A bistatic sonar moving target indication method

Abstract

The application provides a bistatic sonar moving target sequential detection method, and belongs to the technical field of underwater acoustic detection. The method comprises the following steps: receiving a signal containing tonic all-wave interference sent by a bistatic sonar moving target sequential detection device and performing data processing on the received signal; using an extended Kalman filter method in target tracking to realize target detection in a bistatic sonar area; using a combined estimation value of obtained multipath time delay to calculate a likelihood ratio value, comparing the obtained likelihood ratio value with a detection threshold, and obtaining a detection result of whether a target exists. The method realizes moving target detection of a bistatic sonar system under tonic all-wave interference; compared with existing methods, the application can improve the accuracy of parameter estimation values by combining multiple received signals and target motion information, so that the detection rate of weak target signals under strong interference is improved.

Description

Technical Field

[0001] This invention belongs to the field of underwater acoustic detection technology, and more specifically, relates to a method for sequential detection of moving targets using bistatic sonar. Background Technology

[0002] When an underwater target passes through the region between the transmitter and receiver of a bistatic sonar system, the difference in acoustic propagation paths between the directly transmitted wave (the acoustic signal emitted by the transmitter without being reflected by the target) and the target-scattered wave is small. This means that when the target moves close to the transmitter-receiver connection, it is impossible to directly distinguish between the two signals on the received signal. Furthermore, the target's scattering causes the signal-to-direction ratio (the ratio of the target-scattered signal to the directly transmitted wave signal) to be below -20 dB, making target detection extremely difficult. Currently used detection methods mainly include:

[0003] Spatial signal strength enhancement method: Using the channel's own time-reflection focusing intensity as an indicator, the scattered signal reduces the focusing energy while enhancing the sidelobe energy.

[0004] Adaptive cancellation method: The adaptive cancellation principle is used to directly cancel the direct transmitted wave, thereby increasing the ratio of the scattered signal intensity to the direct transmitted wave signal intensity.

[0005] Since the energy of the direct-transmitted wave is much higher than that of the target scattered wave, the above method detects the target independently based on the signal received in each detection cycle, which does not make full use of the target's motion characteristics, and the signal parameter estimation has a deviation, resulting in a low target detection rate. Summary of the Invention

[0006] In view of this, the present invention provides a bistatic sonar sequential detection method for moving targets based on the sequential detection concept, which is used to solve the technical problems of not fully utilizing the target's motion characteristics, having biases in signal parameter estimation, and having a low target detection rate.

[0007] This invention is implemented as follows:

[0008] This invention provides a method for sequential detection of moving targets using bistatic sonar, comprising the following steps:

[0009] S10: Receive signals containing strong direct current transmission interference emitted by the bistatic sonar moving target sequential detection device and perform data processing on the received signals.

[0010] S20: Target detection within a bistatic sonar region is achieved using the extended Kalman filter method in target tracking;

[0011] S30: Calculate the likelihood ratio using the obtained multipath delay joint estimate, and compare the obtained likelihood ratio with the detection threshold to obtain the detection result of whether the target is present or not.

[0012] The bistatic sonar moving target sequential detection device includes a transmitter and a receiver, with a distance of 15km between the transmitter and the receiver. The transmitter transmits a linear frequency modulated (LFM) signal with a pulse width of 1s, a transmission period of 5s, a center frequency of 1000Hz, and a bandwidth of 100Hz.

[0013] Based on the above technical solution, the sequential detection method for bistatic sonar moving targets of the present invention can be further improved as follows:

[0014] The signal containing strong direct-transmission interference includes the direct-transmission wave signal emitted by the sound source in the bistatic sonar moving target sequential detection device and the target scattered wave signal, wherein the number of sound lines of the direct-transmission wave and the target scattered wave are 10 and 9, respectively.

[0015] Specifically, step S10 includes:

[0016] Step 1: Represent the sound source emitted signal in frequency domain form, where the frequency domain form of the sound source emitted signal is... After underwater propagation, the signal received by the receiver is in the frequency domain form of matrix X;

[0017] Step 2: The frequency domain form of the received signal is estimated using a binary hypothesis testing method for a specified number of receptions. Specifically:

[0018] Based on different hypotheses in binary hypothesis testing and The test method for the first Frequency domain form of the received signal Perform parameter estimation

[0019] Step 3: Calculate the direct-transmission wave multipath delay and the target-scattered wave multipath delay using the EM time delay estimation algorithm. Specifically:

[0020] The multipath delay of the direct-transmission wave is obtained using the EM time delay estimation algorithm. The multipath delay of the target scattered wave is The number of acoustic lines for the direct-transmitted wave and the target-scattered wave are respectively set as Tiaohe The time delay estimates can be abbreviated as follows: and .

[0021] Specifically, step S20 includes:

[0022] Step 1: Based on the Extended Kalman Filter (EKF) method, establish the state equation and observation equation for the EKF, specifically as follows:

[0023] Based on the extended Kalman filter method, the state variables of the target motion are set. and observation Establish the state equation and observation equation for the extended Kalman filter:

[0024] ; (3)

[0025] in: It is the state transition matrix, determined by the target's motion. For the observation function, The state noise matrix is ​​represented by the following: and The observation noise matrix follows the rules. ;

[0026] Step 2: Based on the known information at a specified time, use the extended Kalman filter method to obtain the state prediction equation and the predicted covariance matrix for the next time step, specifically:

[0027] according to Given the known information at time, the extended Kalman filter method is used to obtain... State prediction equation at time 1 and the predicted covariance matrix :

[0028] ; (4)

[0029] Step 3: Calculate the functional relationship between the target motion state and the multipath delay, specifically:

[0030] Due to the functional relationship between the time delay parameter and the target motion parameter It is nonlinear. According to the processing method of extended Kalman filtering, it is linearized by using the first-order Taylor formula, which requires obtaining the functional relationship. The expression is used to represent the functional relationship using the virtual source mirror method to obtain the target motion state. Estimated multipath delay The functional relationship between them.

[0031] ; (5)

[0032] Using the virtual source mirroring method to analyze functional relationships By making a linear approximation, the propagation process of the target scattered wave is divided into two segments: the transmitter-target segment and the target-receiver segment. These two segments are described using the virtual source image method.

[0033] For the launcher-target ( (segment) , assuming the number of vocal ray is . The relationship between the vocal tract travel and the target position is as follows:

[0034] (6)

[0035] For the target—the receiver ( (segment, assuming the number of vocal ray is also ) The relationship between the vocal tract travel and the target position is as follows:

[0036] (7)

[0037] , and These represent the coordinates of the transmitter, target, and receiver, respectively. The selection of the number of these two sound ray segments follows... The principle is to ensure that the dimensions of the matrix are consistent. In shallow seas, the sound velocity gradient does not change significantly; therefore, when calculating multipath delay, the sound velocity can be set to a constant value. Therefore, the multipath delay can be expressed as:

[0038] ; (8)

[0039] Therefore, the relationship between the target motion state and the multipath delay is as follows:

[0040] (9)

[0041] Find the observation function The Jacobian matrix is ​​the observation matrix. .

[0042] ; (10)

[0043] Step 4: Calculate the prediction and Kalman gain of the observations, specifically by calculating the prediction of the observations. and Kalman gain The calculation formula is:

[0044] ; ; (11)

[0045] Step 5: Update the observations, using the updated observations to represent the joint multipath time delay estimate obtained by combining target motion information. Specifically:

[0046] In obtaining Observations of time Afterwards, the updated state value is obtained through the update process. And error covariance update matrix .

[0047] ; ;(12)

[0048] Among the observed update values This represents the joint estimate of multipath time delay obtained by combining target motion information.

[0049] Specifically, step S30 includes:

[0050] Step 1: Use the joint estimate of multipath delay as the parameter estimate in generalized likelihood ratio detection, where the joint estimate of multipath delay is expressed as: ;

[0051] Step 2: Calculate the likelihood ratio using the obtained parameter estimates and the likelihood function. Specifically, this involves using the hypothesis... and The likelihood function is as follows: , The likelihood ratio was calculated. ,in

[0052] ; (13)

[0053] Step 3: Compare the obtained likelihood ratio with the detection threshold to obtain the detection result of whether the target is present or not, specifically:

[0054] After simplifying equation (13), the test statistic is obtained. Compare it with the corresponding detection threshold Compare and determine whether the target exists.

[0055] ;(14)

[0056] Where the matrix , It depends only on the multipath delay of the directly transmitted wave and the target scattered wave, respectively, and:

[0057] ; (15)

[0058] yes Projection matrix in space .

[0059] Compared with existing technologies, the beneficial effects of the sequential detection method for bistatic sonar moving targets provided by this invention are as follows: It adopts the algorithm used in target tracking and proposes a sequential detection algorithm based on extended Kalman filtering, realizing the detection of moving targets in a bistatic sonar system under strong direct current interference. Compared with existing methods, this invention can improve the accuracy of parameter estimation by combining multiple received signals and target motion information, thereby increasing the detection rate of weak target signals under strong interference. Attached Figure Description

[0060] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 A flowchart of a bistatic sonar sequential detection method for moving targets provided by the present invention;

[0062] Figure 2 This is a schematic diagram of the geometry of a bistatic sonar.

[0063] Figure 3 The change in signal-to-direct ratio during the motion;

[0064] Figure 4 Extended Kalman filter flowchart;

[0065] Figure 5 This is a schematic diagram of the virtual source mirroring method;

[0066] Figure 6 This is a schematic diagram of the test results;

[0067] Figure 7 The detection performance curves for the two detection methods are shown. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0070] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0071] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0072] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0073] like Figure 1 The diagram shown is a flowchart of a bistatic sonar sequential detection method for moving targets provided by the present invention. Specific embodiments of this method are described below:

[0074] Bistatic sonar sequential detection device for moving targets, such as Figure 2 As shown, the configuration is as follows: the transmitted signal is a linear frequency modulated (LFM) signal with a pulse width of 1s, a transmission period of 5s, a center frequency of 1000Hz, a bandwidth of 100Hz, a transmit / receive distance of 15km, and the coordinates of the transmitting end are... The coordinates of the receiving end are The underwater target is an elongated ellipsoid, at a depth of [missing information]. m. The target is Motion occurs on a plane, with coordinates as follows: The target's initial position coordinates are (8000, 500, 50). During the 20th transmission cycle (i.e., between 95 and 100 seconds), the target moves to the transmit / receive baseline, with coordinates (7500, 0, 50). During the 40th transmission cycle (i.e., at 200 seconds), the target stops moving, with coordinates (7000, -500, 50). The signal-to-weight ratio changes during the target's movement are as follows: Figure 3 As shown.

[0075] This method includes the following steps:

[0076] S10: Receive signals containing strong direct current transmission interference emitted by the bistatic sonar moving target sequential detection device and perform data processing on the received signals;

[0077] S20: Target detection within a bistatic sonar region is achieved using the extended Kalman filter method in target tracking;

[0078] S30: Calculate the likelihood ratio using the obtained multipath delay joint estimate, and compare the obtained likelihood ratio with the detection threshold to obtain the detection result of whether the target is present or not.

[0079] The specific steps of S10 are as follows:

[0080] Based on different hypotheses in the binary hypothesis test and The test method for the first The received signal is in frequency domain form. Parameter estimation is performed. The EM time delay estimation algorithm is used to obtain the direct-transmission wave multipath delay. The multipath delay of the target scattered wave is The number of acoustic lines for the direct-transmitted wave and the target-scattered wave are set to 10 and 9, respectively. Therefore, the time delay estimates can be simplified as follows: and .

[0081] The specific steps of S20 are as follows:

[0082] Using the estimated time delay of the scattered target wave as input The target motion parameters are state variables. Target detection within a bistatic sonar region is achieved using the Extended Kalman Filter (EKF) method in target tracking. The Extended Kalman Filter method is as follows: Figure 4 As shown.

[0083] Step 1: Based on the extended Kalman filter method, set the state variables of the target motion. and observation Establish the state equation and observation equation for the extended Kalman filter:

[0084] ;

[0085] Step 2: According to Given the known information at time, the extended Kalman filter method is used to obtain... State prediction equation at time 1 and the predicted covariance matrix :

[0086] ; ;

[0087] Step 3: Due to the functional relationship between the time delay parameter and the target motion parameter It is nonlinear. According to the processing method of extended Kalman filtering, it is linearized by using the first-order Taylor formula, which requires obtaining the functional relationship. This patent uses the virtual source mirror method to represent the functional relationship and obtain the target motion state. Estimated multipath delay The functional relationship between them.

[0088] ;

[0089] The principle diagram of the virtual source mirroring method is as follows: Figure 5 As shown, the virtual source mirror method is used to analyze the functional relationship. By making a linear approximation, the propagation process of the target scattered wave is divided into two segments: the transmitter-target segment and the target-receiver segment. These two segments are described using the virtual source image method.

[0090] For the launcher-target ( If the number of sound ray segments is 3, then the relationship between the sound ray travel and the target position is:

[0091] ;

[0092] For the target—the receiver ( If the number of vocal ray segments is also 3, then the relationship between the vocal ray travel and the target position is:

[0093] ;

[0094] The number of sound rays in both segments is 3, so the total number of sound rays reaching the receiver from the transmitter after being scattered by the target is 3. strip.

[0095] Since the acoustic gradient does not change significantly in shallow seas, the sound velocity can be set to a constant value when calculating multipath delay. m / s, therefore the multipath delay can be expressed as:

[0096] ;

[0097] Therefore, the relationship between the target motion state and the multipath delay is as follows:

[0098] ;

[0099] Find the observation function The Jacobian matrix is ​​the observation matrix. .

[0100] ;

[0101] Step 4: Calculate the predicted value of the measurement. and Kalman gain .

[0102] ; ;

[0103] Step 5: After obtaining Observations of time Then, the updated state value is obtained. And error covariance update matrix .

[0104] ; ;

[0105] Among the observed update values This indicates that the accuracy of the multipath time delay joint estimate obtained by combining target motion information is improved to a certain extent.

[0106] The specific steps for S30 are as follows:

[0107] The multipath delay estimate obtained by the sequential detection method As parameter estimates in generalized likelihood ratio detection, the hypothesis is used. and The likelihood function is as follows: , Calculate the likelihood ratio. Then with the detection threshold The results are compared to obtain the detection results of whether the target is present or not.

[0108] ;

[0109] After substituting the parameter estimates into the formula, the test statistic is calculated. The value, and the corresponding detection threshold. Compare and determine whether the target exists.

[0110] ;

[0111] Test results as follows Figure 6 As shown, the vertical axis "1" indicates that a target has been detected, represented by "". The vertical axis "0" indicates that no target was detected. This indicates that, within 40 detection cycles of a target passing through the bistatic sonar detection area, the sequential target detection method can detect moving targets, where the combination of... Figure 2 The change in the signal-to-direction ratio shows that: a higher signal-to-direction ratio ( When the signal-to-weight ratio is low, this sequential detection method has a high detection probability of the target, while when the signal-to-weight ratio is low... This method also has good detection performance when used in conjunction with other methods.

[0112] When the number of Monte Carlo runs is 1000, the resulting detection performance curve is as follows: Figure 7 As shown, the traditional method is a target detection method based on generalized likelihood ratio. It can be seen that compared with the traditional method, the sequential target detection method improves the detection rate by about 5%, indicating that the method has a certain performance improvement. However, it can also be seen that when the signal-to-noise ratio continues to decrease, the detection performance of this method will gradually decrease.

[0113] This method has achieved certain results in simulation experiments, and its advantages compared with traditional methods are:

[0114] 1) This sequential detection method can achieve weak target detection under strong interference in environments with low signal-to-noise ratio and low signal-to-sound ratio, with a detection rate of 0.85 within the detection cycle.

[0115] 2) The sequential detection method incorporates the target's motion information, and its detection performance is improved by approximately 5% compared to traditional methods.

[0116] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for sequential detection of moving targets using bistatic sonar, characterized in that, Includes the following steps: S10: Receive signals containing strong direct current transmission interference emitted by the bistatic sonar moving target sequential detection device and perform data processing on the received signals. S20: Target detection within a bistatic sonar region is achieved using the extended Kalman filter method in target tracking; S30: The likelihood ratio is calculated using the joint estimate of multipath delay. The obtained likelihood ratio is compared with the detection threshold to obtain the detection result of whether the target is present or not. The bistatic sonar moving target sequential detection device includes a transmitter and a receiver, with a distance of 15km between the transmitter and the receiver. The transmitter transmits a linear frequency modulated (LFM) signal with a pulse width of 1s, a transmission period of 5s, a center frequency of 1000Hz, and a bandwidth of 100Hz. Step S20 specifically includes: Step 1: Based on the Extended Kalman Filter (EKF) method, establish the state equation and observation equation for the EKF, specifically as follows: Based on the extended Kalman filter method, the state variables of the target motion are set. and observation , This represents an estimate of the multipath delay. Establish the state equation and observation equation for the extended Kalman filter: ； ；（3） in: It is the state transition matrix, determined by the target's motion. For the observation function, The state noise matrix is ​​represented by the following: and The observation noise matrix follows the rules. ; Step 2: Based on the known information at a specified time, use the extended Kalman filter method to obtain the state prediction equation and the predicted covariance matrix for the next time step, specifically: according to Given the known information at time, the extended Kalman filter method is used to obtain... State prediction equation at time 1 and the predicted covariance matrix : ； ； （4） Step 3: Calculate the functional relationship between the target motion state and the multipath delay, specifically: Due to the functional relationship between the time delay parameter and the target motion parameter It is nonlinear. According to the processing method of extended Kalman filtering, it is linearized by using the first-order Taylor formula, which requires obtaining the functional relationship. The expression is used to represent the functional relationship using the virtual source mirror method to obtain the target motion state. Estimated multipath delay The functional relationship between them; ； （5） Using the virtual source mirroring method to analyze functional relationships By making a linear approximation, the propagation process of the target scattered wave is divided into two segments: the transmitter-target segment and the target-receiver segment. These two segments are described using the virtual source image method. For the launcher-target ( (segment) , assuming the number of vocal ray is . The relationship between the vocal tract travel and the target position is as follows: ； ； （6） For the target—the receiver ( (segment) , assuming the number of vocal ray is . H d Represents water depth; The relationship between the vocal tract travel and the target position is as follows: ； ； （7） , and These represent the coordinates of the transmitter, target, and receiver, respectively; the selection of the number of these two sound ray segments follows... The principle is to ensure that the dimensions of the matrix are consistent; N is the total number of sound rays that reach the receiver from the transmitter after being scattered by the target. In shallow seas, the sound velocity gradient does not change significantly, so when calculating multipath delay, the sound velocity is assumed to be constant. Therefore, the multipath delay is expressed as: ；（8） Therefore, the relationship between the target motion state and the multipath delay is as follows: ； （9） Find the observation function The Jacobian matrix is ​​the observation matrix. : ；（10） Step 4: Calculate the prediction and Kalman gain of the observations, specifically by calculating the prediction of the observations. and Kalman gain The calculation formula is: ； ； （11） Step 5: Update the observations, using the updated observations to represent the joint multipath time delay estimate obtained by combining target motion information. Specifically: In obtaining Observations of time Afterwards, the updated state value is obtained through the update process. And error covariance update matrix : ； ； ；（12） Among the observed update values This represents the joint estimate of multipath time delay obtained by combining target motion information.

2. The sequential detection method for bistatic sonar moving targets according to claim 1, characterized in that, The signals containing strong direct-transmission interference include the direct-transmission signal emitted by the sound source in the bistatic sonar moving target sequential detection device and the target scattered wave signal.

3. The sequential detection method for bistatic sonar moving targets according to claim 2, characterized in that, The number of acoustic lines for the direct-transmitting wave and the target-scattered wave are 10 and 9, respectively.

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