A matching phase processing method for active sonar target depth estimation in shallow sea

Abstract

The application discloses a matching phase processing method for shallow sea active sonar target depth estimation, and comprises the following steps: step 1, measurement field construction, processing wideband echo signals received by receiver 1 and receiver 2 to obtain a single-path propagation vector ratio wideband interference structure η (z, r, ω) of the measurement field data (z t ,ω);step 2, copy field construction, based on an ocean waveguide environment, calculating an acoustic field P r1 (z s ,z t ,z r1 ,r,ω) of receiver 1 and an acoustic field P r2 (z s ,z t ,z r1 ,r,ω) of receiver 2 by using a KRAKEN model, and constructing a copy field single-path propagation vector ratio wideband interference structure η rplc (z t ,ω);step 3, ambiguity surface construction, performing matching phase processing on the single-path propagation vector ratio wideband interference structure η data (z t ,ω) of the measurement field and the single-path propagation vector ratio wideband interference structure η rplc (z t ,ω) of the copy field, and constructing an ambiguity surface for target depth estimation. The application can realize active sonar target depth estimation in a shallow sea environment.

Description

Technical field:

[0001] The present invention relates to the field of underwater acoustics, and in particular to a matching phase processing method for shallow sea active sonar target depth estimation. Background technology:

[0002] Target depth estimation in ocean waveguide has been one of the difficult problems in underwater acoustic field. From the research status of passive target depth estimation, the existing depth estimation methods can be mainly divided into two categories: one is the depth estimation method based on matched field processing (MFP), and the other is the depth estimation method based on matched mode processing (MMP) or matched beam processing (MBP). Bucker (see [1] H. P. Bucker. Use of calculated sound fields and matched field detection to locate sound sources in shallow water[J]. Journal of the Acoustical Society of America, 1976, 59: 368–373.) is considered as the first person to formalize the matched field processing. He realized the passive target range and depth estimation by matching the copy field data generated by the sound field model with the actual received field data, and introduced the concept of ambiguity surface. Klemm (see [2] Klemm R. Range and depth estimation by line arrays in shallow water[J]. Signal Processing, 1981, 3(4): 333-344.) introduced a high-resolution generalized maximum entropy (ME) beamformer to perform depth estimation, and obtained better performance than the conventional MFP method. Li Peng (see [3] Li P, Zhang X H, Li L R, et al. Source depth discrimination using wavenumber domain feature with a horizontal array[J]. Applied Acoustics, 2020, 164(2): 107287.) et al. proposed a f-k modal domain distribution extraction method based on horizontal array, which realized the depth discrimination based on modal domain distribution characteristics. Yu Mengxu (see [4] Yu Mengxu, Zhou Shihong, Zhang Yan, et al. Source depth estimation based on coherent / incoherent energy ratio feature matching of shallow water broadband normal mode[J]. Acta Acustica, 2020, 45(3): 309-324.) et al. proposed a target depth estimation method using the energy ratio feature matching of the coherent and incoherent items of multi-order normal modes in the received signal of a single hydrophone, which eliminated the influence of sound source spectrum on passive target depth estimation and improved the robustness of depth estimation.Guo Lianghao et al.

[11] studied the physical phenomenon that near-surface sound sources are difficult to excite low-order modes and used the difference in the wavenumber spectrum structure and wavenumber position of the sound source to distinguish between surface sound sources and underwater sound sources. Zheng et al. (see reference [5] Zheng GY, Zhu F W. Difference factor of vertical beampattern for shallow-water source depth discrimination [J]. Acoustics Australia / Australian Acoustical Society, 2021, 49: 105-123.) considered the difference in the sound field excited by the surface target and the underwater target corresponding to the vertical array beam domain, and defined a feature of implicit target depth information - vertical beam spectrum spatial difference factor, which is used for passive target depth identification, and verified the effectiveness and tolerance of the algorithm based on Swellex-96 data.

[0003] Compared with the research on passive target depth estimation, the research on active sonar target depth estimation is relatively small. Premus et al. (see reference [6] Zheng GY, Zhu F W. Difference factor of vertical beam pattern for shallow-water source depth discrimination [J]. Acoustics Australia / Australian Acoustical Society, 2021, 49: 105-123.) extended their passive target depth identification method based on modal energy distribution to active sonar target depth identification and gave typical simulation analysis results. Han (see reference [7] Han N, Yao S. Discrimination of the active submerged / bottom target based on the total scintillation index [J]. Applied Acoustics, 2021, 172: 107646.) proposed a variant of the traditional modal scintillation index and introduced the mathematical definitions of the modulation scintillation index and the total scintillation index for active sonar target depth identification, which is used to distinguish surface / underwater targets in shallow water waveguides. The premise of the above method is to use a vertical or horizontal receiving array with sufficient aperture to achieve modal filtering. However, for traditional active horizontal towed sonar platforms, the equivalent horizontal physical aperture is not sufficient for modal filtering under the condition of considering the target orientation. Therefore, in the research of active sonar target depth estimation, the matching field positioning technology still receives a certain amount of attention. Yang (see reference [8] Yang T C. Method and system for sensing with an active acoustic array [R]. DEPARTMENT OF THE NAVY WASHINGTON DC, 1996.) proposed a broadband matching field positioning technology using multipath relative amplitude, and used a mode decomposition method based on the eigenvalue decomposition of the received data to achieve the estimation of target depth and distance.Ryan et al. (see reference [9] Goldhahn RA. Waveguide invariant active sonar target detection and depth classification in shallow water [D]. Duke University, 2010.) proposed a waveguide invariant adaptive filter technology, which constructs a frequency domain multi-snap target echo by short-time Fourier transform (STFT) of uniformly subsampled target echoes and then estimates the cross-spectral density matrix (SDM) for active sonar target depth estimation. However, the active matched field processing based on the above method has the following limitations: (1) target scattering will change the amplitude and phase of the sound propagation between the target and the sound source and receiving hydrophone; (2) the scattering function of the target is usually unknown. Therefore, it is meaningful to explore an active sonar target depth estimation method that does not rely on the target scattering characteristics. Summary of the invention:

[0004] The technical problem to be solved by the present invention is to provide a matching phase processing method for shallow water active sonar target depth estimation, so as to realize active sonar target depth estimation in shallow water environment.

[0005] The technical solution of the present invention is to provide a matching phase processing method for shallow water active sonar target depth estimation, comprising the following steps:

[0006] Step 1: Measurement field construction. The broadband echo signals received by receiver 1 and receiver 2 are processed to obtain the broadband interference structure η of the one-way propagation vector ratio of the measurement field. data (z t ,ω);

[0007] Step 2: Copy field construction. Based on the ocean waveguide environment, the KRAKEN model is used to calculate the acoustic field P of receiver 1. r1 (z s ,z t ,z r1 ,r,ω) and the sound field P of receiver 2 r2 (z s ,z t ,z r1 ,r,ω), construct the copy field one-way propagation vector ratio broadband interference structure η rplc (z t ,ω);

[0008] Step 3: Construct the ambiguity surface, and calculate the broadband interferometric structure η of the one-way propagation vector ratio of the measurement field. data(z t ,ω) and the ratio of the single-pass propagation vector of the copy field to the broadband interference structure η rplc (z t ,ω) performs matching phase processing and constructs the ambiguity surface for target depth estimation.

[0009] As a preferred method, the matching phase processing in step 3 is to perform correlation operation by calculating the phase characteristics of the broadband interference structure of the one-way propagation vector ratio of the measurement field and the copy field to construct the ambiguity function A. P (z) as follows,

[0010]

[0011] in, Represent the phase characteristics of the broadband interferometric structure of the one-way propagation vector ratio of the data and copy fields, z represents the estimated target depth, and the ambiguity function A P The depth corresponding to the peak of (z) is the estimated target depth.

[0012] Preferably, a low-frequency broadband signal is emitted by a single sound source, and vertical dual-receiving hydrophones receive target echoes.

[0013] Compared with the prior art, the present invention has the following advantages after adopting the above scheme:

[0014] The present invention utilizes low-frequency broadband target echoes emitted by a single sound source in a shallow-water waveguide and received by vertical dual hydrophones. The ratio of the received broadband echoes is calculated within the signal bandwidth as a measurement field vector. This is then matched with the copied field vector calculated using an acoustic field model for phase feature processing to estimate the target depth. This method eliminates the need to consider the influence of the target's scattering characteristics on depth estimation, reduces computational complexity, and facilitates the realization of real-time processing requirements. Description of the drawings:

[0015] Figure 1 A flow chart of a matching phase processing method for shallow water active sonar target depth estimation provided by an embodiment of the present invention;

[0016] Figure 2 Schematic diagram of single sound source transmission and vertical dual hydrophone reception detection;

[0017] Figure 3 The diagram of the Pekeris waveguide environment parameters and the sound source and receiver layout is shown;

[0018] Figure 4 is the amplitude characteristic of the one-way propagation vector broadband interference structure;

[0019] Figure 5 The phase characteristics of the single-pass propagation vector broadband interference structure;

[0020] Figure 6 To match the single-pass propagation vector broadband interferometry structure using different simple normal wave orders, the relationship between depth estimation error and signal-to-noise ratio;

[0021] Figure 7 The ambiguity surface and 10th-order ambiguity curves of the matching phase processing bathymetry algorithm for different target depths. Specific implementation method:

[0022] The present invention will be further described below with reference to the accompanying drawings:

[0023] like Figure 1-7 As shown, this embodiment provides a matching phase processing method for shallow water active sonar target depth estimation, the method comprising the following steps:

[0024] (1) Measurement field construction: The broadband echo signals received by receivers 1 and 2 are processed to obtain the broadband interference structure η of the one-way propagation vector ratio of the measurement field. data (z t ,ω);

[0025] (2) Copy field construction: Based on the ocean waveguide environment, the KRAKEN model is used to calculate the acoustic field P of receiver 1. r1 (z s ,z t ,z r1 ,r,ω) and the sound field P of receiver 2 r2 (z s ,z t ,z r1 ,r,ω), construct the copy field one-way propagation vector ratio broadband interference structure η rplc (z t ,ω);

[0026] (3) Construction of ambiguity surface, broadband interferometry structure η of the one-way propagation vector ratio of the measurement field data (z t ,ω) and the ratio of the single-pass propagation vector of the copy field to the broadband interference structure η rplc (z t ,ω) performs matching phase processing and constructs the ambiguity surface for target depth estimation.

[0027] Active sonar target depth estimation has always been a difficult problem in the field of underwater acoustics. To address this issue, this paper proposes a method for active sonar target depth estimation in shallow-water waveguides. This method uses a single acoustic source to transmit a low-frequency, broadband signal, dual vertical receiving hydrophones to receive the target echo, and then performs phase matching processing using the phase characteristics of the broadband target echo ratio. This method not only eliminates the influence of target scattering characteristics on active sonar target depth estimation, but also requires only the calculation of the channel transfer function, minimizing computational effort and facilitating real-time processing.

[0028] The present invention is achieved in that:

[0029] Figure 1 A schematic diagram (side view) of the distribution between a single sound source, two vertical receiving hydrophones, and a target is given. It is assumed that the distance between the target and the sound source and the two vertical receiving hydrophones is r.

[0030] From the simple sine wave theory, we know that for a horizontally constant layered waveguide, the depth is z s The unit intensity sound source is at distance r and depth z t The acoustic field excited at can be expressed as the superposition of several simple normal waves:

[0031]

[0032] Distance r, depth z t The sound field excited by the unit intensity target at the receiving depth z ri At:

[0033]

[0034] Where k rm , k rn represents the eigenvalue, ψ m is the corresponding eigenvalue k rm The eigenfunction of n is the corresponding eigenvalue k rn The eigenfunction of M is the order of the simple normal wave, r is the distance from the target to the sound source and the receiving hydrophone, z s is the sound source depth, z ri is the depth of the i-th receiving hydrophone, z t is the depth of the target, ρ is the density of seawater, and ω is the angular frequency.

[0035] In active sonar target depth estimation, the complex scattering characteristics of the target are often an important factor affecting depth estimation. In fact, the scattering function T(ω,θ i ,θ o ) is the frequency, the incident angle θ i , scattering angle θ ofunction, considering that the sound wave propagates over long distances in shallow water, the sound source excites the sound signal to propagate to the target, and the incident grazing angle θ i It is very small, close to horizontal incidence. And after being scattered by the target, it can be transmitted over a long distance to the receiving hydrophones at different depths. The scattering grazing angle θ o Therefore, under the parameter settings discussed in this paper, the following assumptions are made, ignoring the scattering function T(ω,θ i ,θ o ) in the incident grazing angle θ i , the grazing angle of scattering θ o The target scattering function is simplified to T(ω).

[0036] Assuming that there is no significant difference in the horizontal distances between a single sound source, two vertical receiving hydrophones and the target (approximately equal), the sound wave S(ω) transmitted by the sound source propagates to the target p(r, z s ,z t ,ω), is scattered by the target T(ω), and then propagates to the receiving hydrophone p(r,z ri ,z t ,ω), the sound field received by the receiving hydrophone can be expressed as:

[0037]

[0038] Among them, S(ω) represents the spectrum of the sound wave emitted by the sound source, and T(ω) represents the scattering function of the target.

[0039]

[0040] Considering that the broadband target echo P is received by two vertical hydrophones at different depths and emitted by the same sound source, ri (z s ,z t ,z ri ,r,ω) in B(z s ,z t ,r,ω) are consistent, so the received broadband target echo can be used to eliminate the influence of target scattering.

[0041]

[0042] η(z t ,ω) is defined as the broadband interferometric structure of the ratio of the one-way propagation vector of the target echo. This equation is dependent only on factors such as the receiving hydrophone depth, target depth, target distance, and frequency, and is independent of the target scattering characteristics. This means that if we use two hydrophones at different depths to receive broadband target echoes, we can use the echo ratio to eliminate the influence of target scattering.

[0043] The depth information of the target is implicit in η(zt ,ω) with the oscillation distribution of frequency, it is difficult to summarize the relationship between target depth and η(z t ,ω) broadband distribution, a feasible method is to convert η(z t ,ω) is used as the feature of implicit target depth, and the target depth estimation is achieved by matching the feature structure.

[0044] Method 1: Matched Amplitude Processing (MAP) calculates the amplitude characteristics of the broadband interference structure of the one-way propagation vector ratio of the data and the copy field to perform correlation operations and construct the ambiguity function A M (z) as follows,

[0045]

[0046] in, The amplitude characteristics of the broadband interference structure of the one-way propagation vector ratio of the data and copy fields, and the ambiguity function A M The depth corresponding to the peak of (z) is the estimated target depth.

[0047] Method 2: Matched Phase Processing (MPP) calculates the phase characteristics of the broadband interference structure of the one-way propagation vector ratio of the data and the copy field to perform correlation operations and construct the ambiguity function A P (z) as follows,

[0048]

[0049] in, The phase characteristics of the broadband interferometer structure, the ambiguity function A, represent the ratio of the single-pass propagation vectors of the data and copy fields, respectively. P The depth corresponding to the peak of (z) is the estimated target depth.

[0050] Method 3: Matched Amplitude-Phase Processing (MAPP) performs correlation operations by directly calculating the broadband interferometric structure (including amplitude and phase information) of the one-way propagation vector ratio between the data and the copy field, and constructs the ambiguity function A(z) as follows:

[0051]

[0052] Among them, η data (z t ,ω),η rplc(z, ω) represents the broadband interference structure of the one-way propagation vector of the data and copy fields respectively. The depth corresponding to the peak of the ambiguity function A(z) is the estimated target depth.

[0053] The present invention selects Pekeris waveguide for simulation analysis and uses KRAKEN simple normal wave sound field calculation program to simulate the model. The environmental parameters and the layout diagram of the sound source and receiving hydrophone are shown in the figure. Figure 2 shown.

[0054] The simulation conditions are as follows: the water layer depth is 100m, the sound velocity profile in the water layer is constant, the sound velocity is 1500m / s, and the density is 1g / cm 3 ; The sediment depth is 50m, the speed of sound is 1700m / s, and the density is 1.9g / cm 3 The absorption coefficient is 0.1dB / λ. The depth of a single sound source is z s =50m, vertical receiving hydrophone 1 depth z r1 =40m, vertical receiving hydrophone 2 depth z r2 = 60m, and assuming the target depth z t The parameter to be estimated is the target distance r = 30km. The transmitted signal is a hyperbolic frequency modulated (HFM) signal with a signal duration of 4s and a frequency range of 400Hz-800Hz. The Green's function p(r, z) from the sound source to the target is calculated using the KRAKEN model. s ,z t ,ω) and the Green's function p(r,z t ,z ri ,ω), then the sound field received by different vertical receiving hydrophones can be expressed as P ri (z s ,z t ,z ri ,r,ω)=S(ω)p(r,z s ,z t ,ω)T(ω)p(r,z t ,z ri ,ω), the scattering function of the target is set to be a random complex number related to the frequency, and S(ω) is the spectrum of the transmitted signal. ri (z s ,z t ,z ri ,r,ω) is transformed by inverse Fourier transform to obtain the time domain waveform P of the received data ri (z s ,z t ,z ri ,r,t).

[0055] The signal-to-noise ratio (SNR) is one of the important parameters for evaluating the performance of the method. The SNR is set as follows: considering that the spatially distributed white noise that obeys the Gaussian distribution is superimposed on the simulated time domain signal, the white noise is added to the time domain signal received by the receiving hydrophone at a certain SNR.

[0056]

[0057] Among them, P ri (z s ,z t ,z ri ,r,t) and P ri (z s ,z t ,z ri ,r,ω) is the Fourier transform pair, and w(t) is the superimposed white noise. It is important to note that the bandwidth and time duration of the noise should be consistent with those of the signal. In subsequent simulation analysis, the bandwidth and time duration of the added Gaussian white noise will be consistent with those of the signal, so this description will not be repeated.

[0058] The received data time domain waveform P ri (z s ,z t ,z ri ,r,t) are superimposed with Gaussian white noise, and the signal-to-noise ratio is set to 10dB. The ratio of broadband target echo data obtained by the vertical dual-receiver hydrophone is used to eliminate the influence of target scattering, and a single-path propagation vector ratio broadband interferometer structure is obtained. Figure 3 and Figure 4 The amplitude and phase characteristics of the one-way vector ratio broadband interferometer structure based on different order modes are presented. It can be seen that, for both the amplitude and phase characteristics of the one-way vector ratio broadband interferometer structure, the actual sound field oscillates relatively violently due to the influence of all order modes, while the sound field constructed by a limited number of low-order modes depicts the envelope of the oscillations. In other words, the low-order modes depict the envelope structure, and the high-order modes depict the fine structure. As the number of selected simple normal wave orders increases, the variations of the one-way vector ratio broadband interferometer structure at different depths and frequencies become more complex, and its sensitivity to target depth is significantly enhanced. This indicates that selecting a one-way vector ratio broadband interferometer structure with a larger number of simple normal wave orders for matching processing can improve the performance of target depth estimation.

[0059] The broadband interference structure of the one-way propagation vector contributed by all-order simple normal waves is used as the measurement field. Then, the broadband interference structure of the one-way propagation vector ratio contributed by different-order simple normal waves is calculated using KRAKEN as the copy field. The ambiguity function of different matching sounding algorithms is calculated using equations (7) to (9) to estimate the target depth. The simulation conditions are as follows: sea depth 100m, constant sound velocity hydrology, environmental parameters, sound source and receiving hydrophone deployment depths, and transmission signal parameters are the same as above. Target distance r = 30km, target depth z t = 50m. Set the signal-to-noise ratio to continuously change from -20dB to 10dB, with an interval of 1dB. Perform 100 simulations at each signal-to-noise ratio and calculate the average error of depth estimation. Figure 5 (a)-(c) give the target depth z t =50m, the single-pass propagation vector ratio broadband interferometry structure with 5th order, 10th order and all order simple normal waves is used to match and simulate the estimation results under different matching depth sounding algorithms. It can be seen that

[0060] (1) For the matched amplitude processing bathymetry algorithm and the matched amplitude phase processing bathymetry algorithm, regardless of whether a finite-order or full-order simple normal wave is used, the average error of depth estimation tends to remain unchanged as the signal-to-noise ratio increases, and the average error remains at around 20 m. This indicates that the matched amplitude processing bathymetry algorithm and the matched amplitude phase processing bathymetry algorithm based on the single-pass propagation vector ratio broadband interferometry structure are not suitable for target depth estimation;

[0061] (2) For the matched phase processing bathymetry algorithm, the average error of depth estimation tends to decrease as the signal-to-noise ratio increases, and the more simple normal wave orders are used, the greater the decrease in the estimated average error. When all orders of simple normal waves are used and the signal-to-noise ratio is greater than -10dB, the estimated average error is generally within 5m. This shows that the matched phase processing bathymetry algorithm based on the one-way propagation vector ratio broadband interferometer structure can be used to estimate the target depth. The higher the signal-to-noise ratio, the smaller the estimated average error is overall. Under the condition of no parameter mismatch, the more simple normal wave orders are used, the better the depth estimation performance. In all subsequent simulations, the matched phase processing bathymetry algorithm with all order modal contributions is selected for target depth estimation.

[0062] The KRAKEN model copy field is divided into 201×101 grids. Since the target distance is roughly known in the active sonar scenario, assuming the target distance is 30km, the matching positioning search range is 29km~31km in the distance direction, with a distance interval of 10m, and 0m~100m in the depth direction, with a depth interval of 1m, and a signal-to-noise ratio of 0dB. Figure 6The ambiguity surfaces and 10th-order ambiguity curves for depth estimation at target depths of 10m, 50m, and 90m at a target distance of 30km are shown. As can be seen, the target depth is accurately estimated at all target depths, and there are no additional peaks in the ambiguity curves. The main-to-sidelobe ratio for targets in water (target depth of 50m), near the water surface (target depth of 10m), and near the seabed (target depth of 90m) is approximately 6dB.

[0063] The above description is only for the preferred embodiment of the present invention, which should not be understood as limiting the claims. Any equivalent structure or equivalent process transformation made by using the description of the present invention is included in the patent protection scope of the present invention.

Claims

1. A matching phase processing method for shallow water active sonar target depth estimation, characterized by: The following steps are included: Step 1, for a horizontally invariant layered waveguide with a depth of z s The unit intensity sound source is at distance r and depth z t The acoustic field excited at can be expressed as the superposition of several simple normal waves: Distance r, depth z t The sound field excited by the unit intensity target at the receiving depth z ri The place is: Where k rm , k rn represents the eigenvalue, ψ m is the corresponding eigenvalue k rm The eigenfunction of n is the corresponding eigenvalue k rn The eigenfunction of M is the order of the simple normal wave, r is the distance from the target to the sound source and the receiving hydrophone, z s is the sound source depth, z ri is the depth of the i-th receiving hydrophone, z t is the depth of the target, ρ is the density of seawater, and ω is the angular frequency; Assuming that the horizontal distances between a single sound source, two vertical receiving hydrophones and the target are approximately equal, the sound wave S(ω) transmitted by the sound source propagates to the target p(r, z s ,z t ,ω), is scattered by the target and then propagates to the receiving hydrophone p(r,z ri ,z t ,ω), the sound field received by the receiving hydrophone can be expressed as: Among them, S(ω) represents the spectrum of the sound wave emitted by the sound source, and T(ω) represents the scattering function of the target; Considering the broadband target echo P received by two vertical hydrophones at different depths and emitted by the same sound source, ri (z s ,z t ,z ri ,r,ω) in B(z s ,z t ,r,ω) are consistent, so the received broadband target echo can be used to eliminate the influence of target scattering. η(z t ,ω) is defined as the one-way propagation vector ratio broadband interference structure of the target echo; the measurement field is constructed by processing the broadband echo signals received by receiver 1 and receiver 2 to obtain the one-way propagation vector ratio broadband interference structure η of the measurement field data (z t ,ω); Step 2: Copy field construction. Based on the ocean waveguide environment, the KRAKEN model is used to calculate the acoustic field P of receiver 1. r1 (z s ,z t ,z r1 ,r,ω) and the sound field P of receiver 2 r2 (z s ,z t ,z r1 ,r,ω), construct the copy field one-way propagation vector ratio broadband interference structure η rplc (z t ,ω); Step 3: Construct the ambiguity surface, and calculate the broadband interferometric structure η of the one-way propagation vector ratio of the measurement field. data (z t ,ω) and the ratio of the single-pass propagation vector of the copy field to the broadband interference structure η rplc (z t ,ω) performs matching phase processing and constructs the ambiguity surface for target depth estimation.

2. The matching phase processing method for shallow water active sonar target depth estimation according to claim 1, characterized in that: The matching phase processing in step 3 is to perform correlation operation by calculating the phase characteristics of the broadband interference structure of the one-way propagation vector ratio of the measurement field and the copy field, and construct the ambiguity function A P (z) as follows, in, Represent the phase characteristics of the broadband interferometric structure of the one-way propagation vector ratio of the measurement field and the copy field, z represents the estimated target depth, and the ambiguity function A P The depth corresponding to the peak of (z) is the estimated target depth.

3. The matching phase processing method for shallow water active sonar target depth estimation according to claim 1, characterized in that: In step 1, a low-frequency broadband signal is transmitted through a single sound source, and the vertical dual-receiving hydrophone receives the target echo.

Images