A real-time underwater detection, recognition and positioning algorithm

Abstract

The present invention provides a real-time underwater detection, identification and positioning algorithm, including: obtaining collected data through a detector, filtering the collected data using a bandpass filter filtering algorithm to obtain filtered data; establishing a unified orthogonal coordinate system, finding errors caused by non-orthogonality and non-parallelism of three axes in the filtered data, and obtaining detection data; processing the detection data through a detection algorithm for magnetic anomalies using an adaptive minimum entropy filter to obtain normal detection data and abnormal detection data; constructing multiple single-axis and three-axis sensor arrays, and using the Euler deconvolution method to process normal detection data to obtain identification and positioning data; combining the identification and positioning data, characterizing and describing the signal characteristics of the magnetic target, and realizing the positioning of the magnetic dipole source. The present invention improves the signal-to-noise ratio, reduces the errors caused by non-orthogonality and non-parallelism of the three axes of the sensor, and the interference caused by environmental noise, geomagnetic influence and accidental errors.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of underwater detection, and in particular to a real-time positioning underwater detection identification and positioning algorithm. Background Art

[0002] In recent years, civil fields such as seabed topography detection, large-area ocean monitoring, underwater information acquisition, as well as military fields such as detection of enemy ships, submarines, and aircraft carriers all require the support of underwater detection technology.

[0003] According to the six-dimensional precise imaging, identification and positioning technology and method for deep-sea exploration provided by the patent document with application number CN202110055153.6, the method includes a six-dimensional precise imaging technology module of multi-sensor information fusion imaging, a target identification, tracking and positioning technology module, a high-precision underwater navigation and positioning module, a motion control and intelligent decision-making module, and an external transmission communication module; the six-dimensional precise imaging technology module of multi-sensor information fusion imaging is composed of a laser detector, an acoustic detector, an infrared detector, and a camera. On the basis of realizing the functions of deep-water multi-dimensional imaging, underwater precise navigation and positioning, and precise motion control of underwater robots, it realizes the autonomous identification, positioning and tracking functions with generalization and robustness, improves the autonomy and intelligence of underwater robots, and also realizes deep-water high-precision navigation and positioning, which is worthy of vigorous promotion.

[0004] The above-mentioned underwater detection method can perform autonomous identification, positioning and tracking functions, which improves the autonomy and intelligence of underwater robots. However, the traditional underwater detection and positioning methods have low filtering capabilities and are difficult to effectively reduce the impact of the environment, thus affecting the detection and positioning accuracy. Summary of the invention

[0005] The present invention mainly provides a real-time underwater detection and identification positioning algorithm to solve the technical problems raised in the above background technology.

[0006] The technical solution adopted by the present invention to solve the above technical problems is:

[0007] A real-time underwater detection, identification and positioning algorithm comprises the following steps:

[0008] Step 1, acquiring collected data through a detector, and filtering the collected data using a filtering algorithm of a bandpass filter to obtain filtered data;

[0009] Step 2: Establish a unified orthogonal coordinate system, find out the errors caused by the non-orthogonality and non-parallelism of the three axes in the filtered data obtained in step 1, and obtain the detection data;

[0010] Step 3: Processing the detection data obtained in step 2 by using the detection algorithm of magnetic anomaly targets with adaptive minimum entropy filtering to find magnetic anomaly targets with low signal-to-noise ratio, and obtaining normal detection data and abnormal detection data;

[0011] Step 4: construct multiple single-axis and three-axis sensor arrays, and use the Euler deconvolution method to process the normal detection data obtained in step 3 to obtain identification and positioning data;

[0012] Step five, combining the identification and positioning data obtained in step four, characterize and describe the characteristics of the magnetic target signal to locate the magnetic dipole source.

[0013] Furthermore, in the step three, the detection data obtained in step two is processed by the detection algorithm of the magnetic anomaly target of the adaptive minimum entropy filter to obtain the entropy filter output value. When the entropy filter output value drops below the threshold, it is abnormal detection data. When the entropy filter output value does not drop below the threshold, it is normal detection data.

[0014] Furthermore, in step 3, the detection algorithm of the magnetic anomaly target using the adaptive minimum entropy filter is implemented as follows:

[0015] (1) Detection data, whose normal probability density function is:

[0016]

[0017] where x i is the discrete value of the detection data, u is the average value of the detection data, σ 2 is the variance of the detection data;

[0018] (2) Detection data, the average value is calculated as follows:

[0019]

[0020] where x i is the discrete value of the detection data, and u is the average value of the detection data;

[0021] (3) Detection data, whose discrete value x i The probability of a noise sample is:

[0022]

[0023] where x i is the discrete value of the detection data, using a quantization step size of Δx i The analog-to-digital converter,

[0024] (4) The entropy filter calculates the entropy of a sample of a certain length L as:

[0025]

[0026] where x i is the discrete value of the detection data.

[0027] Furthermore, in step 4, the identification and positioning data includes magnetic induction intensity and gradient magnetic tensor.

[0028] Furthermore, in step 4, the magnetic induction intensity based on the Euler deconvolution method is calculated by the following formula:

[0029] (1)

[0030] Where m is the dipole magnetic moment, the unit is Am 2 , r and r0 are the position vectors of the observation point and the magnetic target in the detection data, u=4π×10 -7 H / m is the magnetic permeability of vacuum.

[0031] Furthermore, in step 4, the single-point positioning of the gradient magnetic tensor is calculated by the following formula based on the Euler deconvolution method:

[0032] (1) Right now

[0033] Where r is the position vector of the observation point and the magnetic target in the detection data.

[0034] Furthermore, in step five, the magnetic field components, total field and first-order and second-order magnetic gradient tensor characteristics are matched to characterize and describe the magnetic target signal characteristics.

[0035] Furthermore, in step one, the detector includes a micromagnetic sensor, a sonar sensor, a temperature sensor, and a salinity sensor.

[0036] Furthermore, in the step 2, after obtaining the detection data, dynamic deep learning of the monitoring environment and target scene patterns is performed through self-learning artificial intelligence AI, and the amplitude, fluctuation and other characteristics of the environmental field signal in the time domain and frequency domain are learned and mastered, specific target types are monitored, and a class of characteristic parameters are mastered.

[0037] Furthermore, in the step 2, after establishing a unified orthogonal coordinate system, the three-axis orthogonal calibration and the three-axis parallel calibration of the sensor are performed to find out the errors caused by the three-axis non-orthogonality and non-parallelism in the filtered data obtained in the step 1.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] Firstly, the present invention improves the signal-to-noise ratio and improves the accuracy of target detection and positioning of weak magnetic target signals.

[0040] Secondly, the present invention reduces the errors caused by the non-orthogonality and non-parallelism of the three axes of the sensor, and improves the accuracy of target detection and positioning.

[0041] Thirdly, the present invention reduces the interference caused by singularity problems, environmental noise, geomagnetic influence and accidental errors of a single magnetic gradiometer when the magnetic moment of the target positioning is perpendicular to the observation direction, thereby achieving accurate positioning of the magnetic dipole source.

[0042] Fourthly, the adaptive minimum entropy filtering magnetic anomaly target detection algorithm adopted by the present invention is significantly better than the orthogonal basis function method in detecting magnetic anomaly targets with low signal-to-noise ratio.

[0043] The present invention will be explained in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0045] To facilitate understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings, but the present invention can be implemented in different forms and is not limited to the embodiments described in the text. On the contrary, these embodiments are provided to make the content disclosed in the present invention more thorough and comprehensive.

[0046] It should be noted that when an element is referred to as being "fixed on" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used in this article are for illustrative purposes only.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly connected by technicians in the technical field to which the present invention belongs. The terminology used in the specification of the present invention is for the purpose of describing specific embodiments and is not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0048] For examples, please refer to the attached Figure 1 , a real-time underwater detection and identification positioning algorithm, comprising the following steps:

[0049] Step 1, acquiring collected data through a detector, and filtering the collected data using a filtering algorithm of a bandpass filter to obtain filtered data;

[0050] Step 2: Establish a unified orthogonal coordinate system, find out the errors caused by the non-orthogonality and non-parallelism of the three axes in the filtered data obtained in step 1, and obtain the detection data;

[0051] Step 3: Processing the detection data obtained in step 2 by using the detection algorithm of magnetic anomaly targets with adaptive minimum entropy filtering to find magnetic anomaly targets with low signal-to-noise ratio, and obtaining normal detection data and abnormal detection data;

[0052] Step 4: construct multiple single-axis and three-axis sensor arrays, and use the Euler deconvolution method to process the normal detection data obtained in step 3 to obtain identification and positioning data;

[0053] Step five, combining the identification and positioning data obtained in step four, characterize and describe the characteristics of the magnetic target signal to locate the magnetic dipole source.

[0054] Furthermore, in the step three, the detection data obtained in step two is processed by the detection algorithm of the magnetic anomaly target of the adaptive minimum entropy filter to obtain the entropy filter output value. When the entropy filter output value drops below the threshold, it is abnormal detection data. When the entropy filter output value does not drop below the threshold, it is normal detection data.

[0055] Furthermore, in step 3, the detection algorithm of the magnetic anomaly target using the adaptive minimum entropy filter is implemented as follows:

[0056] (1) Detection data, whose normal probability density function is:

[0057]

[0058] where x i is the discrete value of the detection data, u is the average value of the detection data, σ 2 is the variance of the detection data;

[0059] (2) Detection data, the average value is calculated as follows:

[0060]

[0061] where x i is the discrete value of the detection data, and u is the average value of the detection data;

[0062] (3) Detection data, whose discrete value x i The probability of a noise sample is:

[0063]

[0064] where x i is the discrete value of the detection data, using a quantization step size of Δx iThe analog-to-digital converter,

[0065] (4) The entropy filter calculates the entropy of a sample of a certain length L as:

[0066]

[0067] where x i is the discrete value of the detection data.

[0068] Furthermore, in step 4, the identification and positioning data includes magnetic induction intensity and gradient magnetic tensor.

[0069] Furthermore, in step 4, the magnetic induction intensity based on the Euler deconvolution method is calculated by the following formula:

[0070]

[0071] Where m is the dipole magnetic moment, the unit is Am 2 , r and r0 are the position vectors of the observation point and the magnetic target in the detection data, u=4π×10 -7 H / m is the magnetic permeability of vacuum.

[0072] Furthermore, in step 4, the single-point positioning of the gradient magnetic tensor is calculated by the following formula based on the Euler deconvolution method:

[0073] (2) Right now

[0074] Where r is the position vector of the observation point and the magnetic target in the detection data.

[0075] Furthermore, in step five, the magnetic field components, total field and first-order and second-order magnetic gradient tensor characteristics are matched to characterize and describe the magnetic target signal characteristics, thereby reducing the interference caused by singularity problems, environmental noise, geomagnetic influences and accidental errors of a single magnetic gradiometer when the magnetic moment of the target positioning is perpendicular to the observation direction.

[0076] Furthermore, in step one, the detector includes a micromagnetic sensor, a sonar sensor, a temperature sensor, and a salinity sensor.

[0077] Furthermore, in the step 2, after obtaining the detection data, dynamic deep learning of the monitoring environment and target scene patterns is performed through self-learning artificial intelligence AI, and the amplitude, fluctuation and other characteristics of the environmental field signal in the time domain and frequency domain are learned and mastered, specific target types are monitored, and a class of characteristic parameters are mastered, thereby improving the adaptability, recognition, convergence speed and accuracy of target detection, positioning and attitude measurement.

[0078] Furthermore, in the step 2, after establishing a unified orthogonal coordinate system, the three-axis orthogonal calibration and the three-axis parallel calibration of the sensor are performed to find out the errors caused by the three-axis non-orthogonality and non-parallelism in the filtered data obtained in step 1, thereby improving the accuracy of target detection and positioning.

[0079] The specific operation mode of the present invention is as follows:

[0080] The collected data is obtained through the detector, and the collected data is filtered using the filtering algorithm of the bandpass filter to obtain the filtered data, a unified orthogonal coordinate system is established, and the errors caused by the non-orthogonality and non-parallelism of the three axes in the filtered data are found to obtain the detection data;

[0081] The detection data is processed by the detection algorithm of magnetic anomaly targets with adaptive minimum entropy filtering to find magnetic anomaly targets with low signal-to-noise ratio, obtain normal detection data and abnormal detection data, build multiple single-axis and three-axis sensor arrays, and use the Euler deconvolution method to process the obtained normal detection data to obtain identification and positioning data;

[0082] Combined with the obtained identification and positioning data, the magnetic target signal characteristics are characterized and described to locate the magnetic dipole source.

[0083] The above is an exemplary description of the present invention in combination with the accompanying drawings. It is obvious that the specific implementation of the present invention is not limited to the above-mentioned method. As long as such non-substantial improvements are made by adopting the method concept and technical solution of the present invention, or the concept and technical solution of the present invention are directly applied to other occasions without improvement, they are all within the protection scope of the present invention.

Claims

1. A real-time underwater detection and identification positioning algorithm, characterized in that: The following steps are involved: Step 1, acquiring collected data through a detector, and filtering the collected data using a filtering algorithm of a bandpass filter to obtain filtered data; Step 2: Establish a unified orthogonal coordinate system, find out the errors caused by the non-orthogonality and non-parallelism of the three axes in the filtered data obtained in step 1, and obtain the detection data; Step 3: Processing the detection data obtained in step 2 by using the detection algorithm of magnetic anomaly targets with adaptive minimum entropy filtering to find magnetic anomaly targets with low signal-to-noise ratio, and obtaining normal detection data and abnormal detection data; Step 4: construct multiple single-axis and three-axis sensor arrays, and use the Euler deconvolution method to process the normal detection data obtained in step 3 to obtain identification and positioning data; Step five, combining the identification and positioning data obtained in step four, characterize and describe the characteristics of the magnetic target signal to locate the magnetic dipole source.

2. The underwater detection, identification and positioning algorithm for real-time positioning according to claim 1 is characterized in that: In the step three, the detection data obtained in step two is processed by the detection algorithm of the magnetic anomaly target of the adaptive minimum entropy filter to obtain the entropy filter output value. When the entropy filter output value drops below the threshold, it is abnormal detection data. When the entropy filter output value does not drop below the threshold, it is normal detection data.

3. The underwater detection, identification and positioning algorithm for real-time positioning according to claim 1 is characterized in that: In step 3, the detection algorithm of the magnetic anomaly target using the adaptive minimum entropy filter is implemented as follows: (1) Detection data, whose normal probability density function is: where x i is the discrete value of the detection data, u is the average value of the detection data, σ 2 is the variance of the detection data; (2) Detection data, the average value is calculated as follows: where x i is the discrete value of the detection data, and u is the average value of the detection data; (3) Detection data, whose discrete value x i The probability of a noise sample is: where x i is the discrete value of the detection data, using a quantization step size of Δx i The analog-to-digital converter, (4) The entropy filter calculates the entropy of a sample of a certain length L as: where x i is the discrete value of the detection data.

4. The underwater detection and identification positioning algorithm for real-time positioning according to claim 1 is characterized in that: In the step 4, the identification and positioning data includes magnetic induction intensity and gradient magnetic tensor.

5. The underwater detection, identification and positioning algorithm for real-time positioning according to claim 4 is characterized in that: In the step 4, the magnetic induction intensity based on the Euler deconvolution method is calculated by the following formula: (1) Where m is the dipole magnetic moment, the unit is Am 2 , r and r0 are the position vectors of the observation point and the magnetic target in the detection data, u=4π×10 -7 H / m is the magnetic permeability of vacuum.

6. The underwater detection and identification positioning algorithm for real-time positioning according to claim 5 is characterized in that: In the step 4, the single-point positioning of the gradient magnetic tensor is calculated by the following formula based on the Euler deconvolution method: (1) Right now Where r is the position vector of the observation point and the magnetic target in the detection data.

7. The underwater detection, identification and positioning algorithm for real-time positioning according to claim 1 is characterized in that: In the step five, the magnetic field components, the total field and the first-order and second-order magnetic gradient tensor characteristics are matched to characterize and describe the magnetic target signal characteristics.

8. The underwater detection, identification and positioning algorithm for real-time positioning according to claim 1 is characterized in that: In the step 1, the detector includes a micromagnetic sensor, a sonar sensor, a temperature sensor, and a salinity sensor.

9. The underwater detection, identification and positioning algorithm for real-time positioning according to claim 1, characterized in that: In the step 2, after the detection data is obtained, dynamic deep learning of the monitoring environment and target scene patterns is performed through self-learning artificial intelligence AI to learn and grasp the amplitude and fluctuation characteristics of the environmental field signal in the time domain and frequency domain.

10. The underwater detection, identification and positioning algorithm for real-time positioning according to claim 1, characterized in that: In the step 2, after establishing a unified orthogonal coordinate system, the three-axis orthogonal calibration and the three-axis parallel calibration of the sensor are performed to find out the errors caused by the non-orthogonality and non-parallelism of the three axes in the filtered data obtained in the step 1.

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