A millimeter-wave radar-based device for detecting fishing floats at night and its usage.

Abstract

This invention discloses a night fishing float detection device based on millimeter-wave radar and its usage method, particularly relating to a night fishing float detection device based on millimeter-wave radar. The device includes a millimeter-wave radar sensor, a computing module, a communication module, an information indicator and button module, and a power supply module. The millimeter-wave radar sensor is used to transmit and receive millimeter-wave wireless signals, specifically a 77GHz wireless signal using frequency-modulated continuous wave. This millimeter-wave sensor can output a point cloud of the detected environment, which includes information on water surface ripples, float information, and noise information. This invention solves the problems of existing night fishing devices, such as "inconvenient to carry, requiring head-mounted equipment and high concentration, and still poor lighting conditions."

Description

Technical Field

[0001] This invention relates to the field of night fishing technology, and in particular to a night fishing float detection device based on millimeter-wave radar and its usage method. Background Technology

[0002] As people's living standards improve, their demand for fishing is increasing. Fishing combines entertainment and exercise, pleasing the mind and body, improving concentration, and enhancing physical fitness and skills, making it popular among many. Night fishing, as one of the most beloved methods among fishing enthusiasts, is becoming increasingly popular. However, night fishing has the following problems:

[0003] 1. Poor visibility, as it is nighttime and visibility on the water is poor; 2. Night fishing makes it difficult to concentrate and is particularly physically demanding, and you can easily lose fish if you are not careful.

[0004] The main current solutions to the above problems are: 1. Using visible / invisible light as lighting equipment, but this is inconvenient to carry, requires head-mounted devices, and still requires high concentration. 2. Using glow-in-the-dark devices, but the light is still not good.

[0005] To address the problems existing in night fishing, this invention proposes a night fishing float detection device based on millimeter-wave radar and its usage method. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a night fishing float detection device based on millimeter-wave radar and its usage method to solve the technical problems in the prior art.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution: a night fishing float detection device based on millimeter-wave radar, comprising a millimeter-wave radar sensor, a computing module, a communication module, an information indicator and button module, and a power supply module, characterized in that the millimeter-wave radar sensor is used to transmit and receive millimeter-wave wireless signals, specifically 77GHz wireless signals, employing FMCW (Frequency Modulated Continuous Wave), and the millimeter-wave sensor can output a point cloud of the detection environment, which includes water surface ripple information, float information, noise information, etc.

[0008] The computing module further analyzes and processes the point cloud data obtained from the sensor, separates the float information, and judges the float state. It compares the result with the preset float phase model to determine the size of the fish that has taken the bait. The computing module uses an ESP32 module, which has certain computing and storage capabilities.

[0009] The communication module is used to send information about the appearance of the fish float, and it is sent using Bluetooth broadcast mode.

[0010] The information indicator and button module uses three LED lights and one button to set and display the device status. The device status includes: fishing adaptation, adaptation successful, and fish biting the hook.

[0011] The power module is used to provide power.

[0012] This invention also discloses a method for using a millimeter-wave radar-based night fishing float detection device, comprising the following steps:

[0013] S1: Millimeter-wave radar power-on startup. This radar uses pulse-frequency modulated continuous wave millimeter-wave radar. During operation, parameters such as transmit power, transmit bandwidth, operating time, fast time, and slow time need to be set. After power-on startup, the radar parameters are first configured, including setting the transmit power and transmit bandwidth. Once set, the radar begins operation, transmitting modulated radio waves through the transmitting antenna. The receiving antenna begins operation, receiving the transmitted radio waves and obtaining the intermediate frequency (IF) signals from the transmitted and received signals through a frequency differencer. Then, the received IF signals are digitally quadratured to form corresponding IQ channel data, obtaining the IF sample signal for one complete frequency modulation cycle of the radar, called a chirp.

[0014] S2: Filtering is performed based on the detected water surface ripples. The process is as follows:

[0015] A: Perform a Fast Fourier Transform (FFT) to obtain the frequency domain distribution of the data. By performing a FFT on the data of a chirp, the frequency domain information of that chirp can be obtained (satisfying the maximum sampling theorem). Each frequency corresponds to a distance, i.e., R = C / 4 * Tc / B * f, where C is the speed of light, Tc is the operating time (i.e., the frequency modulation operating time), B is the frequency modulation bandwidth, and f is the frequency data obtained by the Fourier transform.

[0016] B: Frequency filtering is performed based on the set detection location range. Because water waves are continuous waves, they can be considered relatively stationary within the frequency modulation time of millimeter-wave radar. Based on the set detection distance range, the maximum and minimum frequencies corresponding to the distance of the millimeter-wave radar signal can be obtained. The Fourier transform data is then filtered and eliminated, i.e., data outside the frequency range is assigned a value of 0.

[0017] C: Obtain the water surface ripple frequency, extract the phase data of the Fourier transform results corresponding to multiple consecutive chirps, perform a Fourier transform on the phase data of each frequency group, and obtain the frequency corresponding to the maximum energy value in the second Fourier transform result. Then, obtain the maximum and minimum frequencies of these frequency sets. Halve the energy of these maximum and minimum frequencies in the result of the first Fourier transform, i.e., adjust the corresponding energy to half of the original value. Because the float also vibrates with the water ripples, half of the frequency energy value is retained here.

[0018] D: Perform an inverse Fourier transform on the filtered Fourier transform result to obtain a new time-series signal;

[0019] S3: Near-shore debris filtering, i.e., detection and filtering of static or low-speed objects on the shore. First, a RangFFT operation is performed on the acquired time-series data to obtain the distance data corresponding to the debris. Then, a DopplerFFT operation is used to obtain the velocity v of the debris. If v is less than the set velocity of a static object, the debris is considered stationary and can be filtered. The energy to be filtered is set to 0 in frequency, and then converted into time-series data through an inverse Fourier transform.

[0020] S4: Fishing rod and line detection and filtering. The influence of fishing rods and lines on millimeter-wave radar signals is partly filtered out during debris filtering, partly filtered out during surface filtering, and the majority of the remaining influence comes from the fishing rod itself. This invention, based on RangFFT, filters for consecutive distance start and end points. If the distance between the start and end points exceeds a preset detectable fishing rod length, the corresponding frequencies are knocked out, and the knocked-out frequency domain data is converted to frequency domain data.

[0021] S5: Perform a RangFtt operation on the filtered data. The distance to the float with the highest energy is then determined. Next, perform a DopplerFFT at that distance to obtain the velocity at that position. Simultaneously, save the position and velocity as position and velocity arrays, respectively. Then, perform Fourier analysis on the position and velocity arrays to obtain the float's vibration data. The vibration data includes: position information, velocity information, position change frequency, and velocity change frequency. Since the vibration data reflects the float's movement and spatial position on the water surface, it can be considered equivalent to the float's phase data. The data is saved at a slow time interval and is updated regularly. If the initial float data is empty, the initial float phase data is set by averaging the vibration data 10 times (i.e., averaging the position and velocity change frequencies).

[0022] S6: Compare the obtained float vibration data with the initial phase data to calculate the difference. A represents the position difference, B represents the velocity difference, C represents the position frequency change difference, and D represents the velocity frequency change difference. The total change state is denoted as: T = aA + bB + cC + dD, where a, b, c, and d are the important coefficients of the corresponding difference, which are actually 0.2, 0.2, 0.3, and 0.3.

[0023] S7: If T is greater than the preset change state, it indicates a change in the fish's position, and there is a high probability that a fish has taken the bait. An alert sound will be emitted, and this information will be sent to the app via Bluetooth. Otherwise, continue the judgment process.

[0024] This invention provides a night fishing float detection device based on millimeter-wave radar and its usage method, which has the following beneficial effects:

[0025] The present invention solves the problems of existing night fishing devices that are "inconvenient to carry, require head-mounted equipment and require a high degree of concentration, and the light is still poor". Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating the usage method of the night fishing float detection device based on millimeter-wave radar proposed in this invention. Detailed Implementation

[0027] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0028] Example

[0029] refer to Figure 1This invention provides a technical solution: a night fishing float detection device based on millimeter-wave radar, comprising a millimeter-wave radar sensor, a computing module, a communication module, an information indicator and button module, and a power module. The millimeter-wave radar sensor is used to transmit and receive millimeter-wave wireless signals, specifically a 77GHz wireless signal using FMCW (Frequency Modulated Continuous Wave). This millimeter-wave sensor can output a point cloud of the detected environment, which includes water surface ripple information, float information, noise information, etc. The computing module further analyzes and processes the point cloud data obtained from the sensor, separates the float information, determines the float state, and compares it with a preset float phase model to determine the size of the fish that has taken the bait. The system utilizes an ESP32 module, providing some computing and storage capabilities. The communication module transmits information about the float's position using Bluetooth broadcast mode. The float's position refers to the number of segments visible above the water after the float enters the water. Generally, float adjustments are performed with an empty hook, and the adjustment should be done in half-water, meaning the distance from the bottom of the hook to the last segment of the float should be less than the water depth. This is achieved by adding or subtracting lead weights to adjust the float to the desired number of segments. The information indicator and button module uses three LEDs and one button to set and display the device's status, including: fishing compatible, successful compatibility, and a fish biting. The power module provides power.

[0030] This invention also discloses a method for using a millimeter-wave radar-based night fishing float detection device, comprising the following steps:

[0031] S1: Millimeter-wave radar power-on startup. This radar uses pulse-frequency modulated continuous wave millimeter-wave radar. During operation, parameters such as transmit power, transmit bandwidth, operating time, fast time, and slow time need to be set. After power-on startup, the radar parameters are first configured, including setting the transmit power and transmit bandwidth. Once set, the radar begins operation, transmitting modulated radio waves through the transmitting antenna. The receiving antenna begins operation, receiving the transmitted radio waves and obtaining the intermediate frequency (IF) signals from the transmitted and received signals through a frequency differencer. Then, the received IF signals are digitally quadratured to form corresponding IQ channel data, obtaining the IF sample signal for one complete frequency modulation cycle of the radar, called a chirp.

[0032] S2: Filtering is performed based on the detected water surface ripples. The process is as follows:

[0033] A: Perform a Fast Fourier Transform (FFT) to obtain the frequency domain distribution of the data. By performing a FFT on the data of a chirp, the frequency domain information of that chirp can be obtained (satisfying the maximum sampling theorem). Each frequency corresponds to a distance, i.e., R = C / 4 * Tc / B * f, where C is the speed of light, Tc is the operating time (i.e., the frequency modulation operating time), B is the frequency modulation bandwidth, and f is the frequency data obtained by the Fourier transform.

[0034] B: Frequency filtering is performed based on the set detection location range. Because water waves are continuous waves, they can be considered relatively stationary within the frequency modulation time of millimeter-wave radar. Based on the set detection distance range, the maximum and minimum frequencies corresponding to the distance of the millimeter-wave radar signal can be obtained. The Fourier transform data is then filtered and eliminated, i.e., data outside the frequency range is assigned a value of 0.

[0035] C: Obtain the water surface ripple frequency, extract the phase data of the Fourier transform results corresponding to multiple consecutive chirps, perform a Fourier transform on the phase data of each frequency group, and obtain the frequency corresponding to the maximum energy value in the second Fourier transform result. Then, obtain the maximum and minimum frequencies of these frequency sets. Halve the energy of these maximum and minimum frequencies in the result of the first Fourier transform, i.e., adjust the corresponding energy to half of the original value. Because the float also vibrates with the water ripples, half of the frequency energy value is retained here.

[0036] D: Perform an inverse Fourier transform on the filtered Fourier transform result to obtain a new time-series signal;

[0037] S3: Near-shore debris filtering, i.e., detection and filtering of static or low-speed objects on the shore. First, a RangFFT operation is performed on the acquired time-series data to obtain the distance data corresponding to the debris. Then, a DopplerFFT operation is used to obtain the velocity v of the debris. If v is less than the set velocity of a static object, the debris is considered stationary and can be filtered. The energy to be filtered is set to 0 in frequency, and then converted into time-series data through an inverse Fourier transform.

[0038] S4: Fishing rod and line detection and filtering. The influence of fishing rods and lines on millimeter-wave radar signals is partly filtered out during debris filtering, partly filtered out during surface filtering, and the majority of the remaining influence comes from the fishing rod itself. This invention, based on RangFFT, filters for consecutive distance start and end points. If the distance between the start and end points exceeds a preset detectable fishing rod length, the corresponding frequencies are knocked out, and the knocked-out frequency domain data is converted to frequency domain data.

[0039] S5: Perform a RangFtt operation on the filtered data. The distance to the float with the highest energy is then determined. Next, perform a DopplerFFT at that distance to obtain the velocity at that position. Simultaneously, save the position and velocity as position and velocity arrays, respectively. Then, perform Fourier analysis on the position and velocity arrays to obtain the float's vibration data. The vibration data includes: position information, velocity information, position change frequency, and velocity change frequency. Since the vibration data reflects the float's movement and spatial position on the water surface, it can be considered equivalent to the float's phase data. The data is saved at a slow time interval and is updated regularly. If the initial float data is empty, the initial float phase data is set by averaging the vibration data 10 times (i.e., averaging the position and velocity change frequencies).

[0040] S6: Compare the obtained float vibration data with the initial phase data to calculate the difference. A represents the position difference, B represents the velocity difference, C represents the position frequency change difference, and D represents the velocity frequency change difference. The total change state is denoted as: T = aA + bB + cC + dD, where a, b, c, and d are the important coefficients of the corresponding difference, which are actually 0.2, 0.2, 0.3, and 0.3.

[0041] S7: If T is greater than the preset change state, it indicates a change in the fish's position, and there is a high probability that a fish has taken the bait. An alert sound will be emitted, and this information will be sent to the app via Bluetooth. Otherwise, continue the judgment process.

[0042] The working principle of this invention is as follows: Open the APP, press and hold the button on the detector to start searching for radar Bluetooth signals, set the distance to the shore, fishing rod length and other data on the APP, and set the initial float to receive the hook notification message sent by the radar via Bluetooth.

[0043] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention. In the description of the present invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present 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, and therefore should not be construed as a limitation of the present invention.

Claims

1. A night fishing float detection device based on millimeter-wave radar, comprising a millimeter-wave radar sensor, a computing module, a communication module, an information indicator and button module, and a power supply module, characterized in that, The millimeter-wave radar sensor is used to transmit and receive millimeter-wave wireless signals, and in actual use it is a 77GHz wireless signal, using frequency-modulated continuous wave. The millimeter-wave radar sensor can output a point cloud of the detected environment, which includes water surface ripple information, fish float information, and noise information. The computing module further analyzes and processes the point cloud data obtained from the sensor, separates the float information, and determines the float state. It compares the results with a preset float phase model to determine the size of the fish that has taken the bait. This computing module uses an ESP32 module, which has computing and storage capabilities. The communication module, used to send information about the appearance of the fish float, uses Bluetooth broadcast mode for transmission; The information indicator and button module uses three LED lights and one button to set and display the device status. The device status has three states: fishing adaptation, adaptation successful, and fish biting the hook. The power module is used to supply power to the device body; The method of using the millimeter-wave radar-based night fishing float detection device includes the following steps: S1: The millimeter-wave radar is powered on and started. The radar uses pulse frequency modulation continuous wave millimeter-wave radar. During use, parameters such as transmit power, transmit bandwidth, and working time need to be set. After the millimeter-wave radar is powered on, the radar parameters are configured first, that is, the transmit power and transmit bandwidth parameters are set. After the settings are completed, the radar starts to work and transmits modulated radio waves through the transmit antenna. The receive antenna starts to work and begins to receive the transmitted radio waves. The intermediate frequency (IF) signals of the transmit and receive signals are obtained through the frequency differencer. Then, the received IF signals are digitally quadratured to form the corresponding IQ channel data. The IF sample signal of one complete frequency modulation cycle of the radar is obtained, which is called a chirp. S2: Filtering is performed based on the detected water surface ripples. The process is as follows: A: Perform Fast Fourier Transform (FFT) to obtain the frequency domain distribution of the data. By performing FFT on the data of a chirp, the frequency domain information of the chirp can be obtained, which satisfies the maximum sampling theorem. Each frequency domain corresponds to a distance, i.e., R=C / 4*Tc / B*f, where C is the speed of light, Tc is the working time, i.e., the frequency modulation working time, B is the frequency modulation bandwidth, and f is the frequency data obtained by the Fourier transform. B: Frequency filtering is performed based on the set detection location range. Since water waves are continuous waves, they are considered to be relatively stationary within the frequency modulation time of millimeter-wave radar. Based on the set detection distance range, the maximum and minimum frequencies corresponding to the distance of the millimeter-wave radar signal are obtained. The Fourier transform data is then filtered and knocked out, i.e., data outside the frequency range is assigned a value of 0. C: Obtain the frequency of water surface ripples, extract the phase data of the Fourier transform results corresponding to multiple consecutive chirps, perform Fourier transform on the phase data of each frequency group respectively, and obtain the frequency corresponding to the maximum energy value in the second Fourier transform result. Then obtain the maximum and minimum frequencies of these frequency sets, and perform a halving filter on the result of the first Fourier transform on these maximum and minimum frequencies, that is, adjust the corresponding energy to half of the original value. Since the float will also vibrate with the water waves, half of the frequency energy value is retained here. D: Perform an inverse Fourier transform on the filtered Fourier transform result to obtain a new time-series signal; S3: Near-shore debris filtering, i.e. detection and filtering of static or low-speed objects on the shore. First, the obtained time series data is subjected to a RangFFT operation to obtain the distance data corresponding to the debris. Then, the velocity v of the debris is obtained through a DopplerFFT operation. If v is less than the set velocity of static objects, the debris is considered to be stationary and filtered. The energy to be filtered is set to 0 in the frequency range. Then, it is converted into time series data through inverse Fourier transform. S4: Fishing rod and line detection and filtering. The influence of fishing rod and line on millimeter-wave radar signals is partly filtered out during debris filtering and partly filtered out during surface filtering. The remaining influence is mainly caused by the fishing rod. Based on RangFFT, this invention filters the start and end points of the distance that are continuously corresponding to the distance. If the distance between the start and end points is greater than the preset detectable fishing rod length, the frequency corresponding to this distance is knocked out, and the knocked-out frequency domain data is converted into frequency domain data. S5: Perform a RangFtt operation on the filtered data. The distance of the float with the highest energy is the location of the float. Then, perform a DopplerFFT at that distance to obtain the velocity at that location. Simultaneously, save the position and velocity as position arrays and velocity arrays, respectively. Then, perform Fourier analysis on the position data and velocity arrays to obtain the float vibration data. The vibration data includes: position information, velocity information, position change frequency, and velocity change frequency. Since the vibration data reflects the movement and spatial position of the float on the water surface, it is considered equivalent to the float phase data. The storage time is a slow time interval and is updated and replaced. If the initial float phase data is empty, the initial float phase data is set by averaging the vibration data 10 times, i.e., using the position change frequency and velocity change frequency. S6: Compare the obtained float vibration data with the initial phase data to calculate the difference. A represents the position difference, B represents the velocity difference, C represents the position frequency change difference, and D represents the velocity frequency change difference. The total change state is denoted as: T = aA + bB + cC + dD, where a, b, c, and d are the important coefficients of the corresponding difference, which are actually 0.2, 0.2, 0.3, and 0.

3. S7: If T is greater than the preset change state, it indicates that the phase has changed and there is a high probability that a fish has taken the bait. It will emit a baiting alarm sound and send the information to the APP via the Bluetooth communication module. Otherwise, it will continue to make judgments.

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