Method for optimally extracting azimuth information of weak target

Abstract

The invention discloses a method for optimally extracting azimuth information of a weak target, and belongs to the field of underwater acoustic equipment design and manufacturing. The method comprises the steps of firstly specifying an application scene and making a reasonable hypothesis, then deducing the change of a target orientation, then performing orientation prediction, then predicting the target orientation, and finally verifying simulation data. According to the invention, after the signal received by the passive sonar is subjected to beam forming and other signal processing, the azimuth history diagram of the target is formed. In order to obtain the azimuth information of the target, the azimuth history chart is processed to extract the target azimuth. A conventional target orientation extraction method is a peak value detection method, but processing of a weak target is omitted outside a detection threshold due to weak energy of the target. The objective of the invention is to optimize the peak detection method, predict the azimuth when the temporary detection of the target azimuth disappears, and improve the detection rate of the target azimuth.

Description

Technical Field

[0001] The invention relates to the technical field of underwater acoustic equipment design and manufacturing, and in particular to a method for optimizing and extracting weak target azimuth information. Background Art

[0002] Passive sonar does not actively transmit signals, but detects targets by receiving the target's own radiation noise or signals. Therefore, it is difficult for the target to detect its position and intention. It has good concealment and anti-interference properties, so it has received widespread attention. Due to the complexity and particularity of the underwater environment, it is still a difficult problem for passive sonar to detect targets with weak radiation noise, especially in far-field conditions. The radiation noise signal of the target becomes very weak and intermittent when it propagates to the sensor through the complex underwater environment. Therefore, the subsequent signal processing algorithm is particularly important.

[0003] Passive sonar does not actively transmit signals. Therefore, in far-field scenarios, the target information obtained by a single passive array can only obtain the target's azimuth map after beamforming and other processing. In order to obtain the specific coordinate information of the target, multi-array joint cross-positioning is often required. The premise of cross-positioning is to extract the azimuth of the azimuth map. The peak detection method is a traditional azimuth extraction algorithm. When extracting the azimuth of weak targets, the target azimuth at certain moments is excluded by the detection threshold. If the threshold is set too low, a large number of false targets will enter the detection threshold. For the above reasons, there is an urgent need for a method to predict the azimuth when weak targets are missed in a short period of time. Summary of the invention

[0004] The purpose of the present invention is to provide a method for optimizing the extraction of weak target position information to solve the problems in the background technology.

[0005] In order to solve the above technical problems, the present invention provides a method for optimizing and extracting weak target position information, comprising:

[0006] Step 1: Define the scenario for far-field target detection. The target moves in a straight line along A→B→C at a uniform speed in a short period of time. The coordinates of A are (x k-1 ,y k-1 ) is the azimuth angle θ k-1 , the coordinates of B are (x k ,y k ) is the azimuth angle θ k , the coordinates of C are (x k+1 ,y k+1 ) is the azimuth angle θ k+1 ;

[0007] Step 2: Based on the assumptions of the first step, the target's movement direction changes in a short period of time:

[0008]

[0009] The velocity of the target in the x direction is v x , the velocity in the y direction is v y ,have to:

[0010]

[0011] v y and v x is a constant, if Δθ k+1 -Δθ k Approaching zero, it is considered that the target's azimuth changes at a uniform speed; assuming that the target's motion equation in the y direction is:

[0012] y=a+bt(4)

[0013] The equation of motion in the x direction is:

[0014] x=c+dt(5)

[0015]

[0016] Among them, a, b, c, d are all constants; a, c are known coordinates of the target, b, d are the speeds in the x and y directions respectively; if the second-order derivative of formula (6) approaches zero, it proves that the orientation of the target is approximately uniform motion, and the second-order derivative of formula (6) is obtained:

[0017]

[0018] Simplifying formula (6) yields formula (7):

[0019]

[0020] At present, the maximum speed of underwater ships and other military equipment does not exceed 20m / s, so:

[0021]

[0022] The detection direction of the far field is about 1-10 kilometers, so the value of a and c is 1000-10000, so the maximum value of |da-bc| is 300000; according to the Pythagorean theorem:

[0023]

[0024] where p 2 =x 2 +y 2 , in summary,

[0025]

[0026] Where P>1000, so θ"<0.008

[0027] From the above, we know that the change of direction in a short time is considered to be a uniform change;

[0028] Step 3: The change in orientation obtained in the second step is approximately a uniform motion model, which is as follows:

[0029] θ(t)=α(t)+β(t)*t (11)

[0030] Among them, α(t) and β(t) are time-related coefficients. Generally, the closer the historical position data is to the current moment, the better it can represent the position information to be predicted. Therefore, α(t) and β(t) are updated in real time. The calculation formula is as follows:

[0031]

[0032] in: Among them, γ is the empirical value, which controls the weight value; N is the empirical value that controls the amount of data used for prediction;

[0034] Step 4: Set the initial position θ of the weak target α to be tracked. It is known that the target α can be detected by the peak detection method within the first k moments. k , at time k+1, the energy of the target orientation is lower than the detection threshold, resulting in missed detection of the target orientation; if the target is judged to have disappeared at this time, the relevant information of this target will be missed; the target orientation information is deduced using the derivation in the third step, and if the target orientation cannot be detected at N consecutive moments, the target is judged to have disappeared; otherwise, the derived target orientation is used to represent the true target orientation, thereby improving the detection rate of weak target orientations;

[0035] Step 5: At non-peak points, the prediction method is used to successfully extract the target orientation to avoid missed target detection.

[0036] The method for optimizing the extraction of weak target position information provided by the present invention has the following beneficial effects:

[0037] 1) The method of the present invention is an optimization of the traditional peak detection algorithm technology, and the algorithm has low complexity, is concise and easy to understand, and is simple to implement;

[0038] 2) The present invention improves the detection rate of target orientation;

[0039] 3) The present invention can be applied to other signal processing scenarios such as weak target tracking and azimuth extraction, not limited to passive sonar, and has a wide range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1It is a schematic diagram of the motion model provided by the present invention.

[0041] Figure 2 It is a schematic diagram of the relationship between p, x and y provided by the present invention.

[0042] Figure 3 It is a schematic diagram of the algorithm flow provided by the present invention.

[0043] Figure 4 It is a macro effect diagram of target extraction.

[0044] Figure 5 It is a local orientation prediction effect diagram. DETAILED DESCRIPTION

[0045] The following is a further detailed description of a method for optimizing the extraction of weak target azimuth information proposed by the present invention in conjunction with the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer according to the following description. It should be noted that the accompanying drawings are all in a very simplified form and are not in precise proportions, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0046] The present invention provides a method for optimizing the extraction of weak target azimuth information. The method is based on a peak detection algorithm and a reasonable azimuth prediction method. It solves the problem of detection loss during weak target azimuth extraction to a large extent, improves the extraction efficiency of the target azimuth, and provides more supporting data for subsequent cross-positioning and other tasks.

[0047] The present invention is mainly used in the signal processing of passive sonar, and is specifically applied to the azimuth extraction of azimuth history diagram, and comprises the following steps:

[0048] Step 1: Define the application scenario and make reasonable assumptions

[0049] The present invention is applied in the scenario of detecting far-field targets. Targets at sea or underwater can be considered to be moving in a uniform straight line in a short period of time, such as Figure 1 As shown, the relationship between the orientation and the target coordinates is established. k-1 is the coordinate (x k-1 ,y k-1 ) azimuth, θ k is the coordinate (x k ,y k ) azimuth, θ k+1 is the coordinate (x k+1 ,y k+1 )’s azimuth.

[0050] Step 2: Simulate the change of target position

[0051] Based on the assumptions of the first step, the target's movement direction changes in a short period of time:

[0052]

[0053] The velocity of the target in the x direction is v x , the velocity in the y direction is v y ,but:

[0054]

[0055] According to the setting of the first step, the goal is to make uniform linear motion, so v y and v x is a constant. If Δθ k+1 -Δθ k If it approaches zero, it can be considered that the target's azimuth changes at a uniform speed. Assume that the target's motion equation in the y direction is:

[0056] y=a+bt(4)

[0057] The equation of motion in the x direction is:

[0058] x=c+dt(5)

[0059]

[0060] Among them, a, b, c, d are all constants; a, c are known coordinates of the target, b, d are the speeds in the x and y directions respectively. If the second-order derivative of formula (6) approaches zero, it proves that the orientation of the target can be approximated as uniform motion. The second-order derivative of formula (6) is obtained as follows:

[0061]

[0062] Simplifying formula (6) yields formula (7):

[0063]

[0064] At present, the maximum speed of underwater ships and other military equipment does not exceed 20m / s, so:

[0065]

[0066] The detection direction of the far field is about 1-10 kilometers, so the value of a and c is 1000-10000, so the maximum value of |da-bc| is 300000. According to the Pythagorean theorem, it can be deduced that:

[0067]

[0068] The relationship between p and x, y is as follows Figure 2 As shown above,

[0069]

[0070] Where P>1000, so θ″<0.008

[0071] From the above, we know that the change of direction in a short period of time can be considered as a uniform change.

[0072] Step 3: Azimuth prediction method:

[0073] From the second step, we can know that the change of orientation can be approximated as a uniform motion model, which is as follows:

[0074] θ(t)=α(t)+β(t)*t (11)

[0075] Among them, α(t) and β(t) are time-related coefficients. Generally, the closer the historical position data is to the current moment, the more it can represent the position information to be predicted. Therefore, α(t) and β(t) are updated in real time. The calculation formula is as follows:

[0076]

[0077] in:

[0078] Among them, γ is an empirical value that controls the size of the weight. N is an empirical value that controls the amount of data used for prediction.

[0079] Step 4: Predict target location

[0080] Set the initial position θ of the weak target α to be tracked. It is known that the target α can be detected by the peak detection method within the first k moments. k , at time k+1, the energy of the target position is lower than the detection threshold, resulting in missed detection of the target position. If the target is judged to have disappeared at this time, the relevant information of this target will be missed. The target position information is inferred by the derivation in the third step. If the target position cannot be detected at N consecutive moments, the target is judged to have disappeared; otherwise, the derived target position is used to represent the true target position, thereby improving the detection rate of weak target positions. The algorithm flow is as follows Figure 3 shown.

[0081] Step 5: Simulation data verification:

[0082] Figure 4 This is a macro effect diagram of target extraction. The initial azimuth of the extracted target is 93 degrees. From the azimuth history diagram, it can be seen that the target signal is weak, but the extraction effect is good. Figure 5 It can be seen that at the non-peak point, the prediction method is used to successfully extract the target orientation and avoid the phenomenon of target leakage.

[0083] The present invention forms a target azimuth history diagram after performing signal processing such as beamforming on the signal received by the passive sonar. In order to obtain the azimuth information of the target, the azimuth history diagram must be processed to extract the target azimuth. The conventional target azimuth extraction method is a peak detection method, but when processing weak targets, the target will be omitted from the detection threshold because of its weak energy. The purpose of the present invention is to optimize the peak detection method, predict the target azimuth when the target azimuth detection disappears temporarily, and improve the detection rate of the target azimuth.

[0084] The above description is only a description of the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention. Any changes or modifications made by a person skilled in the art in the field of the present invention based on the above disclosure shall fall within the scope of protection of the claims.

Claims

1. A method for optimizing and extracting weak target position information, characterized in that: include: Step 1: Define the scenario for far-field target detection. The target moves in a straight line along A→B→C at a uniform speed in a short period of time. The coordinates of A are (x k-1 ,y k-1 ) is the azimuth angle θ k-1 , the coordinates of B are (x k ,y k ) is the azimuth angle θ k , the coordinates of C are (x k+1 ,y k+1 ) is the azimuth angle θ k+1 ; Step 2: Based on the assumptions of the first step, the target's movement direction changes in a short period of time: The velocity of the target in the x direction is v x , the velocity in the y direction is v y ,have to: v y and v x is a constant, if Δθ k+1 -Δθ k Approaching zero, it is considered that the target's azimuth changes at a uniform speed; assuming that the target's motion equation in the y direction is: y=a+bt(4) The equation of motion in the x direction is: x=c+dt(5) Among them, a, b, c, d are all constants; a, c are known coordinates of the target, b, d are the speeds in the x and y directions respectively; if the second-order derivative of formula (6) approaches zero, it proves that the orientation of the target is approximately uniform motion, and the second-order derivative of formula (6) is obtained: Simplifying formula (6) yields formula (7): At present, the maximum speed of underwater ships and other military equipment does not exceed 20m / s, so: The detection direction of the far field is about 1-10 kilometers, so the value of a and c is 1000-10000, so the maximum value of |da-bc| is 300000; according to the Pythagorean theorem: where p 2 =x 2 +y 2 , in summary, Where P>1000, so θ"<0.008 From the above, we know that the change of direction in a short time is considered to be a uniform change; Step 3: The change in orientation obtained in the second step is approximately a uniform motion model, which is as follows: θ(t)=α(t)+β(t)*t (11) Among them, α(t) and β(t0) are time-related coefficients. Generally, the closer the historical position data is to the current moment, the better it can represent the position information to be predicted. Therefore, α(t) and β(t) are updated in real time. The calculation formula is as follows: in: Among them, γ is the empirical value, which controls the weight value; N is the empirical value that controls the amount of data used for prediction; Step 4: Set the initial position θ of the weak target α to be tracked. It is known that the target α can be detected by the peak detection method within the first k moments. k , at time k+1, the energy of the target orientation is lower than the detection threshold, resulting in missed detection of the target orientation; if the target is judged to have disappeared at this time, the relevant information of this target will be missed; the target orientation information is deduced using the derivation in the third step, and if the target orientation cannot be detected at N consecutive moments, the target is judged to have disappeared; otherwise, the derived target orientation is used to represent the true target orientation, thereby improving the detection rate of weak target orientations; Step 5: At non-peak points, the prediction method is used to successfully extract the target orientation to avoid missed target detection.

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