Broadband system angle-speed joint estimation method and device

Abstract

The invention provides a broadband system angle-speed joint estimation method and device which are used for estimating the target angle and speed by a uniform linear array according to a received signal, and the method comprises the steps: carrying out the line frequency solving pulse pressure processing of the received signal, and obtaining a pulse pressure signal; performing time domain sampling on the pulse pressure signal, and converting an analog signal into a discrete time signal; arranging discrete time signals, designing a corresponding over-complete dictionary, and constructing a corresponding multi-dictionary sparse recovery problem; solving a multi-dictionary sparse recovery problem by using a multi-dictionary sparse Bayesian algorithm; solving results are rearranged to obtain an angle-velocity spectrum, and angle-velocity joint estimation is achieved. The method has the advantages that high-resolution and accurate angle-speed joint estimation can be realized under the condition of small samples; the solution speed is high, and the real-time requirement can be met; accurate angle-speed joint estimation can be realized under the condition that fast-slow time coupling exists and an airspace guide vector is changed along with frequency.

Description

Technical Field

[0001] The present application belongs to the field of sonar detection technology, and specifically relates to a broadband system angle-velocity joint estimation method and device. Background Art

[0002] Azimuth, distance and speed are important parameters of moving targets. Their estimation is a hot topic in the field of array signal processing and is widely used in sonar systems. Sonar systems estimate the angle, speed and distance of targets by estimating the direction of arrival (DOA), Doppler shift and delay of echo signals. Compared with other signals, frequency modulated continuous wave (FMCW) signals have a larger time-bandwidth product, high measurement accuracy and low average transmission power, so they are widely used in sonar systems. By performing de-modulation and de-skewing on the echo signals, the received signals can be regarded as the linear superposition of multiple multidimensional complex sinusoidal signals, whose frequencies are determined by the angle of the target echo, the speed and distance of the target. Therefore, the parameter estimation problem of moving targets can be converted into a multidimensional spectrum estimation problem.

[0003] In order to break through the Rayleigh limit, signal subspace-based methods represented by multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT) are widely used in spectrum estimation problems. In order to extend the subspace method to high dimensions, Hua et al. proposed a matrix enhancement and matrix pencil (MEMP) method to achieve two-dimensional spectrum estimation. By introducing the idea of ​​matrix pencil into the MUSIC algorithm, they further proposed a two-dimensional MUSIC algorithm. Later, Haardt et al. proposed a two-dimensional unitary ESPRIT algorithm, which extended the ESPRIT algorithm to two dimensions.

[0004] In recent years, in order to meet the growing demand for high-precision range resolution, the use of broadband signals has become more and more common. With the increase of bandwidth, the distance that a high-speed target moves in a coherent processing interval (CPI) may exceed the range unit, making the coupling terms of fast time and slow time impossible to be ignored. Currently, the Keystone transform and matched filtering methods are commonly used to eliminate the above coupling terms. In addition, the change of the array's steering vector with frequency cannot be ignored due to the increase of bandwidth, making it difficult to directly apply the narrowband DOA estimation method. To solve this problem, the incoherent signal-subspace method (ISSM) and the coherent signal-subspace method (CSSM) have been proposed one after another.

[0005] Subspace algorithms are all based on the covariance matrix and require a sufficient number of samples to ensure performance. Compared with subspace methods, methods based on compressed sensing and sparse recovery can accurately estimate parameters under the condition of limited sample size, and even achieve good results in the case of a single snapshot. In order to avoid solving the NP-hard l0-norm minimization problem, sparse algorithms based on l1-norm minimization, such as the basis pursuit algorithm, are widely used in the problem of moving target parameter estimation. However, when the problem scale is large, the computational complexity of the basis pursuit algorithm cannot meet the application requirements. Unlike the basis pursuit algorithm, the sparse Bayesian learning (SBL) algorithm treats the coefficient vector with sparse characteristics as a random vector with unknown parameters, and obtains an estimate of the unknown parameters by maximizing its evidence. Since the estimate of the unknown parameters satisfies the sparsity, sparse coefficient estimates can also be obtained. Compared with the basis pursuit algorithm, the computational speed of the SBL algorithm is greatly improved, so it has attracted more and more attention.

[0006] Although Keystone transform and matched filtering can eliminate coupling terms, they require a large amount of data to complete interpolation, so super-resolution estimation cannot be achieved. In addition, in order to achieve a large bandwidth, a longer pulse repetition interval is usually required, which leads to velocity ambiguity. The ISSM and CSSM methods suitable for broadband signals are both based on the covariance matrix and cannot adapt to small sample scenarios. Summary of the invention

[0007] The purpose of the present application is to overcome the defects of the prior art that it cannot achieve super-resolution estimation, requires a longer pulse repetition interval, thus causing velocity blur, or cannot adapt to small sample scenes.

[0008] In order to achieve the above object, the present application proposes a broadband system angle-velocity joint estimation method for estimating the target angle and velocity based on the received signal of a uniform linear array, comprising:

[0009] Step 1: Perform frequency modulation pulse compression processing on the received signal to obtain a pulse compression signal;

[0010] Step 2: Perform time domain sampling on the pulse pressure signal to convert the analog signal into a discrete time signal;

[0011] Step 3: Arrange the discrete-time signals, design the corresponding over-complete dictionary, and construct the corresponding multi-dictionary sparse recovery problem;

[0012] Step 4: Use the multi-dictionary sparse Bayesian algorithm to solve the multi-dictionary sparse recovery problem;

[0013] Step 5: Rearrange the solution results to obtain the angle-velocity spectrum and realize angle-velocity joint estimation.

[0014] As an improvement of the above method, step 1 comprises:

[0015] The pulse pressure signal obtained by demultiplexing the line frequency modulated pulse pressure of the signal received by the lth array element is:

[0016]

[0017] Among them, N Tar Indicates the number of targets to be tested; Indicates noise; The transmitting array element transmits a series of Chirp signals in a coherent processing interval, T c represents the pulse duration of the signal, T p Represents the pulse repetition interval of the signal, N p represents the number of pulses of the signal, B represents the pulse width of the signal, and f c Indicates the starting frequency of the signal; t m =mT p ,m∈{1,2,...,N p};

[0018] Represents the reflected signal from the i-th target received by the l-th array element The difference frequency output obtained by correlating with the reference signal s is:

[0019]

[0020] in, Represents the loss coefficient of the transmitted signal after propagation and reflection; R irepresents the radial distance of the i-th target from the sonar; θ i represents the azimuth of the i-th target from the sonar; v i represents the radial velocity of the i-th target from the sonar; c represents the speed of sound in water; μ = B / T c ;

[0021] As an improvement of the above method, step 2 comprises:

[0022] The pulse pressure signal is f s The sampling rate is used for time domain sampling, and the discrete time signal after sampling is for:

[0023]

[0024] Where k represents the fast time sampling index.

[0025] As an improvement of the above method, step 3 comprises:

[0026] make have:

[0027]

[0028] Among them, N m represents the number of array elements; T represents transpose;

[0029]

[0030] Y k Vectorized, we get:

[0031]

[0032] in, represents the Kronecker product,

[0033] A k The estimated parameters are obtained by the compressed sensing algorithm. Specifically, the range of radial velocity v and azimuth angle θ is divided into fine grids to form the set and set Θ; let

[0034] |·| represents the cardinality of a set; for A k The expanded supercomplete dictionary is used Indicates that there are:

[0035]

[0036] Construct the corresponding multi-dictionary sparse recovery problem:

[0037]

[0038] in, is a sparse vector whose support set It is obtained by the compressed sensing algorithm, and the estimated parameters v and θ are obtained from it; N t represents the total number of fast time samples; ∈ k represents the noise threshold; ||·||2 represents the 2-norm of the matrix; ||·|| 2,1 Represents the 2,1 norm of the matrix.

[0039] As an improvement of the above method, step 4 includes:

[0040] All The covariance matrix of is the same, both are Λ=diag(λ), where

[0041] Estimation of λ for:

[0042]

[0043] Where H represents a conjugate matrix;

[0044] Solving by iterative algorithm, we get:

[0045]

[0046] in, N p N m ×N p N m The identity matrix of b represents an adjustable parameter that controls the convergence rate; Represents [λ] i The result of the previous iteration; Represents [λ] i The result after the next iteration; Tr represents the trace of the matrix; Represents the noise power.

[0047] As an improvement of the above method, the noise power The estimation method is:

[0048]

[0049] in, represents the support set of λ, that is, the largest N in λ Tar The index corresponding to the element; represents the pseudo-inverse.

[0050] As an improvement of the above method, step 5 comprises:

[0051] The joint spectrum Spec of azimuth angle θ and radial velocity v is estimated by Rearrange to The matrix is ​​obtained, that is:

[0052]

[0053] in, Indicates that With |Θ| elements as a column, arrange them into Columns, arranged as The matrix of .

[0054] The present application also provides a broadband system angle-velocity joint estimation device, which is implemented based on the above method, and the device includes:

[0055] The demultiplexing module is used to perform demultiplexing on the received signal to obtain a pulse pressure signal.

[0056] A time domain sampling module is used to perform time domain sampling on the pulse pressure signal and convert the analog signal into a discrete time signal;

[0057] Construct a multi-dictionary sparse recovery problem module, which is used to arrange discrete-time signals, design corresponding over-complete dictionaries, and construct corresponding multi-dictionary sparse recovery problems;

[0058] A module for solving multi-dictionary sparse recovery problems, for solving multi-dictionary sparse recovery problems using a multi-dictionary sparse Bayesian algorithm; and

[0059] The angle-velocity joint estimation module is used to rearrange the solution results to obtain the angle-velocity spectrum and realize the angle-velocity joint estimation.

[0060] Compared with the prior art, the advantages of this application are:

[0061] 1. Compared with the traditional subspace method, the present invention can achieve high-resolution and accurate angle-velocity joint estimation in the case of small samples;

[0062] 2. Compared with the basis pursuit method based on l1 optimization, the present invention has a higher solution speed and can meet the requirements of real-time performance;

[0063] 3. Compared with the existing narrowband methods, the present invention can achieve accurate angle-velocity joint estimation in the presence of fast-slow time coupling and when the spatial steering vector changes with frequency;

[0064] 4. The present invention can also improve the problem of speed ambiguity and realize speed estimation of high-speed targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 The figure shows a schematic diagram of a uniform linear array (ULA) receiving a sonar echo signal reflected by a target;

[0066] Figure 2 The figure shows a flow chart of a method for joint angle-velocity estimation in a broadband system;

[0067] Figure 3(a) shows the effect of angle-velocity joint estimation in the absence of noise (narrowband MUSIC algorithm);

[0068] Figure 3(b) shows the effect of angle-velocity joint estimation (ISSM algorithm) in the absence of noise;

[0069] FIG3( c ) shows the angle-velocity joint estimation effect in the absence of noise (the algorithm of this application);

[0070] Figure 4(a) shows the relationship between angle estimation RMSE and SNR;

[0071] Figure 4(b) shows the relationship between RMSE and SNR of velocity estimation. DETAILED DESCRIPTION

[0072] The technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0073] The present invention proposes a method and device for joint angle-velocity estimation of a broadband system, which is implemented based on a multi-dictionary SBL algorithm and a broadband FMCW sonar joint angle-velocity estimation algorithm. By deriving and analyzing the signal model, the present invention converts the joint angle-velocity estimation problem into a multi-dictionary sparse recovery problem, and uniformly solves the fast-slow time coupling problem, velocity ambiguity problem, and the problem of spatial guidance vector variation with frequency brought by broadband signals. In order to improve the solution speed, the present invention uses a multi-dictionary SBL algorithm to solve the sparse recovery problem.

[0074] 1. Principle description

[0075] The signal processed by the method of the present application is a sonar echo signal reflected by a target and received by a uniform linear array (ULA), wherein the transmitting signal is a broadband frequency modulated continuous wave signal, and the function is to obtain the angle and speed estimation of the target, as shown in the schematic diagram Figure 1 shown.

[0076] The method first performs frequency modulation pulse compression processing on the received signal, then samples the pulse compression signal in the time domain, and converts the analog signal into a discrete time signal for processing. Next, the sampled discrete time signal is arranged in a certain way and the corresponding over-complete dictionary is designed to construct the corresponding multi-dictionary sparse recovery problem. Then, the multi-dictionary sparse recovery problem is quickly solved using the multi-dictionary sparse Bayesian algorithm. Finally, the solution results are rearranged to obtain the angle-velocity spectrum, realizing the angle-velocity joint estimation. The method flow is as follows Figure 2 shown.

[0077] 2. Method description

[0078] Assume that there are N Tar There are targets to be detected, where the radial distance, radial velocity and azimuth of the i-th target from the sonar are R i 、v i and θ i ,i∈{1,2,3,...,N Tar The transmitting array element transmits a series of Chirp signals in a coherent processing interval, with a pulse width of B and a starting frequency of f. c , the pulse duration is T c , the pulse repetition interval is T p , the number of pulses is N p , CPI is T CPI =T p N p . Transmit signal It can be expressed as:

[0079]

[0080] in t m =mT p ,m∈{1,2,...,N p},μ=B / T c .

[0081] The transmitted signal is reflected by the target and then received by the receiving array, which consists of N m A uniform linear array with array elements and an element spacing of d, where

[0082] In order to describe the received signal, we first briefly analyze the delay of the received signal. Assume that the speed of sound in water is c = 1500 m / s. Let the delay of the signal transmitted at time t after being reflected by the i-th target and reaching the receiving array be τ i (t), which satisfies:

[0083] R i -v i t=(v i +c)τi / twenty two)

[0084] From this we can get:

[0085]

[0086] The corresponding reflected signal received by the receiving array can be expressed as:

[0087] r i (t+τ i (t)) = α i s(t), (4)

[0088] in is the loss coefficient of the transmitted signal after propagation and reflection. So that:

[0089]

[0090] Easy to prove So we have:

[0091] r i (t) = α i s(t-τ i (t)), (6)

[0092] Right now:

[0093]

[0094] Substituting (1) into (7), we can obtain:

[0095]

[0096] in:

[0097]

[0098] From this we can get r i The instantaneous frequency f i for:

[0099]

[0100] Let the reflected signal from the i-th target received by the l-th array element be Since the time delay of the signal reflected by the i-th target reaching the l-th array element relative to the reference array element is So we have:

[0101]

[0102] Combined with (10), we can get:

[0103]

[0104] In order to realize the decoupling of line frequency modulation pulse pressure, r i,l Correlating with the reference signal s, the difference frequency output is:

[0105]

[0106] When μT c dN m / c<<1 When (13) can be ignored, and the signal can be approximated by a narrowband model. Right now When , the target moves no more than one distance unit in one CPI. At this time, (13) However, in order to obtain higher distance resolution, the bandwidth B of the transmitted signal also needs to be wider, so that the above two items cannot be ignored, resulting in the inability to directly apply the classic narrowband processing method.

[0107] In order to facilitate subsequent processing, (13) is rearranged into the following form:

[0108]

[0109] in,

[0110] The output of the signal received by the lth array element after demultiplexing the line frequency modulation pulse pressure is have:

[0111]

[0112] where n l is noise. s The sampling rate is used for time domain sampling, and t m =mT p , the sampled signal for:

[0113]

[0114] The subsequent Processing is performed to estimate the parameters to be estimated.

[0115] It is worth mentioning that processing based on the signal model as shown in (16) can also improve the velocity ambiguity. The traditional processing method ignores the coupling between fast time and slow time, and the velocity Doppler term is obtained as: In order to avoid speed ambiguity, the speed to be measured should not exceed Broadband signal Tp Usually, the speed Doppler term is obtained to be very large, which makes the speed range that can be measured very narrow and difficult to meet the needs of high-speed target speed measurement in practice. In the present invention, the coupling of fast time and slow time is considered, and the speed Doppler term obtained is To avoid velocity ambiguity, a maximum velocity is required satisfy:

[0116]

[0117] Where LCM represents the least common multiple, N t is the number of fast time sampling points. To simplify the discussion, we consider f c for It is easy to prove that the maximum speed that can be measured at this time is Greatly increases the range of measurable speeds and improves speed blur.

[0118] fixed The fast time sampling index k is:

[0119]

[0120] in Let [Y k ] l,m =Y k (l,m), we have:

[0121]

[0122] in

[0123]

[0124] Y k Vectorized, we get:

[0125]

[0126] in represents the Kronecker product,

[0127] From the last equation of (20), we can see that by k Expanded to an overcomplete dictionary, the estimated parameters can be obtained by the compressed sensing algorithm. Specifically, first divide the value range of v and θ into fine grids, forming the set and set Θ. Let in |·| represents the cardinality of a set. k The expanded supercomplete dictionary is used Indicates that there are:

[0128]

[0129] When the mesh is fine enough, So that:

[0130]

[0131] at this time is a sparse vector, whose support set It can be obtained by the compressed sensing algorithm, and the estimated parameters v and θ are obtained from it, where make:

[0132]

[0133] Where N t is the total number of fast time samples. It can be seen that With group sparsity, the following optimization problem can be constructed for solution:

[0134]

[0135] Among them, ∈ k represents the noise threshold; ||·||2 represents the 2-norm of the matrix; ||·|| 2,1 Represents the 2,1 norm of the matrix.

[0136] Although the above optimization problem is solvable, it is complex to solve due to its large scale. In order to meet the real-time requirements, the multi-dictionary SBL method is used to estimate the parameters.

[0137] Assumptions k=1,2,...,N t They are independent of each other and all conform to the zero-mean circularly symmetric Gaussian distribution. With group sparsity, we can assume k=1,2,...,N t Satisfy the covariance matrix prior, that is, all The covariance matrix of is the same, both are Λ=diag(λ), where So we have:

[0138]

[0139] Assumptions The posterior probability can be obtained as:

[0140]

[0141] Combining (25) and (26) we can obtain:

[0142]

[0143] Taking the logarithm of (27) yields:

[0144]

[0145] The estimate of λ can be obtained by maximizing the above logarithmic probability density Right now:

[0146]

[0147] (29) can be solved by an iterative algorithm. Specifically,

[0148]

[0149] in N p N m ×N p N m The identity matrix of b is an adjustable parameter that controls the convergence rate, is [λ] i The result of the previous iteration, is [λ] i The result after the next iteration. Noise power is required in (30) When it is unknown, it can be estimated as follows:

[0150]

[0151] in is the support set of λ, that is, the largest N in λ Tar The index corresponding to the element, represents the pseudo-inverse.

[0152] Finally, the joint spectrum Spec of azimuth angle θ and velocity v can be estimated by Rearrange to The matrix is ​​obtained, that is:

[0153]

[0154] in Indicates that With |Θ| elements as a column, arrange them into Columns, arranged as The matrix of .

[0155] The algorithm flow chart is shown in Table 1.

[0156] Table 1 Angle-velocity joint estimation method for broadband system

[0157]

[0158] 3. Simulation experiment

[0159] Let the number of targets be N Tar =3, the distances of the target from the sonar are 4m, 19m and 10m, the azimuths are 11°, 12° and 13°, and the radial velocities are 10m / s, -10m / s and 9m / s. Set the starting frequency of the transmitting signal f c The frequency is 1500Hz, the bandwidth is 1500Hz, the pulse duration is 0.1s, the pulse repetition interval is 0.5s, the number of pulses is 5, the receiving array is a uniform linear array, the number of array elements is 5, and the array element spacing is d = c / (2f c ). It can be seen that if the coupling of fast time and slow time is ignored, the maximum speed that can be estimated at this time is v max =0.5m / s, there will be velocity ambiguity. After considering the coupling term, according to the description of the method of this application, the maximum velocity that can be estimated is v max =20m / s, and there is no velocity ambiguity at this time. When the noise is not considered, the results of using the narrowband MUSIC algorithm, ISSM algorithm and the proposed wideband system angle-velocity joint estimation method are shown in Figure 3(a), Figure 3(b) and Figure 3(c).

[0160] As can be seen from Figure 3(a), the narrowband MUSIC algorithm is completely unable to distinguish the signal, which shows that the coupling term in (13) cannot be ignored. In Figure 3(b), the broadband ISSM algorithm has a large number of pseudo peaks in the estimation result due to the insufficient number of snapshots, and cannot distinguish the two target parameters when they are similar. In contrast, the algorithm proposed in the present invention in Figure 3(c) can achieve accurate estimation of the target azimuth and velocity, with the sharpest peak, the lowest side lobe, and the highest resolution.

[0161] To verify the noise robustness of the proposed algorithm, Figure 4(a) and Figure 4(b) show the estimation accuracy of angle and distance under different signal-to-noise ratios (SNR). Gaussian white noise is added, and when the SNR is 0-50dB, 100 Monte Carlo simulations are performed, and the root mean square error (RMSE) of the angle and velocity estimation is shown in Figure 4.

[0162] It can be seen from Figure 4(a) and Figure 4(b) that when the signal-to-noise ratio is higher than 20 dB, the proposed method can achieve high accuracy in estimating both the angle and velocity, demonstrating the robustness of the proposed algorithm.

[0163] The present application also provides a broadband system angle-velocity joint estimation device, which is implemented based on the above method, and the device includes:

[0164] The demultiplexing module is used to perform demultiplexing on the received signal to obtain a pulse pressure signal.

[0165] A time domain sampling module is used to perform time domain sampling on the pulse pressure signal and convert the analog signal into a discrete time signal;

[0166] Construct a multi-dictionary sparse recovery problem module to sample and arrange discrete-time signals, design the corresponding over-complete dictionary, and construct the corresponding multi-dictionary sparse recovery problem;

[0167] A module for solving multi-dictionary sparse recovery problems, for solving multi-dictionary sparse recovery problems using a multi-dictionary sparse Bayesian algorithm; and

[0168] The angle-velocity joint estimation module is used to rearrange the solution results to obtain the angle-velocity spectrum and realize the angle-velocity joint estimation.

[0169] The present application may also provide a computer device, comprising: at least one processor, a memory, at least one network interface and a user interface. The various components in the device are coupled together through a bus system. It is understood that the bus system is used to achieve 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.

[0170] The user interface may include a display, a keyboard or a pointing device, such as a mouse, a trackball, a touch pad or a touch screen.

[0171] 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 random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memories.

[0172] In some embodiments, the memory stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system and applications.

[0173] The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present disclosure can be included in the application.

[0174] In the above embodiment, the processor may also call a program or instruction stored in the memory, specifically, a program or instruction stored in an application program, and is used to:

[0175] Execute the steps of the above method.

[0176] The above method can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The above disclosed methods, steps and logic block diagrams can be implemented or executed. The general processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the above disclosed method can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field 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. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0177] It is understood that the embodiments described in the present application can be implemented by 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 (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application or a combination thereof.

[0178] For software implementation, the technology of the present application can be implemented by executing the functional modules (such as 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 outside the processor.

[0179] 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.

[0180] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application is described in detail with reference to the embodiments, a person skilled in the art should understand that any modification or equivalent replacement of the technical solution of the present application does not depart from the spirit and scope of the technical solution of the present application and should be included in the scope of the claims of the present application.

Claims

1. A broadband system angle-velocity joint estimation method, used for uniform linear array to estimate target angle and velocity based on received signals, comprising: Step 1: Perform frequency modulation pulse compression processing on the received signal to obtain a pulse compression signal; Step 2: Perform time domain sampling on the pulse pressure signal to convert the analog signal into a discrete time signal; Step 3: Arrange the discrete-time signals, design the corresponding over-complete dictionary, and construct the corresponding multi-dictionary sparse recovery problem; Step 4: Use the multi-dictionary sparse Bayesian algorithm to solve the multi-dictionary sparse recovery problem; Step 5: Rearrange the solution results to obtain the angle-velocity spectrum and realize angle-velocity joint estimation.

2. The broadband system angle-velocity joint estimation method according to claim 1, characterized in that: The step 1 comprises: The pulse pressure signal obtained by demultiplexing the line frequency modulated pulse pressure of the signal received by the lth array element is: Among them, N Tar Indicates the number of targets to be tested; Indicates noise; The transmitting array element transmits a series of Chirp signals in a coherent processing interval, T c represents the pulse duration of the signal, T p Represents the pulse repetition interval of the signal, N p represents the number of pulses of the signal, B represents the pulse width of the signal, and f c Indicates the starting frequency of the signal; t m =mT p ,m∈{1,2,...,N p }; Represents the reflected signal from the i-th target received by the l-th array element The difference frequency output obtained by correlating with the reference signal s is: in, Represents the loss coefficient of the transmitted signal after propagation and reflection; R i represents the radial distance of the i-th target from the sonar; θ i represents the azimuth of the i-th target from the sonar; v i represents the radial velocity of the i-th target from the sonar; c represents the speed of sound in water; μ = B / T c ; 3. The broadband system angle-velocity joint estimation method according to claim 2, characterized in that: The step 2 comprises: The pulse pressure signal is f s The sampling rate is used for time domain sampling, and the discrete time signal after sampling is for: Where k represents the fast time sampling index.

4. The broadband system angle-velocity joint estimation method according to claim 3, characterized in that: The step 3 comprises: make have: Among them, N m represents the number of array elements; T represents transpose; Y k Vectorized, we get: in, represents the Kronecker product, A k The estimated parameters are obtained by the compressed sensing algorithm. Specifically, the range of radial velocity v and azimuth angle θ is divided into fine grids to form the set and set Θ; let |·| represents the cardinality of a set; for A k The expanded supercomplete dictionary is used Indicates that there are: Construct the corresponding multi-dictionary sparse recovery problem: in, is a sparse vector whose support set It is obtained by the compressed sensing algorithm, and the estimated parameters v and θ are obtained from it; N t represents the total number of fast time samples; ∈ k represents the noise threshold; ||·||2 represents the 2-norm of the matrix; ||·|| 2,1 Represents the 2,1 norm of the matrix.

5. The broadband system angle-velocity joint estimation method according to claim 4, characterized in that: The step 4 comprises: All The covariance matrix of is the same, both are Λ=diag(λ), where Estimation of λ for: Where H represents the conjugate matrix; Solving by iterative algorithm, we get: in, N p N m ×N p N m The identity matrix of b represents an adjustable parameter that controls the convergence rate; Represents [λ] i The result of the previous iteration; Represents [λ] i The result after the next iteration; Tr represents the trace of the matrix; Represents the noise power.

6. The broadband system angle-velocity joint estimation method according to claim 5, characterized in that: The noise power The estimation method is: in, represents the support set of λ, that is, the largest N in λ Tar The index corresponding to the element; represents the pseudo-inverse.

7. The broadband system angle-velocity joint estimation method according to claim 5, characterized in that: The step 5 comprises: The joint spectrum Spec of azimuth angle θ and radial velocity v is estimated by Rearrange to The matrix is ​​obtained, that is: in, Indicates that With |Θ| elements as a column, arrange them into Columns, arranged as The matrix of .

8. A broadband system angle-velocity joint estimation device, implemented based on any of the methods of claims 1-7, characterized in that: The device comprises: The demultiplexing module is used to perform demultiplexing on the received signal to obtain a pulse pressure signal. A time domain sampling module is used to perform time domain sampling on the pulse pressure signal and convert the analog signal into a discrete time signal; Construct a multi-dictionary sparse recovery problem module, which is used to arrange discrete-time signals, design corresponding over-complete dictionaries, and construct corresponding multi-dictionary sparse recovery problems; A module for solving multi-dictionary sparse recovery problems, for solving multi-dictionary sparse recovery problems using a multi-dictionary sparse Bayesian algorithm; and The angle-velocity joint estimation module is used to rearrange the solution results to obtain the angle-velocity spectrum and realize the angle-velocity joint estimation.

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