CN117706507A - Sea wave echo extraction method based on cloud and rain area identification and adaptive clutter suppression - Google Patents
Sea wave echo extraction method based on cloud and rain area identification and adaptive clutter suppression Download PDFInfo
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Abstract
The invention discloses a sea wave echo extraction method based on cloud and rain area identification and self-adaptive clutter suppression, which is characterized in that a fuzzy identification function is established according to the statistical characteristics of phase and amplitude information of a coherent system radar echo baseband signal distance dimension, the cloud and rain area is identified, the cloud and rain are eliminated in the identified cloud and rain area by adopting the self-adaptive clutter suppression technology, and sea wave echo is extracted, so that the interference of the cloud and rain clutter on sea state parameter inversion is eliminated. Compared with the traditional radar cloud and rain recognition and suppression algorithm, the method utilizes the consistency detection of Doppler and the difference coefficient of the cloud and rain amplitude distribution to construct a fuzzy judgment function, and carries out differentiation processing on different airspace according to the cloud and rain region recognition result and the Doppler characteristic of cloud and rain clutter to realize the refined suppression of clutter.
Description
Technical Field
The invention relates to the technical field of radar signal processing.
Background
The wave-measuring radar is arranged on a ship platform such as a shore-based or marine survey ship and the like, carries out high-precision monitoring on marine power environment element information (wave height, wave direction, wave period, wavelength, surface flow velocity and surface flow direction) around the coast or the ship, and provides effective information support for marine information survey, ship maneuvering navigation safety, use of a ship-based weapon, take-off and landing of a ship-based aircraft, ship landing and the like. The wave-measuring radar is used as an important means for measuring the information of the marine dynamic environment elements in a large range at present, and is required to have all-weather working capacity. In cases of severe weather conditions, such as typhoons, there is often a dramatic change in the marine power environment, at which time the marine power environment measurements become more significant. However, weather cloud and rain clutter can cover a large area in a sea radar detection area, so that wave echoes are submerged by the weather clutter, and effective wave information cannot be directly extracted.
In recent years, the wave-measuring radar generally performs image processing or zero-intensity pixel point percentage on the identification of cloud and rain clutter, and the methods utilize the uniform distribution characteristic of cloud and rain echo intensity in a space domain to calculate statistical indexes such as variance, difference coefficient and the like on the amplitude, and adopt frequency domain filtering processing in the aspect of suppression. However, the noise wave intensity of cloud and rain is greatly affected by rain drop, so that the statistical characteristic based on the amplitude is not suitable for cloud and rain judgment under complex meteorological conditions, and the method based on the zero-intensity pixel point percentage is easily affected by sea conditions, and the accuracy is limited.
Disclosure of Invention
The invention provides a sea wave echo extraction method based on cloud and rain area identification and self-adaptive clutter suppression, which is suitable for a shore-based fixed platform, a survey ship and other moving platforms and is used for solving the problem of low reliability of sea wave information inversion results caused by influence of cloud and rain clutter or precipitation under complex meteorological conditions of a wave-measuring radar.
The technical solution for realizing the purpose of the invention is as follows:
step 1: obtaining coherent radar echo baseband data X m×n ＝[x 11 x 12 ...x 1n ；x 21 x 22 ...x 2n ；…；x m1 x m2 ...x mn ]，X m×n The pulse group is baseband IQ data of m coherent pulses, wherein each pulse has n distance units, and m is required to be more than or equal to 8;
step 2: for X m×n According to the traversal of the distance units, calculating the phase change rate of the energy contained in each distance unit and the error of the phase change rate by adopting a cycle correlation method by m IQ data of the same distance unit, thereby obtaining the Doppler velocity estimation vector v of each distance unit in the pulse group 1×n And a Doppler velocity spectrum width estimation vector delta 1×n And to X m×n Non-coherent accumulation processing is carried out on m pulses of the pulse number to obtain an echo amplitude vector A 1×n ；
Step 3: estimating vector v for Doppler velocity by sliding window 1×n Consistency detection is carried out on non-coherent accumulated data A 1×n Calculating a difference coefficient, generating a judging logic by a speed consistency detection value VT and the difference coefficient CV, and marking a distance unit meeting a certain condition as a cloud and rain area;
step 4: estimating vector v from Doppler velocity for range bin data identified as a cloud and rain region 1×n And a Doppler velocity spectrum width estimation vector delta 1×n Selecting a proper moving target filter set for X in radar echo m×n Filtering the main energy of the data matrix to obtain a suppressed data matrixWherein p is the number of filter banks;
step 5: outputting pulse group coherent accumulation data for the distance units which are not marked as the main energy cloud and rain areas, and outputting the pulse group coherent accumulation data for the distance units which are marked as the main energy cloud and rain areas for the distance units obtained in the step 4And (5) obtaining and outputting data of the middle p Doppler channels.
Compared with the traditional radar cloud and rain recognition and suppression algorithm, the method utilizes the consistency detection of Doppler and the difference coefficient of cloud and rain amplitude distribution to construct a fuzzy judgment function, and carries out differentiation processing on Doppler characteristics of different airspace cloud and rain clutter according to recognition results to realize refined suppression. According to the invention, cloud and rain identification is performed through multi-dimensional characteristics, and better robustness is achieved.
Drawings
FIG. 1 is a computational flow diagram.
Fig. 2 cloud rain echo data.
Fig. 3 is a diagram of cloud and rain recognition effect.
FIG. 4 raw data of weather clutter interference.
Figure 5 is a graph of the invention for suppressing cloud rain extraction ocean wave contrast.
Fig. 6 shows the actual wave extraction effect of the invention.
Detailed Description
The technical scheme of the invention is further explained below with reference to the accompanying drawings.
The preferred implementation steps of the invention are shown in figure 1:
step 1: the coherent radar transmitting signal is subjected to receiving amplification link, down-conversion, digital-to-analog conversion, digital quadrature, anti-interference and pulse compression to obtain an obtained echo baseband data matrix X m×n ＝[x 11 x 12 ...x 1n ；x 21 x 22 ...x 2n ；…；x m1 x m2 ...x mn ]As shown in S101, X m×n Baseband IQ data representing m coherent pulses of a pulse group of the radar, wherein each pulse has n distance units, and m is required to be more than or equal to 8;
step 2: as shown in S102, the spatial, time and frequency domain characteristics of the echo are calculated mainly for X m×n According to distance unit traversal, m IQ data of the same distance unit, such as ith distance unit data x m×i Calculating the phase change rate of the energy contained in each distance unit and the error of the phase change rate by adopting a period correlation method to obtain the Doppler velocity estimation vector v of each distance unit in the pulse group 1×n And a Doppler velocity spectrum width estimation vector delta 1×n And to X m×n Non-coherent accumulation processing is carried out on m pulses of the number to obtain a return amplitude vector A 1×n ；
Step 3: as shown in S103, the processing is performed by a sliding window method, and the doppler velocity estimation vector v is estimated assuming that the processing window length is l 1×n The consistency detection can be realized by solving the variance of the data in the processing window or other consistency detection modes, and the non-coherent accumulated data A is obtained 1×n Calculating a difference coefficient; as shown in S104, a determination logic is generated from the speed consistency detection value VT and the coefficient of variation CV; as shown in S105, for satisfying VT<T VT ,CV<T CV Is marked as a cloud and rain area, wherein T VT For speed consistency detection threshold, T CV The difference coefficient detection threshold is an adjustable parameter and is determined according to the actual echo; as shown in fig. 2, the cloud and rain echo covers most areas, and the cloud and rain area identification effect by adopting the method is shown in fig. 3;
step 4: as shown in S107, a vector v is estimated from the doppler velocity for the distance cell data identified as the cloud and rain region 1×n And a Doppler velocity spectrum width estimation vector delta 1×n Selecting a proper moving target filter set for X in radar echo m×n The main energy of the (a) is filtered, and the optimization principle of the moving target filter bank is that the frequency response notch spectrum center of the moving target filter bank with the distance unit of i is v 1×n (i) The frequency response notch widths are all delta 1×n (i) Obtaining the suppressed data matrixWherein p is the number of filter banks;
step 5; as shown in S106 and S108, pulse group coherent accumulation data is output for the distance units not marked as the main energy cloud and rain area, and the pulse group coherent accumulation data is output for the distance units marked as the main energy cloud and rain area for the distance units obtained in step 4And (5) obtaining and outputting data of the middle p Doppler channels.
As shown in fig. 4 to 6, fig. 4 is wave echo data covered by cloud and rain clutter, fig. 5 is a comparison chart of the invention for inhibiting cloud and rain extraction, and it can be seen that cloud and rain are completely inhibited, and the wave is extracted, and fig. 6 is a final effect chart of the invention.
Claims (1)
1. The sea wave echo extraction method based on cloud and rain area identification and adaptive clutter suppression is characterized by comprising the following steps of: the method comprises the following steps:
step 1: obtaining coherent radar echo baseband data X m×n ＝[x 11 x 12 ...x 1n ；x 21 x 22 ...x 2n ；…；x m1 x m2 ...x mn ]，X m×n The baseband pulse group I/Q data are m coherent pulses, wherein m is more than or equal to 8, and each pulse in the baseband pulse group has n distance units;
step 2: for X m×n According to the traversal of the distance units, calculating the phase change rate of the energy contained in each distance unit and the error of the phase change rate by adopting a cycle correlation method by m I/Q data of the same distance unit, thereby obtaining the Doppler velocity estimation vector v of each distance unit in the baseband pulse group 1×n And a Doppler velocity spectrum width estimation vector delta 1×n And to X m×n Non-coherent accumulation processing is carried out on m pulses of the pulse number to obtain an echo amplitude vector A 1×n ；
Step 3: estimating vector v for Doppler velocity by sliding window 1×n Consistency detection is carried out on non-coherent accumulated data A 1×n Calculating a coefficient of differenceGenerating a judging logic by the speed consistency detection value VT and the difference coefficient CV, and marking a distance unit meeting a certain condition as a cloud and rain area;
step 4: estimating vector v from Doppler velocity for range bin data identified as a cloud and rain region 1×n And a Doppler velocity spectrum width estimation vector delta 1×n Selecting a proper moving target filter set for X in radar echo m×n Filtering the main energy of the data matrix to obtain a suppressed data matrixWherein p is the number of filter banks;
step 5: outputting pulse group coherent accumulation data for the distance units which are not marked as the main energy cloud and rain areas, and outputting the pulse group coherent accumulation data for the distance units which are marked as the main energy cloud and rain areas for the distance units obtained in the step 4And (5) obtaining and outputting data of the middle p Doppler channels.
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