；
The measurement information corresponding to the direct wave path is expressed as
The measurement information corresponding to the water surface reflected wave path is expressed as
The measurement information corresponding to the underwater reflected wave path is expressed as
5. The underwater multi-target tracking method as claimed in any of claims 1-4, wherein in step S200, the sensor continuously acquires Nw frame data;
in step S400, an underwater target model is built according to the log-likelihood ratio, expressed as:
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