CN110716203A - Time-frequency analysis and tracking method of passive sonar target - Google Patents
Time-frequency analysis and tracking method of passive sonar target Download PDFInfo
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- CN110716203A CN110716203A CN201911051681.3A CN201911051681A CN110716203A CN 110716203 A CN110716203 A CN 110716203A CN 201911051681 A CN201911051681 A CN 201911051681A CN 110716203 A CN110716203 A CN 110716203A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/66—Sonar tracking systems
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
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Abstract
The invention discloses a time-frequency analysis and tracking method of a passive sonar target, and belongs to the technical field of underwater acoustic signal detection. Carrying out Fourier transform on each path of wave beam domain signal independently; superposing the data after Fourier transform to obtain a group of frequency domain data; selecting corresponding spectral line data from the data after Fourier transform according to the requirement of the required frequency band; performing frequency domain time accumulation and smoothing; and obtaining the tracking orientation of the recorded target frequency spectrum, and tracking through Kalman filtering. The method provided by the invention can obtain the frequency spectrum information with higher resolution, has more visual data on tracking display, makes the frequency information of the noise signal more definite, and provides more favorable data information for subsequent target identification and judgment.
Description
Technical Field
The invention relates to the technical field of underwater acoustic signal detection, in particular to a time-frequency analysis and tracking method of a passive sonar target.
Background
The interference of marine environment noise, local ship noise and the like forms a random noise field, and the noise of the target ship and signals sent by the active sonar form unknown random signals to be detected. The traditional random noise signal time-frequency analysis algorithm receives wave beam domain signals which are subjected to notch processing and azimuth normalization processing. The traditional noise signal time frequency analysis mainly adopts Fourier transform, line spectrum superposition and frequency domain time accumulation and smoothing processing; meanwhile, the system has a tracking function and can acquire the azimuth change condition of a line spectrum in a frequency domain.
In the traditional noise signal time-frequency analysis, frequency domain information is obtained through Fourier transform, and line spectrums in the frequency domain information are subjected to superposition operation, so that the resolution ratio of the frequency spectrums is reduced. The frequency of the acquired noise signal is an approximate estimate, and the frequency value is not accurate. Frequency information is not marked on the display breadth, and the frequency value of some positions is only displayed in the azimuth direction and is higher. In the process of tracking the noise target by aiming at higher frequency information, only the azimuth information of the target can be acquired, and the frequency of the current target cannot be detected. Therefore, the method brings difficulty in distinguishing the noise target and is not beneficial to subsequent detection work such as pattern recognition of the noise target.
Disclosure of Invention
The invention aims to provide a time-frequency analysis and tracking method of a passive sonar target, which aims to solve the problem that frequency information cannot be clearly determined in the conventional random noise signal time-frequency analysis algorithm.
In order to solve the technical problem, the invention provides a time-frequency analysis and tracking method of a passive sonar target, which comprises the following steps:
carrying out Fourier transform on each path of wave beam domain signal independently;
superposing the data after Fourier transform to obtain a group of frequency domain data;
selecting corresponding spectral line data from the frequency domain data after Fourier transform according to the requirement of a required frequency band;
performing frequency domain time accumulation and smoothing;
and obtaining the tracking orientation of the recorded target frequency spectrum, and tracking through Kalman filtering.
Optionally, the number of the beam domain signals subjected to fourier transform is 48, the number of the fourier transform points is 4096, and the spectral line interval △ F is 7.8125 Hz.
Optionally, the number of spectral lines is 768.
Optionally, the frequency domain time accumulation and smoothing process includes:
marking 768 selected spectral lines as Zk_tWhere k is 1 to 768 and t represents the tth frame beam; carrying out time accumulation smoothing processing by a smoothing filter, wherein the length M of the smoothing filter is 64, the number of updating frames is N times when each time of calculation is carried out, and N is 2, 4 or 8; calculating an arithmetic mean value for the data of the length M of the smoothing filter;
the smoothed output is Jk_iI denotes the ith frame beam, pair Jk_iCarrying out normalization processing to obtain the final processing result D sent to the display devicek_i；Dk_i＝255*Jk_iG, wherein G is a normalization coefficient and is selected according to the absolute value of the processing result data; k is a channel number of 1-768.
Optionally, obtaining a tracking position of the recording target frequency spectrum, and tracking through kalman filtering includes:
(1) the interpolation direction finding method comprises the following steps:
the core of the interpolation direction-finding method is an adjacent three-beam azimuth interpolation method, the tracking azimuth of the recording target frequency spectrum is obtained by the adjacent three-beam azimuth interpolation method, the adjacent three-beam azimuth interpolation method utilizes the adjacent three beams of the target, sets parameters, and obtains the azimuth of the actual target by the following formula:
wherein the content of the first and second substances,
Rn-1representing the left beam of three beams, RnRepresenting the middle beam of the three beams, Rn+1Representing the right beam of the three beams, theta is a parameter,
is the horizontal separation angle of the two elements;
determining and tracking a certain frequency on display equipment through manual operation, finding three adjacent beams of the tracking frequency by using the position of the frequency, and then carrying out interpolation on the position near the acquired target point by using an interpolation direction-finding method to estimate the target position; wherein each channel occupies an angle 360/48 of 7.5;
(2) the Kalman filtering working method comprises the following steps:
the equation of motion of Kalman filtering is set as uniform accelerated motion, and the motion model is as follows:
X(t+1)＝X(t)+v0(t)T+1/2T2a
v(t)＝v0(t)+aT
where a is acceleration, X (t +1) is the next cycle's orientation, X (t) is the current orientation, v0(T) is the initial velocity, T is time, T is the number of cycles, v (T) is the actual velocity;
the state vector of the motion model includes azimuth and speed, and the control input variable is acceleration a (t), so the state equation of the motion model is:
wherein XtIs the azimuth of the current cycle, vtIs the speed of the current cycle, Xt-1Is the azimuth of the previous cycle, vt-1Is the speed of the last cycle of the cycle,
here corresponding matrix
The size is 2 x 2 and the size is,
the size is 2 × 1; the measured value is mapped out by the system state variable, and the formula is as follows: z is a radical ofk＝Hxk+vk；
The measurement equation for the system is therefore:
zkis a measured value with a size of 2 x 1, and H is a transformation matrix of the state variable to the measured value，H＝[1 0]The magnitude is 2 × 1, the random variable v is the measurement noise, and for the system noise w and the measurement noise v in the state equation, it is assumed that the following multivariate gaussian noise distribution is obeyed:
p (w) N (0, Q), P (v) N (0, R), where w, v are independent of each other, Q, R is a covariance matrix of noise variables,
assuming that noise is present in both bearing and velocity, and the noise variance is 0.01, then:r is 0.1, and the value of R can be adjusted according to actual conditions.
Optionally, the time-frequency analysis and tracking method for the passive sonar target further includes a matching function:
recording the value of the target information as a predicted value of a first period, matching the predicted value with the output obtained by the interpolation direction-finding algorithm, comparing the difference with a wave gate, considering that the target is matched in the wave gate, and taking the output obtained by the interpolation direction-finding algorithm as a matching value to enter Kalman filtering, otherwise, considering that no target is matched;
if the number of cycles is more than or equal to 3, entering Kalman filtering, and extrapolating for three times of reporting;
if the number of cycles is less than 3, the report is directly lost.
Optionally, the size of the wave gate is set according to requirements, and the initial value is 2.5.
The invention provides a time-frequency analysis and tracking method of a passive sonar target, which independently performs Fourier transform on each path of wave beam domain signal; superposing the data after Fourier transform to obtain a group of frequency domain data; selecting corresponding spectral line data from the frequency domain data after Fourier transform according to the requirement of a required frequency band; performing frequency domain time accumulation and smoothing; and obtaining the tracking orientation of the recorded target frequency spectrum, and tracking through Kalman filtering. The method provided by the invention can obtain the frequency spectrum information with higher resolution, has more visual data on tracking display, makes the frequency information of the noise signal more definite, and provides more favorable data information for subsequent target identification and judgment.
Drawings
Fig. 1 is a schematic flow chart of a time-frequency analysis and tracking method of a passive sonar target provided by the present invention;
FIG. 2 is a schematic of frequency domain time accumulation and smoothing;
fig. 3 is a flow chart of a tracking method in the time-frequency analysis and tracking method of the passive sonar target.
Detailed Description
The time-frequency analysis and tracking method of the passive sonar target provided by the invention is further described in detail with reference to the accompanying drawings and specific embodiments. Advantages and features of the present invention will become apparent from the following description and from the claims. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.
Example one
The invention provides a time-frequency analysis and tracking method of a passive sonar target, the flow of which is shown in figure 1 and comprises the following steps: carrying out Fourier transform on each path of wave beam domain signal independently; superposing the data after Fourier transform to obtain a group of frequency domain data; selecting corresponding spectral line data from the frequency domain data after Fourier transform according to the requirement of a required frequency band; performing frequency domain time accumulation and smoothing; and obtaining the tracking orientation of the recorded target frequency spectrum, and tracking through Kalman filtering.
And (3) performing Fourier transform on each path of signals of the multi-path beam domain signals independently, wherein the number of Fourier transform points is 4096, the spectral line interval △ F is 7.8125Hz, the number of the beam domain signals subjected to Fourier transform is 48, and data obtained after Fourier transform is performed on each path of beam domain signals are subjected to superposition operation, so that a group of 4096-point frequency domain data is obtained finally.
Selecting corresponding spectral lines on the basis of the operation according to the requirement of the required frequency band, and marking 768 selected spectral lines as Zk_tWhere k is 1 to 768 and t represents the tth frame beam; carrying out time accumulation smoothing processing by a smoothing filter, wherein the length M of the smoothing filter is 64, the number of updating frames is N times when each time of calculation is carried out, and N is 2, 4 or 8; to pairCalculating an arithmetic mean value of data of the length M of the smoothing filter; the smoothed output is Jk_iI denotes the ith frame beam, pair Jk_iNormalization processing is carried out to obtain the final processing result D which is sent to displayk_i；Dk_i＝255*Jk_iG is a normalization coefficient, and is selected according to the absolute value of the processing result data; and k is a channel number, the value range is 1-768, and a schematic diagram of frequency domain time accumulation and smoothing processing is shown in figure 2.
And finally, obtaining the tracking orientation of the recorded target frequency spectrum, and tracking through Kalman filtering:
(1) the interpolation direction finding method comprises the following steps:
the core of the interpolation direction-finding method is an adjacent three-beam azimuth interpolation method, the tracking azimuth of the recording target frequency spectrum is obtained by the adjacent three-beam azimuth interpolation method, the adjacent three-beam azimuth interpolation method utilizes the adjacent three beams of the target, sets parameters, and obtains the azimuth of the actual target by the following formula:
wherein the content of the first and second substances,Rn-1representing the left beam of three beams, RnRepresenting the middle beam of the three beams, Rn+1Representing the right beam of the three beams, theta is a parameter,
is the horizontal separation angle of two primitives.
Determining and tracking a certain frequency on display equipment through manual operation, finding three adjacent beams of the tracking frequency by using the position of the frequency, interpolating in the direction of the target point by using an interpolation direction-finding method, and estimating the target direction; wherein each channel occupies an angle 360/48 of 7.5;
(2) the Kalman filtering working method comprises the following steps:
the equation of motion of Kalman filtering is set as uniform accelerated motion, and the motion model is as follows:
X(t+1)＝X(t)+v0(t)T+1/2T2a
v(t)＝v0(t)+aT
where a is acceleration, X (t +1) is the next cycle's orientation, X (t) is the current orientation, v0(T) is the initial velocity, T is time, T is the number of cycles, v (T) is the actual velocity;
the state vector of the motion model includes azimuth and speed, and the control input variable is acceleration a (t), so the state equation of the motion model is:wherein XtIs the azimuth of the current cycle, vtIs the speed of the current cycle, Xt-1Is the azimuth of the previous cycle, vt-1Is the speed of the previous cycle, here the corresponding matrix
The size is 2 x 2 and the size is,
the size is 2 × 1; the measured value is mapped out by the system state variable, and the formula is as follows: z is a radical ofk＝Hxk+vk；
The measurement equation for the system is therefore:
zkis the measured value, size 2 × 1, H is the state variable to measurement transformation matrix, H ═ 10]The magnitude is 2 × 1, the random variable v is the measurement noise, and for the system noise w and the measurement noise v in the state equation, it is assumed that the following multivariate gaussian noise distribution is obeyed:
p (w) N (0, Q), P (v) N (0, R), where w, v are independent of each other, Q, R is a covariance matrix of noise variables,
assuming that noise is present in both bearing and velocity, and the noise variance is 0.01, then:
r is 0.1, and the value of R can be adjusted according to actual conditions.
Referring to a tracking flow chart shown in fig. 3, a value of target information is recorded as a predicted value of a first period, a period counter starts counting, the predicted value is matched with the output obtained by the interpolation direction-finding method, the difference between the predicted value and the output obtained by the interpolation direction-finding method is compared with a wave gate, the target is considered to be matched in the wave gate, the output obtained by the interpolation direction-finding method is used as a matching value to enter Kalman filtering, and otherwise, the target is considered not to be matched; if the number of cycles is more than or equal to 3, entering Kalman filtering, and extrapolating for three times of reporting; if the periodicity is less than 3, directly reporting to be lost; the size of the wave gate is set according to requirements, and the initial value is 2.5.
The above description is only for the purpose of describing the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention, and any variations and modifications made by those skilled in the art based on the above disclosure are within the scope of the appended claims.
Claims (7)
1. A time-frequency analysis and tracking method of a passive sonar target is characterized by comprising the following steps:
carrying out Fourier transform on each path of wave beam domain signal independently;
superposing the data after Fourier transform to obtain a group of frequency domain data;
selecting corresponding spectral line data from the frequency domain data after Fourier transform according to the requirement of a required frequency band;
performing frequency domain time accumulation and smoothing;
and obtaining the tracking orientation of the recorded target frequency spectrum, and tracking through Kalman filtering.
2. The time-frequency analysis and tracking method of the passive sonar target according to claim 1, wherein the number of the beam domain signals subjected to the fourier transform is 48, the number of the fourier transform points is 4096, and the line interval △ F is 7.8125 Hz.
3. The time-frequency analysis and tracking method of a passive sonar target according to claim 1, wherein the number of spectral lines is 768 lines.
4. The time-frequency analysis and tracking method of a passive sonar target of claim 3, wherein performing frequency-domain time accumulation and smoothing comprises:
marking 768 selected spectral lines as Zk_tWhere k is 1 to 768 and t represents the tth frame beam; carrying out time accumulation smoothing processing by a smoothing filter, wherein the length M of the smoothing filter is 64, the number of updating frames is N times when each time of calculation is carried out, and N is 2, 4 or 8; calculating an arithmetic mean value for the data of the length M of the smoothing filter;
the smoothed output is Jk_iI denotes the ith frame beam, pair Jk_iCarrying out normalization processing to obtain the final processing result D sent to the display devicek_i；Dk_i＝255*Jk_iG, wherein G is a normalization coefficient and is selected according to the absolute value of the processing result data; k is a channel number of 1-768.
5. The time-frequency analysis and tracking method of the passive sonar target according to claim 1, wherein obtaining a tracking orientation of a recorded target spectrum, and tracking through kalman filtering includes:
(1) the interpolation direction finding method comprises the following steps:
the core of the interpolation direction-finding method is an adjacent three-beam azimuth interpolation method, the tracking azimuth of the recording target frequency spectrum is obtained by the adjacent three-beam azimuth interpolation method, the adjacent three-beam azimuth interpolation method utilizes the adjacent three beams of the target, sets parameters, and obtains the azimuth of the actual target by the following formula:
wherein the content of the first and second substances,Rn-1representing three wavesLeft beam of the beam, RnRepresenting the middle beam of the three beams, Rn+1Representing the right beam of the three beams, theta is a parameter,
is the horizontal separation angle of the two elements;
determining and tracking a certain frequency on display equipment through manual operation, finding three adjacent beams of the tracking frequency by using the position of the frequency, and then carrying out interpolation on the position near the acquired target point by using an interpolation direction-finding method to estimate the target position; wherein each channel occupies an angle 360/48 of 7.5;
(2) the Kalman filtering working method comprises the following steps:
the equation of motion of Kalman filtering is set as uniform accelerated motion, and the motion model is as follows:
X(t+1)＝X(t)+v0(t)T+1/2T2a
v(t)＝v0(t)+aT
where a is acceleration, X (t +1) is the next cycle's orientation, X (t) is the current orientation, v0(T) is the initial velocity, T is time, T is the number of cycles, v (T) is the actual velocity;
the state vector of the motion model includes azimuth and speed, and the control input variable is acceleration a (t), so the state equation of the motion model is:
wherein XtIs the azimuth of the current cycle, vtIs the speed of the current cycle, Xt-1Is the azimuth of the previous cycle, vt-1Is the speed of the last cycle of the cycle,
here corresponding matrixThe size is 2 x 2 and the size is,
the size is 2 × 1; the measured value is mapped out by the system state variable, and the formula is as follows: z is a radical ofk＝Hxk+vk；
The measurement equation for the system is therefore:
zkis the measured value, size 2 × 1, H is the state variable to measurement transformation matrix, H ═ 10]The magnitude is 2 × 1, the random variable v is the measurement noise, and for the system noise w and the measurement noise v in the state equation, it is assumed that the following multivariate gaussian noise distribution is obeyed:
p (w) N (0, Q), P (v) N (0, R), where w, v are independent of each other, Q, R is a covariance matrix of noise variables,
assuming that noise is present in both bearing and velocity, and the noise variance is 0.01, then:
r is 0.1, and the value of R can be adjusted according to actual conditions.
6. The time-frequency analysis and tracking method of a passive sonar target of claim 1, further comprising a matching function:
recording the value of the target information as a predicted value of a first period, matching the predicted value with the output obtained by the interpolation direction-finding algorithm, comparing the difference with a wave gate, considering that the target is matched in the wave gate, and taking the output obtained by the interpolation direction-finding algorithm as a matching value to enter Kalman filtering, otherwise, considering that no target is matched;
if the number of cycles is more than or equal to 3, entering Kalman filtering, and extrapolating for three times of reporting;
if the number of cycles is less than 3, the report is directly lost.
7. The time-frequency analysis and tracking method of the passive sonar target according to claim 6, wherein a size of the gate is set according to a requirement, and an initial value is 2.5.
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