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CN-116449351-B - Active sonar processing method and system based on COSTAS waveform

CN116449351BCN 116449351 BCN116449351 BCN 116449351BCN-116449351-B

Abstract

The invention discloses an active sonar processing method and system based on a COSTAS waveform, which are used for generating a COSTAS waveform signal through frequency intervals and frequency hopping numbers, realizing frequency domain beam forming based on a guide vector and fast Fourier transform, improving the signal to noise ratio of a received signal, increasing azimuth information, realizing normalized matched filtering through maximum frequency module selection and noise estimation, and obtaining an azimuth history map through approximate coherence weighted fusion. The invention can effectively overcome the selective fading of the channel frequency and obviously improve the signal detection performance. Meanwhile, the invention fully utilizes the coherent components in each frequency and combines a fusion method based on signal-to-noise ratio weighting, thereby effectively improving the echo gain under the azimuth calendar and improving the detection accuracy and reliability.

Inventors

  • WANG LEI
  • PENG CONG
  • YANG CHI
  • Long Huibo
  • JI HAORAN
  • ZHANG SHUHAO

Assignees

  • 湖南大学

Dates

Publication Date
20260505
Application Date
20230331

Claims (7)

  1. 1. The active sonar processing method based on the COSTAS waveform is characterized by comprising the following steps of: s1, carrying out beam forming on multichannel data of a receiving array, acquiring a frequency domain beam forming result by adopting a frequency domain beam forming method, combining a steering vector, realizing beam forming in a single beam direction, and realizing beam forming in other directions by modifying the steering vector; s2, selecting the maximum spectral line after beam forming to obtain a matched filtering result; s3, dividing the matched filtering result of single direction, single frequency hopping and single snapshot by the estimation of the noise standard deviation to obtain an updated output result; S4, repeatedly executing the steps S1-S3 on L snapshots and M beam directions, wherein all updated output results form a complex azimuth history chart of the nth hop frequency, N is more than or equal to 0 and less than or equal to N-1, and N is the length of a COSTAS coding sequence; S5, calculating the signal-to-noise ratio of a complex azimuth history chart of each frequency jump, carrying out delay weighted summation on the complex azimuth history chart of each frequency jump according to the signal-to-noise ratio, and changing 1-L snapshot data corresponding to the complex azimuth history chart into 1+delta L-L+delta L snapshot data to obtain a complex azimuth history chart matrix Q, wherein L is the number of snapshot data, delta L= (n-1) T sp /T L ,T L is the time interval between snapshots, and T sp is the duration of a single COSTAS code element; s6, performing modular square on each element in the complex azimuth calendar matrix Q to obtain an azimuth calendar matrix E.
  2. 2. The method of claim 1, wherein in step S1, the frequency domain beam forming result R m,p,l of the mth beam direction, the P-th spectral line, and the l snapshot is denoted as R m,p,l ＝g p (A m,p ,Fr l , wherein R l represents a matrix composed of the data of the l snapshot received by the D channels of the receiving array, the size is P×D, P is the number of sampling points included in each snapshot, F represents a Fourier transform matrix with the size of P×P, and the (k, u) -th element in F is denoted as K is more than or equal to 1 and less than or equal to P, u is more than or equal to 1 and less than or equal to P, A m,p represents a guide vector corresponding to the mth beam direction and the P-th spectral line and is a 1*D matrix, wherein M is more than or equal to 1 and less than or equal to M, P is more than or equal to 1 and less than or equal to P, and the (1, k) th element of A m,p is D is the linear array element spacing, θ m is the angle of the mth beam direction, c is the speed of sound, f (p) is the frequency corresponding to the p-th spectral line, and f (p) = (p-1) ·f s /P;g p (A m,p ,Fr l ) represents performing an inner product operation on the p-th line of Fr l and a m,p .
  3. 3. The method for active sonar processing based on a COSTAS waveform according to claim 1, wherein in step S2, the matching filtering result of the mth direction, the nth frequency hopping, the first snapshot is obtained Expressed as: Wherein complexmax {.cndot }' represents the operation of finding the element with the largest modulus in all elements in the matrix, R m,p,l is the frequency domain beam forming result of the mth beam direction, the p-th spectral line and the first snapshot, For the m-th beam direction, the p 1,n th spectral line, and the frequency domain beam forming result of the first snapshot, For the m-th beam direction, p 2,n spectral lines, and the frequency domain beam forming result of the l snapshot, p 1,n ～p 2,n represents the spectral line sequence numbers corresponding to the frequency ranges f n -f window to f n +f window , f n is the single-frequency signal frequency corresponding to the nth symbol after being modulated by the COSTAS sequence, that is, the nth frequency hopping, and f window is the set frequency.
  4. 4. The method of claim 1, wherein in step S4, the complex azimuth calendar of the nth hop frequency is represented by a matrix Q n : wherein M is the number of beam directions, The updated output result of the mth direction, the nth frequency hopping and the first snapshot is obtained.
  5. 5. The method for active sonar processing based on a COSTAS waveform according to claim 4, wherein in step S5, a calculation formula of a signal-to-noise ratio SNR n of the nth hop frequency is: Where Q n (i, j) represents an element of the i-th row, j-th column in the matrix Q n .
  6. 6. The method of claim 4, wherein in step S5, the expression of the complex azimuth calendar matrix Q is: SNR n is the signal-to-noise ratio of the nth hop frequency, delay (-) represents the delay operation.
  7. 7. An active sonar processing system based on a COSTAS waveform, comprising: One or more processors; A memory having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the steps of the method of any of claims 1-6.

Description

Active sonar processing method and system based on COSTAS waveform Technical Field The invention relates to the field of underwater acoustic signal generation and processing, in particular to an active sonar processing method and system based on a COSTAS waveform. Background The active sonar detects the target in an echo positioning mode, so that the underwater target is detected and positioned. The underwater acoustic channel is used as a transmission channel of acoustic signals, has a complex structure and a plurality of influencing factors, wherein the factors such as low propagation speed, increased transmission loss along with frequency, high ocean noise and the like are adverse conditions of the underwater acoustic channel, and meanwhile, the underwater acoustic channel has the characteristics of strong time variability, complex multipath propagation and the like (M.Stojanovic and J.Preisig,"Underwater acoustic communication channels:Pro-pagation models and statistical characterization,"in IEEE Communications Magazine,vol.47,no.1,pp.84-89,January 2009.)., and serious signal distortion occurs after acoustic waves pass through the underwater acoustic channel, so that the subsequent processing of echo detection is not facilitated. Among these, the most obvious phenomena are frequency selective fading and time selective fading, which further aggravate the distortion degree of the signal, and put higher demands on the processing of the detected and received signal. The traditional active sonar generally adopts single-frequency wave for detection, and combines narrowband filtering, fast Fourier transform and pulse compression technology for echo processing. In recent years, some new active sonar signals such as a chirp signal (Line Frequency Modulation, LFM), a hyperbolic tone signal (Hyperbolic Frequency Modulation, HFM), a coded phase modulation pulse (Pulse Code Modulation, PCM), a Pseudo-Random signal (PR), an inter-pulse modulation signal (Interpulse Modulated signal, IM), a composite signal, and the like, appear successively. These signals are characterized, but the performance of the signals is usually contradictory, such as between speed estimation capability and time delay estimation capability, between measurement accuracy, different target resolution capability and multi-value ambiguity (Zhang Yao. Active sonar target detection under shallow sea conditions several methods research [ D ]. Harbine engineering university, 2013.). In long-distance target detection, speed and resolution are not the primary targets, and echo detection is the primary target, so that an active sonar signal applicable to a time-varying and frequency-selective fading underwater acoustic channel and a related processing method are needed. Disclosure of Invention Aiming at the defects of the prior art, the invention provides an active sonar processing method and an active sonar processing system based on a COSTAS waveform, which improve the gain of the traditional COSTAS (COSTAS waveform) processing waveform. In order to solve the technical problems, the invention adopts the technical scheme that the active sonar processing method based on the COSTAS waveform comprises the following steps: s1, carrying out beam forming on multichannel data of a receiving array, acquiring a frequency domain beam forming result by adopting a frequency domain beam forming method, combining a steering vector, realizing beam forming in a single beam direction, and realizing beam forming in other directions by modifying the steering vector; s2, selecting the maximum spectral line after beam forming to obtain a matched filtering result; s3, dividing the matched filtering result of single direction, single frequency hopping and single snapshot by the estimation of the noise standard deviation to obtain an updated output result; S4, repeatedly executing the steps S1-S3 on L snapshots and M beam directions, wherein all updated output results form a complex azimuth history chart of the nth hop frequency, N is more than or equal to 0 and less than or equal to N-1, and N is the length of a COSTAS coding sequence; S5, calculating the signal-to-noise ratio of a complex azimuth history chart of each frequency jump, carrying out delay weighted summation on the complex azimuth history chart of each frequency jump according to the signal-to-noise ratio, and changing 1-L snapshot data corresponding to the complex azimuth history chart into 1+delta L-L+delta L snapshot data to obtain a complex azimuth history chart matrix Q, wherein L is the number of snapshot data, delta L= (n-1) T sp/TL,TL is the time interval between snapshots, and T sp is the duration of a single COSTAS code element; s6, performing modular square on each element in the complex azimuth calendar matrix Q to obtain an azimuth calendar matrix E. The invention fully utilizes the space array gain and time gain of the received data, improves the received signal-to-noise ratio and can effectively re