Fast broadband coherent difference frequency beam forming method and system and phase-only coherent difference frequency beam forming method and system

Abstract

The invention provides a fast broadband coherent difference frequency beam forming method and system and a phase-only coherent difference frequency beam forming method and system, and the method comprises the steps: carrying out the difference frequency self-product processing of different frequency components of a broadband signal, and carrying out the coherent processing of a self-product term through the phase information obtained after the spatial filtering of the self-product term, and finally, carrying out coherent averaging in the frequency domain and the difference frequency domain to obtain a broadband signal orientation estimation spectrum. The method inherits the space anti-aliasing capability of a traditional difference frequency beam forming method, has low operation complexity, and can easily achieve real-time processing in engineering application. In a multi-target azimuth estimation scene, the method can effectively suppress interference caused by difference frequency self-product amplification, improve the multi-target resolution capability and obviously reduce the background sidelobe level of an azimuth spectrum, so that the weak target detection capability is improved.

Description

Technical Field

[0001] This application belongs to the field of underwater acoustic array signal processing, and particularly 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 a sound source from the output spatial spectrum of a receiving sensor array, and is an important research content in array signal processing. Conventional Beamforming (CBF) is a widely used array data processing method, and its basic principle is the way of delay-summation, which has the advantages of small computational complexity and good robustness. However, for a sparse array, when the array element spacing is greater than half of the wavelength of the received signal, due to spatial aliasing, the CBF method will have grating lobes, seriously affecting the azimuth estimation performance of the target. In addition, due to the complex and changeable ocean environment, there may be phenomena of sound speed profile mismatch and array shape 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 the information between multiple frequency points to suppress grating lobes by reducing the operating frequency.

[0003] However, the reduction of the operating frequency after frequency difference processing will increase the beam width of the azimuth pattern and reduce the target resolution. It is difficult to distinguish when the target azimuth angles are close; at the same time, since the self-integration process introduces the product of noise and the sound source term, amplifying the influence of noise, the sidelobe level will be increased as a whole; in addition, in the case of multiple targets, the signal difference frequency self-integration will bring additional cross terms, which will also interfere with target detection, and will cause problems such as weak targets being masked in complex ocean environments or multiple target situations.

[0004] Traditional broadband FDB methods suppress cross-term interference by non-coherently averaging the single difference frequency results in the frequency domain and the difference frequency domain. Although it has a certain effect, in the case of multiple targets or low signal-to-noise ratio, there are many cross terms and large noise interference. Direct frequency domain accumulation will lead to a significant increase in the background level, and the sidelobes of strong targets are extremely likely to mask weak targets, resulting in failure of target azimuth estimation. Summary of the Invention

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

[0006] To achieve the above purpose, this application proposes a method for fast broadband coherent difference frequency beamforming and phase-only coherent difference frequency beamforming, including:

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

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

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

[0010] Step 4: Select the corresponding frequency points within the received signal frequency band, and conjugate multiply the high-frequency signals to construct the autocorrelation product;

[0011] Step 5: Perform spatial domain filtering on the autocorrelation product terms to obtain the phase information, and perform coherence correction on the difference frequency autocorrelation product to obtain the normalized coherent difference frequency autocorrelation product and the phase-only coherent difference frequency autocorrelation product;

[0012] Step 6: Use the steering vector to perform difference frequency beamforming on the normalized coherent autocorrelation product terms and the phase-only coherent autocorrelation product terms to obtain the results corresponding to the difference frequency;

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

[0014] As an improvement of the above method, Step 2 includes:

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

[0016] As an improvement of the above method, Step 3 includes:

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

[0018]

[0019] where N represents the number of difference frequencies included 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] where θ i represents the i-th angle in the angle scanning range; m represents the element number;

[0023] Select the high-frequency frequency point f n according to the difference frequency Δf l +Δfn 。

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

[0025] Select the frequency-domain signal x m (f l ) and x m (f l +Δf n ) received by the m-th array element from the k-th sound source within the received frequency band, and construct the auto-product term according to the following formula

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

[0027] where * represents conjugate;

[0028] Adopt the steering vector w n corresponding to the current difference frequency Δf θ (n) to perform spatial filtering on the auto-product term to obtain the phase information

[0029]

[0030] where represents the auto-product corresponding to the high-frequency frequency f and the difference frequency Δf n ; w θ (n) represents the steering vector corresponding to the difference frequency Δf n and the angle θ i within the scan angle domain; represents the phase information obtained by performing spatial filtering on the auto-product term of the array received signal; H represents the conjugate matrix;

[0031] Use the phase information to perform coherence correction on the normalized auto-product term to obtain

[0032]

[0033] where j represents the imaginary unit;

[0034] Only use the phase information of the corrected auto-product to obtain the phase-only coherence auto-product term

[0035]

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

[0037] Performing difference-frequency beamforming on the normalized coherent auto-correlation term and the phase-only coherent auto-correlation term using the steering vector to obtain the corresponding difference frequency Δf n and the coherent difference-frequency and phase-only coherent difference-frequency beamforming results 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 ) represents the steering vector corresponding to the m-th array element for the current difference frequency Δf n and the angle θ i within the scanning angle range.

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

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

[0042]

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

[0044] Repeating steps 2 to 6 to process the next selected high-frequency frequency point until all frequency points within the received frequency band are traversed to obtain the coherent average 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 auto-correlation in the frequency domain;

[0047] Repeating steps 2 to 7 to traverse the angle search range to obtain the target estimated azimuth spectrum p i at each θ CFDB (θ i ) and p POCFDB (θ i ).

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

[0049] An angle scanning domain definition module, which is used to define an angle scanning domain;

[0050] A processing frequency domain selection module, which is used to select a processing frequency domain according to the signal reception spectrum;

[0051] A steering vector calculation module, which is used to select an appropriate heterodyne frequency and calculate the steering vector according to the array configuration;

[0052] An autocorrelation construction module, which is used to select corresponding frequency points within the received signal frequency band and conjugate multiply the high-frequency signals to construct an autocorrelation;

[0053] A coherence correction module, which is used to perform spatial filtering on the autocorrelation terms to obtain phase information, and perform coherence correction on the heterodyne autocorrelation to obtain a normalized coherent heterodyne autocorrelation and a phase-only coherent heterodyne autocorrelation;

[0054] A heterodyne beamforming module, which is used to perform heterodyne beamforming on the normalized coherent autocorrelation terms and phase-only coherent autocorrelation terms using the steering vector to obtain results corresponding to the heterodyne frequencies;

[0055] A coherence averaging module, which is used to traverse the heterodyne frequency domain and the frequency domain, and perform coherence averaging in the heterodyne frequency domain and the frequency domain respectively to obtain the target azimuth estimation result.

[0056] Compared with the prior art, the advantages of the present application are as follows:

[0057] Both the simulation and experimental results show that in the scenario of multi-target azimuth estimation with a low signal-to-noise ratio, compared with the existing broadband incoherent FDB method, the proposed method can effectively improve the target resolution ability, reduce the sidelobe level, and at the same time improve the detection ability for weak targets. This method inherits the spatial anti-aliasing ability of the traditional heterodyne beamforming method and has a low computational complexity, making it relatively easy to achieve real-time processing in engineering applications. In the scenario of multi-target azimuth estimation, this method can effectively suppress the interference amplified by the heterodyne autocorrelation, improve the resolution ability for multi-targets, significantly reduce the background sidelobe level of the azimuth spectrum, and thus improve the detection ability for weak targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 Shows the broadband single-target azimuth spectrum diagrams under different signal-to-noise ratios;

[0059] Figure 2 Shows the broadband multi-target azimuth spectrum diagrams;

[0060] Figure 3 Shows the variation of the weak target direction finding success rate with the signal-to-noise ratio;

[0061] Figure 4 The mean square error of the direction finding result is shown as varying with the signal-to-noise ratio;

[0062] Figure 5 The broadband multi-target azimuth history diagram is shown; the first diagram from top to bottom represents high-frequency CBF; the second diagram represents broadband ICFDB; the third diagram represents broadband CFDB; the fourth diagram represents broadband POCFDB;

[0063] Figure 6 The comparison diagram of the azimuth spectrum results of four methods when the azimuths of two targets approach at the 8th minute is shown;

[0064] Figure 7 The resolution probability is shown as varying with the angular separation;

[0065] Figure 8 The horizontal array configuration used for the actual measured data at sea is shown;

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

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

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

[0069] Figure 10 The comparison diagram of the azimuth spectrum of three difference frequency methods at the 30th minute is shown;

[0070] Figure 11 The flowcharts of the fast broadband coherent difference frequency beamforming and the phase-only coherent difference frequency beamforming methods are shown. Specific implementation manners

[0071] The technical solutions of the present application will be described in detail below in conjunction with the accompanying drawings.

[0072] Aiming at the problem of high background level of azimuth spectrum in frequency difference beamforming (FDB) method of broadband signal, the present invention uses the phase information of self-integration in the difference frequency method to correct the self-integration term, and proposes a fast broadband coherent frequency difference beamforming (CFDB) and phase-only coherent frequency difference beamforming (POCFDB) method. The invention first performs difference frequency self-integration processing between different frequency components of broadband signal, thereby reducing the processing frequency to meet the spatial Nyquist sampling requirements of sparse array of array elements, and 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. Simulation shows that this method inherits the spatial anti-aliasing capability of the traditional difference frequency beamforming method, and has low computational complexity, and 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-product, 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. By processing the horizontal array data of the offshore test, the azimuth history diagrams obtained by the traditional broadband difference frequency beamforming algorithm and the proposed coherent algorithm are compared and analyzed, verifying the superiority 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 in the received signal frequency band, and construct the self-product by conjugating and multiplying the high-frequency signal pair;

[0078] Step 5: Filter the autointegral term in the spatial domain to obtain phase information, perform coherent correction on the difference frequency autointegral, and 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 self-product term and the phase-only coherent self-product term to obtain the result at the corresponding difference frequency;

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

[0081] Simulation parameters: We consider a uniform linear array with M equally spaced array elements. Assume that the signal from a far - away point source is a broadband plane wave with frequency f ∈ [f L , f H . The frequency - domain signal received by the m - th array element from the k - th sound source can be written as:

[0082]

[0083] where f is the signal frequency, s k (f) is the spectrum of the k - th source signal, n m (f) is the additive Gaussian noise uncorrelated with the signal on the m - th array element, d is the array element spacing, θ k is the angle between the incident plane wave of the k - th signal and the broadside direction of the receiving array, c is the speed of sound, denotes the imaginary unit.

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

[0085] Step 2: Determine the processing frequency points. Select the processing frequency points f l according to the received signal spectrum. l represents the index in the frequency domain. Let l = 1,

[0086] Step 3: Select the difference frequency domain, {Δf} = [Δf 1 , Δf 2 ,..., Δf n ,..., Δf N . N represents the number of difference frequencies included in the difference frequency domain, n represents the index in the difference frequency domain. Let n = 1, and calculate the beam - forming steering vector According to the difference frequency Δf n , select the high - frequency frequency point f l + Δf n .

[0087] Step 4: Calculate the difference - frequency auto - product. Select the frequency - domain signals x m (f l ) and x m (f l + Δf n ) received by the m - th array element from the k - th sound source in the received frequency band. According to the formula Conjugate - multiply to construct the auto - product * represents conjugate; Use the current difference frequency Δf nThe corresponding steering vector For the autocorrelation term Perform spatial filtering to obtain the phase information That is

[0088] Use this phase information to perform coherent correction on the normalized autocorrelation term to obtain That is:

[0089]

[0090] Further consider only using the phase information of the corrected autocorrelation to obtain the phase-only coherent autocorrelation term That is:

[0091]

[0092] Step 5: Heterodyne beamforming. Use the steering vector to perform heterodyne beamforming on the normalized coherent autocorrelation term and the phase-only coherent autocorrelation term to obtain the corresponding heterodyne frequency Δf n The coherent heterodyne frequency and the phase-only coherent heterodyne beamforming results at:

[0093]

[0094] where, w m (Δf n , θ i ) represents the steering vector corresponding to the m-th array element for the current heterodyne frequency Δf n and the angle θ within the scan angle domain i .

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

[0096]

[0097] where, * represents taking the conjugate of the steering vector.

[0098] Step 7: Frequency-domain coherent averaging. Let l = l + 1, and repeat steps 2 - 6 to process the next selected high-frequency point until all points in the received frequency band are traversed. F represents the number of frequency pairs used to construct the heterodyne autocorrelation in the frequency domain, and obtain the frequency-domain coherent averaging results of the coherent heterodyne frequency and the phase-only coherent heterodyne frequency:

[0099]

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

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

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

[0103] Simulation A:

[0104] Select the difference frequency range as 70 - 90 Hz, the step size is 1 Hz, a total of 21 difference frequency points, the processing frequency band is 950 - 1050 Hz, and the step size is 1 Hz. Process the broadband signal according to the steps of this application, and compare the results of three methods, ICFDB, CFDB, and POCFDB, under different signal-to-noise ratios. The results are as Figure 1 shown. Comparing the three methods, the sidelobe background levels of CFDB and POCFDB are significantly lower than that of ICFDB, proving that the method proposed in this application can enhance the main peak and thus play a role in suppressing sidelobes. Add a weak target with an amplitude half of the amplitude values of the other two targets at -10°. The azimuth spectrum diagrams of the three methods at a signal-to-noise ratio of -10 dB are as Figure 2 shown. It can be seen that when there is a weak target under low signal-to-noise ratio, a relatively high sidelobe appears near 0° in the result of the ICFDB method, 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 ability for weak targets.

[0105] Simulation B:

[0106] Conduct 200 times of Monte Carlo simulation for each of the three methods respectively, and statistically count the success rate of the positioning results for weak targets as Figure 3 shown. The curves of the mean square error of the direction finding results of the three methods varying with the signal-to-noise ratio are as Figure 4 shown. From Figure 3 it can be seen that when the signal-to-noise ratio is -10 - 0 dB, ICFDB has direction finding failure for weak targets. In contrast, the two coherent processing methods are almost all successful in direction finding. From 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] Evaluate the performance of the method proposed in this application under the condition of the azimuth change of two targets. Set the initial azimuth angles of the two targets to be -20° and 30° respectively, and the azimuth angles of the two targets change linearly. Set the signal-to-noise ratio to 0 dB, and compare the azimuth history diagrams of the traditional beamforming method and the three FDB methods, as Figure 5 shown; intercept the azimuth spectrograms of the four methods when the azimuths of the two targets are close at the 8th minute as Figure 6 shown. From Figure 5 the first figure in it, it can be seen that in the high-frequency case, due to spatial aliasing, multiple grating lobes appear in the CBF result, and the direction cannot be accurately measured. Comparing Figure 5 the second, third, and fourth figures, it can be seen that the main lobes of the CFDB method and the POCFDB method proposed in this application are narrower, and the background sidelobe level is also lower; from Figure 6 it can be seen that when the incident angles of the 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] Evaluate the angle resolution performance of the method proposed in this application under the condition of the azimuth change of two targets. Set the same conditions as in Simulation C, adopt 100 Monte Carlo simulations, set the azimuth interval of the two targets to change from 0 to 10°, and compare the resolution probability results of the three FDB methods for the two targets as Figure 7 shown. It can be observed that the ICFDB method can basically completely resolve when the azimuth interval of the two targets is more than 6°. The two coherent methods can both improve the resolution. Among them, the CFDB method has the best resolution probability for the targets, and can basically accurately resolve the two targets when the azimuth interval of the targets is greater than 3°.

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

[0110] Select the difference frequency domain to be 90 - 100 Hz for the 600 - 800 Hz frequency band signal of the experimental received signal, and perform ICFDB, CFDB and POCFDB respectively. The results are as Figure 9(a) - Figure 9(c)As shown, Fig. 9(a) shows the results of the ICFDB method. It can be seen that although broadband difference frequency processing can effectively remove grating lobes, the background sidelobe level is relatively high. At the same time, in the comparison figure, after 25 minutes, due to multiple targets, the self-accumulation cross terms increase, and the background level of the ICFDB method results increases significantly. The trajectory of weak target 1 is relatively blurred, and the trajectory of weak target 2 is completely submerged, making it difficult to accurately estimate the direction; Figs. 9(b) and 9(c) show the results of the coherent processing difference frequency method, which are the azimuth history diagrams obtained by using the CFDB and POCFDB methods respectively. It can be seen that in the case of a single target, after coherent processing, the background sidelobe level is significantly reduced, 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 in Fig. 9(a), and weak target 2 is completely submerged by the sidelobes. In (b) and (c), the trajectories of both weak target 1 and weak target 2 can be seen simultaneously; Figure 10 The azimuth estimation results of the three methods at the 61st frame (the 30th minute) are plotted. It can be seen that although the ICFDB can see the trajectory of weak target 1, the amplitude of weak target 1 in the single-frame output 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 the positions of 228° and 288°. The CFDB method has the lowest sidelobe level, proving that the method proposed in the present invention has a certain improvement in azimuth estimation performance.

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

[0112] An angle scanning domain definition module for defining the angle scanning domain;

[0113] A processing frequency domain selection module for selecting the processing frequency domain according to the signal reception spectrum;

[0114] A steering vector calculation module for selecting an appropriate difference frequency and calculating the steering vector according to the array shape;

[0115] An auto-accumulation construction module for selecting corresponding frequency points within the received signal frequency band and conjugate multiplying the high-frequency signals to construct auto-accumulation;

[0116] A coherence correction module for spatially filtering the auto-accumulation terms to obtain phase information, and performing coherence correction on the difference frequency auto-accumulation to obtain the normalized coherent difference frequency auto-accumulation and only phase coherent difference frequency auto-accumulation;

[0117] A difference frequency beamforming module for performing difference frequency beamforming on the normalized coherent auto-accumulation terms and only phase coherent auto-accumulation terms using the steering vector to obtain the results corresponding to the difference frequency;

[0118] A coherence averaging module for traversing the difference frequency domain and the frequency domain, and coherently averaging in the difference frequency domain and the frequency domain respectively to obtain the target azimuth estimation results.

[0119] The present application can also provide a computer device, including: at least one processor, a memory, at least one network interface, and a user interface. Each component in the device is coupled together through a bus system. It can be understood that the bus system is used to implement the connection and communication between these components. In addition to the data bus, the bus system also includes a power bus, a control bus, and a status signal bus.

[0120] Among them, the user interface can include a display, a keyboard, or a pointing device. For example, a mouse, a trackball, a touchpad, or a touch screen, etc.

[0121] It can be understood that the memory in the disclosed embodiments of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can 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 can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchlink dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM). The memory described herein is intended to include but not be 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 subsets thereof, or extended sets thereof: an operating system and an application program.

[0123] Among them, the operating system includes various system programs, such as the framework layer, the core library layer, the driver layer, etc., which are used to implement various basic services and handle hardware-based tasks. The application programs include various application programs, such as the Media Player, the Browser, etc., which are used to implement various application services. The program for implementing the method of the embodiments of the present disclosure may be included in the application programs.

[0124] In the above-mentioned embodiments, the program or instruction stored in the memory can also be called. Specifically, it can be the program or instruction stored in the application program. The processor is used for:

[0125] Executing the steps of the above method.

[0126] The above method can be applied to the processor or implemented by the processor. The processor may be an integrated circuit chip with signal processing capabilities. During the implementation process, each step of the above method can be completed by the integrated logic circuit in the hardware of the processor or the instruction in the form of software. The above-mentioned 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, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed above. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. Combining the steps of the above-disclosed method can be directly embodied as being executed by the hardware decoding processor, or completed by the combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0127] It can be understood that the embodiments described in this application can be implemented using hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can 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, other electronic units for performing the functions described in this application, or a combination thereof.

[0128] For software implementation, the technology of this application can be implemented by executing the functional modules of this application (such as procedures, functions, etc.). The software code can be stored in a memory and executed by a processor. The memory can be implemented inside or outside the processor.

[0129] This application can also provide a non-volatile storage medium for storing a computer program. When the computer program is executed by a processor, the various steps in the above method embodiments can be implemented.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit them. Although this application has been described in detail with reference to the embodiments, those of ordinary skill in the art should understand that any modification or equivalent replacement of the technical solutions of this application does not depart from the spirit and scope of the technical solutions of this application, and they should all be covered within the scope of 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 in the received signal frequency band, and construct the self-product by conjugating and multiplying the high-frequency signal pair; Step 5: Filter the autointegral term in the spatial domain to obtain phase information, perform coherent correction on the difference frequency autointegral, and 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 self-product term and the phase-only coherent self-product term to obtain the result at the corresponding difference frequency; Step 7: Traverse the difference frequency domain and the frequency domain, and obtain the target direction estimation result by coherent averaging in the difference frequency domain and the frequency domain respectively.

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 2 comprises: Select the processing frequency point 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.

3. The fast broadband coherent difference frequency beamforming and phase-only coherent difference frequency beamforming method according to claim 2, characterized in that: 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 .

4. The fast broadband coherent difference frequency beamforming and phase-only coherent difference frequency beamforming method according to claim 3, characterized in that: 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 ), the self-integral term is constructed according to the following formula Wherein, * indicates conjugation; Using the current difference frequency Δf n The corresponding steering vector For the self-product 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 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 self-integral is used to obtain the phase-coherent self-integral term 5. The fast broadband coherent difference frequency beamforming and phase-only coherent difference frequency beamforming method according to claim 4, characterized in that: The step 5 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-only 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.

6. The fast broadband coherent difference frequency beamforming and phase-only coherent difference frequency beamforming method according to claim 5, characterized in that: The step 6 comprises: 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 ):

7. The fast broadband coherent difference frequency beamforming and phase-only coherent difference frequency beamforming method according to claim 6, characterized in that: The step 7 comprises: Repeat steps 2 to 6 to process the next selected high-frequency point until all the frequencies in the receiving band are traversed, and the coherent difference frequency and the phase-only coherent difference frequency in the frequency domain are obtained: Where F represents the number of frequency pairs used to construct the difference frequency self-product in the frequency domain; Repeat steps 2 to 7 to traverse the angle search range and obtain each θ i The target estimated direction spectrum p under CFDB (θ i ) and p POCFDB (θ i ).

8. A fast broadband coherent difference frequency beamforming and phase-only coherent difference frequency beamforming system, implemented based on any of the methods of claims 1-7, characterized in that: The system comprises: Define angle scanning domain module, used to define angle scanning domain; A frequency domain selection processing module is used to select a frequency domain for processing according to a received signal spectrum; A steering vector calculation module is used to select a suitable difference frequency and calculate a steering vector according to the formation; Constructing a self-integration module, which is used to select a corresponding frequency point in the received signal frequency band and construct a self-integration by conjugating and multiplying the high-frequency signal pairs; A coherent correction module is used to obtain phase information by spatially filtering the self-product term, and to perform coherent correction on the difference frequency self-product to obtain a normalized coherent difference frequency self-product and a phase-only coherent difference frequency self-product; 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 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 result by coherent averaging in the difference frequency domain and the frequency domain respectively.

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