Method for detecting echo signals of fish finding device, fish finding device and storage medium

Abstract

The application provides a fish finder echo signal detection method, a fish finder and a storage medium. The method comprises the following steps: S1, receiving an echo signal; S2, performing Kalman filtering on the echo signal to obtain a target frequency signal; S3, calculating the noise energy A of the target frequency signal, and identifying a signal segment formed by signal points with an amplitude smaller than a small signal threshold in the target frequency signal as a small signal data segment; S4, performing IIR band-pass filtering processing on the small signal data segment; S5, adjusting the small signal data segment after the IIR band-pass filtering; S6, re-splicing the adjusted small signal data segment and other signal data segments of the target frequency signal to form a complete signal; and S7, performing Kalman filtering processing on the complete signal formed by splicing again to obtain an image signal to be displayed. By using the technical scheme, specific frequency components in the underwater acoustic signal are effectively extracted, and signal deformation caused by the filtering process is avoided.

Description

Methods for detecting echo signals from fish finders, fish finders and their storage media Technical Field

[0001] This invention relates to the field of underwater acoustic signal processing technology, and in particular to a method for detecting echo signals from a fish finder, the fish finder itself, and a storage medium. Background Technology

[0002] With the development of marine exploration and development, the problem of sensing and transmitting information about underwater targets has received increasing attention. Underwater acoustics is the science of information transmission and processing in the ocean. It primarily studies the radiation, transmission, and reception of sound waves underwater to solve various acoustic problems related to underwater target detection and information transmission. The spatiotemporal structure of the sound field formed by sound waves as information carriers in the ocean has become a fundamental research area in modern underwater acoustics. Extracting the spatiotemporal structure of the sound field information in the ocean is a means we use for underwater detection, identification, communication, and environmental monitoring, such as long-range target detection, marine resource development, and fish school detection.

[0003] Accurate and timely assessment of fish resources and quantities is of great significance for fisheries management, production, resource development, and related industries. Fish finding equipment, such as fish finders, is an important means of detecting aquatic biological resources and is widely used in fisheries resource development. Fish finding equipment, such as ultrasonic fish finders, consists of a display unit, ultrasonic sensors, and other components. It mainly utilizes the principles of ultrasonic wave emission, reflection, and reception. Currently, the application of sonar technology for fish resource assessment is quite common internationally. Using ultrasound as a detection method requires both generating and receiving ultrasonic waves. The device that performs this function is the ultrasonic sensor, conventionally called an ultrasonic transducer. When the fish finder is working, the ultrasonic sensor first emits a signal, which propagates in the water. When the emitted signal encounters a medium different from water, such as a solid or gas, a portion of the signal is reflected back. The reflected signal is filtered and input into the display unit for analysis and processing.

[0004] Currently, various filters are commonly used to separate frequencies and reduce noise in the acoustic echoes (reflected signals) emitted by fish finders, in order to clearly display the position and shape of underwater objects in the image. Among these techniques, frequently used filters include bandpass filters and low-pass filters. These filters can effectively separate signals of specific frequencies and reduce noise, but for strong signals, using these filters may cause severe distortion of the signal shape. Furthermore, for weak signals against a noisy background, these techniques often struggle to extract useful information. Summary of the Invention

[0005] To address at least one of the aforementioned problems in the prior art, embodiments of the present invention provide a method for detecting echoes from a fish finder, a fish finder, and a storage medium.

[0006] To achieve the above objectives, on the one hand, a method for detecting echo signals from a fish finder is provided, wherein the fish finder includes an ultrasonic sensor, and the method includes:

[0007] S1, receive echo signals from the ultrasonic sensor;

[0008] S2, perform Kalman filtering on the echo signal to obtain the target frequency signal;

[0009] S3, calculate the noise energy A of the target frequency signal, set a small signal threshold based on the noise energy A, and identify the signal segment composed of signal points in the target frequency signal with amplitudes less than the small signal threshold as a small signal data segment;

[0010] S4, perform IIR bandpass filtering on the small signal data segment;

[0011] S5, Based on the change in noise energy of the small signal data segment before and after IIR bandpass filtering, adjust the small signal data segment after IIR bandpass filtering so that the noise energy of the small signal data segment before and after IIR bandpass filtering is consistent;

[0012] S6, reassemble the signal data segments of the target frequency signal, excluding the small signal data segment, and the adjusted small signal data segment to form a complete signal;

[0013] S7. Perform Kalman filtering on the complete signal formed by splicing to obtain the image signal to be displayed.

[0014] Preferably, in the detection method, the noise energy A is set as a predetermined multiple as a small signal threshold for identifying small signal data segments.

[0015] Preferably, in the detection method, five times the noise energy A is set as the small signal threshold.

[0016] Preferably, in the detection method, the target frequency is 9.6 kHz.

[0017] Preferably, in the detection method, a sliding window is used to calculate the noise energy of the target frequency signal or the small signal data segment.

[0018] Preferably, in the detection method, calculating the noise energy of the target frequency signal or the small signal data segment using a sliding window includes:

[0019] The sliding window starts from the end of the target frequency signal or the small signal data segment and moves towards the beginning of the target frequency signal or the small signal data segment;

[0020] For each window, calculate the variance of the signal segment within that window;

[0021] When the first signal segment that satisfies a variance less than a predetermined variance threshold is found, the average value of the amplitude of each signal point in the signal segment is calculated, and the average value is used as the noise energy of the target frequency signal or the small signal data segment.

[0022] Preferably, in the detection method, the IIR bandpass filter is a fourth-order IIR filter with a bandwidth of 1 kHz.

[0023] Preferably, in the detection method, step S5, adjusting the small signal data segment after IIR bandpass filtering based on the change in noise energy of the small signal data segment before and after IIR bandpass filtering, includes:

[0024] Calculate the noise energy A1 of the small signal data segment before IIR bandpass filtering and the noise energy B1 of the small signal data segment after IIR bandpass filtering;

[0025] The amplitude of each signal point in the small signal data segment after IIR bandpass filtering is multiplied by A1 / B1.

[0026] On the other hand, a fish finder is provided, including an ultrasonic sensor, which further includes a memory and a processor, the memory storing at least one program, the at least one program being executed by the processor to implement the detection method as described above.

[0027] In another aspect, a computer-readable storage medium is provided, wherein the storage medium stores at least one program that is executed by a processor to implement the detection method as described in any of the above descriptions.

[0028] The above technical solution has the following technical effects:

[0029] This invention employs Kalman filtering to process the echo signal, optimizing signals at specific frequencies and extracting small-signal data segments while maintaining signal quality. Since IIR bandpass filters can introduce severe signal distortion into large signals, this invention ensures image quality and accuracy by specifically isolating small signals for IIR bandpass filtering. Furthermore, when the noise energy of the small-signal data segments changes before and after bandpass filtering, noise energy adjustment is performed on the bandpass-filtered small-signal data segments to ensure consistency within the overall data packet, preventing abrupt image distortion. Therefore, using the technical solution of this invention, specific frequency components in underwater acoustic signals are effectively extracted through Kalman filtering and IIR bandpass filtering, while simultaneously avoiding signal distortion introduced by the filtering process. Attached Figure Description

[0030] Figure 1 is a flowchart illustrating a method for detecting echo signals from a fish finder according to an embodiment of the present invention. Detailed Implementation

[0031] To further illustrate the various embodiments, the present invention provides accompanying drawings. These drawings are part of the disclosure of the present invention, primarily used to illustrate the embodiments and to explain the operating principles of the embodiments in conjunction with the relevant descriptions in the specification. With reference to these drawings, those skilled in the art should be able to understand other possible implementations and the advantages of the present invention. Components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0032] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0033] Example 1:

[0034] Figure 1 is a flowchart illustrating a method for detecting echo signals from a fish finder according to an embodiment of the present invention. As shown in Figure 1, the method for detecting echo signals from a fish finder in this embodiment includes the following steps:

[0035] S1, receives echo signals from the ultrasonic sensor;

[0036] After receiving the echo signal from the ultrasonic sensor, it is usually sampled by an analog-to-digital converter (ADC) to convert the analog echo signal into a digital echo signal.

[0037] S2, perform Kalman filtering on the echo signal to obtain the target frequency signal;

[0038] A Kalman filter is used to extract the effective signal from the echo signal, for example, at a target frequency of 9.6 kHz. This step reduces the influence of noise and extracts the most dominant frequency components. Specifically, this step includes:

[0039] a. Set the relevant frequency parameters, including: sampling frequency, sampling period, and target frequency; and calculate the target angular frequency;

[0040] b. Initialize the Kalman filter, including: initializing the state estimation vector as a zero vector and setting the state covariance matrix as an identity matrix; setting the process noise covariance matrix as an identity matrix and the measurement noise variance as 1; defining the state transition matrix as a rotation matrix with a rotation angle equal to the target angular frequency multiplied by the sampling period; wherein the measurement matrix is ​​defined as a vector [1 0].

[0041] c. Signal processing using Kalman filters, including:

[0042] Prediction phase: Predict the state based on the state transition matrix, and update the state covariance based on the prediction results.

[0043] Update phase: Calculate the prediction error and obtain the prediction error covariance, then calculate the Kalman gain; based on the above parameters, update the state estimate and state covariance.

[0044] Signal amplitude calculation: Calculate the signal amplitude at the current moment using the latest state estimate.

[0045] The detailed method for using a Kalman filter to perform Kalman filtering on a signal is a prior art method. This invention does not intend to protect the Kalman filtering algorithm itself, so it will not be described in detail here.

[0046] S3, calculate the noise energy A of the target frequency signal, set a small signal threshold based on the noise energy A, and identify the signal segment composed of signal points in the target frequency signal with an amplitude less than the small signal threshold as a small signal data segment;

[0047] Preferably, a predetermined multiple of noise energy A is set as the small signal threshold for identifying small signal data segments; preferably, 5 times the noise energy A is set as the small signal threshold.

[0048] In one specific implementation, a sliding window is used to calculate the noise energy of the target frequency signal extracted after Kalman filtering.

[0049] In one specific implementation, calculating the noise energy of a target frequency signal using a sliding window includes: moving the sliding window from the tail of the target frequency signal towards the head; calculating the variance of the signal segment within each window; and when the first signal segment with a variance less than a predetermined variance threshold is found, calculating the average amplitude of each signal point in that segment, and using this average as the noise energy A of the target frequency signal. Generally, in the absence of a valid signal, the noise variance is usually less than 0.05; therefore, in one specific implementation, the variance threshold is set to 0.05; in other implementations, depending on the specific scenario, the variance threshold can be set to other selected appropriate values.

[0050] S4, perform IIR bandpass filtering on the above small signal data segment;

[0051] In this step, an IIR bandpass filter is used to process the identified small signal data segments to further extract the effective signals from these segments. Preferably, a fourth-order IIR filter with a bandwidth of 1kHz is used to ensure that the IIR bandpass filter does not cause excessive image distortion.

[0052] S5. Based on the change in noise energy of the small signal data segment before and after IIR bandpass filtering, adjust the small signal data segment after IIR bandpass filtering so that the noise energy of the small signal data segment before and after IIR bandpass filtering is consistent.

[0053] This step maintains the consistency of the entire signal, i.e., the data packet, before and after IIR bandpass filtering, avoiding abrupt changes in the image.

[0054] Specifically, the steps include:

[0055] Calculate the noise energy A1 of the small signal data segment before IIR bandpass filtering and the noise energy B1 of the small signal data segment after IIR bandpass filtering; in one specific implementation, the same sliding window method as the aforementioned calculation of the target frequency signal noise energy is used to calculate the noise energy A1 and B1 of the small signal segment;

[0056] Multiply the amplitude of each signal point in the small signal data segment after IIR bandpass filtering by A1 / B1, which is the ratio of the noise energy before IIR bandpass filtering to the noise energy after filtering. The noise energy of the small signal data segment after adjustment will be equal to the noise energy before IIR bandpass filtering.

[0057] S6, reassemble the large signal data segment (excluding the small signal data segment) and the adjusted small signal data segment in the target frequency signal to form a complete signal, i.e., a complete data packet.

[0058] For example, assuming the target frequency signal has 1000 points, and the [300:400] points are identified as small signals, then the [300:400] small signal data segment is subjected to the above IIR bandpass filtering and adjustment steps separately. Then, the original [1:299], the adjusted [300:400], and the [401:1000] signal points are spliced ​​together to form a complete signal or data packet.

[0059] S7. Perform Kalman filtering on the complete signal formed by the above splicing to obtain the image signal to be displayed, that is, the final image to be displayed by the fish finder.

[0060] Example 2:

[0061] The present invention also provides a fish finder, which includes an ultrasonic sensor, a processor, a memory, and a computer program stored in the memory and executable on the processor. The processor includes one or more processing cores, and the memory is connected to the processor and is used to store program instructions. When the processor executes the program, it implements the steps in the detection method of the above embodiments of the present invention.

[0062] Furthermore, as an executable solution, the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The processor is the control center of the computer unit, connecting various parts of the entire computer unit via various interfaces and lines.

[0063] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the computer unit by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0064] Example 3:

[0065] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the detection method described above in the embodiments of the present invention.

[0066] If the modules / units integrated in the computer unit are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0067] Although the invention has been specifically shown and described in conjunction with preferred embodiments, those skilled in the art should understand that various changes in form and detail may be made to the invention without departing from the spirit and scope of the invention as defined in the appended claims, all of which shall be within the scope of protection of the invention.

Claims

1. A method for detecting echo signals from a fish finder, the fish finder comprising an ultrasonic sensor, characterized in that, include: S1, receive the echo signal from the ultrasonic sensor; S2, perform Kalman filtering on the echo signal to obtain the target frequency signal; S3, calculate the noise energy A of the target frequency signal, set a small signal threshold based on the noise energy A, and identify the signal segment composed of signal points in the target frequency signal with amplitudes less than the small signal threshold as a small signal data segment; S4, perform IIR bandpass filtering on the small signal data segment; S5, adjust the small signal data segment after IIR bandpass filtering based on the change in noise energy of the small signal data segment before and after IIR bandpass filtering, so that the noise energy of the small signal data segment is consistent before and after IIR bandpass filtering; S6, reassemble the signal data segments of the target frequency signal excluding the small signal data segment and the adjusted small signal data segment to form a complete signal; S7, perform Kalman filtering on the spliced ​​complete signal again to obtain the image signal to be displayed.

2. The detection method according to claim 1, characterized in that, The noise energy A, which is a predetermined multiple, is set as the small signal threshold for identifying small signal data segments.

3. The detection method according to claim 2, characterized in that, Set the noise energy A by 5 times as the small signal threshold.

4. The detection method according to claim 1, characterized in that, The target frequency is 9.6 kHz.

5. The detection method according to claim 1, characterized in that, A sliding window is used to calculate the noise energy of the target frequency signal or the small signal data segment.

6. The detection method according to claim 5, characterized in that, Calculating the noise energy of the target frequency signal or the small signal data segment using a sliding window includes: moving the sliding window from the tail of the target frequency signal or the small signal data segment toward the head of the target frequency signal or the small signal data segment; calculating the variance of the signal segment within each window; and when the first signal segment whose variance is less than a predetermined variance threshold is found, calculating the average amplitude of each signal point in the signal segment, and using the average value as the noise energy of the target frequency signal or the small signal data segment.

7. The detection method according to claim 1, characterized in that, The IIR bandpass filtering is performed using a fourth-order IIR filter with a bandwidth of 1 kHz.

8. The detection method according to claim 1, characterized in that, In step S5, adjusting the small signal data segment after IIR bandpass filtering based on the change in noise energy before and after IIR bandpass filtering includes: calculating the noise energy A1 of the small signal data segment before IIR bandpass filtering and the noise energy B1 of the small signal data segment after IIR bandpass filtering; and multiplying the amplitude of each signal point in the small signal data segment after IIR bandpass filtering by A1 / B1.

9. A fish finder, comprising an ultrasonic sensor, characterized in that, It also includes a memory and a processor, the memory storing at least one program, which is executed by the processor to implement the detection method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one program, which is executed by a processor to implement the detection method as described in any one of claims 1 to 8.

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