Marine small target detection method, device and equipment and storage medium

Abstract

The embodiment of the invention provides a sea small target detection method, device and equipment and a storage medium, and relates to the technical field of radar signal processing. According to the method, sea clutter data and target prior information are utilized to perform signal compounding on a linear frequency modulation signal and a phase coding signal to generate a transmitting signal, an antenna array is utilized to acquire a receiving signal to estimate an angle of arrival, a weighted vector of the antenna array is adjusted according to the angle of arrival, and an echo signal is obtained. And performing time domain compression on the echo signal to obtain a time domain compressed signal, converting the time domain compressed signal to a frequency domain, and performing information fusion according to the distance information of the time domain, the speed information of the frequency domain and the arrival angle of the space domain to realize target detection. The transmitting signal is a composite waveform obtained by dynamically adjusting signal parameters according to the statistical characteristics of the sea clutter and the prior information of the target, the matching degree of the transmitting signal, the target and the sea clutter can be improved, combined processing is carried out in the time domain, the frequency domain and the space domain, and the detection accuracy and reliability of the weak and small target on the sea are improved.

Description

Technical Field

[0001] The present application relates to the field of radar signal processing technology, and in particular to a method, device, equipment and storage medium for detecting small targets at sea. Background Art

[0002] With the booming marine economy and the increasing complexity of maritime security, the need for accurate detection of maritime targets, especially small and weak ones, is becoming increasingly prominent. These targets (including small unmanned vehicles, buoys, and small ships) have a small radar cross-section (RCS) and are easily drowned out by noise in complex sea clutter environments.

[0003] Related technologies for maritime target detection primarily use fixed waveform transmission signals, extracting target features through conventional pulse compression and Doppler processing techniques. However, sea clutter exhibits significant non-stationary characteristics, with echo characteristics fluctuating intricately depending on factors such as wave dynamics, wind intensity, ocean currents, and real-time meteorological conditions. In such dynamic environments, fixed waveform radar systems struggle to adapt to varying sea conditions, resulting in poor target detection accuracy and reliability. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose a method, device, equipment and storage medium for detecting small targets at sea, so as to improve the detection accuracy and reliability of small targets at sea.

[0005] To achieve the above objectives, a first aspect of an embodiment of the present application provides a method for detecting small targets at sea, comprising:

[0006] acquiring sea clutter data and target prior information in real time, and performing signal combination on a linear frequency modulation signal and a multi-binary phase coded signal according to the target prior information and the sea clutter data to generate a transmission signal;

[0007] Obtaining a received signal corresponding to the transmitted signal using an antenna array, estimating an angle of arrival based on the received signal, adjusting a weighting vector of the antenna array according to the angle of arrival, and obtaining an echo signal based on the weighting vector;

[0008] Performing time domain compression on the echo signal to obtain a time domain compressed signal, and converting the time domain compressed signal into a frequency domain to obtain a frequency domain received signal;

[0009] Distance information is obtained according to the time domain compression signal, speed information is obtained according to the frequency domain received signal, parameter estimation results are obtained by performing information fusion based on the distance information, the speed information and the arrival angle, and target detection is performed based on the parameter estimation results.

[0010] In some embodiments, the target prior information is a speed range of the target, the sea clutter data includes at least Doppler frequency shift information, and generating a transmission signal according to the target prior information, the sea clutter data, the linear frequency modulation signal, and the phase coding signal includes:

[0011] generating a frequency modulation slope corresponding to a linear frequency modulation signal based on the speed interval and the Doppler frequency shift information to obtain a target linear frequency modulation signal;

[0012] generating a target phase coding sequence corresponding to the phase coding signal by using a genetic algorithm, and obtaining a target phase coding signal according to the target phase coding sequence;

[0013] The transmit signal is generated according to the target linear frequency modulation signal and the target phase coded signal.

[0014] In some embodiments, generating a target phase coding sequence corresponding to a phase coding signal using a genetic algorithm includes:

[0015] Randomly generating a group of initial phase coding sequences to form a population, and calculating the fitness function value of each of the initial phase coding sequences based on the autocorrelation sidelobe level and the cross-correlation peak value;

[0016] Selecting a candidate coding sequence from the population according to the fitness function value, performing crossover and mutation operations on the candidate coding sequence to obtain a new offspring sequence;

[0017] The population is updated using the offspring sequence until a preset termination condition is reached, and a target phase coding sequence is selected from the final population.

[0018] In some embodiments, the sea clutter data includes at least power spectrum and Doppler shift change information, and generating a transmit signal according to the target linear frequency modulation signal and the target phase coded signal includes:

[0019] Performing frequency analysis on the power spectrum to obtain frequency segment information;

[0020] If the frequency segment information includes a frequency segment with concentrated energy, the target phase coded signal is updated based on the frequency segment, and the target linear frequency modulation signal is updated according to the Doppler frequency shift change information.

[0021] In some embodiments, compressing the echo signal in the time domain to obtain a time domain compressed signal, and converting the time domain compressed signal into the frequency domain to obtain a frequency domain received signal includes:

[0022] Inputting the echo signals into different matched filters respectively to obtain frequency modulated echo signals and coded echo signals;

[0023] The frequency modulation echo signal and the coded echo signal are weighted to obtain the time domain compression signal.

[0024] In some embodiments, the antenna array includes multiple antenna units, and estimating the angle of arrival based on the received signal and adjusting the weighting vector of the antenna array according to the angle of arrival includes:

[0025] Obtaining a covariance matrix according to a received component corresponding to each antenna unit in the received signal, and performing eigendecomposition on the covariance matrix to obtain a signal subspace;

[0026] determining a spatial spectrum function according to the array manifold vector and the signal subspace, and determining the angle of arrival based on a peak value of the spatial spectrum function;

[0027] A target direction vector is obtained based on the arrival angle and the array manifold vector, and the weighted vector is optimized and solved based on the target direction vector to obtain the updated weighted vector.

[0028] In some embodiments, the fusing the distance information, the speed information, and the arrival angle to obtain a parameter estimation result includes:

[0029] Obtaining a distance likelihood, a speed likelihood, and an angle likelihood according to the distance information, the speed information, and the arrival angle, respectively, and obtaining a likelihood function based on the distance likelihood, the speed likelihood, and the angle likelihood;

[0030] A priori probability is obtained, a posterior probability is obtained according to the priori probability and the likelihood function, and the parameter estimation result is obtained based on the posterior probability.

[0031] To achieve the above objectives, a second aspect of an embodiment of the present application provides a small target detection device at sea, comprising:

[0032] Transmitting signal generation module: used to obtain linear frequency modulation signal and multi-ary phase coding signal, and obtain sea clutter data and target prior information in real time, and generate a transmitting signal according to the target prior information, the sea clutter data, the linear frequency modulation signal and the phase coding signal;

[0033] Beamforming module: used to obtain a received signal corresponding to the transmitted signal using an antenna array, estimate an arrival angle based on the received signal, adjust a weighting vector of the antenna array according to the arrival angle, and obtain an echo signal based on the weighting vector;

[0034] The time-frequency domain processing module is used to perform time-domain compression on the echo signal to obtain a time-domain compressed signal, and convert the time-domain compressed signal into the frequency domain to obtain a frequency-domain received signal;

[0035] Target detection module: used to obtain distance information based on the time domain compressed signal, obtain speed information based on the frequency domain received signal, perform information fusion based on the distance information, the speed information and the arrival angle to obtain a parameter estimation result, and perform target detection based on the parameter estimation result.

[0036] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0037] To achieve the above-mentioned purpose, the fourth aspect of the embodiment of the present application proposes a storage medium, which is a storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0038] The embodiments of the present application provide a method, apparatus, device, and storage medium for detecting small targets at sea. These methods, apparatuses, and storage media acquire sea clutter data and target prior information in real time, composite a linear frequency modulation (LFM) signal and a multi-bit phase-coded signal based on the target prior information and the sea clutter data to generate a transmitted signal, acquire a received signal corresponding to the transmitted signal using an antenna array, estimate the angle of arrival based on the received signal, adjust a weighting vector of the antenna array based on the angle of arrival, obtain an echo signal based on the weighting vector, perform time-domain compression on the echo signal to obtain a time-domain compressed signal, convert the time-domain compressed signal to the frequency domain to obtain a frequency-domain received signal, obtain distance information based on the time-domain compressed signal, obtain velocity information based on the frequency-domain received signal, fuse the distance information, velocity information, and angle of arrival to obtain a parameter estimation result, and perform target detection based on the parameter estimation result. The transmitted signal in the embodiments of the present application is a composite waveform obtained by dynamically adjusting signal parameters based on the statistical characteristics of sea clutter and target prior information. The composite waveform has the characteristics of high range resolution of the LFM signal and strong anti-interference capability of the phase-coded signal, thereby improving the matching degree between the transmitted signal and the target and sea clutter. At the same time, adaptive beamforming is combined to suppress interference, and the received signals are jointly processed in the time domain, frequency domain and spatial domain, which enhances the angular resolution and adaptability to complex sea conditions, and significantly improves the accuracy and reliability of detection of weak targets at sea. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of a method for detecting small targets at sea provided in an embodiment of the present application.

[0040] Figure 2This is a flowchart of generating a transmission signal by combining a linear frequency modulation signal and a multi-bit phase coded signal according to target prior information and sea clutter data provided in an embodiment of the present application.

[0041] Figure 3 This is a flowchart of using a genetic algorithm to generate a target phase coding sequence corresponding to a phase coding signal provided by an embodiment of the present application.

[0042] Figure 4 This is a flowchart of generating a transmission signal based on a target linear frequency modulation signal and a target phase coding signal provided by an embodiment of the present application.

[0043] Figure 5 This is a flowchart of an embodiment of the present application for estimating the arrival angle based on the received signal and adjusting the weighting vector of the antenna array according to the arrival angle.

[0044] Figure 6 This is a flowchart of performing time domain compression on an echo signal to obtain a time domain compressed signal provided by an embodiment of the present application.

[0045] Figure 7 This is a flowchart of an embodiment of the present application for obtaining parameter estimation results by fusing information based on distance information, speed information and arrival angle.

[0046] Figure 8 This is a structural block diagram of a small target detection device at sea provided in another embodiment of the present application.

[0047] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0049] It should be noted that although the functional modules are divided in the device schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in a different order than the module division in the device or the order in the flowchart.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0051] With the booming marine economy and the increasing complexity of maritime security, the need for accurate detection of maritime targets, especially small and weak ones, is becoming increasingly prominent. These targets (including small unmanned vehicles, buoys, and small ships) have a small radar cross-section (RCS) and are easily drowned out by noise in complex sea clutter environments.

[0052] For example, in strong sea clutter environments, the backscattered energy of the sea clutter can be far greater than the echo energy of a small target, causing the target signal to be obscured by the sea clutter and resulting in missed detection. In the case of multipath propagation, the target echo will be reflected along multiple paths, creating complex multipath interference, further increasing the difficulty of target detection. Furthermore, radar resolution is relatively limited, making it difficult to accurately determine the location and characteristics of small targets.

[0053] Related technologies for maritime target detection primarily use fixed waveform transmission signals, extracting target features through conventional pulse compression and Doppler processing techniques. However, sea clutter exhibits significant non-stationary characteristics, with echo characteristics fluctuating intricately depending on factors such as wave dynamics, wind intensity, ocean currents, and real-time meteorological conditions. In such dynamic environments, fixed waveform radar systems struggle to adapt to varying sea conditions, resulting in poor target detection accuracy and reliability.

[0054] Based on this, embodiments of the present application provide a method, apparatus, device, and storage medium for detecting small targets at sea. These methods obtain a linear frequency modulation (LFM) signal and a multi-bit phase-coded signal, and acquire sea clutter data and target prior information in real time. A transmission signal is generated based on the target prior information, sea clutter data, the LFM signal, and the phase-coded signal. An antenna array is used to obtain a received signal corresponding to the transmission signal. An angle of arrival is estimated based on the received signal. A weighting vector of the antenna array is adjusted based on the angle of arrival. The received signal is adaptively updated based on the weighting vector. The received signal is time-domain compressed to obtain a time-domain compressed signal. The time-domain compressed signal is converted to the frequency domain to obtain a frequency-domain received signal. Distance information is obtained from the time-domain compressed signal. Velocity information is obtained from the frequency-domain received signal. Information fusion is performed based on the distance information, velocity information, and angle of arrival to obtain a parameter estimation result. Target detection is performed based on the parameter estimation result. In embodiments of the present application, the transmission signal is a composite waveform obtained by dynamically adjusting signal parameters based on the statistical characteristics of sea clutter and target prior information. The composite waveform has the characteristics of high distance resolution of the LFM signal and strong anti-interference capability of the phase-coded signal, which can improve the matching degree between the transmission signal and the target and sea clutter. At the same time, adaptive beamforming is combined to suppress interference, and the received signals are jointly processed in the time domain, frequency domain and spatial domain, which enhances the angular resolution and adaptability to complex sea conditions, and significantly improves the accuracy and reliability of detection of weak targets at sea.

[0055] The embodiments of the present application provide a method, apparatus, device and storage medium for detecting small targets at sea, which are specifically illustrated by the following embodiments. First, the method for detecting small targets at sea in the embodiments of the present application is described.

[0056] The small target detection method at sea provided by the embodiment of the present application relates to the field of radar signal processing technology. The small target detection method at sea provided by the embodiment of the present application can be applied to a terminal, can also be applied to a server side, and can also be a computer program running in a terminal or a server side. For example, the computer program can be a native program or software module in an operating system; it can be a local (Native) application (Application, APP), that is, a program that needs to be installed in the operating system to run, such as a client that supports small target detection at sea, that is, a program that can be run only by downloading it into a browser environment; it can also be a small program that can be embedded in any APP. In short, the above-mentioned computer program can be an application, module or plug-in in any form. Among them, the terminal communicates with the server via a network. The small target detection method at sea can be executed by a terminal or a server, or by a terminal and a server in collaboration.

[0057] In some embodiments, the terminal can be a smartphone, tablet computer, laptop computer, desktop computer, or smartwatch. The server can be an independent server, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. It can also be a service node in a blockchain system, where each service node in the blockchain system forms a peer-to-peer (P2P) network. The P2P protocol is an application layer protocol that runs on top of the Transmission Control Protocol (TCP). The terminal and the server can be connected via a communication connection method such as Bluetooth, Universal Serial Bus (USB), or a network, which is not limited in this embodiment.

[0058] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0059] The following describes a method for detecting small targets at sea in an embodiment of the present application.

[0060] Figure 1 This is an optional flowchart of the method for detecting small targets at sea provided by an embodiment of the present application. Figure 1 The method may include but is not limited to steps 110 to 140. It is also understood that this embodiment is Figure 1 The order of step 110 to step 140 is not specifically limited, and the order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0061] Step 110: Acquire sea clutter data and target prior information in real time, and perform signal combination on the linear frequency modulation signal and the multi-level phase coded signal according to the target prior information and the sea clutter data to generate a transmission signal.

[0062] In one embodiment, the variability of sea clutter and the weakness of small target signals in complex marine environments require waveform design to comprehensively consider multiple factors to achieve effective target detection and identification. Therefore, to generate a transmit signal with good characteristics and adaptability to complex marine environments, this embodiment of the application uses a composite waveform combining a multi-bit phase-coded signal and a linear frequency modulation signal as the transmit signal.

[0063] Among them, the multi-level phase coding signal s MPSK (t) is expressed as:

[0064]

[0065] Among them, a n is the amplitude coefficient, φ n is the phase of the nth symbol, T sis the symbol period, f0 is the carrier frequency, and rect(·) is the rectangular window function. Multi-ary phase-coded signals carry information by varying the carrier phase within each symbol period. For example, the 4-ary phase-coded signal is QPSK, whose phase values ​​are typically 0, π / 2, π, and 3π / 2. This characteristic gives phase-coded signals high spectral efficiency, allowing them to transmit more information within a limited bandwidth and, to a certain extent, provide immunity to interference.

[0066] At the same time, the linear frequency modulation signal s LFM (t) is expressed as:

[0067]

[0068] Where A is the signal amplitude, T is the pulse width, and μ is the frequency modulation slope.

[0069] Furthermore, the frequency of a linear frequency modulation signal varies linearly over time, offering a significant advantage in its large time-bandwidth product. Pulse compression technology can compress wide pulses into narrow pulses, thereby improving range resolution. For example, when transmitting a linear frequency modulation signal with a bandwidth of B and a pulse width of T, the pulse width can be compressed to a level of 1 / B after matched filtering, improving range resolution to c / 2B (where c is the speed of light).

[0070] Next, in the embodiment of the present application, the linear frequency modulation signal and the multi-bit phase coding signal are combined into a composite waveform, which is expressed as:

[0071] s(t)=s MPSK (t)S LFM (t),

[0072] The composite waveform fully leverages the advantages of both. The multi-level phase-coded signal provides multiple phase states to increase signal diversity, while the linear frequency modulation signal ensures good range resolution. The combination of the two provides a more powerful signal foundation for detecting weak targets at sea.

[0073] In one embodiment, the signal parameters of the composite waveform are related to the actual environment. Parameter optimization is performed based on real-time acquired sea clutter data and target prior information to ensure good orthogonality between different waveforms, reduce mutual interference between waveforms, and obtain a transmitted signal. The target prior information is the possible speed range of the target determined based on actual experience, and the sea clutter data includes at least Doppler shift information, where the Doppler shift information includes maximum Doppler shift and minimum Doppler shift. Figure 2 , Figure 2 This is a flowchart of generating a transmit signal by combining a linear frequency modulation signal and a multi-bit phase coded signal based on target prior information and sea clutter data, provided in an embodiment of the present application. The flowchart specifically includes the following steps:

[0074] Step 210: Generate a frequency modulation slope corresponding to the linear frequency modulation signal based on the speed interval and the Doppler frequency shift information to obtain a target linear frequency modulation signal.

[0075] In one embodiment, the target linear frequency modulation signal is obtained by adjusting the frequency modulation slope μ of the linear frequency modulation signal. Since the frequency modulation bandwidth and pulse width of the linear frequency modulation signal determine its detectable Doppler shift range, in order to ensure effective pulse compression within the target speed range while suppressing the impact of Doppler broadening of sea clutter on target detection, the frequency modulation slope μ in this embodiment should meet the following conditions:

[0076]

[0077] Among them, 2v max Indicates the maximum possible speed of the target, 2v min Indicates the minimum possible speed of the target. The maximum speed and minimum speed constitute the speed range. represents the maximum Doppler shift of sea clutter, represents the minimum Doppler shift of sea clutter, and λ represents the radar wavelength.

[0078] Step 220: Generate a target phase coding sequence corresponding to the phase coding signal using a genetic algorithm, and obtain a target phase coding signal according to the target phase coding sequence.

[0079] In one embodiment, referring to Figure 3 , Figure 3 This is a flowchart of using a genetic algorithm to generate a target phase coding sequence corresponding to a phase coding signal provided by an embodiment of the present application, specifically including the following steps:

[0080] Step 310: randomly generate a group of initial phase coding sequences to form a population, and calculate the fitness function value of each initial phase coding sequence based on the autocorrelation sidelobe level and the cross-correlation peak.

[0081] In one embodiment, each initial phase coding sequence represents a potential coding scheme, and the fiber value can be discretely selected from {0, π} or {0, π / 2, π, 3π / 2}.

[0082] Since the optimization goals for phase coding are: low autocorrelation sidelobes and low cross-correlation peaks, the purpose of low autocorrelation sidelobes is to concentrate the mainlobe energy to facilitate distance resolution after pulse compression, while the purpose of low cross-correlation peaks is to reduce mutual interference between different beams. Therefore, based on the autocorrelation sidelobe level and the cross-correlation peak, the fitness function value of each initial phase coding sequence is calculated. The fitness function value is expressed as:

[0083] Fitness=w1·ISL+w2·PCC

[0084]

[0085] Wherein, Fitness represents the fitness function value, ISL represents the autocorrelation sidelobe level of the corresponding initial phase-coded sequence, PCC represents the cross-correlation peak of the corresponding initial phase-coded sequence, w1 and w2 represent weight coefficients, and their sum is 1. The specific value can be set according to the actual situation.

[0086] Furthermore, φ n represents the nth phase value in the corresponding initial phase coding sequence, R(k) represents the autocorrelation function of the corresponding initial phase coding sequence at time delay k, N represents the length of the initial phase coding sequence, τ max Indicates the maximum delay, usually N-1, k≠0 means only the sidelobe energy is calculated. ij (k) represents the cross-correlation function value between the i-th initial phase coding sequence and the j-th initial phase coding sequence at time delay k, and M represents the number of initial phase coding sequences in the population.

[0087] It's understandable that the autocorrelation function measures the similarity between a signal and its own delayed state. Ideally, the autocorrelation function should have a sharp peak at zero delay and values ​​close to zero at other delays. This allows low autocorrelation sidelobes to accurately identify the signal itself when performing matched filtering at the receiver, reducing sidelobe interference. The cross-correlation function, on the other hand, measures the similarity between different initial phase-coded sequences. A low cross-correlation peak minimizes interference between different waveforms.

[0088] Step 320: Select candidate coding sequences from the population according to the fitness function value, perform crossover and mutation operations on the candidate coding sequences, and obtain new offspring sequences.

[0089] In one embodiment, a roulette wheel selection or tournament selection method is used to select multiple initial phase coding sequences with high fitness function values ​​from a population as candidate coding sequences. A crossover operation is then performed on each of these candidate coding sequences to simulate the gene exchange process in biological genetics and generate new offspring sequences. For example, a single-point crossover or multi-point crossover method is used to randomly select a crossover point, at which two candidate coding sequences are exchanged to generate new offspring sequences. Mutation operations can also be performed on the offspring sequences with a certain mutation probability to simulate genetic mutations in biological genetics, changing certain phase values ​​in the offspring sequences to prevent the optimization process from falling into local optimal solutions. Multiple offspring sequences are obtained through repeated crossover and mutation operations.

[0090] Step 330: Update the population using the offspring sequence until a preset termination condition is reached, and select the target phase coding sequence from the final population.

[0091] In one embodiment, the population is updated using offspring sequences. An iterative process is performed, repeating the fitness function value calculation, candidate coding sequence selection, crossover, and compilation operations until a preset number of iterations is reached or the fitness function value converges, satisfying a preset termination condition. The coding sequence with the largest fitness function value in the population after the last iteration is selected as the target phase coding sequence. Finally, a target phase coding signal is generated based on the phase value in the target phase coding sequence.

[0092] Step 230: Generate a transmit signal according to the target linear frequency modulation signal and the target phase coding signal.

[0093] In one embodiment, the target linear frequency modulation signal and the target phase coded signal may be directly used as the transmission signal in the form of the above-mentioned composite waveform.

[0094] In addition, real-time updates can also be performed during this process. Figure 4 , Figure 4 This is a flowchart of generating a transmission signal according to a target linear frequency modulation signal and a target phase coding signal provided by an embodiment of the present application, which specifically includes the following steps:

[0095] Step 410: Perform frequency analysis on the power spectrum to obtain frequency segment information.

[0096] In one embodiment, the waveform parameters of the transmitted signal are adaptively adjusted based on the statistical characteristics of sea clutter (sea clutter data) to improve the matching degree between the transmitted signal and the target and sea clutter. The statistical characteristics of the sea clutter can be acquired through real-time monitoring, for example, by measuring the mean, variance, power spectrum, etc. of the sea clutter using sea clutter echo acquisition equipment. When a change in the statistical characteristics of the sea clutter is detected, such as a sudden increase in the power of the sea clutter, indicating that the sea conditions have deteriorated, the target phase coding sequence of the phase coding signal and the frequency modulation slope of the linear frequency modulation signal can be adjusted.

[0097] In one embodiment, frequency analysis is performed on the acquired power spectrum in real time to obtain energy distribution of sea clutter in different frequency bands as frequency band information.

[0098] Step 420: If the frequency segment information includes a frequency segment with concentrated energy, update the target phase coded signal based on the frequency segment, and update the target linear frequency modulation signal according to the Doppler shift change information.

[0099] In one embodiment, if frequency band information indicates that sea clutter energy is concentrated in certain frequency bands, a genetic algorithm is used to optimize the target phase-coded sequence to reduce the correlation between the target phase-coded signal and the sea clutter in these frequency bands. Furthermore, Doppler shift variation information of the sea clutter is acquired in real time. The Doppler broadening of the sea clutter is determined based on this Doppler shift variation information, and the frequency modulation slope of the chirp signal is adjusted accordingly, allowing the target chirp signal to better adapt to changes in sea clutter.

[0100] It is understood that for linear frequency modulation signals, if the target prior information changes, such as a change in the target's speed range, the frequency modulation slope can be adjusted based on the changed speed range. If the target prior information includes the possible location of the target, the target phase encoding sequence can also be regenerated based on the location, so that the phase encoding signal has better signal characteristics in the direction where the target is likely to appear.

[0101] On the one hand, the embodiments of the present application use a composite waveform that combines multi-level phase coding and linear frequency modulation, optimizing the phase coding sequence and frequency modulation slope to achieve good orthogonality between different waveforms and reduce mutual interference. On the other hand, the waveform parameters are adaptively adjusted based on sea clutter characteristics and target prior information. This significantly improves the matching degree between the transmitted signal and the target and sea clutter, enabling the radar to maintain good detection performance in complex and changing marine environments, effectively enhancing the radar's ability to resolve weak target signals under different sea conditions, and significantly improving detection performance.

[0102] Step 120: Utilize the antenna array to obtain a received signal corresponding to the transmitted signal, estimate the angle of arrival based on the received signal, adjust the weighting vector of the antenna array according to the angle of arrival, and adaptively update the received signal based on the weighting vector.

[0103] In one embodiment, after the transmission signal is generated, it is transmitted to the target sea area using a transmitter, and the received signal is obtained using an antenna array. The antenna array here can be a uniform linear array or a planar array composed of multiple antenna units arranged at equal intervals.

[0104] In one embodiment, the received signal includes an echo signal and a noise signal, and adaptive beamforming is performed based on the received signal. Figure 5 , Figure 5 This is a flowchart of estimating the angle of arrival based on the received signal and adjusting the weighting vector of the antenna array according to the angle of arrival provided by an embodiment of the present application, including the following steps:

[0105] Step 510: Obtain a covariance matrix according to the received components corresponding to each antenna element in the received signal, and perform eigendecomposition on the covariance matrix to obtain a signal subspace.

[0106] In one embodiment, the received signal is represented by X(t)=[x1(t),x2(t),…,x N (t)] T , N represents N antenna units, x m (t) represents the received component of the mth antenna element. Taking the uniform linear array as an example, the received component corresponding to the mth antenna element is expressed as:

[0107]

[0108] Where d is the antenna element spacing, θ is the target's angle of arrival, λ is the radar wavelength, and s(t) is the received signal, representing the noise signal corresponding to the mth antenna element. A planar array is an antenna array extended in two dimensions. A similar approach can be used to model the received signal, enabling richer spatial information to be obtained.

[0109] In one embodiment, the covariance matrix is ​​expressed as:

[0110] R=E[X(t)X H (t)]

[0111] Among them, E[·] means to find the expectation, (·) H In specific calculations, a finite number of snapshots are usually used to estimate the covariance matrix, that is, the estimated covariance matrix is ​​expressed as:

[0112]

[0113] Where K is the number of snapshots.

[0114] Next, the estimated covariance matrix Perform eigendecomposition, expressed as:

[0115]

[0116] Among them, U=[u1,u2,…,u N ] is the eigenvector matrix, Λ=diag(λ1,λ2,…,λ N ) is a diagonal matrix of eigenvalues, and λ1≥λ2≥…≥λ N According to the characteristics of the signal subspace and the noise subspace, the eigenvectors corresponding to the first M′ larger eigenvalues ​​form the signal subspace:

[0117] U s =[u1,u2,…,u M′ ]

[0118] The eigenvectors corresponding to the remaining NM′ smaller eigenvalues ​​form the noise subspace:

[0119] U n=[u M′+1 ,u M′+2 …,u N ]

[0120] Where M' is the number of targets.

[0121] Step 520: Determine a spatial spectrum function according to the array manifold vector and the signal subspace, and determine an arrival angle based on a peak value of the spatial spectrum function.

[0122] In one embodiment, the array manifold vector a(θ) is defined as:

[0123]

[0124] Determine the spatial spectrum function P based on the array manifold vector and signal subspace MUSIC (θ), expressed as:

[0125]

[0126] After obtaining the spatial spectrum function, the arrival angle of the target can be estimated by searching for the peak value of the spatial spectrum function within a certain angle range. For example, searching within the range of [-π / 2,π / 2] with a certain step size to find the peak value that makes P MUSIC The angle value where (θ) reaches its maximum value is the arrival angle of the target.

[0127] Step 530: Obtain a target direction vector based on the arrival angle and the array manifold vector, and optimize and solve the weighted vector based on the target direction vector to obtain an updated weighted vector.

[0128] In one embodiment, once the arrival angle is known, adaptive beamforming technology is combined to adaptively adjust the antenna array's weighting coefficients based on the spatial distribution of the target and interference. For example, the minimum variance distortionless response algorithm aims to minimize the antenna array's output power while ensuring the target signal is distortion-free, thereby suppressing interference signals.

[0129] Assume that the weight vector w of the antenna array is expressed as:

[0130] w=[w1,w2…,w N ] T

[0131] If the arrival angle is θ0, then the target direction vector is a(θ0), and the optimization problem is expressed as:

[0132] min w w H R

[0133] stw H a(θ0)=1

[0134] The optimization goal of the above optimization problem is to minimize the output power of the antenna array while ensuring a distortion-free response at the arrival angle θ0. The Lagrange multiplier method can be used to solve this optimization problem. The weighted vector obtained after the solution is expressed as:

[0135]

[0136] With this updated weighting vector, the antenna array can form a high-gain beam in the direction of the target's arrival angle, while simultaneously creating a null in the interference direction, suppressing sea clutter and other interfering signals. For example, when sea clutter primarily comes from a certain direction, adjusting the weighting coefficients can reduce the antenna array's gain in that direction, forming a beam directed toward the target while suppressing interference signals from sea clutter and other interference sources.

[0137] In one embodiment, the weighted vector is applied to the current received signal, and the result is used as the echo signal, which is expressed as: r(t)=w H *s(t).

[0138] In related technologies, array radars have limited angular resolution due to limited aperture, and their ability to suppress interference is limited. The embodiments of the present application use a uniform linear array or planar array to receive echo signals, and perform spatial spectrum estimation based on a subspace algorithm to effectively improve angular resolution and achieve accurate direction finding of small targets at sea. At the same time, combined with adaptive beamforming technology, the weighting vector of the antenna array is adaptively adjusted according to the spatial distribution of the target and interference, and the weighting coefficient of each antenna unit is adjusted accordingly to form a beam pointing to the target and suppress interference signals. Not only can the target direction be determined more accurately, but it can also effectively suppress interference in complex sea clutter and interference environments, thereby improving the accuracy and stability of target detection.

[0139] Step 130: compress the echo signal in the time domain to obtain a time domain compressed signal, and convert the time domain compressed signal into the frequency domain to obtain a frequency domain received signal.

[0140] In one embodiment, multi-dimensional signal joint processing is performed on the echo signal. The core of this process is to organically integrate the transmission signal of the composite wave signal and the array signal processing. By collaboratively processing the echo signal in the time domain, frequency domain and spatial domain, accurate detection and parameter estimation of weak targets at sea can be achieved.

[0141] In one embodiment, referring to Figure 6 , Figure 6 This is a flowchart of performing time domain compression on an echo signal to obtain a time domain compressed signal, provided by an embodiment of the present application, which specifically includes the following steps:

[0142] Step 610: Input the echo signals into different matched filters respectively to obtain frequency-modulated echo signals and coded echo signals.

[0143] Step 620: Perform weighted processing on the FM echo signal and the coded echo signal to obtain a time-domain compressed signal.

[0144] In one embodiment, the transmitted signal is a composite signal generated by a linear frequency modulation (LFM) signal and a multi-bit phase-coded signal. Therefore, the echo signal can also be considered a composite waveform. Different matched filters are designed for the phase-coded portion and the linear frequency modulation (LFM) portion of the echo signal, respectively, and time-domain pulse compression is performed using the matched filters. For example, the echo signal can first be input into a LFM-related matched filter to obtain an FM echo signal, and then the echo signal can be input into a phase-coded matched filter to obtain a coded echo signal. With the FM echo signal and the coded echo signal, they are weighted to produce a time-domain compressed signal.

[0145] As you can understand, for coded echo signals, the matched filter is designed based on the target phase-coding sequence, ensuring accurate restoration of the transmitted phase information at the receiver and reducing phase ambiguity. For FM echo signals, the matched filter is designed based on the frequency modulation slope and pulse width. Through convolution, the wide pulse of the FM echo signal is compressed into a narrow pulse, thereby improving range resolution.

[0146] In one embodiment, after obtaining a time-domain compressed signal through time-domain pulse compression, the compressed pulse signal is Fourier transformed, and the spectral distribution of the time-domain compressed signal is converted to the frequency domain to obtain a frequency-domain received signal. Furthermore, windowing can be used to improve the accuracy of Doppler shift estimation. Windowing can reduce spectral leakage, and can employ methods such as Hanning windows and Hamming windows. Because the time-domain compressed signal is weighted before Fourier transform, spectral leakage can be effectively reduced and frequency resolution can be improved.

[0147] Step 140: Obtain distance information based on the time domain compressed signal, obtain speed information based on the frequency domain received signal, perform information fusion based on the distance information, speed information and arrival angle to obtain parameter estimation results, and perform target detection based on the parameter estimation results.

[0148] In one embodiment, after obtaining the time-domain compressed signal, the target's distance information is obtained based on the time-domain peak position. Furthermore, in maritime target detection, the target's motion can cause a Doppler shift in the echo signal. According to the Doppler effect, the Doppler shift is related to the target's speed and the radar wavelength. Therefore, by analyzing the Doppler shift corresponding to the received frequency-domain signal, the target's speed information can be extracted.

[0149] In one embodiment, with the distance information in the time domain, the speed information in the frequency domain, and the arrival angle in the spatial domain, the multi-dimensional information fusion of the weak targets at sea can be performed according to the Bayesian fusion algorithm to improve the accuracy and reliability of target detection. Figure 7 , Figure 7 This is a flowchart of obtaining parameter estimation results by fusing distance information, speed information, and arrival angle according to an embodiment of the present application, which specifically includes the following steps:

[0150] Step 710: Obtain the distance likelihood, speed likelihood and angle likelihood according to the distance information, speed information and arrival angle respectively, and obtain a likelihood function based on the distance likelihood, speed likelihood and angle likelihood.

[0151] In one embodiment, the Bayesian fusion algorithm is based on Bayes' theorem. Its core idea is to calculate the posterior probability based on the prior probability and observation data, thereby achieving information fusion. Assume that D represents distance information, V represents speed information, and A represents arrival angle. The target state is expressed as:

[0152] X=[D true ,V true ,A true ]

[0153] Among them, D true Denotes the predicted distance, V true Indicates the prediction speed, A true Represents the prediction angle.

[0154] According to the statistical characteristics of radar measurement errors, such as the standard deviation of distance measurement error σ D , standard deviation of velocity measurement error σ V and the standard deviation of the angle measurement error σ A , establish the corresponding probability distribution function.

[0155] Assume that the distance error follows a normal distribution The distance likelihood is expressed as:

[0156]

[0157] At this time, it is assumed that the velocity error obeys the normal distribution The velocity likelihood is expressed as:

[0158]

[0159] At this time, it is assumed that the angle error follows a normal distribution The angular likelihood is expressed as:

[0160]

[0161] Step 720: Obtain a priori probability, obtain a posterior probability according to the priori probability and the likelihood function, and obtain a parameter estimation result based on the posterior probability.

[0162] In one embodiment, P(X) represents the prior probability of the target state, which can be estimated based on the historical data and prior knowledge of the target. If there is no historical data, it is set to a uniform distribution. If there is historical data, it is obtained based on Kalman filter observations.

[0163] At this time, the posterior probability P(X|D,V,A) obtained according to the prior probability and likelihood function is expressed as:

[0164]

[0165] P(D,V,A|X)=P(D|X)·P(V|X)·P(A|X)

[0166] P(D,V,A)=∫P(D,V,A|X)P(X)d(X)

[0167] P(D,V,A|X) is calculated based on the distance likelihood, velocity likelihood, and angle likelihood, and represents the probability of observing distance, velocity, and angle information when the target state is X. P(D,V,A) is a normalization constant used to ensure that the sum of the posterior probabilities is 1. By multiplying the likelihood functions of multiple dimensions of information and combining them with the prior probability, the posterior probability of the target state can be obtained, thus achieving information fusion.

[0168] In one embodiment, after obtaining the posterior probability, the target state X with the highest posterior probability is found, and the parameter estimation result is obtained based on the corresponding predicted distance, predicted speed, and predicted angle. After obtaining the parameter estimation result, target detection can be performed based on the likelihood ratio test.

[0169] First, the log-likelihood ratio is calculated based on the parameter estimation results. If the log-likelihood ratio is greater than or equal to a preset threshold η, the target is detected and tracked in real time using the Kalman filter algorithm. Otherwise, the result is ignored and detection continues. The threshold η in the likelihood ratio test requires a comprehensive consideration of the detection probability and the false alarm probability, achieving a balance between the two by adjusting the threshold.

[0170] When using the Kalman filter algorithm to track a target in real time, the predicted state at the kth moment is first obtained based on the target's motion model. Taking the uniform linear motion model as an example, the predicted state at the kth moment is expressed as:

[0171] X k|k-1 =FX k-1|k-1

[0172] Among them, F represents the state transfer matrix, X k-1|k-1represents the predicted state at the k-1th moment. At the same time, the covariance matrix corresponding to the predicted state at the kth moment is expressed as:

[0173] P k|k-1 =FP k-1|k-1 F T +Q

[0174] Where Q is the process noise covariance matrix, P k-1|k-1 represents the covariance matrix at the k-1th moment.

[0175] Next comes the update phase.

[0176] In the update phase, the parameter estimation results are used as new observation data Z k , correct the predicted state and get the updated state estimate:

[0177] X k|k =X k|k-1 +K k (Z k -HX k|k-1 )

[0178] K k =P k|k-1 H T (HP k|k-1 H T +R′) -1

[0179] Among them, K k is the Kalman gain, H is the observation matrix, and R′ is the observation noise covariance matrix.

[0180] Through continuous prediction and updating, the Kalman filter algorithm can track parameters such as the target's position, speed, and angle in real time, and continuously detect small targets at sea.

[0181] In the embodiments of the present application, waveform design of the transmitted signal, array signal processing of the received signal, and multi-dimensional signal joint processing are performed to effectively solve the problems of low detection performance, insufficient resolution, and poor adaptability to complex sea conditions in radar detection of weak targets at sea.

[0182] The following is a specific embodiment of the method for detecting small targets at sea according to an embodiment of the present application.

[0183] First, determine the waveform parameter range: In the initial stage of radar system construction, based on key indicators such as the radar's carrier frequency f0, bandwidth B, pulse repetition frequency PRF, and comprehensive consideration of factors such as the radar's operating range, resolution, and signal processing complexity, ensure that the parameter range of the linear frequency modulation signal and multi-level phase coded signal is accurately determined while meeting detection performance, so that system resources are reasonably utilized.

[0184] For example, the radar carrier frequency is 10 GHz, the bandwidth is 100 MHz, and the pulse repetition frequency is 1000 Hz. According to the system performance requirements and signal processing capabilities, the base number of the phase coding signal is set to 4 (i.e., QPSK), the initial phase coding length is 64 bits, the bandwidth of the linear frequency modulation signal is 80 MHz, and the pulse width is 10 μs.

[0185] Next, a genetic algorithm is used to optimize the phase coding of the multi-bit phase coding signal to obtain the target phase coding signal. First, a population of 100 random initial phase coding sequences is initialized, and the length of each initial phase coding sequence is 64 bits. The fitness function value of each initial phase coding sequence is calculated based on the autocorrelation sidelobe level and the cross-correlation peak. After entering the iterative process, in each generation of evolution, the initial phase coding sequence with a higher fitness function value is selected as the candidate phase coding sequence by the roulette wheel selection method, and a single-point crossover and mutation operation is performed on it. Among them, the crossover probability is set to 0.8 and the mutation probability is set to 0.01. After 50 generations of iterative optimization, a phase coding sequence with good autocorrelation and cross-correlation characteristics is obtained, from which the target phase coding sequence is selected. The generated phase coding signal can effectively reduce the interference between waveforms.

[0186] At the same time, according to the possible speed range of the target (assuming it is -50m / s to 50m / s) and the Doppler characteristics of sea clutter (according to historical data statistics, the Doppler frequency shift range of sea clutter is -100Hz to 100Hz), the frequency modulation slope of the linear frequency modulation signal is calculated, and the frequency modulation slope is calculated to be 8×10 12 Hz / s. This ensures that the linear frequency modulation signal can achieve effective pulse compression within the target's speed range, while suppressing the impact of the Doppler broadening of sea clutter on target detection.

[0187] During radar operation, the radar uses sea clutter echo acquisition equipment to monitor the statistical characteristics of sea clutter in real time, such as mean, variance, and power spectrum. This information is also updated based on the target's potential presence in a certain area and speed range of 10 to 30 m / s. When the sea clutter power spectrum is detected to have energy concentrated near 50 Hz, a genetic algorithm is used to re-optimize the target phase coding sequence of the phase coding signal to reduce its correlation with sea clutter in this frequency range. Furthermore, the linear frequency modulation slope is fine-tuned based on the target's speed range to achieve better pulse compression within this speed range, thereby improving the signal's match with the target and sea clutter.

[0188] After determining the transmitted signal according to the above process, the radar's antenna array receives the echo signal. Assume that a uniform linear array consisting of 16 antenna elements, with a spacing of half a wavelength, is used to receive the echo signal from a maritime target. The received signal is first amplified by a low-noise amplifier with a gain of 20dB to enhance signal strength. It then passes through a bandpass filter with a bandwidth of 120MHz to remove out-of-band noise. Finally, the received signal is sampled at a frequency of 200MHz to ensure distortion-free signal.

[0189] The received signal is then used to estimate the spatial spectrum. Specifically, the covariance matrix of the received signal is calculated using 100 snapshots. Eigendecomposition is then performed on the covariance matrix to obtain the signal and noise subspaces. Finally, the peak of the spatial spectrum function is searched within the range [-π / 2, π / 2] with a step size of 0.1° to determine the target's angle of arrival. For example, if the peak of the spatial spectrum function is found at an angle of 30°, the target's angle of arrival is 30°.

[0190] Next, the antenna calculates the array's weighting coefficients based on the target's arrival angle and the spatial distribution of the interference source. Assuming the arrival angle is 30° and the interference source primarily comes from a 120° direction, the calculated weighting vector is used to adjust the weighting coefficients of each antenna element in the array. This allows the antenna array to form a high-gain beam in the estimated direction of the target, increasing the gain by 15dB. At the same time, a null is formed in the interference direction, effectively suppressing sea clutter and other interfering signals.

[0191] The multidimensional signals are then processed jointly. First, time-domain pulse compression is performed. Matched filters are designed for echo signals of different waveforms. The matched filters for the transmitted signal are convolved with the echo signal. After matched filtering, the pulse width of the linear frequency modulation signal is compressed from 10μs to 12.5ns, improving the range resolution to 1.875m, effectively extracting target range information.

[0192] Next, frequency domain processing is performed, applying a Fourier transform to the compressed time-domain signal and applying a Hanning window function with the same length as the signal. Refined spectrum analysis techniques are used to locally amplify and fine-tune analysis of the frequency range of interest, improving the accuracy of Doppler shift estimation. For example, after processing, changes in target velocity of 1 m / s can be accurately discerned, effectively extracting target velocity information.

[0193] With the time-domain compressed signal, the target's range information is derived from the time-domain peak position, and the target's velocity information can be extracted by analyzing the corresponding Doppler shift of the received frequency-domain signal. A Bayesian fusion algorithm is used to fuse the time-domain range information, the frequency-domain velocity information, and the spatial-domain angle of arrival. During the fusion process, the prior probability of the target state is estimated based on historical data and prior knowledge. Based on the statistical characteristics of radar measurement errors, such as a standard deviation of 2 m for distance measurement error, 2 m / s for velocity measurement error, and 1° for angle measurement error, a corresponding likelihood function is established. By multiplying the likelihood functions of multiple dimensions and combining them with the prior probabilities, the posterior probability of the target state is obtained, achieving information fusion and obtaining parameter estimation results.

[0194] Finally, based on the fused parameter estimation results, a detection algorithm based on the likelihood ratio test is used to determine the presence of a target. For example, a likelihood ratio threshold of 1.5 is set; when the likelihood ratio exceeds this threshold, the target is considered present. If a target is detected, the Kalman filter algorithm is used to track the target in real time. Assuming the target is moving in a uniform linear motion, the target's state at the next moment is predicted based on its initial state and motion model, and this state is updated based on new observations. For example, during the tracking process, parameters such as the target's position, velocity, and angle can be accurately updated in real time, enabling continuous monitoring of the target.

[0195] The embodiment of the present application is aimed at the detection of small and weak targets at sea, and jointly processes the echo signal in the time domain, frequency domain, and spatial domain. The target's distance and velocity information is extracted through time-domain pulse compression and frequency-domain Doppler processing, and combined with the arrival angle in the spatial domain, multi-dimensional parameter estimation and detection of small and weak targets at sea are achieved. Data fusion technology is used to fuse multi-dimensional information, fully tapping into the useful information in the signal, enabling the radar to more comprehensively and accurately detect and track small and weak targets at sea, improving the accuracy and reliability of target detection and achieving precise direction finding, positioning, and tracking of targets.

[0196] The technical solution provided by the embodiments of the present application obtains sea clutter data and target prior information in real time, combines a linear frequency modulation signal and a multi-bit phase-coded signal based on the target prior information and the sea clutter data to generate a transmit signal, obtains a received signal corresponding to the transmit signal using an antenna array, estimates the angle of arrival based on the received signal, adjusts the weighting vector of the antenna array based on the angle of arrival, obtains an echo signal based on the weighting vector, compresses the echo signal in the time domain to obtain a time-domain compressed signal, converts the time-domain compressed signal to the frequency domain to obtain a frequency-domain received signal, obtains distance information based on the time-domain compressed signal, obtains velocity information based on the frequency-domain received signal, fuses the distance information, velocity information, and angle of arrival to obtain a parameter estimation result, and performs target detection based on the parameter estimation result. The transmit signal in the embodiments of the present application is a composite waveform obtained by dynamically adjusting signal parameters based on the statistical characteristics of sea clutter and the target prior information. The composite waveform has the characteristics of high range resolution of the linear frequency modulation signal and strong anti-interference capability of the phase-coded signal, which can improve the matching degree between the transmit signal and the target and sea clutter. At the same time, adaptive beamforming is combined to suppress interference, and the received signals are jointly processed in the time domain, frequency domain and spatial domain, which enhances the angular resolution and adaptability to complex sea conditions, and significantly improves the accuracy and reliability of detection of weak targets at sea.

[0197] The present application also provides a small target detection device at sea, which can implement the above-mentioned small target detection method at sea. Figure 8 , the device comprises:

[0198] Transmit signal generation module 810: used to obtain linear frequency modulation signals and multi-bit phase coded signals, and obtain sea clutter data and target prior information in real time, and generate a transmit signal based on the target prior information, sea clutter data, linear frequency modulation signals and phase coded signals.

[0199] Beamforming module 820: used to obtain a received signal corresponding to the transmitted signal using the antenna array, estimate the arrival angle based on the received signal, adjust the weighting vector of the antenna array according to the arrival angle, and obtain the echo signal based on the weighting vector.

[0200] The time-frequency domain processing module 830 is configured to perform time-domain compression on the echo signal to obtain a time-domain compressed signal, and convert the time-domain compressed signal into the frequency domain to obtain a frequency-domain received signal.

[0201] Target detection module 840: used to obtain distance information based on the time domain compressed signal, obtain speed information based on the frequency domain received signal, fuse the distance information, speed information and arrival angle to obtain parameter estimation results, and perform target detection based on the parameter estimation results.

[0202] The specific implementation of the marine small target detection device of this embodiment is basically the same as the specific implementation of the marine small target detection method described above, and will not be repeated here.

[0203] An embodiment of the present application further provides an electronic device, including:

[0204] at least one memory;

[0205] at least one processor;

[0206] at least one program;

[0207] The program is stored in the memory, and the processor executes the at least one program to implement the above-mentioned method for detecting small targets at sea. The electronic device can be any smart terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), a car computer, etc.

[0208] See also Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0209] The processor 901 may be implemented as a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0210] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program codes are stored in the memory 902 and are called by the processor 901 to execute the method for detecting small targets at sea in the embodiments of this application.

[0211] Input / output interface 903, used to implement information input and output;

[0212] Communication interface 904, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0213] Bus 905 , which transmits information between various components of the device (e.g., processor 901 , memory 902 , input / output interface 903 , and communication interface 904 );

[0214] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0215] An embodiment of the present application also provides a storage medium, which is a storage medium that stores a computer program. When the computer program is executed by a processor, the above-mentioned method for detecting small targets at sea is implemented.

[0216] The memory, as a non-transient storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0217] The present invention provides a method, apparatus, device, and storage medium for detecting small targets at sea. The method, apparatus, and storage medium provide a method for acquiring sea clutter data and target prior information in real time. The method combines a linear frequency modulation (LFM) signal and a multi-bit phase-coded signal based on the target prior information and the sea clutter data to generate a transmitted signal. The method uses an antenna array to acquire a received signal corresponding to the transmitted signal. The antenna array estimates the angle of arrival based on the received signal. The antenna array's weighting vector is adjusted based on the angle of arrival. The antenna array's weighting vector is used to obtain an echo signal based on the weighting vector. The echo signal is compressed in the time domain to obtain a time-domain compressed signal. The time-domain compressed signal is converted to the frequency domain to obtain a frequency-domain received signal. Distance information is obtained from the time-domain compressed signal. Velocity information is obtained from the frequency-domain received signal. Information fusion is performed based on the distance information, velocity information, and angle of arrival to obtain a parameter estimation result. Target detection is performed based on the parameter estimation result. The transmitted signal in the present invention is a composite waveform obtained by dynamically adjusting signal parameters based on the statistical characteristics of sea clutter and target prior information. The composite waveform combines the high range resolution of the LFM signal with the strong anti-interference capability of the phase-coded signal, thereby improving the matching degree between the transmitted signal and the target and sea clutter. At the same time, adaptive beamforming is combined to suppress interference, and the received signals are jointly processed in the time domain, frequency domain and spatial domain, which enhances the angular resolution and adaptability to complex sea conditions, and significantly improves the accuracy and reliability of detection of weak targets at sea.

[0218] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0219] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0220] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0221] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0222] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0223] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0224] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0225] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0226] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0227] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0228] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A method for detecting small targets at sea, characterized in that: include: acquiring sea clutter data and target prior information in real time, and performing signal combination on a linear frequency modulation signal and a multi-binary phase coded signal according to the target prior information and the sea clutter data to generate a transmission signal; Obtaining a received signal corresponding to the transmitted signal using an antenna array, estimating an angle of arrival based on the received signal, adjusting a weighting vector of the antenna array according to the angle of arrival, and obtaining an echo signal based on the weighting vector; Performing time domain compression on the echo signal to obtain a time domain compressed signal, and converting the time domain compressed signal into a frequency domain to obtain a frequency domain received signal; Distance information is obtained according to the time domain compression signal, speed information is obtained according to the frequency domain received signal, parameter estimation results are obtained by performing information fusion based on the distance information, the speed information and the arrival angle, and target detection is performed based on the parameter estimation results.

2. The method for detecting small targets at sea according to claim 1, wherein: The target priori information is a speed range of the target, the sea clutter data includes at least Doppler frequency shift information, and the linear frequency modulation signal and the multi-binary phase coded signal are combined according to the target priori information and the sea clutter data to generate a transmission signal, including: generating a frequency modulation slope corresponding to a linear frequency modulation signal based on the speed interval and the Doppler frequency shift information to obtain a target linear frequency modulation signal; generating a target phase coding sequence corresponding to the phase coding signal by using a genetic algorithm, and obtaining a target phase coding signal according to the target phase coding sequence; The transmit signal is generated according to the target linear frequency modulation signal and the target phase coded signal.

3. The method for detecting small targets at sea according to claim 2, wherein: The method of generating a target phase coding sequence corresponding to a phase coding signal by using a genetic algorithm includes: Randomly generating a group of initial phase coding sequences to form a population, and calculating the fitness function value of each of the initial phase coding sequences based on the autocorrelation sidelobe level and the cross-correlation peak value; Selecting a candidate coding sequence from the population according to the fitness function value, performing crossover and mutation operations on the candidate coding sequence to obtain a new offspring sequence; The population is updated using the offspring sequence until a preset termination condition is reached, and a target phase coding sequence is selected from the final population.

4. The method for detecting small targets at sea according to claim 2, wherein: The sea clutter data includes at least power spectrum and Doppler shift change information, and generating a transmit signal according to the target linear frequency modulation signal and the target phase coded signal includes: Performing frequency analysis on the power spectrum to obtain frequency segment information; If the frequency segment information includes a frequency segment with concentrated energy, the target phase coded signal is updated based on the frequency segment, and the target linear frequency modulation signal is updated according to the Doppler frequency shift change information.

5. The method for detecting small targets at sea according to claim 1, wherein: The step of compressing the echo signal in the time domain to obtain a time domain compressed signal, and converting the time domain compressed signal into the frequency domain to obtain a frequency domain received signal comprises: Inputting the echo signals into different matched filters respectively to obtain frequency modulated echo signals and coded echo signals; The frequency modulation echo signal and the coded echo signal are weighted to obtain the time domain compression signal.

6. The method for detecting small targets at sea according to claim 1, characterized in that: The antenna array includes a plurality of antenna units, and the estimating the arrival angle based on the received signal and adjusting the weighting vector of the antenna array according to the arrival angle include: Obtaining a covariance matrix according to a received component corresponding to each antenna unit in the received signal, and performing eigendecomposition on the covariance matrix to obtain a signal subspace; determining a spatial spectrum function according to the array manifold vector and the signal subspace, and determining the angle of arrival based on a peak value of the spatial spectrum function; A target direction vector is obtained based on the arrival angle and the array manifold vector, and the weighted vector is optimized and solved based on the target direction vector to obtain the updated weighted vector.

7. The method for detecting small targets at sea according to claim 1, wherein: The obtaining a parameter estimation result by performing information fusion according to the distance information, the speed information, and the arrival angle includes: Obtaining a distance likelihood, a speed likelihood, and an angle likelihood according to the distance information, the speed information, and the arrival angle, respectively, and obtaining a likelihood function based on the distance likelihood, the speed likelihood, and the angle likelihood; A priori probability is obtained, a posterior probability is obtained according to the priori probability and the likelihood function, and the parameter estimation result is obtained based on the posterior probability.

8. A small target detection device at sea, characterized in that: include: Transmitting signal generation module: used to obtain sea clutter data and target prior information in real time, and perform signal combination on the linear frequency modulation signal and the multi-binary phase coded signal according to the target prior information and the sea clutter data to generate a transmitting signal; Beamforming module: used to obtain a received signal corresponding to the transmitted signal using an antenna array, estimate an arrival angle based on the received signal, adjust a weighting vector of the antenna array according to the arrival angle, and obtain an echo signal based on the weighting vector; The time-frequency domain processing module is used to perform time-domain compression on the echo signal to obtain a time-domain compressed signal, and convert the time-domain compressed signal into the frequency domain to obtain a frequency-domain received signal; Target detection module: used to obtain distance information based on the time domain compressed signal, obtain speed information based on the frequency domain received signal, perform information fusion based on the distance information, the speed information and the arrival angle to obtain a parameter estimation result, and perform target detection based on the parameter estimation result.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method for detecting small targets at sea as described in any one of claims 1 to 7 when executing the computer program.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for detecting small targets at sea according to any one of claims 1 to 7 is implemented.

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