A fast broadband coherent difference frequency beamforming and phase-only coherent difference frequency beamforming method and system

Abstract

The present application provides a method and system for fast broadband coherent difference frequency beamforming and phase-only coherent difference frequency beamforming. The method first performs difference frequency self-integration processing on the different frequency components of the broadband signal, then uses the phase information obtained after spatial filtering of the self-integration term to perform coherent processing on the self-integration term, and finally obtains the broadband signal azimuth estimation spectrum by coherent averaging in the frequency domain and difference frequency domain. This method inherits the spatial anti-aliasing capability of the traditional difference frequency beamforming method and has low computational complexity. It can be easily implemented in real time in engineering applications. In the multi-target azimuth estimation scenario, this method can effectively suppress the interference amplified by the difference frequency self-integration, improve the resolution of multiple targets, and significantly reduce the background sidelobe level of the azimuth spectrum, thereby improving the detection capability of weak targets.

Description

Technical Field

[0001] The present application belongs to the field of underwater acoustic array signal processing, and specifically relates to a method and system for fast broadband coherent difference-frequency beamforming and phase-only coherent difference-frequency beamforming. Background Art

[0002] Direction of Arrival (DOA) estimation refers to the process of finding the direction of the sound source from the spatial spectrum of the output of the receiving sensor array and is an important research topic in array signal processing. Conventional beamforming (CBF) is a widely used array data processing method. Its basic principle is the delay-sum method, which has the advantages of low computational complexity and good robustness. However, for sparse arrays, when the array element spacing is greater than half the wavelength of the received signal, the CBF method will produce grating lobes due to spatial aliasing, which seriously affects the target's azimuth estimation performance. In addition, due to the complex and changeable ocean environment, there may be sound velocity profile mismatch and array mismatch. High-frequency processing is more sensitive to mismatch and is prone to performance degradation. The frequency difference (FD) method is a method that combines information between multiple frequency points to suppress grating lobes by reducing the operating frequency.

[0003] However, the lower operating frequency after difference frequency processing will increase the azimuth pattern beamwidth and reduce the target resolution, making it difficult to distinguish when the target azimuth angle is close. At the same time, the self-integration processing introduces the product of noise and sound source terms, which amplifies the noise effect and increases the overall sidelobe level. In addition, in the case of multiple targets, the self-integration of the signal difference frequency will bring additional cross terms, which will also interfere with target detection. In complex ocean environments or multi-target situations, it will cause problems such as weak targets being masked.

[0004] The traditional wideband FDB method suppresses cross-term interference by incoherently averaging the single difference frequency results in the frequency domain and difference frequency domain. Although it has a certain effect, under conditions of multiple targets or low signal-to-noise ratio, there are many cross-terms and strong noise interference. Direct frequency domain accumulation will lead to a significant increase in the background level. The sidelobes of strong targets can easily mask weak targets, resulting in the failure of target direction estimation. Summary of the Invention

[0005] The purpose of this application is to provide a coherent processing method based on a conventional broadband difference frequency beamforming method, which can quickly reduce the sidelobe level, improve target resolution and improve the detection capability of weak targets.

[0006] To achieve the above objectives, the present application proposes a fast broadband coherent difference-frequency beamforming and phase-only coherent difference-frequency beamforming method, comprising:

[0007] Step 1: Define the angle scanning domain;

[0008] Step 2: Select the processing frequency domain according to the signal reception spectrum;

[0009] Step 3: Select the appropriate difference frequency and calculate the steering vector according to the formation;

[0010] Step 4: Select the corresponding frequency point within the received signal frequency band and construct the self-product by conjugate multiplying the high-frequency signal pair;

[0011] Step 5: Spatial filtering is performed on the autointegral term to obtain phase information, and coherence correction is performed on the difference frequency autointegral to obtain the normalized coherent difference frequency autointegral and the phase-only coherent difference frequency autointegral.

[0012] Step 6: Use the steering vector to perform difference-frequency beamforming on the normalized coherent autoproduct term and the phase-only coherent autoproduct term to obtain the result at the corresponding difference frequency.

[0013] Step 7: Traverse the difference frequency domain and frequency domain, and perform coherent averaging in the difference frequency domain and frequency domain to obtain the target direction estimation result.

[0014] As an improvement to the above method, step 2 includes:

[0015] Select the processing frequency f according to the signal receiving spectrum l , l represents the index in the frequency domain; [f L ,f H ] represents the frequency range of the signal from a distant point source; d represents the array element spacing; and c represents the speed of sound.

[0016] As an improvement to the above method, step 3 includes:

[0017] Select the difference frequency domain {Δf}:

[0018]

[0019] Wherein, N represents the number of difference frequency points contained in the difference frequency domain; n represents the index in the difference frequency domain;

[0020] Calculate the beamforming steering vector w m (Δf n ,θ i ):

[0021]

[0022] Among them, θ i represents the i-th angle in the angle scanning domain; m represents the array element number;

[0023] According to the difference frequency Δf n Select the high frequency point f l +Δfn .

[0024] As an improvement to the above method, step 4 includes:

[0025] Select the frequency domain signal x received by the mth array element from the kth sound source within the receiving frequency band m (f l ) and x m (f l +Δf n ), construct the self-integral term according to the following formula

[0026] AP m (Δf n ,f)={x m (f)} * x m (f+Δf n )

[0027] Where * indicates conjugation;

[0028] Use the current difference frequency Δf n The corresponding steering vector w θ (n) For the self-integral Perform spatial filtering to obtain phase information

[0029]

[0030] in, Indicates that the corresponding high frequency is f and the difference frequency is Δf n The self-product of θ (n) Indicates the difference frequency Δf n and the angle θ in the scanning angle domain i The steering vector of represents the phase information obtained by spatial domain filtering of the self-product term of the array received signal; H represents the conjugate matrix;

[0031] Using phase information The normalized self-integral term is coherently corrected to obtain

[0032]

[0033] Wherein, j represents the imaginary unit;

[0034] Only the phase information of the corrected autointegral is used to obtain the phase-coherent autointegral term

[0035]

[0036] As an improvement to the above method, step 5 includes:

[0037] The steering vector is used to perform difference frequency beamforming on the normalized coherent self-product term and the phase-only coherent self-product term to obtain the corresponding difference frequency Δf n The coherent difference frequency and phase-coherent difference frequency beamforming results under A c (θ i ) and A poc (θ i ):

[0038]

[0039] Where M represents the number of array elements; w m (Δf n ,θ i ) indicates that the mth array element corresponds to the current difference frequency Δf n and the angle θ in the scanning angle domain i The guiding vector.

[0040] As an improvement to the above method, step 6 includes:

[0041] Repeat steps 3 to 5 until all difference frequencies in the difference frequency domain are traversed to obtain the average result B of the coherent difference frequency and the phase-coherent difference frequency in the difference frequency domain. CFDB {Δf} (f,θ i ) and B POCFDB {Δf} (f,θ i ):

[0042]

[0043] As an improvement to the above method, step 7 includes:

[0044] Repeat steps 2 to 6 to process the next selected high-frequency point until all frequency points in the receiving band are traversed, and obtain the coherent averaging results of the coherent difference frequency and the phase-only coherent difference frequency in the frequency domain:

[0045]

[0046] Where F represents the number of frequency pairs used to construct the difference frequency self-product in the frequency domain;

[0047] Repeat steps 2 to 7 to traverse the angle search range and get each θ i The target estimated direction spectrum p under CFDB (θ i ) and p POCFDB (θ i ).

[0048] The present application also provides a fast broadband coherent difference-frequency beamforming and phase-only coherent difference-frequency beamforming system, which is implemented based on the above method, and includes:

[0049] Define angle scanning domain module, used to define the angle scanning domain;

[0050] A frequency domain selection module is used to select a frequency domain for processing based on the received signal spectrum.

[0051] The steering vector calculation module is used to select the appropriate difference frequency and calculate the steering vector according to the formation;

[0052] Constructing an auto-integration module, which is used to select a corresponding frequency point within the received signal frequency band and construct an auto-integration by conjugate multiplication of high-frequency signal pairs;

[0053] A coherent correction module is used to filter the autointegration term in the spatial domain to obtain phase information, and to perform coherent correction on the difference frequency autointegration to obtain normalized coherent difference frequency autointegration and phase-only coherent difference frequency autointegration;

[0054] A difference frequency beamforming module is used to perform difference frequency beamforming on the normalized coherent self-product term and the phase-only coherent self-product term using a steering vector to obtain a result at the corresponding difference frequency;

[0055] The coherent averaging module is used to traverse the difference frequency domain and the frequency domain, and obtain the target direction estimation results by coherent averaging in the difference frequency domain and the frequency domain respectively.

[0056] Compared with the prior art, the advantages of this application are:

[0057] Both simulation and experimental results demonstrate that, in multi-target azimuth estimation scenarios with low signal-to-noise ratios, the proposed method effectively improves target resolution, reduces sidelobe levels, and enhances weak target detection compared to existing broadband incoherent FDB methods. This method inherits the spatial anti-aliasing capabilities of traditional difference-frequency beamforming methods and exhibits low computational complexity, making real-time processing relatively easy to implement in engineering applications. In multi-target azimuth estimation scenarios, this method effectively suppresses interference amplified by difference-frequency self-products, enhances multi-target resolution, and significantly reduces the background sidelobe levels of the azimuth spectrum, thereby improving weak target detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 Shown are broadband single-target azimuth spectra at different signal-to-noise ratios;

[0059] Figure 2 Shown is a broadband multi-target azimuth spectrum;

[0060] Figure 3 The figure shows the change of weak target direction finding success rate with signal-to-noise ratio;

[0061] Figure 4 Shown is the mean square error of the direction finding result as the signal-to-noise ratio changes;

[0062] Figure 5 The figure shows the broadband multi-target azimuth course diagram; from top to bottom, the first figure shows the high-frequency CBF; the second figure shows the broadband ICFDB; the third figure shows the broadband CFDB; the fourth figure shows the broadband POCFDB;

[0063] Figure 6 The figure shows the comparison of the azimuth spectrum results of the four methods when the two targets are close in azimuth at the 8th minute;

[0064] Figure 7 Shown is the variation of resolution probability with angular separation;

[0065] Figure 8 Shown is the horizontal array formation used for the measured data at sea;

[0066] Figure 9(a) shows the azimuth history of the marine measured data processed by the ICFDB method;

[0067] Figure 9(b) shows the azimuth history diagram of the offshore measured data processed by the CFDB method;

[0068] Figure 9(c) shows the azimuth history diagram of the offshore measured data processed by the POCFDB method;

[0069] Figure 10 The following is a comparison of the azimuth spectra at the 30th minute using the three difference frequency methods;

[0070] Figure 11 Shown is a flow chart of the fast broadband coherent difference-frequency beamforming and phase-only coherent difference-frequency beamforming methods. DETAILED DESCRIPTION

[0071] The technical solution of this application is described in detail below with reference to the accompanying drawings.

[0072] To address the high background level of the azimuth spectrum in the frequency difference beamforming (FDB) method for broadband signals, this paper proposes a fast broadband coherent frequency difference beamforming (CFDB) and phase-only coherent frequency difference beamforming (POCFDB) method by utilizing the phase information of the autointegration term in the difference frequency method to correct the autointegration term. This method first performs difference frequency autointegration processing on the different frequency components of the broadband signal, thereby reducing the processing frequency to meet the spatial Nyquist sampling requirements of a sparsely arranged array. The autointegration term is then coherently processed using the phase information obtained after spatial filtering. Finally, the broadband signal azimuth spectrum is obtained by coherently averaging in the frequency and difference frequency domains. Simulations demonstrate that this method inherits the spatial anti-aliasing capability of traditional difference frequency beamforming methods while having lower computational complexity, making it relatively easy to implement real-time processing in engineering applications. In multi-target azimuth estimation scenarios, this method effectively suppresses interference amplified by difference frequency self-integration, improves multi-target resolution, and significantly reduces the background sidelobes of the azimuth spectrum, thereby enhancing detection of weak targets. By processing horizontal array data from offshore trials, a comparative analysis of azimuth history maps obtained using a conventional wideband difference frequency beamforming algorithm and the proposed coherent processing algorithm verifies the superior performance of the coherent processing algorithm.

[0073] like Figure 11 As shown, the present invention provides a fast broadband coherent difference frequency beamforming and phase-only coherent difference frequency beamforming method, the method comprising the following steps:

[0074] Step 1: Define the angle scanning grid Θ;

[0075] Step 2: Select the processing frequency domain according to the signal reception spectrum;

[0076] Step 3: Select the appropriate difference frequency and calculate the steering vector according to the formation;

[0077] Step 4: Select the corresponding frequency point within the received signal frequency band and construct the self-product by conjugate multiplying the high-frequency signal pair;

[0078] Step 5: Spatial filtering is performed on the autointegral term to obtain phase information, and coherence correction is performed on the difference frequency autointegral to obtain the normalized coherent difference frequency autointegral and the phase-only coherent difference frequency autointegral.

[0079] Step 6: Use the steering vector to perform difference-frequency beamforming on the normalized coherent autoproduct term and the phase-only coherent autoproduct term to obtain the result at the corresponding difference frequency.

[0080] Step 7: Traverse the difference frequency domain and frequency domain, and perform coherent averaging in the difference frequency domain and frequency domain to obtain the target direction estimation result.

[0081] Simulation parameters: We consider a uniform linear array with M equally spaced elements and assume that the signal from a distant point source is of frequency f∈[f L ,f H ] broadband plane wave, the frequency domain signal received by the mth array element from the kth sound source can be written as:

[0082]

[0083] Where f is the signal frequency, s k (f) is the spectrum of the kth source signal, n m (f) is the additive Gaussian noise on the mth array element that is uncorrelated with the signal, d is the array element spacing, θ k is the angle between the incident plane wave of the kth signal and the positive transverse direction of the receiving array, c is the speed of sound, Represents an imaginary unit.

[0084] Step 1: Set the search range and define the angle scanning domain Θ, let i = 1, θ i ∈Θ.

[0085] Step 2: Determine the processing frequency and select the processing frequency f according to the signal reception spectrum l , l represents the index in the frequency domain, let l = 1,

[0086] Step 3: Select the difference frequency domain, {Δf}=[Δf1,Δf2,...,Δf n ,...,Δf N ], N represents the number of difference frequency points contained in the difference frequency domain, n represents the index in the difference frequency domain, let n = 1, calculate the beamforming steering vector According to the difference frequency Δf n , select the high frequency point f l +Δf n .

[0087] Step 4: Calculate the difference frequency self-product and select the frequency domain signal x received by the mth array element from the kth sound source within the receiving frequency band. m (f l ) and x m (f l +Δf n ), according to the formula Conjugate multiplication to construct self-product * indicates conjugation; uses the current difference frequency Δf n The corresponding steering vector For the self-integral Perform spatial filtering to obtain phase information Right now

[0088] The phase information is used to perform coherent correction on the normalized self-integral term to obtain Right now:

[0089]

[0090] Further considering only the phase information of the modified self-integral to obtain the phase-coherent self-integral term Right now:

[0091]

[0092] Step 5: Difference frequency beamforming: Use the steering vector to perform difference frequency beamforming on the normalized coherent self-product term and the phase-only coherent self-product term to obtain the corresponding difference frequency Δf n The coherent difference frequency and phase-only coherent difference frequency beamforming results are:

[0093]

[0094] Among them, w m (Δf n ,θ i ) indicates that the mth array element corresponds to the current difference frequency Δf n and the angle θ in the scanning angle domain i The guiding vector.

[0095] Step 6: Estimate the azimuth spectrum. Set n = n + 1 and repeat steps 3 to 5 until all difference frequencies in the difference frequency domain are traversed to obtain the average results of coherent difference frequencies and phase-coherent difference frequencies in the difference frequency domain:

[0096]

[0097] Where * represents the conjugate of the steering vector.

[0098] Step 7: Frequency domain coherent averaging. Let l = l + 1, and repeat steps 2 to 6 for the next selected high-frequency point until all frequency points in the receiving band are traversed. F represents the number of frequency pairs used to construct the difference frequency self-product in the frequency domain. The coherent difference frequency and the phase-only coherent difference frequency are obtained in the frequency domain coherent averaging results:

[0099]

[0100] Step 8: Traverse the angle search range, set i = i + 1, repeat steps 2 to 7, and calculate each θ i The following p CFDB (θ i ) and p POCFDB (θi );

[0101] Step 9: Output target estimated orientation spectrum p CFDB (θ) and p POCFDB (θ).

[0102] Example 1: The target direction estimation performance of the proposed algorithm is verified through simulation. The proposed method is compared with the traditional incoherent average difference frequency beamforming method (ICFDB) in the case of single target, multiple targets, and weak targets. The reliability of the method is analyzed through Monte Carlo simulation experiments. The simulation experiment uses a 20-element horizontal linear array with an array element spacing of d = 10m and a sound speed of c = 1500m / s. The signal-to-noise ratio is defined as the ratio of the signal power σ within the processing bandwidth to the noise power η under the same bandwidth, that is,

[0103] Simulation A:

[0104] The difference frequency domain is selected as 70~90Hz, the step size is 1Hz, a total of 21 difference frequency points, the processing frequency band is 950~1050Hz, and the step size is 1Hz. The broadband signal is processed according to the steps of this application, and the results of the three methods of ICFDB, CFDB and POCFDB are compared at different signal-to-noise ratios. The results are as follows Figure 1 As shown in the figure, comparing the three methods, the sidelobe background level of CFDB and POCFDB is significantly lower than that of ICFDB, proving that the method proposed in this application can enhance the main peak and thus suppress the sidelobes. Adding a weak target with an amplitude half the amplitude of the other two targets at -10°, the azimuth spectra of the three methods are as follows when the signal-to-noise ratio is -10dB. Figure 2 As shown, it can be seen that when there is a weak target under low signal-to-noise ratio conditions, the ICFDB method results in a higher sidelobe near 0°, and the weak target at -10° is submerged by the sidelobe of the strong target, resulting in direction finding failure; the two coherent processing methods can still detect the weak target, proving that the method proposed in this application can effectively reduce the sidelobe level and enhance the detection capability of weak targets.

[0105] Simulation B:

[0106] 200 Monte Carlo simulations were performed on the three methods respectively, and the success rates of the positioning results for weak targets were statistically analyzed as follows: Figure 3 As shown in the figure, the mean square error of the direction finding results of the three methods changes with the signal-to-noise ratio. Figure 4 As shown by Figure 3 It can be seen that when the signal-to-noise ratio is -10 to 0 dB, ICFDB fails to find the direction of weak targets. In contrast, the two coherent processing methods almost all succeed in finding the direction. Figure 4It can be seen that due to the failure of ICFDB to detect weak targets, when the signal-to-noise ratio is -10 to 0 dB, the overall mean square error of ICFDB is significantly higher than that of CFDB and POCFDB.

[0107] The performance of the proposed method in this application is evaluated when the azimuths of the two targets change. The initial azimuths of the two targets are set to -20° and 30° respectively. The azimuths of the two targets change linearly. The signal-to-noise ratio is set to 0dB. The azimuth history diagrams of the traditional beamforming method and the three FDB methods are compared. Figure 5 As shown; intercept the 8th minute when the two targets are close to each other, the four methods of azimuth spectra are as follows Figure 6 As shown. Figure 5 As can be seen from the first figure, due to spatial aliasing, multiple grating lobes appear in the CBF result at high frequencies, making it impossible to accurately find the direction. Figure 5 As can be seen from the second, third, and fourth figures, the main lobe of the CFDB method and the POCFDB method proposed in this application is narrower and the background side lobe level is also lower; Figure 6 It can be seen that when the incident angles of two targets are very close, the traditional method can no longer distinguish the two targets, and the method proposed in this application has better azimuth resolution.

[0108] The angular resolution performance of the proposed method in this application is evaluated when the two targets' azimuths change. The same conditions as in simulation C are set, 100 Monte Carlo simulations are performed, and the azimuth interval between the two targets is set to change from 0 to 10°. The resolution probability results of the three FDB methods for the two targets are compared as shown in the figure below. Figure 7 It can be observed that the ICFDB method can basically completely distinguish the two targets when the azimuth interval between the two targets is more than 6°. Both coherence methods can improve the resolution of the results. Among them, the CFDB method has the best target resolution probability and can basically accurately distinguish the two targets when the target azimuth interval is greater than 3°.

[0109] Example 2: The azimuth estimation performance of CBF, ICFDB and the two methods proposed in this invention (CFDB and POCFDB) is verified and analyzed using sea trial data. The sea trial data used is from a seabed horizontal array acoustic experiment. The sea depth of the experimental sea area is about 73m. The array used is a 32-element horizontal array deployed on the seabed (the actual number of working array elements is 29). The seawater sound speed at the receiving array is about 1480m / s. The received signal is ship noise with a sampling rate of 6000Hz. The array element position distribution of the horizontal array is as follows: Figure 8 shown.

[0110] For the experimental received signal 600-800Hz band signal, the difference frequency domain is selected as 90-100Hz, and ICFDB, CFDB and POCFDB are performed respectively. The results are as follows Figure 9(a)-Figure 9(c)As shown, Figure 9(a) is the result of the ICFDB method. It can be seen that although the broadband difference frequency processing can effectively remove the grating lobes, the background sidelobe level is high. At the same time, the comparison chart shows that after 25 minutes, due to the increase in the self-integration cross terms caused by multiple targets, the ICFDB method results show a significant increase in the background level. The trajectory of weak target 1 is relatively blurred, and the trajectory of weak target 2 is completely submerged, making it difficult to accurately find the direction; Figures 9(b) and 9(c) are the results of the coherent processing difference frequency method, which are the azimuth history diagrams obtained by the CFDB and POCFDB methods respectively. It can be seen that in the case of a single target, the background sidelobe level is significantly reduced after coherent processing, the main lobe is narrower, and the azimuth history diagram is clearer. After 25 minutes, only the trajectory of weak target 1 can be observed from Figure 9(a), and weak target 2 is completely submerged by the sidelobe. (b) and (c) can see the trajectories of weak target 1 and weak target 2 at the same time; Figure 10 The azimuth estimation results of the 61st frame (30th minute) of the three methods are plotted. It can be seen that although ICFDB can see the trajectory of weak target 1, the amplitude of weak target 1 output in a single frame is lower than the sidelobe level near 340-360°. In contrast, both coherent methods have an enhancement effect on weak targets. Two weak targets can be observed at positions of 228° and 288°. The sidelobe level of the CFDB method is the lowest, which proves that the method proposed in this invention has a certain improvement on the azimuth estimation performance.

[0111] The present application also provides a fast broadband coherent difference-frequency beamforming and phase-only coherent difference-frequency beamforming system, which is implemented based on the above method, and includes:

[0112] Define angle scanning domain module, used to define the angle scanning domain;

[0113] A frequency domain selection module is used to select a frequency domain for processing based on the received signal spectrum.

[0114] The steering vector calculation module is used to select the appropriate difference frequency and calculate the steering vector according to the formation;

[0115] Constructing an auto-integration module, which is used to select a corresponding frequency point within the received signal frequency band and construct an auto-integration by conjugate multiplication of high-frequency signal pairs;

[0116] A coherent correction module is used to filter the autointegration term in the spatial domain to obtain phase information, and to perform coherent correction on the difference frequency autointegration to obtain normalized coherent difference frequency autointegration and phase-only coherent difference frequency autointegration;

[0117] A difference frequency beamforming module is used to perform difference frequency beamforming on the normalized coherent self-product term and the phase-only coherent self-product term using a steering vector to obtain a result at the corresponding difference frequency;

[0118] The coherent averaging module is used to traverse the difference frequency domain and the frequency domain, and obtain the target direction estimation results by coherent averaging in the difference frequency domain and the frequency domain respectively.

[0119] The present application may also provide a computer device comprising: at least one processor, memory, at least one network interface, and a user interface. The various components in the device are coupled together via a bus system. It will be understood that the bus system is used to enable communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus.

[0120] The user interface may include a display, a keyboard, or a pointing device, such as a mouse, a trackball, a touchpad, or a touch screen.

[0121] It is understood that the memory in the embodiments disclosed in the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced synchronous DRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memory.

[0122] In some embodiments, the memory stores the following elements, executable modules or data structures, or a subset or an extension thereof: an operating system and applications.

[0123] The operating system includes various system programs, such as the framework layer, core library layer, and driver layer, which are used to implement various basic services and handle hardware-based tasks. Application programs include various application programs, such as media players and browsers, which are used to implement various application services. The program that implements the method of the embodiment of the present disclosure can be included in the application program.

[0124] In the above embodiment, the processor may also call a program or instruction stored in the memory, specifically, a program or instruction stored in the application program, to:

[0125] Perform the steps of the above method.

[0126] The above method can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor or by software instructions. The above processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The above-disclosed methods, steps, and logic block diagrams can be implemented or executed. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the above-disclosed method can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0127] It is understood that the embodiments described herein may be implemented using hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit may be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, or other electronic units or combinations thereof for performing the functions described herein.

[0128] For software implementation, the technology of the present application can be implemented by executing the functional modules (e.g., procedures, functions, etc.) of the present application. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or external to the processor.

[0129] The present application may also provide a non-volatile storage medium for storing a computer program. When the computer program is executed by a processor, each step in the above method embodiment can be implemented.

[0130] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of this application and are not intended to limit the scope of the present invention. Although this application has been described in detail with reference to the embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions to the technical solutions of this application do not depart from the spirit and scope of the technical solutions of this application and should be encompassed by the claims of this application.

Claims

1. A fast broadband coherent difference-frequency beamforming and phase-only coherent difference-frequency beamforming method, comprising: Step 1: Define the angle scanning domain; Step 2: Select the processing frequency domain according to the signal reception spectrum; Step 3: Select the appropriate difference frequency and calculate the steering vector according to the formation; Step 4: Select the corresponding frequency point within the received signal frequency band and construct the self-product by conjugate multiplying the high-frequency signal pair; Step 5: Spatial filtering is performed on the autointegral term to obtain phase information, and coherence correction is performed on the difference frequency autointegral to obtain the normalized coherent difference frequency autointegral and the phase-only coherent difference frequency autointegral. Step 6: Use the steering vector to perform difference-frequency beamforming on the normalized coherent autoproduct term and the phase-only coherent autoproduct term to obtain the result at the corresponding difference frequency. Repeat steps 3 to 5 until all difference frequencies in the difference frequency domain are traversed to obtain the average results of coherent difference frequencies and phase-coherent difference frequencies in the difference frequency domain; Step 7: Traverse the frequency domain and obtain the target direction estimation result by coherent averaging in the frequency domain; The step 2 includes: Select the processing frequency f according to the signal receiving spectrum l , l represents the index in the frequency domain; [f L ,f H ] represents the frequency range of the signal from a distant point source; d represents the array element spacing; c represents the speed of sound; The step 3 comprises: Select the difference frequency domain {Δf}: Wherein, N represents the number of difference frequency points contained in the difference frequency domain; n represents the index in the difference frequency domain; Calculate the beamforming steering vector w m (Δf n ,θ i ): Among them, θ i represents the i-th angle in the angle scanning domain; m represents the array element number; According to the difference frequency Δf n Select the high frequency point f l +Δf n ; The step 4 comprises: Select the frequency domain signal x received by the mth array element from the kth sound source within the receiving frequency band m (f l ) and x m (f l +Δf n ), construct the self-integral term according to the following formula Where * indicates conjugation; The step 5 comprises: Use the current difference frequency Δf n The corresponding steering vector For the self-integral Perform spatial filtering to obtain phase information in, Indicates that the corresponding high frequency is f and the difference frequency is Δf n The self-integration of Indicates the difference frequency Δf n and scanning angle θ i The steering vector of represents the phase information obtained by spatial domain filtering of the self-product term of the array received signal; H represents the conjugate matrix; Using phase information The normalized self-integral term is coherently corrected to obtain Wherein, j represents the imaginary unit; Only the phase information of the corrected autointegral is used to obtain the phase-coherent autointegral term The step 7 comprises: Repeat steps 2 to 6 to process the next selected high-frequency point until all frequencies in the receiving band are traversed, and obtain the coherent averaging results of the coherent difference frequency and the phase-only coherent difference frequency in the frequency domain; Repeat steps 2 to 7 to traverse the angle search range and obtain the target estimated azimuth spectrum at each scanning angle.

2. The fast broadband coherent difference frequency beamforming and phase-only coherent difference frequency beamforming method according to claim 1, characterized in that: The step 5 further comprises: The steering vector is used to perform difference frequency beamforming on the normalized coherent self-product term and the phase-only coherent self-product term to obtain the corresponding difference frequency Δf n The coherent difference frequency and phase-coherent difference frequency beamforming results under A c (θ i ) and A poc (θ i ): Where M represents the number of array elements; w m (Δf n ,θ i ) indicates that the mth array element corresponds to the current difference frequency Δf n and the angle θ in the scanning angle domain i The guiding vector.

3. The fast broadband coherent difference frequency beamforming and phase-only coherent difference frequency beamforming method according to claim 2, characterized in that: The coherent difference frequency and the phase-coherent difference frequency in step 6 are averaged in the difference frequency domain. CFDB {Δf} (f,θ i ) and B POCFDB {Δf} (f,θ i ) is expressed as:

4. The fast broadband coherent difference frequency beamforming and phase-only coherent difference frequency beamforming method according to claim 3, characterized in that: The coherent averaging result of the coherent difference frequency and the phase-only coherent difference frequency in the frequency domain in step 7 is expressed as: Where F represents the number of frequency pairs used to construct the difference-frequency self-product in the frequency domain.

5. A fast broadband coherent difference frequency beamforming and phase-only coherent difference frequency beamforming system, implemented based on the method of any one of claims 1 to 4, characterized in that: The system comprises: Define angle scanning domain module, used to define the angle scanning domain; A frequency domain selection module is used to select a frequency domain for processing based on the received signal spectrum. The steering vector calculation module is used to select the appropriate difference frequency and calculate the steering vector according to the formation; Constructing an auto-integration module, which is used to select a corresponding frequency point within the received signal frequency band and construct an auto-integration by conjugate multiplication of high-frequency signal pairs; A coherent correction module is used to filter the autointegration term in the spatial domain to obtain phase information, and to perform coherent correction on the difference frequency autointegration to obtain normalized coherent difference frequency autointegration and phase-only coherent difference frequency autointegration; a difference frequency beamforming module, configured to perform difference frequency beamforming on the normalized coherent self-product term and the phase-only coherent self-product term using a steering vector to obtain a result at a corresponding difference frequency; and The coherent averaging module is used to traverse the difference frequency domain and the frequency domain, and obtain the target direction estimation results by coherent averaging in the difference frequency domain and the frequency domain respectively.

Images