CN107728109A - A kind of noncooperative target radiated noise measurement and positioning technology - Google Patents

Abstract

The invention discloses a non-cooperative target radiation noise measurement and positioning technology. Based on the vector hydrophone measurement technology, the sound pressure and vibration velocity reception model of the vector hydrophone is constructed, and the joint processing of the sound pressure and vibration velocity of the vector hydrophone is used. Technology, using the cross-spectrum sound intensity method to obtain the measurement azimuth of the baseline; through the triangle intersection method to solve the target position coordinate information measured by each set of baselines. For the positioning of multiple vector hydrophones, it can be regarded as a pairwise combination of vector hydrophones, and then through data fusion processing technology, the measurement results of all baselines are integrated to determine the position of each measurement point of the moving target. Finally, the Kalman filter algorithm is used for post-processing to further optimize the trajectory of the moving target. The data fusion technology combined with the Kalman filter algorithm can improve the positioning accuracy, quickly and accurately locate the trajectory of the target in a small range, and solve the problems of poor positioning accuracy and slow tracking speed of the two hydrophones.

Description

A Non-cooperative Target Radiation Noise Measurement and Positioning Technology

technical field

The invention relates to the technical field of noise testing, in particular to a non-cooperative target radiation noise measurement and positioning technology.

Background technique

Noise measurement technology (noise measuring technique) noise measurement includes the measurement of various noise sources and basic characteristic parameters of the noise field; the acoustic performance measurement of sound absorption and sound insulation materials and vibration damping materials used in noise control; sound absorption, sound insulation , noise reduction, vibration reduction, vibration isolation and other control measures for technical performance evaluation measurement. In addition, the study of the impact and harm of noise on the human body, the subjective evaluation of noise, and the formulation of various environmental noise standards also require scientific basis for noise measurement. Accurately completing these measurements requires the use of various technical means.

Noise test technology can receive the radiated noise of the target, obtain its acoustic information, and provide guidance and suggestions for vibration and noise reduction. The accurate measurement of the distance between the underwater target and the measurement point is the key to the calculation of the target sound source level. The target ship radiation noise of passive sonar is broadband, and the noise intensity on different frequencies is different. The quantity that reflects the dependence of the radiated noise intensity of the sound source on the frequency is called the sound source spectral level, which is the decibel number of the sound intensity of the radiated noise within a 1HZ bandwidth near a single frequency relative to the reference sound intensity at a distance of 1 meter from the sound source. SLs said. Due to the complexity of the mechanism of ship radiation noise, SLs are difficult to obtain through theoretical calculations, and actual measurements are required. In traditional noise measurement systems, cooperative beacons are used to obtain synchronous ranging information. However, for non-cooperative targets, this method cannot be achieved. Because the vector hydrophone can obtain both the scalar and vector information of the target, it can improve the measurement capability, and has now been used in the noise measurement system. Therefore, based on the vector hydrophone measurement technology, this paper uses the vector hydrophone azimuth estimation results and adopts the data fusion method to make full use of the data information obtained by multiple vector hydrophones, and quickly and accurately locate the The trajectory of the target can solve the problem that the current domestic noise measurement system cannot accurately locate non-cooperative targets.

Deng Xiuhua and others have studied the method of precise measurement of synchronous distance (Deng Xiuhua. Research on the measurement method of self-guided deep missile miss distance. Ship Electronic Engineering, 2012, Vol.32No.10), which has a synchronous acoustic beacon installed on the target, which cannot Realize the measurement of non-cooperative moving targets; Wu Yanqun and others studied the method of vector hydrophone azimuth-only target motion analysis (Wu Yanqun, Hu Yongming. Water surface target motion analysis based on single vector hydrophone. Acoustic Technology, 2010, 1000- 3630(2010)-04-0361-04), which uses a single vector hydrophone to estimate the orientation of the target and track and measure the moving target in a large range, but the tracking speed of this method is slow and the positioning accuracy cannot be achieved. Meet the measurement requirements.

Based on this, the present invention combines the vector hydrophone measurement technology with the data fusion method, obtains the information amount of the tested target and the environment to a greater extent, integrates the target information obtained by multiple sets of measurement systems, and effectively improves the performance of the system , which improves the positioning accuracy. On the other hand, the Kalman filter algorithm is combined to filter the target motion, so that the target's motion trajectory is optimized to meet the needs of noise measurement. The method proposed by the present invention utilizes the data fusion technology to process the data information measured by multiple vector hydrophones, so that the measured data can play a maximum role, and is suitable for quickly and accurately locating the movement of the target in a small range The trajectory improves the efficiency of the experiment and is easier to implement in engineering.

Contents of the invention

The invention proposes a non-cooperative target radiation noise measurement and positioning technology, which combines vector data fusion and Kalman filter algorithm, can effectively track and measure non-cooperative moving targets, obtain motion parameters, and better improve vector hydrophone The positioning accuracy and the stability of the measurement system.

The technical solution adopted by the present invention to solve its technical problems comprises the following steps:

(1) Respectively establish a linear measurement array model and a square measurement array model composed of four array elements. The four array elements form six baselines, which respectively form six triangles with the target and form 12 azimuth angles;

(2) Establish the receiving signal model of the vector hydrophone, obtain the sound pressure data P(t) received by the vector hydrophone, the vibration velocity V _x (t) in the X direction, and the vibration velocity V _y (t) in the Y direction. The joint processing technology of sound pressure and vibration velocity of hydrophones uses the cross-spectrum sound intensity method to estimate the orientation;

(3) Spectrum analysis is performed on the received signal, the envelope is extracted to obtain an effective frequency band, and frequency domain fusion processing is performed within a narrow band range;

(4) Combine the 6 baselines and the obtained 12 azimuth angles, and solve the target position measured by each baseline through the triangle intersection method; perform data re-fusion on the target positions calculated by multiple sets of vector hydrophones;

(5) The Kalman filter algorithm is used to optimize the moving target measurement trajectory generated in (4).

The sound pressure vibration velocity joint processing technology of the vector hydrophone of described step (2) specifically includes:

Combining the target with the six baselines respectively, the received signal at the i-th array element is obtained as:

Among them, the subscript s indicates the amount of signal, and the subscript n indicates the amount of noise; p _i (t) indicates the sound pressure signal received by the i-th array element, v _xi (t) indicates the horizontal direction received by the i-th array element The vibration velocity signal of , v _yi (t) represents the vibration velocity signal in the vertical direction received by the i-th array element, and θ is the horizontal azimuth angle of the incident sound wave.

The cross-spectrum sound intensity method of described step (2) specifically comprises:

The obtained sound pressure p _i (r, t), vibration velocity v _xi (r, t), and v _yi (r, t) are Fourier transformed, and signal processing in the frequency domain can obtain the frequency domain sound intensity information:

The horizontal azimuth angle of each frequency is estimated by using the cross-spectrum sound intensity method as:

where ω is the angular frequency, is the average sound intensity in the x direction, is the average sound intensity in the y direction, is the estimated value of the horizontal azimuth.

Described step (3) specifically comprises:

First use FFT to estimate the power spectrum in the entire frequency domain, find the line spectrum of the signal after analysis, extract the envelope from the line spectrum, and find a narrow band range (f ₁ , f ₂ , f ₃ ,…,f _n ) where the envelope is located Do frequency refinement analysis; for the same target signal, use the cross-spectrum sound intensity method to obtain a series of target azimuth estimates; The result should be a weighted composite of all baseline positioning results

The weight selection is the reciprocal variance method, where D _i represents the variance of the i-th group of measurement data:

Described step (4) specifically comprises:

The distance between array element i and array element j:

The schematic diagram of double hydrophone cross positioning is shown in Fig. 1, and R and for:

Described step (5) specifically comprises:

The recursive formula of the Kalman filter algorithm is as follows:

P(k+1|k)=Φ·P(k|k)·Φ'+Γ·Q(k)·Γ';

K(k+1)=P(k+1|k)·H'(k+1|k)·S ^-1 (k+1);

S(k+1)=H(k+1)·P(k+1|k)·H'(k+1)+R(k+1);

P(k+1|k+1)=[I-K(k+1) H(k+1)] P(k+1|k);

Wherein, Q(k).δ _kl =E[g(k).g'(l)], R(k).δ _kl =E[w(k).w'(l)].

The beneficial effect of the present invention is that: the method fully utilizes the data information obtained by the vector hydrophone, and effectively combines the joint processing technology of the sound pressure and vibration velocity of the vector hydrophone with the data fusion technology. And the Kalman filter algorithm is used to optimize the trajectory of the moving target, so that the positioning accuracy of the moving target has been significantly improved, which can meet the measurement requirements of non-cooperative targets and has strong engineering practicability.

Description of drawings

Fig. 1 is a flowchart of the present invention;

Fig. 2 is a schematic diagram of double-vector hydrophone cross positioning;

Figure 3 is a schematic diagram of a square layout;

Fig. 4 is the simulation analysis result of target motion trajectory;

Fig. 5 is the simulation analysis result of speed detection in X direction;

Fig. 6 is the simulation analysis result of speed detection in Y direction;

Fig. 7 is the simulation analysis result of X-axis position detection error;

Figure 8 is the simulation analysis result of the Y-axis position detection error.

detailed description

The present invention will be further described below in conjunction with accompanying drawings and examples.

(1) The measurement model of the four-element vector hydrophone is established, and the four array elements are combined in pairs to form six measurement baselines, which respectively form six triangles with the target, thus obtaining 12 measurement azimuths.

Taking the square measurement array as an example, both the square array and the moving target are located in the xoy plane, and the four-element vector hydrophones [1, 2, 3, 4] are distributed on the four vertices of the square whose side length is a, according to the counterclockwise The directions are arranged, and the coordinates are (x _i , y _i ), i=1, 2, 3, 4. Array element 1 and array element 2 are located on the x-axis, equally spaced on both sides of the origin O. Array element 3 and array element 4 are located directly above array element 2 and array element 3 respectively. The initial position of the target is at the midpoint of the intersection of array element 1 and array element 4, as shown in Figure 3.

(2) Establish the receiving signal model of the vector hydrophone, and obtain the sound pressure data P(t) received by the vector hydrophone, the vibration velocity V _x (t) in the X direction, and the vibration velocity V _y (t) in the Y direction. Combining the target with the six baselines respectively, the received signal at the i-th array element can be obtained as:

The subscript s indicates the amount of signal, and the subscript n indicates the amount of noise. Where p _i (t) represents the sound pressure signal received by the i-th array element, v _xi (t) represents the vibration velocity signal in the horizontal direction received by the i-th array element, and v _yi (t) represents the i-th The vibration velocity signal in the vertical direction received by the array element. θ is the horizontal azimuth angle of the incident sound wave.

The obtained sound pressure p _i (r, t), vibration velocity v _xi (r, t), and v _yi (r, t) are Fourier transformed, and signal processing in the frequency domain can obtain the frequency domain sound intensity information:

The horizontal azimuth angle of each frequency is estimated by using the cross-spectrum sound intensity method as:

where ω is the angular frequency, is the average sound intensity in the x direction, is the average sound intensity in the y direction, is the estimated value of the horizontal azimuth.

(3) In order to improve the positioning accuracy, it is necessary to analyze the spectrum of the received signal to obtain the effective frequency band, and perform frequency domain fusion processing in the narrow band range to synthesize the positioning results of all baselines of the measurement array to the target.

First use FFT to estimate the power spectrum in the entire frequency domain, find the line spectrum of the signal after analysis, extract the envelope from the line spectrum, and find a narrow band range (f ₁ , f ₂ , f ₃ ,…,f _n ) where the envelope is located Do frequency analysis.

The vector hydrophone uses the cross-spectrum sound intensity method for direction finding, corresponding to each frequency f ₁ , f ₂ , f ₃ ,…,f _n of the received signal, a direction information can be estimated according to the formula (2.4) Therefore, for the same target signal, the azimuth estimation value of a series of targets can be obtained by using the cross-spectrum sound intensity method For each group of vector hydrophones, corresponding to each frequency of the received signal, a set of target position coordinate data S ₁ (x, y), S ₂ (x, y), S ₃ (x,y),…,S _n (x,y), using the sound intensity of each frequency to combine the target position results for each frequency.

Then, data re-fusion is performed on the target position calculated by multiple sets of vector hydrophones, and the precise positioning result of the target position should be the weighted synthesis of all baseline positioning results, namely:

The weight selection is the reciprocal variance method, where D _i represents the variance of the i-th group of measurement data:

(4) Six baselines are composed of four array elements, which respectively form six triangles with the target, forming 12 azimuth angles. Using the distance between the obtained horizontal orientation value θ and the baseline, the position coordinates of the target are determined by the triangle intersection method. Specifically:

The distance between array element i and array element j:

The schematic diagram of double hydrophone cross positioning is shown in Fig. 1, and R and for:

(5) Use Kalman filter to filter the target motion. The Kalman filter algorithm (recursive formula) is as follows:

P(k+1|k)＝Φ·P(k|k)·Φ'+Γ·Q(k)·Γ'

K(k+1)＝P(k+1|k)·H'(k+1|k)·S ^-1 (k+1)

S(k+1)＝H(k+1)·P(k+1|k)·H'(k+1)+R(k+1)

P(k+1|k+1)＝[I-K(k+1)·H(k+1)]·P(k+1|k)

Wherein: Q(k).δ _kl =E[g(k).g'(l)], R(k).δ _kl =E[w(k).w'(l)].

The specific implementation of each part of the content of the invention has been described above. The multi-vector hydrophone joint tracking and positioning technology that integrates the data fusion technology and the Kalman filter algorithm can effectively improve the positioning accuracy of the system. The following takes the square station layout as an example to analyze the simulation results.

The example parameters are set as follows: The layout of the four-element square array is shown in Figure 2. The horizontal position of the No. 1 vector hydrophone is (-100, 0); the horizontal position of the No. 2 vector hydrophone is (100, 0), and the horizontal position of the No. 3 vector hydrophone is ( 100, 200); the horizontal position of the No. 4 vector hydrophone is (-100, 200). Suppose the target is a single-frequency signal, the signal frequency is 150Hz, the sampling rate is 4096, the signal-to-noise ratio at the initial moment is 20dB, the initial position of the target on the horizontal plane is (-100,100), and the initial velocity in the X direction is V _x = 1m/s, The initial velocity in the Y direction is V _y =0.

Fig. 3 is the simulation analysis result of target motion trajectory, Fig. 4 is the simulation analysis result of speed detection, and Fig. 5 is the simulation analysis result of position detection error.

Based on the simulation results of Figure 3, Figure 4 and Figure 5, it can be seen that:

(1) The multivariate vector hydrophone passive positioning solution algorithm can accurately describe the target's trajectory, which proves the reliability of the algorithm and the effectiveness of the positioning method. The multi-vector hydrophone combined with the Kalman filter algorithm can further improve the positioning accuracy.

(2) The distance between the target and the hydrophone can be accurately obtained by using the azimuth information, and the sound propagation loss can be calculated, so as to accurately obtain the radiated noise level of the target.

Claims (6)

1. A non-cooperative target radiation noise measurement and positioning technology is characterized in that it specifically comprises the steps: (1) Respectively establish a linear measurement array model and a square measurement array model composed of four array elements. The four array elements form six baselines, which respectively form six triangles with the target and form 12 azimuth angles; (2) Establish the receiving signal model of the vector hydrophone, obtain the sound pressure data P(t) received by the vector hydrophone, the vibration velocity V _x (t) in the X direction, and the vibration velocity V _y (t) in the Y direction. The joint processing technology of sound pressure and vibration velocity of hydrophones uses the cross-spectrum sound intensity method to estimate the orientation; (3) Spectrum analysis is performed on the received signal, the envelope is extracted to obtain an effective frequency band, and frequency domain fusion processing is performed within a narrow band range; (4) Combine the 6 baselines and the obtained 12 azimuth angles, and solve the target position measured by each baseline through the triangle intersection method; perform data re-fusion on the target positions calculated by multiple sets of vector hydrophones; (5) The Kalman filter algorithm is used to optimize the moving target measurement trajectory generated in (4). 2. a kind of non-cooperative target radiation noise measurement positioning technology according to claim 1, is characterized in that, the sound pressure vibration velocity joint processing technology of the vector hydrophone of described step (2) specifically comprises: Combining the target with the six baselines respectively, the received signal at the i-th array element is obtained as: <mrow><mfenced open = "{" close = ""><mtable><mtr><mtd><mrow><msub><mi>p</mi><mi>i</mi></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>=</mo><msub><mi>p</mi><mrow><mi>s</mi><mi>i</mi></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>+</mo><msub><mi>p</mi><mi>n</mi></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></mtd></mtr><mtr><mtd><mrow><msub><mi>v</mi><mrow><mi>x</mi><mi>i</mi></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>=</mo><msub><mi>v</mi><mrow><mi>s</mi><mi>i</mi></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>&amp;CenterDot;</mo><mi>c</mi><mi>o</mi><mi>s</mi><mrow><mo>(</mo><mi>&amp;theta;</mi><mo>)</mo></mrow><mo>+</mo><msub><mi>v</mi><mrow><mi>x</mi><mi>n</mi></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></mtd></mtr><mtr><mtd><mrow><msub><mi>v</mi><mrow><mi>y</mi><mi>i</mi></mrow></msub><mrow><mo>(< / mo><mi>t</mi><mo>)</mo></mrow><mo>=</mo><msub><mi>v</mi><mrow><mi>s</mi><mi>i</mi></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>&amp;CenterDot;</mo><mi>sin</mi><mrow><mo>(</mo><mi>&amp;theta;</mi><mo>)</mo></mrow><mo>+</mo><msub><mi>v</mi><mrow><mi>y</mi><mi>n</mi></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></mtd></mtr></mtable></mfenced><mo>;</mo></mrow> Among them, the subscript s indicates the amount of signal, and the subscript n indicates the amount of noise; p _i (t) indicates the sound pressure signal received by the i-th array element, v _xi (t) indicates the horizontal direction received by the i-th array element The vibration velocity signal of , v _yi (t) represents the vibration velocity signal in the vertical direction received by the i-th array element, and θ is the horizontal azimuth angle of the incident sound wave. 3. a kind of non-cooperative target radiation noise measurement positioning technique according to claim 1, is characterized in that, the cross-spectrum sound intensity method of described step (2) specifically comprises: The obtained sound pressure p _i (r, t), vibration velocity v _xi (r, t), and v _yi (r, t) are Fourier transformed, and signal processing in the frequency domain can obtain the frequency domain sound intensity information: <mrow><mover><msub><mi>I</mi><mi>x</mi></msub><mo>&amp;OverBar;</mo></mover><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>&amp;omega;</mi><mo>)</mo></mrow><mo>=</mo><mover><mrow><msub><mi>p</mi><mi>i</mi></msub><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>&amp;omega;</mi><mo>)</mo></mrow><mo>.</mo><msub><mi>v</mi><mrow><mi>x</mi><mi>i</mi></mrow></msub><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>&amp;omega;</mi><mo>)</mo></mrow></mrow><mo>&amp;OverBar;</mo></mover><mo>=</mo><mover><mi>I</mi><mo>&amp;OverBar;</mo></mover><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>&amp;omega;</mi><mo>)</mo></mrow><mo>.</mo><mi>c</mi><mi>o</mi><mi>s</mi><mrow><mo>(</mo><mi>&amp;theta;</mi><mo>)</mo></mrow><mo>;</mo></mrow> <mrow><mover><msub><mi>I</mi><mi>y</mi></msub><mo>&amp;OverBar;</mo></mover><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>&amp;omega;</mi><mo>)</mo></mrow><mo>=</mo><mover><mrow><msub><mi>p</mi><mi>i</mi></msub><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>&amp;omega;</mi><mo>)</mo></mrow><mo>.</mo><msub><mi>v</mi><mrow><mi>y</mi><mi>i</mi></mrow></msub><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>&amp;omega;</mi><mo>)</mo></mrow></mrow><mo>&amp;OverBar;</mo></mover><mo>=</mo><mover><mi>I</mi><mo>&amp;OverBar;</mo></mover><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>&amp;omega;</mi><mo>)</mo></mrow><mo>.</mo><mi>s</mi><mi>i</mi><mi>n</mi><mrow><mo>(</mo><mi>&amp;theta;</mi><mo>)</mo></mrow><mo>;</mo></mrow> The horizontal azimuth angle of each frequency is estimated by using the cross-spectrum sound intensity method as: <mrow><mover><mi>&amp;theta;</mi><mo>^</mo></mover><mrow><mo>(</mo><mi>&amp;omega;</mi><mo>)</mo></mrow><mo>=</mo><msup><mi>tan</mi><mrow><mo>-</mo><mn>1</mn></mrow></msup><mo>(</mo><mfrac><mrow><mover><msub><mi>I</mi><mi>y</mi></msub><mo>&amp;OverBar;</mo></mover><mrow><mo>(</mo><mrow><mi>r</mi><mo>,</mo><mi>&amp;omega;</mi></mrow><mo>)</mo></mrow></mrow><mrow><mover><msub><mi>I</mi><mi>x</mi></msub><mo>&amp;OverBar;</mo></mover><mrow><mo>(</mo><mrow><mi>r</mi><mo>,</mo><mi>&amp;omega;</mi></mrow><mo>)</mo></mrow></mrow></mfrac><mo>)</mo><mo>;</mo></mrow> where ω is the angular frequency, is the average sound intensity in the x direction, is the average sound intensity in the y direction, is the estimated value of the horizontal azimuth. 4. a kind of non-cooperative target radiation noise measurement positioning technology according to claim 1, is characterized in that, described step (3) specifically comprises: First use FFT to estimate the power spectrum in the entire frequency domain, find the line spectrum of the signal after analysis, extract the envelope from the line spectrum, and find a narrow band range (f ₁ , f ₂ , f ₃ ,…,f _n ) where the envelope is located Do frequency refinement analysis; for the same target signal, use the cross-spectrum sound intensity method to obtain a series of target azimuth estimates; The result should be a weighted composite of all baseline positioning results <mrow><mfenced open = "{" close = ""><mtable><mtr><mtd><mrow><mi>x</mi><mo>=</mo><mfrac><mrow><munder><mi>&amp;Sigma;</mi><mrow><mi>i</mi><mo>&amp;NotEqual;</mo><mi>j</mi></mrow></munder><munder><mi>&amp;Sigma;</mi><mi>j</mi></munder><msub><mi>&amp;omega;</mi><mrow><mi>x</mi>mi><mi>i</mi><mi>j</mi></mrow></msub><msub><mi>x</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub></mrow><mrow><munder><mi>&amp;Sigma;</mi><mrow><mi>i</mi><mo>&amp;NotEqual;</mo><mi>j</mi></mrow></munder><munder><mi>&amp;Sigma;</mi><mi>j</mi></munder><msub><mi>&amp;omega;</mi><mrow><mi>x</mi><mi>i</mi><mi>j</mi></mrow></msub></mrow></mfrac></mrow></mtd></mtr><mtr><mtd><mrow><mi>y</mi><mo>=</mo><mfrac><mrow><munder><mi>&amp;Sigma;</mi><mrow><mi>i</mi><mo>&amp;NotEqual;</mo><mi>j</mi></mrow></munder><munder><mi>&amp;Sigma;</mi><mi>j</mi></munder><msub><mi>&amp;omega;</mi><mrow><mi>y</mi><mi>i</mi><mi>j</mi></mrow></msub><msub><mi>y</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub></mrow><mrow><munder><mi>&amp;Sigma;</mi><mrow><mi>i</mi><mo>&amp;NotEqual;</mo><mi>j</mi></mrow></munder><munder><mi>&amp;Sigma;</mi><mi>j</mi></munder><msub><mi>&amp;omega;</mi><mrow><mi>y</mi><mi>i</mi><mi>j</mi></mrow></msub></mrow></mfrac></mrow></mtd></mtr></mtable></mfenced><mo>;</mo></mrow> The weight selection is the reciprocal variance method, where D _i represents the variance of the i-th group of measurement data: <mrow><msub><mi>&amp;omega;</mi><mi>i</mi></msub><mo>=</mo><msup><msub><mi>D</mi><mi>i</mi></msub><mrow><mo>-</mo><mn>1</mn></mrow></msup><mo>/</mo><munderover><mi>&amp;Sigma;</mi><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>N</mi></munderover><msup><msub><mi>D</mi><mi>i</mi></msub><mrow><mo>-</mo><mn>1</mn></mrow></msup><mo>.</mo></mrow> 5. a kind of non-cooperative target radiation noise measurement positioning technique according to claim 1, is characterized in that, described step (4) specifically comprises: The distance between array element i and array element j: <mrow><msub><mi>d</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub><mo>=</mo><msqrt><mrow><msup><mrow><mo>(</mo><msub><mi>x</mi><mi>i</mi></msub><mo>-</mo><msub><mi>x</mi><mi>j</mi></msub><mo>)</mo></mrow><mn>2</mn></msup><mo>+</mo><msup><mrow><mo>(</mo><msub><mi>y</mi><mi>i</mi></msub><mo>-</mo><msub><mi>y</mi><mi>j</mi></msub><mo>)</mo></mrow><mn>2</mn></msup></mrow></msqrt><mo>,</mo><mrow><mo>(</mo><mi>i</mi><mo>&amp;NotEqual;</mo><mi>j</mi><mo>)</mo></mrow><mo>;</mo></mrow> The schematic diagram of double hydrophone cross positioning is shown in Fig. 1, and R and for: 6. a kind of non-cooperative target radiation noise measurement positioning technique according to claim 1, is characterized in that, described step (5) specifically comprises: The recursive formula of the Kalman filter algorithm is as follows: P(k+1|k)=Φ·P(kk)·Φ'+Γ·Q(k)·Γ'; K(k+1)=P(k+1|k)·H'(k+1k)·S ^-1 (k+1); S(k+1)＝H(k+1)·P(k+1|k)·H'(k+1)+R(k+1); P(k+1|k+1)=[I-K(k+1)·H(k+1)]·P(k+1|k); X(k+1|k+1)=X(k +1|k)+K(k+1|k)+K(k+1) V(k+1); Wherein, Q(k).δ _kl =E[g(k).g'(l)], R(k).δ _kl =E[w(k).w'(l)].