CN110515065A - A kind of Linear Spectrum of Radiated Noise Depth discrimination method - Google Patents
A kind of Linear Spectrum of Radiated Noise Depth discrimination method Download PDFInfo
- Publication number
- CN110515065A CN110515065A CN201910817051.6A CN201910817051A CN110515065A CN 110515065 A CN110515065 A CN 110515065A CN 201910817051 A CN201910817051 A CN 201910817051A CN 110515065 A CN110515065 A CN 110515065A
- Authority
- CN
- China
- Prior art keywords
- radiated noise
- spectrum
- linear spectrum
- sequence
- amplitude
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
- G01S11/14—Systems for determining distance or velocity not using reflection or reradiation using ultrasonic, sonic, or infrasonic waves
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
Abstract
The invention discloses a kind of Linear Spectrum of Radiated Noise Depth discrimination methods, comprising the following steps: (1) initialization process parameter；(2) Linear Spectrum of Radiated Noise range value is read in, line spectrum amplitude sequence is updated；(3) judge the corresponding time span of Linear Spectrum of Radiated Noise amplitude sequenceTWhether meet the requirements, if satisfied, carrying out step 4, otherwise return step 2；(4) Linear Spectrum of Radiated Noise amplitude sequence is divided into multiple subsequences, calculates separately the scintillation index of each subsequence Linear Spectrum of Radiated Noise amplitude, obtain line spectrum amplitude scintillation exponential sequence；(5) the line spectrum amplitude scintillation exponential sequence middle line spectral amplitude scintillation index that statistic procedure 4 obtains is more than the probability of setting threshold value, if this probability judges Linear Spectrum of Radiated Noise source for waterborne target greater than 0.5；If probability less than 0.5, judges Linear Spectrum of Radiated Noise source for submarine target；If probability is equal to 0.5, line spectrum source attribute can not be judged.
Description
Technical field
The present invention relates to a kind of Linear Spectrum of Radiated Noise Depth identification techniques, belong to Acoustic Object incident-noise signal characteristic
It extracts and identification technology field.
Background technique
The underwater acoustic systems such as sonar receive acoustical signal in water using hydrophone array, realize target detection, positioning, identification in water
Etc. functions, wherein target identification and positioning are particularly important links, are related to that commanding correctly judges and decision.Due to
The submarine navigation devices such as submarine and the surface vessel depth locating in navigation have a significant difference, thus the identification of target depth for
Differentiate that the classification of target has important supporting function.
Acoustic Object identification mainly distinguishes target type and information by extracting target characteristic amount.Target signature information
To be in target initial data include or extractible accurately and can simplify the information that show dbjective state and identity.Acoustic Object master
It to include the characteristic informations such as noise, movement, wake flow and geometry, the characteristic information of different Acoustic Objects is different.The water such as submarine
Lower aircraft and surface vessel are inevitably generated radiated noise under sail, therefore the radiated noise of target is current in water
The chief source of information of passive sonar work, radiated noise are characterized in the main foundation of passive sonar target recognition.
Target radiated noise line spectrum (abbreviation line spectrum) refers to discrete frequency component stable in radiated noise power in water, special
Be not that low frequency spectrum lines propagation distance is remote, there is better stability, it is carried out detection be low noise, calm type Underwater Target Detection and
The important channel of identification.Influenced by sea wave, interior wave etc., the submarine navigation devices such as submarine as Linear Spectrum of Radiated Noise source and
The depth of surface vessel can usually fluctuate, and this phenomenon is referred to as ocean and acts on the depth modulation of Ship Target.
And according to underwater sound propagation theory, the fluctuation of depth, which will lead to its amplitude in communication process of the line spectrum in radiated noise, have been occurred
Volt.Although line spectrum amplitude scintillation and distance, sea situation dissipate that many factors such as refraction are related, existing result of study shows second grade
Signal fluctuation is by sound source in positional fluctuation bring vertically and horizontally.Therefore, the otherness to be risen and fallen using line spectrum is right
It is located at the water surface in effectively discrimination line spectrum source to be still of great significance under water.
The present invention analyzes the line spectrum amplitude sequence of passive sonar processing output, calculates the sequence middle line spectral amplitude
Scintillation index LSAFI (Line Spectrum Amplitude Fluctuation Index), by counting line spectrum amplitude scintillation
Index is more than the probability of setting threshold value, realizes the discrimination of line spectrum Depth.
Summary of the invention
The purpose of the invention is to be directed to the depth discrimination problem in Linear Spectrum of Radiated Noise source, a kind of radiated noise line is provided
Compose Depth discrimination method.This method first judges the time span of given target radiated noise line spectrum amplitude sequence,
The Linear Spectrum of Radiated Noise amplitude sequence of elongate member, is divided into multiple subsequences when for meeting, and calculates separately every sub- sequence
The line spectrum amplitude scintillation index LSAFI of column, then counting line spectrum amplitude sequence middle line spectral amplitude scintillation index is more than setting thresholding
The probability of value differentiates that line spectrum source is waterborne target or submarine target according to this probability value.
In order to achieve the above objectives, the method that the present invention uses is: a kind of Linear Spectrum of Radiated Noise Depth discrimination method, including
Following steps:
(1) initialization process parameter；
(2) Linear Spectrum of Radiated Noise range value is read in, is updated line spectrum amplitude sequence A (n)；
(3) judge whether the corresponding time span T of Linear Spectrum of Radiated Noise amplitude sequence meets the requirements, if satisfied, being walked
Rapid 4, otherwise return step 2；
(4) Linear Spectrum of Radiated Noise amplitude sequence A (n) is divided into Q subsequence, calculates radiated noise in each subsequence
The scintillation index of line spectrum amplitude, and constitute line spectrum amplitude scintillation exponential sequence LSAFI (q)；
(5) in the line spectrum amplitude scintillation exponential sequence LSAFI (q) that statistic procedure 4 obtains each index be more than thresholding λ probability
P judges corresponding line spectrum source for waterborne target if p > 0.5；If p < 0.5, judge corresponding line spectrum source for submarine target；p
When=0.5, then corresponding line spectrum source attribute can not judge.
As a further improvement of that present invention, step 1 initializes following parameter:
(1.1) sequence for saving Linear Spectrum of Radiated Noise range value is set as A (n), n=1,2 ..., N, and enable N=1；
(1.2) Δ t is divided between setting Linear Spectrum of Radiated Noise range value renewal time；
(1.3) setting Linear Spectrum of Radiated Noise amplitude sequence updates accumulated time length Γ；
(1.4) line spectrum amplitude scintillation exponential threshold value is set as λ.
As a further improvement of that present invention, step 2 specifically comprises the following steps:
It reads in a Linear Spectrum of Radiated Noise range value and is assigned to variable a, and enable A (N)=a.
As a further improvement of that present invention, step 3 specifically comprises the following steps:
(3.1) it is corresponding to calculate A (n) by the Linear Spectrum of Radiated Noise amplitude sequence A (n), n=1,2 ... obtained according to step 2, N
Time span T=N × Δ t；
(3.2) if T >=Γ, step 4 is carried out；Otherwise, N=N+1, and return step 2 are enabled.
As a further improvement of that present invention, step 4 specifically comprises the following steps:
(4.1) Linear Spectrum of Radiated Noise amplitude sequence A (n) is divided into Q subsequence (Q >=300), the length of each subsequence
Degree is L (L>=50), and two neighboring subsequence data overlap length is K (L/2≤K<L), and q-th of subsequence is Aq(kq), 1+ (L-
K)(q-1)≤kq≤ 2L+ (L-K) (q-1), q=1,2 ..., Q.
(4.2) according to formula (1), (2), the single order Arithmetic mean function and three rank Arithmetic mean letters of each subsequence are calculated separately
Number constitutes sequence PMF1(q)、PMF3(q)。
(4.3) according to obtained sequence PMF1(q)、PMF3(q), every sub- sequence of Linear Spectrum of Radiated Noise amplitude sequence is calculated
The line spectrum amplitude scintillation index of column, and Linear Spectrum of Radiated Noise amplitude scintillation exponential sequence LSAFI (q) is constituted, as shown in formula (3).
As a further improvement of that present invention, step 5 specifically comprises the following steps:
(5.1) LSAFI (q) obtained according to step 4 counts the Probability p that wherein each scintillation index is more than thresholding λ, such as formula
(4) shown in:
M { } indicates the scintillation index value for being more than or equal to thresholding λ in Linear Spectrum of Radiated Noise amplitude scintillation exponential sequence in formula
Number.
(5.2) if p > 0.5, judge the Linear Spectrum of Radiated Noise source for waterborne target；If p < 0.5, the radiated noise line is judged
Spectrum source is submarine target；If p=0.5, the Linear Spectrum of Radiated Noise source can not be judged in waterborne target or submarine target.
A kind of Linear Spectrum of Radiated Noise Depth discrimination method disclosed by the invention.This method first makes an uproar to given target emanation
The time span of sound ray spectrum amplitude degree series judged, the Linear Spectrum of Radiated Noise amplitude sequence of elongate member when for meeting, by it
Multiple subsequences are divided into, the line spectrum amplitude scintillation index LSAFI of each subsequence is calculated separately, then count line spectrum amplitude sequence
Column middle line spectral amplitude scintillation index be more than setting threshold value probability, according to this probability value differentiate line spectrum source be waterborne target or
Submarine target.
The utility model has the advantages that
Compared with prior art, method disclosed by the invention has the advantage that underwater acoustic target radiated noise time-frequency is special
Sign analysis method is combined with the analysis of underwater sound propagation Characterization method, takes full advantage of the fluctuation characteristic of radiated noise power spectrum line spectrum
And the monotonic nature of Arithmetic mean function, do not need the prior information and ocean environment parameter information of target line spectral frequencies, algorithm
Implement simple, explicit physical meaning, identification is high-efficient.
Detailed description of the invention
Fig. 1 is the implementation flow chart of the method for the present invention.
Fig. 2 is the 240Hz line spectrum amplitude curve of 1 sound source radiation noise in embodiment.
Fig. 3 is the 240Hz line spectrum amplitude scintillation exponential curve of 1 sound source radiation noise in embodiment.
Fig. 4 is the 362Hz line spectrum amplitude curve of 2 sound source radiation noise in embodiment.
Fig. 5 is the 362Hz line spectrum amplitude scintillation exponential curve of 2 sound source radiation noise in embodiment.
Specific embodiment
Present invention is further described in detail with reference to the accompanying drawings and detailed description:
The present invention analyzes the line spectrum amplitude sequence of passive sonar processing output, calculates the sequence middle line spectral amplitude
Scintillation index LSAFI is more than the probability of setting threshold value by statistics line spectrum amplitude scintillation index, realizes distinguishing for line spectrum Depth
Not.
Embodiment 1:
With reference to the accompanying drawings and detailed description, the present invention is furture elucidated.
A kind of Linear Spectrum of Radiated Noise Depth discrimination method, as shown in Figure 1, including the following steps:
Step 1, initialization process parameter, specifically:
(1.1) sequence for saving Linear Spectrum of Radiated Noise range value is set as A (n), n=1,2 ..., N, and enable N=1；
(1.2) it sets and is divided into Δ t as 1 second between Linear Spectrum of Radiated Noise range value renewal time；
(1.3) it sets Linear Spectrum of Radiated Noise amplitude sequence and updates accumulated time length Γ as 350 seconds；
(1.4) line spectrum amplitude scintillation exponential threshold value λ=1.5.
Step 2 specifically: read in one of the line spectrum that frequency is 240Hz in an actual measurement water surface sound source radiation noise power spectrum
Range value is simultaneously assigned to variable a, and enables A (N)=a.
Step 3 specifically:
(3.1) A (n) corresponding time span T=N × Δ t is calculated；
(3.2) if T >=Γ, next step is carried out；Otherwise, N=N+1, and return step 2 are enabled.
As N=350, the corresponding time span T=N of sequence A (n) × Δ t=350 × 1=350 seconds then meets condition T
>=Γ, carries out step 4, and the sequence A (n) obtained at this time is as shown in Figure 2.
Step 4 specifically:
(4.1) Linear Spectrum of Radiated Noise amplitude sequence A (n) is divided into Q=301 subsequence, the length of each subsequence
For L=50, two neighboring subsequence data overlap length is K=49, and q-th of subsequence is Aq(kq), q≤kq≤ q+49, q=
1,2,…,301。
(4.2) according to formula (1), (2), the single order Arithmetic mean function and three rank Arithmetic mean letters of each subsequence are calculated separately
Number constitutes sequence PMF1(q)、PMF3(q)。
(4.3) according to obtained sequence PMF1(q)、PMF3(q), Linear Spectrum of Radiated Noise amplitude sequence is calculated according to formula (3)
The line spectrum amplitude scintillation index of each subsequence, and Linear Spectrum of Radiated Noise amplitude scintillation exponential sequence LSAFI (q) is constituted, such as Fig. 3
It is shown.
Step 5, specifically:
(5.1) LSAFI (q) obtained according to step 4, wherein the subsequence number that each scintillation index is more than thresholding λ is
290, Probability p=0.9435 that each scintillation index is more than thresholding λ is counted according to formula (4):
(5.2) due to p > 0.5, then determine that the corresponding radiated noise source of 240Hz line spectrum is water surface sound source, with actual conditions one
It causes.
Embodiment 2:
A kind of Linear Spectrum of Radiated Noise Depth discrimination method, as shown in Figure 1, including the following steps:
Step 1, initialization process parameter, specifically:
(1.1) sequence for saving Linear Spectrum of Radiated Noise range value is set as A (n), n=1,2 ..., N, and enable N=1；
(1.2) it sets and is divided into Δ t as 1 second between Linear Spectrum of Radiated Noise range value renewal time；
(1.3) it sets Linear Spectrum of Radiated Noise amplitude sequence and updates accumulated time length Γ as 350 seconds；
(1.4) line spectrum amplitude scintillation exponential threshold value λ=1.5.
Step 2 specifically: read in one of the line spectrum that frequency is 362Hz in an actual measurement underwater sound source radiated noise power spectrum
Range value is simultaneously assigned to variable a, and enables A (N)=a.
Step 3 specifically:
(3.1) A (n) corresponding time span T=N × Δ t is calculated；
(3.2) if T >=Γ, next step is carried out；Otherwise, N=N+1, and return step 2 are enabled.
As N=350, the corresponding time span T=N of sequence A (n) × Δ t=350 × 1=350 seconds then meets condition T
>=Γ, carries out step 4, and the sequence A (n) obtained at this time is as shown in Figure 4.
Step 4 specifically:
(4.1) Linear Spectrum of Radiated Noise amplitude sequence A (n) is divided into Q=301 subsequence, the length of each subsequence
For L=50, two neighboring subsequence data overlap length is K=49, and q-th of subsequence is Aq(kq), q≤kq≤ q+49, q=
1,2,…,301。
(4.2) according to formula (1), (2), the single order Arithmetic mean function and three rank Arithmetic mean letters of each subsequence are calculated separately
Number constitutes sequence PMF1(q)、PMF3(q)。
(4.3) according to obtained sequence PMF1(q)、PMF3(q), Linear Spectrum of Radiated Noise amplitude sequence is calculated according to formula (3)
The line spectrum amplitude scintillation index of each subsequence, and Linear Spectrum of Radiated Noise amplitude scintillation exponential sequence LSAFI (q) is constituted, such as Fig. 5
It is shown.
Step 5, specifically:
(5.1) LSAFI (q) obtained according to step 4, wherein the subsequence number that each scintillation index is more than thresholding λ is 25,
Probability p=0.0831 that each scintillation index is more than thresholding λ is counted according to formula (4):
(5.2) due to p < 0.5, then determine that the corresponding radiated noise source of 362Hz line spectrum is underwater sound source, with actual conditions one
It causes.
Above-described embodiment shows that the amplitude scintillation of the underwater surface target radiated noise line spectrum calculated using above method is referred to
Number has preferable discrimination, can effectively realize the identification of Linear Spectrum of Radiated Noise Depth.
The above described is only a preferred embodiment of the present invention, being not the limit for making any other form to the present invention
System, and made any modification or equivalent variations according to the technical essence of the invention, still fall within present invention model claimed
It encloses.
Claims (6)
1. a kind of Linear Spectrum of Radiated Noise Depth discrimination method, it is characterised in that include the following steps:
(1) initialization process parameter；
(2) Linear Spectrum of Radiated Noise range value is read in, is updated line spectrum amplitude sequence A (n)；
(3) judge whether the corresponding time span T of Linear Spectrum of Radiated Noise amplitude sequence meets the requirements, if satisfied, step 4 is carried out,
Otherwise return step 2；
(4) Linear Spectrum of Radiated Noise amplitude sequence A (n) is divided into Q subsequence, calculates Linear Spectrum of Radiated Noise in each subsequence
The scintillation index of amplitude, and constitute line spectrum amplitude scintillation exponential sequence LSAFI (q)；
(5) in the line spectrum amplitude scintillation exponential sequence LSAFI (q) that statistic procedure 4 obtains each index be more than thresholding λ Probability p, if
P > 0.5 then judges corresponding line spectrum source for waterborne target；If p < 0.5, judge corresponding line spectrum source for submarine target；P=
When 0.5, then corresponding line spectrum source attribute can not judge.
2. a kind of Linear Spectrum of Radiated Noise Depth discrimination method according to claim 1, which is characterized in that step 1 is initial
Change following parameter:
(1.1) sequence for saving Linear Spectrum of Radiated Noise range value is set as A (n), n=1,2 ..., N, and enable N=1；
(1.2) Δ t is divided between setting Linear Spectrum of Radiated Noise range value renewal time；
(1.3) setting Linear Spectrum of Radiated Noise amplitude sequence updates accumulated time length Γ；
(1.4) line spectrum amplitude scintillation exponential threshold value is set as λ.
3. a kind of Linear Spectrum of Radiated Noise Depth discrimination method according to claim 1, which is characterized in that step 2 is specific
Include the following steps: to read in a Linear Spectrum of Radiated Noise range value and be assigned to variable a, and enables A (N)=a.
4. a kind of Linear Spectrum of Radiated Noise Depth discrimination method according to claim 1, which is characterized in that step 3 is specific
Include the following steps:
(3.1) the Linear Spectrum of Radiated Noise amplitude sequence A (n), n=1,2 ... obtained according to step 2, N, when calculating A (n) is corresponding
Between length T=N × Δ t；
(3.2) if T >=Γ, step 4 is carried out；Otherwise, N=N+1, and return step 2 are enabled.
5. a kind of Linear Spectrum of Radiated Noise Depth discrimination method according to claim 1, which is characterized in that step 4 is specific
Include the following steps:
(4.1) Linear Spectrum of Radiated Noise amplitude sequence A (n) is divided into Q subsequence, the length of Q >=300, each subsequence is
L, L>=50, two neighboring subsequence data overlap length are K, L/2≤K<L, and q-th of subsequence is Aq(kq), 1+ (L-K) (q-
1)≤kq≤ 2L+ (L-K) (q-1), q=1,2 ..., Q；
(4.2) according to formula (1), (2), the single order Arithmetic mean function and three rank Arithmetic mean functions of each subsequence, structure are calculated separately
At sequence PMF1(q)、PMF3(q)；
(4.3) according to obtained sequence PMF1(q)、PMF3(q), each subsequence of Linear Spectrum of Radiated Noise amplitude sequence is calculated
Line spectrum amplitude scintillation index, and Linear Spectrum of Radiated Noise amplitude scintillation exponential sequence LSAFI (q) is constituted, as shown in formula (3):
6. a kind of Linear Spectrum of Radiated Noise Depth discrimination method according to claim 1, which is characterized in that step 5 is specific
Include the following steps:
(5.1) LSAFI (q) obtained according to step 4 counts the Probability p that wherein each scintillation index is more than thresholding λ, such as formula (4) institute
Show:
M { } indicates the scintillation index value number for being more than or equal to thresholding λ in Linear Spectrum of Radiated Noise amplitude scintillation exponential sequence in formula；
(5.2) if p > 0.5, judge the Linear Spectrum of Radiated Noise source for waterborne target；If p < 0.5, the Linear Spectrum of Radiated Noise source is judged
For submarine target；If p=0.5, the Linear Spectrum of Radiated Noise source can not be judged in waterborne target or submarine target.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910817051.6A CN110515065B (en) | 2019-08-30 | 2019-08-30 | Radiation noise line spectrum source depth identification method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910817051.6A CN110515065B (en) | 2019-08-30 | 2019-08-30 | Radiation noise line spectrum source depth identification method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110515065A true CN110515065A (en) | 2019-11-29 |
| CN110515065B CN110515065B (en) | 2022-03-25 |
Family
ID=68630026
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910817051.6A Active CN110515065B (en) | 2019-08-30 | 2019-08-30 | Radiation noise line spectrum source depth identification method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110515065B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111736158A (en) * | 2020-08-25 | 2020-10-02 | 东南大学 | Target line spectrum feature identification method based on distributed multi-buoy matching |
| CN111929665A (en) * | 2020-09-01 | 2020-11-13 | 中国科学院声学研究所 | Target depth identification method and system based on wave number spectrum main lobe position |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102243302A (en) * | 2011-04-15 | 2011-11-16 | 东南大学 | Method for extracting line spectrum time accumulation characteristics of hydro-acoustic target radiation noise |
| CN108919240A (en) * | 2018-04-23 | 2018-11-30 | 东南大学 | A kind of underwater acoustic target radiated noise modulation spectrum reconstruction method based on group sparsity structure |
| CN110118962A (en) * | 2019-04-30 | 2019-08-13 | 东南大学 | A kind of radiated noise emulation mode of Acoustic Object maneuvering condition |
| CN110135316A (en) * | 2019-05-07 | 2019-08-16 | 中国人民解放军海军潜艇学院 | The automatic detection and extracting method of low frequency spectrum lines in a kind of ship-radiated noise |
-
2019
- 2019-08-30 CN CN201910817051.6A patent/CN110515065B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102243302A (en) * | 2011-04-15 | 2011-11-16 | 东南大学 | Method for extracting line spectrum time accumulation characteristics of hydro-acoustic target radiation noise |
| CN108919240A (en) * | 2018-04-23 | 2018-11-30 | 东南大学 | A kind of underwater acoustic target radiated noise modulation spectrum reconstruction method based on group sparsity structure |
| CN110118962A (en) * | 2019-04-30 | 2019-08-13 | 东南大学 | A kind of radiated noise emulation mode of Acoustic Object maneuvering condition |
| CN110135316A (en) * | 2019-05-07 | 2019-08-16 | 中国人民解放军海军潜艇学院 | The automatic detection and extracting method of low frequency spectrum lines in a kind of ship-radiated noise |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111736158A (en) * | 2020-08-25 | 2020-10-02 | 东南大学 | Target line spectrum feature identification method based on distributed multi-buoy matching |
| CN111929665A (en) * | 2020-09-01 | 2020-11-13 | 中国科学院声学研究所 | Target depth identification method and system based on wave number spectrum main lobe position |
| CN111929665B (en) * | 2020-09-01 | 2024-02-09 | 中国科学院声学研究所 | Target depth identification method and system based on wave number spectrum main lobe position |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110515065B (en) | 2022-03-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108600135A (en) | A kind of recognition methods of signal modulation mode | |
| CN113259288B (en) | Underwater sound modulation mode identification method based on feature fusion and lightweight hybrid model | |
| Nosal | Methods for tracking multiple marine mammals with wide-baseline passive acoustic arrays | |
| CN109828266A (en) | A kind of moving target depth sorting method suitable for the negative spring layer condition in shallow sea | |
| CN104749568A (en) | Shallow sea target depth classification method based on hydrophone array | |
| CN104732970A (en) | Ship radiation noise recognition method based on comprehensive features | |
| CN110929842B (en) | Accurate intelligent detection method for non-cooperative radio signal burst time region | |
| CN105158734B (en) | A kind of single vector hydrophone Passive Location based on battle array invariant | |
| CN110515065A (en) | A kind of Linear Spectrum of Radiated Noise Depth discrimination method | |
| CN108318876A (en) | A method of estimating submarine target depth and distance using single hydrophone | |
| CN112462355A (en) | Sea target intelligent detection method based on time-frequency three-feature extraction | |
| CN114492540B (en) | Training method and device of target detection model, computer equipment and storage medium | |
| CN110850420B (en) | Fisher SVM sonar signal discrimination method based on marble loss | |
| CN113591733B (en) | Underwater acoustic communication modulation mode classification identification method based on integrated neural network model | |
| CN104665875A (en) | Ultrasonic Doppler envelope and heart rate detection method | |
| CN118245873A (en) | Domain adaptive neural architecture search method for cross-room non-line-of-sight acoustic signal recognition | |
| CN108572349B (en) | Sound source depth setting method based on model calculation under deep sea environment | |
| CN113608193A (en) | Radar multi-target distance and speed estimation method based on UNet | |
| CN110389335B (en) | Underwater sound propagation loss reliable calculation method based on ocean background field | |
| CN107122764B (en) | ShipTargets detection method based on KpN model | |
| CN116383719A (en) | MGF radio frequency fingerprint identification method for LFM radar | |
| KR102433912B1 (en) | Multi-operational depth determination device and method of variable depth sonar | |
| CN112305502A (en) | Water surface and underwater sound source binary discrimination method based on array invariants | |
| CN112270294A (en) | Underwater vehicle vortex identification method and device and underwater vehicle | |
| CN110555370B (en) | Channel effect inhibition method based on PLDA factor analysis method in underwater target recognition |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |