Fish school quantity estimation method based on extended Kalman filtering combined with nearest neighbor clustering algorithm

A technology of extended Kalman and clustering algorithm, which is applied in the field of fish population estimation based on extended Kalman filter combined with nearest neighbor clustering algorithm, can solve the problems of large error and damage to fish resources, etc., and the method is simple and easy, high precision effect

Inactive Publication Date: 2017-05-24
ZHEJIANG UNIV
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Problems solved by technology

The traditional method mainly relies on sampling fishing, which is harmful to the fish resources itself; or using a metering fish finder, using the

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  • Fish school quantity estimation method based on extended Kalman filtering combined with nearest neighbor clustering algorithm
  • Fish school quantity estimation method based on extended Kalman filtering combined with nearest neighbor clustering algorithm
  • Fish school quantity estimation method based on extended Kalman filtering combined with nearest neighbor clustering algorithm

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Embodiment Construction

[0056] The present invention will be described in further detail below in conjunction with specific embodiments and accompanying drawings, but the present invention is not limited thereto.

[0057] The dual-frequency identification sonar used in this example is mainly a multi-beam system composed of 3 lenses and a sonar array. It can transmit ultrasonic waves with a frequency of 1.8MHz or 1.1MHz underwater. The minimum beam detection range is 5 meters. The maximum is 40 meters, the rate of receiving data is up to 20 frames of images per second, the detection field of view is 29° in the horizontal direction, and 14° in the vertical direction, the weight in the air is about 7 kg, and the power is about 30W. figure 1 It is a flow chart of the realization of fish population estimation using dual-frequency identification sonar. The main process is described as follows:

[0058] The first step is to fix the dual-frequency identification sonar on the survey vessel. like figure 2 A...

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Abstract

The invention discloses a fish school quantity estimation method based on extended Kalman filtering combined with the nearest neighbor clustering algorithm. The method comprises the steps that (1) a dual frequency identification sonar is fixed; (2) underwater detection is carried out in the way of tracking to acquire acoustic data; (3) data processing is carried out, extended Kalman filtering is combined with the nearest neighbor clustering algorithm to count the number of fishes; (4) the volume of swept water body is calculated, and the fish school density is calculated; and (5) according to the water storage of a known water area, the quantity of fishes in the whole water area is estimated. According to the invention, the dual frequency identification sonar is used to transmit underwater acoustic wave, and an acoustic wave signal is returned when an obstacle is encountered; the echo signal is received, and signal processing is carried out to acquire underwater fish school information to estimate the fish school quantity; the method is simple, practicable and efficient, and does not damage fish resources; the method is more accurate than a traditional target intensity integrating method, and provides a new way for the assessment of fishery resources.

Description

technical field [0001] The invention belongs to the technical field of fishery resources assessment, and in particular relates to a method for estimating the number of fish schools based on extended Kalman filtering combined with the nearest neighbor clustering algorithm. Background technique [0002] The assessment of fishery resources is an important link in the process of modern fishery development, and the statistics of fish stocks is the most basic requirement of fishery resources assessment. The traditional method mainly relies on sampling fishing, which is harmful to the fish resources itself; or using a metering fish finder, using the echo integration method or echo counting method for measurement, which can only roughly estimate the number of fish schools, with large errors. Modern society puts forward higher requirements for improving the quality and output of fishery resources, effectively protecting marine ecosystems, and realizing the sustainable development of ...

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Application Information

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IPC IPC(8): G01S15/96G01S7/539
CPCG01S7/539G01S15/96G01S15/107
Inventor 韩军荆丹翔王杰英杜鹏飞章旻昊任佳
Owner ZHEJIANG UNIV
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