A radar distributed target detection method and system based on data dimension reduction

Abstract

The present application belongs to but is not limited to the technical field of target detection, and particularly relates to a radar distributed target detection method and system based on data dimension reduction. First, the to-be-detected data and auxiliary data are projected into a signal subspace to realize data dimension reduction. Then, the noise power mismatch, target echo amplitude vector, signal direction vector coordinate vector and noise covariance matrix are estimated based on the dimension-reduced test data and auxiliary data. Further, a detection statistic is calculated, and a detection threshold is determined according to the false alarm probability. Finally, the size of the detection statistic and the detection threshold is compared. When the detection statistic is greater than the detection threshold, the target exists, and vice versa. The present application projects the to-be-detected data and auxiliary data into the signal subspace, effectively reduces the data dimension, simplifies the target detection mathematical model, and realizes reliable detection performance when the amount of auxiliary data is limited.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of target detection technology, and in particular relates to a radar distributed target detection method and system based on data dimensionality reduction. Background Art

[0002] Adaptive target detection is a fundamental research area in radar signal processing. When observed using high-resolution radar, a target can be resolved into several isolated scattering points distributed across multiple range resolution units. Ideally, the echoes from these scattering points can be described as the same steering vector weighted by different echo intensities. However, due to factors such as array calibration errors, the assumed steering vector deviates from the true value. To account for deviations in the steering vector and ensure the reliability of detection results, a distributed model can be used to describe the entire target echo, where the steering vector belongs to a known subspace, but the specific coordinates are unknown.

[0003] The radar received signal also contains unknown environmental noise. Auxiliary data containing only noise can be obtained from adjacent range-resolution units to estimate the noise covariance matrix. Due to noise fluctuations, there is a noise power mismatch between the measured data within the observation unit and the auxiliary data from adjacent units. This situation is called a partially homogeneous environment.

[0004] To address the problem of detecting distributed targets in partially uniform environments, Olivier Besson et al. designed a generalized adaptive direction detector (GADD) based on the generalized likelihood ratio criterion in 2006. GADD is robust to steering vector deviations. In 2020, Liu Jun et al. derived the same detector based on the generalized likelihood ratio criterion and the Wald criterion under the assumption that the noise covariance matrix has a fully symmetric structure. This detector has a similar structure to GADD and is also robust to steering vector deviations.

[0005] In the presence of Gaussian colored noise, existing detectors rely on highly accurate estimation of the noise covariance matrix to ensure good detection performance. This requires obtaining sufficient auxiliary data from neighboring cells, typically at least twice the dimensionality of the data model. However, the real environment is continuously fluctuating, and the number of neighboring cells with the same noise characteristics as the cell under test is limited. Under these circumstances, existing detectors face the risk of drastically deteriorating performance or even failure. Therefore, effective target detection in the presence of limited auxiliary data is a serious and pressing problem that needs to be addressed.

[0006] In view of the above analysis, the technical problem that urgently needs to be solved in the existing technology is: how to perform effective target detection when the amount of auxiliary data is limited. Summary of the Invention

[0007] To address the challenges of the existing technology, the present invention provides a radar distributed target detection method and system based on data dimensionality reduction. The goal is to improve the detection performance of radar distributed targets in partially homogeneous environments when the amount of auxiliary data is limited. This method projects the target data and auxiliary data into the signal subspace to achieve data dimensionality reduction. This simplifies the target detection model while reducing the dimensionality of the noise covariance matrix to be estimated, effectively reducing the amount of auxiliary data required to estimate the noise covariance matrix and ensuring reliable detection performance even with limited auxiliary data.

[0008] The present invention is implemented as follows: a radar distributed target detection method based on data dimensionality reduction, comprising:

[0009] S1: Get radar in K p The data to be tested sampled in the continuous units to be tested The signal subspace to which the steering vector of the unit to be detected belongs is represented by the column-full rank matrix Zhang Cheng, the coordinate vector of the steering vector in the signal subspace is Under the target assumption H1, the target signal amplitude vector is Where M is the number of radar channels or data model dimension, Q is the signal subspace dimension, represents a complex matrix of dimension m×n, (·) * indicates conjugation;

[0010] S2: Get the K of the radar from the unit under test s Auxiliary data sampled in adjacent cells

[0011] S3: The data to be tested X p and auxiliary data X s Are projected into the signal subspace for data dimensionality reduction, and new test data are obtained respectively. and new auxiliary data

[0012] S4: Under the target hypothesis H1, according to the new test data Y p and new auxiliary data Y s The joint probability density function f1(Y p ,Y s ) and the signal subspace matrix H, solve the estimated value of the power mismatch γ of the Gaussian colored noise in the measured data and the auxiliary data Solve the coordinate vector of the target signal in the signal subspace spanned by the matrix H in the measured data Estimated value of Solve the conjugate amplitude vector of the target signal in the measured data Estimated value of Solve the covariance matrix of the new Gaussian colored noise Estimated value of

[0013] S5: Conjugate the target signal to the estimated amplitude vector Column vectorization and storage in the relevant parameter vector The estimated value of the Gaussian colored noise power mismatch between the measured data and the auxiliary data is The estimated value of the coordinate vector of the target signal in the measured data in the signal subspace spanned by the matrix H New covariance matrix estimate of Gaussian colored noise Vectorize the columns and save them in the redundant parameter vector In the equation: r and the redundant parameter vector θ s The columns are vectorized and stored in the parameter vector middle;

[0014] S6: Calculate the detection statistic t based on the Wald criterion according to the parameter vector θ Wald ;

[0015] S7: According to the false alarm probability P fa Determine the detection threshold T Wald ;

[0016] S8: Comparison of the test statistic t based on the Wald criterion Wald and the detection threshold T Wald The size relationship is used to determine whether there is a target signal of interest in the data to be tested.

[0017] Furthermore, in step S3, the data to be tested X p and auxiliary data X s All are projected into the signal subspace for data dimensionality reduction to obtain the new test data Y p and new auxiliary data Y s They are: Y p =(H H H) -1 H H X p and Y s =(H H H) -1 H H X s ,in(·) G represents the conjugate transpose of the matrix, (·) --1 Represents the inversion of a reversible matrix.

[0018] Furthermore, the estimated value of the Gaussian colored noise power mismatch γ between the measured data and the auxiliary data in step S4 is for: It is a multivariate equation of one variable The only positive solution of , where N = min{K p ,Q},ε1≥ε2≥...≥ε N is the new test data Y p Hermitian inner product matrix after quasi-whitening The non-zero eigenvalues ​​of is the new auxiliary data Y s The sampling covariance matrix of .

[0019] Furthermore, the estimated value of the coordinate vector p of the target signal in the data to be measured in the signal subspace spanned by the matrix H in step S4 is for: yes The eigenvector corresponding to the largest eigenvalue of Indicates dimension K s The unit matrix of the target signal; the estimated value of the conjugate amplitude vector α in the measured data for:

[0020] Furthermore, the estimated value of the covariance matrix ∑ of the new Gaussian colored noise in step S4 is for:

[0021] Furthermore, the relevant parameter vector θ in step S5 r for: Redundant parameter vector θ s for: Where vec(·) means to vectorize the matrix columns, (·) T represents the transpose; the parameter vector θ is:

[0022] Furthermore, the detection statistic t based on the Wald criterion in step S6 is Wald The expression is: in i,j∈{r,s}, E[·] means to find the expectation, ln(·) means the natural logarithm function, f1(Y p ,Y s ) is the new test data Y p and new auxiliary data Y s Joint probability density function under the target hypothesis H1.

[0023] Further, in step S7, according to the false alarm probability P fa Determine the detection threshold T Wald The specific steps are:

[0024] S71: Under the targetless hypothesis H0, calculate the test statistic t based on the Wald criterion Wald ;

[0025] S72: Execute M on step S71 c The Monte Carlo simulation is performed and the calculated detection statistic t based on the Wald criterion is used. Wald Stored in column vectors Among them represents a real matrix of dimension m×n;

[0026] S73: Vector V Wald The elements in V are rearranged in ascending order. Wald Middle The element is the false alarm probability P fa The corresponding detection threshold T Wald ,in Indicates rounding down.

[0027] Furthermore, in step S8, the detection statistic t based on the Wald criterion is compared. Wald and the detection threshold T Wald The specific process of judging whether there is a target signal of interest in the data to be tested is as follows: If t Wald >T Wald , then the target signal of interest exists; if t Wald ≤T Wald , then the target signal of interest does not exist.

[0028] Another object of the present invention is to provide a radar distributed target detection system based on data dimensionality reduction for implementing the radar distributed target detection method based on data dimensionality reduction, comprising:

[0029] Data acquisition module: used to obtain the data to be tested and auxiliary data;

[0030] Parameter acquisition module: used to obtain the subspace column full rank matrix to which the steering vector of the unit to be detected belongs and the required false alarm probability;

[0031] Data dimensionality reduction module: used to project the test data and auxiliary data into the signal subspace to perform data dimensionality reduction, so as to obtain new test data and new auxiliary data respectively;

[0032] Parameter calculation module: used to calculate the sampling covariance matrix of the new auxiliary data using the new data to be measured, the new auxiliary data and the signal subspace matrix, calculate the power mismatch of the Gaussian colored noise in the data to be measured and the auxiliary data, calculate the coordinate vector of the target signal in the data to be measured in the subspace spanned by the signal subspace matrix, calculate the amplitude vector of the target signal in the data to be measured, and calculate the covariance matrix of the new Gaussian colored noise;

[0033] Detection statistic calculation module: used to calculate the detection statistic based on the Wald criterion using the results output by the parameter calculation module;

[0034] Detection threshold calculation module: used to calculate the detection threshold based on the false alarm probability;

[0035] Target detection output module: used to compare the detection statistics and the detection threshold, and output the judgment result of whether the target signal of interest exists in the data to be tested.

[0036] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the radar distributed target detection method based on data dimensionality reduction.

[0037] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the radar distributed target detection method based on data dimensionality reduction.

[0038] Another object of the present invention is to provide an information data processing terminal, which includes the radar distributed target detection system based on data dimensionality reduction.

[0039] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0040] First, by projecting the test data and auxiliary data into the signal subspace, this invention achieves data dimensionality reduction, significantly simplifying the mathematical model for target detection. This reduces the dimensionality of the noise covariance matrix to be estimated, reducing the amount of auxiliary data required by the detector. This reduces the computational complexity of the detection statistic and enables higher distributed target detection performance even with limited auxiliary data.

[0041] Second, the technical solution of the present invention solves the technical problems that people have long been eager to solve but have never been able to solve successfully:

[0042] ① In reality, factors such as array calibration errors, radar channel or array element consistency issues, and radar antenna beam pointing deviations can cause the target steering vector to deviate in different directions. Assuming the target steering vector is known exactly in these situations can lead to reliability issues with conventional detectors. By using a signal subspace model to cover the deviation range of the steering vector, this modeling approach slightly degrades performance when the target echo steering vector is unbiased. However, it can ensure reliable detection results when uncertainty exists about the steering vector's deviation and its degree of deviation.

[0043] ② When performing target detection, it is necessary to sample sufficient auxiliary data from the neighboring units of the unit to be observed to estimate the noise covariance matrix in the unit to be observed. However, the current detection environment is becoming more and more complex and changeable, and the noise characteristics in different distance resolution units fluctuate greatly. In order to ensure the accuracy of the noise covariance matrix estimation results in the unit to be observed, auxiliary data can only be sampled from several units adjacent to the unit to be observed. At this time, there is a problem of insufficient auxiliary data and the performance of the existing detector deteriorates or even fails. Therefore, how to perform effective and reliable target detection under the condition of limited auxiliary data is a very necessary, meaningful and urgent problem to be solved. The present invention reduces the dimension of the detection problem and the dimension of the noise covariance matrix to be estimated by projecting data into the target signal subspace as a preprocessing method, thereby effectively reducing the detector's demand for auxiliary data. In addition, data projection also reduces the computational complexity of the detection statistic, which is conducive to achieving real-time detection.

[0044] The technical solution of the present invention overcomes technical prejudice: the existing technical solutions are all based on specific criteria (such as the generalized likelihood ratio criterion, Rao criterion, Wald criterion, etc.) to design detectors. This design process is carried out in the entire observation space, that is, the dimension of the processed data is equal to the number of radar channels. However, the core of target detection is to determine whether there is a target signal of interest in the signal subspace, so detection can be performed only in the signal subspace. The technical solution of the present invention is to introduce a data preprocessing process, that is, first projecting the data into the signal subspace to achieve data dimensionality reduction, and then designing the detector based on specific criteria. Since the dimension of the signal subspace is smaller than or even much smaller than the number of radar channels in practice, the complexity of the detector design is significantly reduced after data dimensionality reduction. In addition, projecting the data into the signal subspace can eliminate the need to consider the influence of noise in the orthogonal signal subspace, which is beneficial to improving detection performance.

[0045] Third, the technical solution of the present invention solves several key problems in the prior art in industrial applications and achieves significant technological progress, which is specifically reflected in the following aspects:

[0046] 1. Data dimensionality reduction improves processing efficiency: The existing radar target detection system in the face of complex environment, processing large-scale data often consumes a lot of computing resources, and the detection accuracy is greatly affected by noise. The present application introduces a data dimensionality reduction module, which projects the data to be measured and auxiliary data into the signal subspace, effectively reducing the dimensionality of the data and reducing the computational complexity. This not only improves the processing efficiency of the system, but also reduces the occupation of resources while ensuring the accuracy.

[0047] 2. Suppress noise outside the signal subspace: The traditional radar detection method is carried out in the whole observation space filled with noise, and the noise in the orthogonal signal subspace will cause false detection and missed detection. The present application projects the data into the signal subspace, which excludes the influence of noise in the orthogonal signal subspace, thereby improving the accuracy of detection, especially in complex environment.

[0048] 3. Reduce false alarm probability and improve detection sensitivity: The traditional radar detection system often has difficulty in maintaining high detection sensitivity in complex background while maintaining low false alarm probability. The present application introduces the projection of the signal subspace matrix and the accurate detection statistic calculation, which can greatly improve the detection sensitivity of weak target signals while maintaining low false alarm probability, especially suitable for application in radar distributed target detection field.

[0049] 4. Adapt to part of the uniform environment: The existing radar detection system has the problem of detection performance degradation when dealing with part of the uniform environment. The present application introduces an unknown mismatch quantity to represent the noise fluctuation difference between the unit to be measured and the adjacent unit, and estimates the mismatch quantity into the design process of the detector, so as to ensure that the present application is not sensitive to the noise fluctuation difference between the unit to be measured and the adjacent unit, effectively cope with the detection challenge in part of the uniform environment, and greatly improve the performance in practical application.

[0050] 5. Wide industrial application prospect: The present application solves the problem of large data processing, detection accuracy and efficiency in radar target detection field, and the technical scheme can be widely applied in radar, communication, national defense and other industries, especially in distributed radar system, target detection in complex environment and other fields, which has significant application value and market prospect.

[0051] In summary, the present application solves the problems of the prior art by means of data dimensionality reduction, noise suppression outside the signal subspace, and detection statistic calculation, and achieves significant technical progress, which has strong industrial application potential. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 is the radar distributed target detection method flowchart based on data dimensionality reduction provided by the embodiment of the present application;

[0053] Figure 2 1 is a structural diagram of a radar distributed target detection system based on data dimensionality reduction provided by an embodiment of the present invention;

[0054] Figure 3 2. This is a comparison diagram of detection probabilities of the present invention and the prior art solution under different amounts of auxiliary data provided by an embodiment of the present invention;

[0055] Figure 4 This is a comparison diagram of detection probabilities under different signal-to-noise ratios between the present invention and the prior art provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] The present invention proposes a radar distributed target detection method based on data dimensionality reduction, focusing on solving technical problems such as low detection accuracy, high false alarm rate, and heavy computational burden in traditional radar target detection methods in complex environments. It also improves detection efficiency and accuracy through data dimensionality reduction and parameter optimization, and represents a significant technological advancement.

[0058] First, traditional radar target detection suffers from high computational complexity and reduced detection accuracy when faced with high-dimensional data and complex environments. This invention utilizes data dimensionality reduction techniques to project high-dimensional radar data into a signal subspace, effectively reducing the data dimension and significantly reducing the computational burden. Furthermore, this dimensionality reduction operation not only preserves the key information of the target signal but also mitigates the effects of background noise and interference, enabling the system to more efficiently process large amounts of test data.

[0059] Secondly, in terms of balancing false alarm rate and detection rate, traditional methods are often limited by the influence of noise interference, making it difficult to achieve a low false alarm rate. Based on the Wald criterion, the present invention combines the extraction of relevant and redundant parameters. Through optimized detection statistics, the detection threshold is dynamically adjusted to ensure that the false alarm rate is controlled within a certain range while maintaining high sensitivity for target signal detection. This improvement not only improves the robustness of detection, but also further enhances the adaptability and accuracy of the system.

[0060] Furthermore, to address the difficulty of traditional methods in effectively estimating noise mismatch and signal strength, the present invention uses a joint probability density function in the reduced-dimensional data space to accurately estimate parameters such as the power mismatch of Gaussian colored noise, the amplitude vector of the target signal, and the covariance matrix. This reduces the system's dependence on preset parameters. Using these parameters, more accurate detection statistics are constructed, effectively improving the system's stability and adaptability in different environments.

[0061] The detection method of the present invention not only effectively improves the detection efficiency of the radar target detection system in complex scenarios and reduces the false alarm rate, but also significantly improves the detection accuracy and robustness, providing a more reliable and intelligent solution for the application of radar target detection in military, aviation, transportation and other industries, showing significant technological progress and industrial application prospects.

[0062] The following are two specific application examples of the radar distributed target detection method based on the present invention in actual industry:

[0063] Example 1: Object Detection System in Autonomous Driving

[0064] In the autonomous driving industry, vehicles must accurately identify and detect surrounding moving and stationary targets, such as pedestrians, other vehicles, and obstacles. This invention, through data dimensionality reduction and noise optimization, can reduce the impact of noise in complex urban environments and achieve more accurate target detection. By utilizing continuous radar scanning data and adjusting the detection threshold using an optimized detection algorithm and the Wald criterion, the system can efficiently detect a variety of targets while maintaining a low false alarm rate and a high response speed, thereby ensuring the safety and reliability of the autonomous driving system.

[0065] Example 2: Airport Ground Monitoring Radar System

[0066] Airport ground surveillance radars must monitor the dynamics of areas such as runways, taxiways, and aprons in real time to ensure the safe operation of aircraft and ground vehicles. Traditional radar detection is susceptible to interference from complex ground echoes. However, the method proposed in this paper effectively filters out ground clutter through signal subspace dimensionality reduction and noise estimation optimization. The system accurately detects ground activity in all weather and lighting conditions, ensuring efficient, all-weather monitoring and improving the overall safety and efficiency of airport operations.

[0067] It is known that a radar detection system has M channels, from K p The test data sampled in the continuous units to be tested is in is the test data in the kth unit to be tested, k=1,2,…,K p , represents a complex matrix of dimension m×n. Under the untargeted hypothesis test H0, the test data X p Contains only Gaussian colored noise N p All column vectors of are independent and identically distributed and have a mean of zero, and the covariance matrix is The Gaussian distribution of , the scalar γ characterizes the noise power mismatch between the unit to be detected and its adjacent units. Under the target hypothesis detection H1, the test data X p Contains Gaussian colored noise N p and distributed target echo sα H ,in is the target orientation vector, is the target echo conjugate amplitude vector, is the target echo amplitude in the kth unit to be detected, k=1,2,…,K p , (·) T represents transpose, (·) * Indicates conjugate. Due to factors such as radar channel calibration error, the steering vector s is not precisely known but belongs to a known column full rank matrix The spanned signal subspace is s = Hp, where Is an unknown coordinate vector, indicating the position of the steering vector s in the signal subspace. In order to estimate the noise covariance matrix γR in the data to be tested, from the K s The auxiliary data sampled in adjacent cells is Auxiliary data X s Contains only Gaussian colored noise Represents the auxiliary data in the lth adjacent unit, l=1,2,…,K s . Auxiliary data X s =N s All column vectors in are independent and identically distributed and obey a Gaussian distribution with mean zero and covariance matrix R. Therefore, the binary hypothesis testing problem for distributed targets in a partially uniform environment can be expressed as:

[0068] The purpose of the present invention is to improve the detection performance of radar distributed targets in a partially homogeneous environment when the amount of auxiliary data is limited. To this end, this embodiment provides a radar distributed target detection method based on data dimensionality reduction in a partially homogeneous environment, such as Figure 1 As shown, the steps of the method include:

[0069] S1: Get radar in K p The data to be tested sampled in the continuous units to be tested The signal subspace to which the steering vector of the unit to be detected belongs is represented by the column-full rank matrix Zhang Cheng, the coordinate vector of the steering vector in the signal subspace is Under the target assumption H1, the target signal amplitude vector is Where M is the number of radar channels or data model dimension, Q is the signal subspace dimension, represents a complex matrix of dimension m×n, (·) * indicates conjugation;

[0070] S2: Get the K of the radar from the unit under test s Auxiliary data sampled in adjacent cells

[0071] S3: The data to be tested X p and auxiliary data X s Are projected into the signal subspace for data dimensionality reduction, and new test data are obtained respectively. and new auxiliary data

[0072] S4: Under the target hypothesis H1, according to the new test data Y p and new auxiliary data Y s The joint probability density function f1(Y p ,Y s ) and the signal subspace matrix H, solve the estimated value of the power mismatch γ of the Gaussian colored noise in the measured data and the auxiliary data Solve the coordinate vector of the target signal in the signal subspace spanned by the matrix H in the measured data Estimated value of Solve the conjugate amplitude vector of the target signal in the measured data Estimated value of Solve the covariance matrix of the new Gaussian colored noise Estimated value of

[0073] S5: Conjugate the target signal to the estimated amplitude vector Column vectorization and storage in the relevant parameter vector The estimated value of the Gaussian colored noise power mismatch between the measured data and the auxiliary data is The estimated value of the coordinate vector of the target signal in the measured data in the signal subspace spanned by the matrix H New covariance matrix estimate of Gaussian colored noise Vectorize the columns and save them in the redundant parameter vector In the equation: r and the redundant parameter vector θ s The columns are vectorized and stored in the parameter vector middle;

[0074] S6: Calculate the detection statistic t based on the Wald criterion according to the parameter vector θWald ;

[0075] S7: According to the false alarm probability P fa Determine the detection threshold T Wald ;

[0076] S8: Comparison of the test statistic t based on the Wald criterion Wald and the detection threshold T Wald The size relationship is used to determine whether there is a target signal of interest in the data to be tested.

[0077] In step S3, the data to be tested X p and auxiliary data X s All are projected into the signal subspace for data dimensionality reduction to obtain the new test data Y p and new auxiliary data Y s They are: Y p =(H H H) -1 H H X p and Y s =(H H H) -1 H H X s ,in(·) H represents the conjugate transpose of the matrix, (·) -1 Represents the inversion of a reversible matrix.

[0078] The estimated value of the Gaussian colored noise power mismatch γ between the measured data and the auxiliary data in step S4 for: It is a multivariate equation of one variable The only positive solution of , where N = min{K p ,Q},ε1≥ε2≥…≥ε N is the new test data Y p Hermitian inner product matrix after quasi-whitening The non-zero eigenvalues ​​of is the new auxiliary data Y s The sampling covariance matrix of .

[0079] The estimated value of the coordinate vector p of the target signal in the data to be measured in the signal subspace spanned by the matrix H in step S4 for: yes The eigenvector corresponding to the largest eigenvalue of Indicates dimension K s The unit matrix of the target signal; the estimated value of the conjugate amplitude vector α in the measured data for:

[0080] The estimated value of the covariance matrix Σ of the new Gaussian colored noise in step S4 is for:

[0081] The relevant parameter vector θ in step S5 r for: Redundant parameter vector θ s for: Where vec(·) means to vectorize the matrix columns, (·) T represents the transpose; the parameter vector θ is:

[0082] The detection statistic t based on the Wald criterion in step S6 Wald The expression is: in i,j∈{r,s}, E[·] means to find the expectation, ln(·) means the natural logarithm function, f1(Y p ,Y s ) is the new test data Y p and new auxiliary data Y s Joint probability density function under the target hypothesis H1.

[0083] In step S7, according to the false alarm probability P fa Determine the detection threshold T Wald The specific steps are:

[0084] S71: Under the targetless hypothesis H0, calculate the test statistic t based on the Wald criterion Wald ;

[0085] S72: Execute M on step S71 c The Monte Carlo simulation is performed and the calculated detection statistic t based on the Wald criterion is used. Wald Stored in column vectors Among them represents a real matrix of dimension m×n;

[0086] S73: Vector V Wald The elements in V are rearranged in ascending order. Wald Middle The element is the false alarm probability P fa The corresponding detection threshold T Wald ,in Indicates rounding down.

[0087] In step S8, the detection statistic t based on the Wald criterion is compared. Wald and the detection threshold T Wald The specific process of judging whether there is a target signal of interest in the data to be tested is as follows: If t Wald >T Wald , then the target signal of interest exists; if t Wald ≤T Wald , then the target signal of interest does not exist.

[0088] See also Figure 2 As shown, the present invention provides a radar distributed target detection system based on data dimensionality reduction in a partially uniform environment, comprising:

[0089] Data acquisition module: used to obtain the data to be tested and auxiliary data;

[0090] Parameter acquisition module: used to obtain the subspace column full rank matrix to which the steering vector of the unit to be detected belongs and the required false alarm probability;

[0091] Data dimensionality reduction module: used to project the test data and auxiliary data into the signal subspace to perform data dimensionality reduction, so as to obtain new test data and new auxiliary data respectively;

[0092] Parameter calculation module: used to calculate the sampling covariance matrix of the new auxiliary data using the new data to be measured, the new auxiliary data and the signal subspace matrix, calculate the power mismatch of the Gaussian colored noise in the data to be measured and the auxiliary data, calculate the coordinate vector of the target signal in the data to be measured in the subspace spanned by the signal subspace matrix, calculate the amplitude vector of the target signal in the data to be measured, and calculate the covariance matrix of the new Gaussian colored noise;

[0093] Detection statistic calculation module: used to calculate the detection statistic based on the Wald criterion using the results output by the parameter calculation module;

[0094] Detection threshold calculation module: used to calculate the detection threshold based on the false alarm probability;

[0095] Target detection output module: used to compare the detection statistics and the detection threshold, and output the judgment result of whether the target signal of interest exists in the data to be tested.

[0096] The radar distributed target detection system based on data dimensionality reduction provided by the present invention first acquires the test data and auxiliary data through the data acquisition module. These data include the original radar signal and related environmental or background information for subsequent target detection processing.

[0097] Secondly, the column-full-rank matrix of the signal subspace and the system's preset false alarm probability are obtained from the parameter acquisition module to ensure that the statistical confidence of the detection meets the design requirements, thereby improving the detection accuracy of the system.

[0098] Next, the data dimensionality reduction module projects the target data and auxiliary data onto the signal subspace, achieving data dimensionality reduction. This process generates new target data and auxiliary data. This dimensionality reduction removes excess noise and redundant information, improving the ability to distinguish target signals from background noise while reducing computational complexity.

[0099] Based on the reduced-dimensional data, the parameter calculation module begins calculating various key parameters. First, it computes the sampling covariance matrix of the new auxiliary data and, based on this matrix, derives the power mismatch of the Gaussian colored noise. Simultaneously, the module calculates the coordinate vector and amplitude vector of the target signal in the measured data in the signal subspace. These calculations help accurately characterize the target signal.

[0100] The detection statistic calculation module then uses the Wald criterion to calculate the detection statistic based on the aforementioned calculation results. The Wald criterion is an effective detection method that can quickly determine whether a target signal exists in the test data. By calculating this statistic, the system can generate a series of valid values ​​for target identification.

[0101] Finally, the detection threshold calculation module calculates the detection threshold based on the preset false alarm probability and compares it with the detection statistic. Based on this comparison, the target detection output module determines whether the target signal of interest is present in the data under test and outputs the final detection decision. This result provides the radar system with reliable target detection capabilities, especially in partially uniform environments.

[0102] The following is a simulation experiment to further illustrate the effect of the present invention on detecting distributed targets in a partially uniform environment.

[0103] Figure 3 The comparison chart of detection probability between the technical solution of the present invention and the existing generalized adaptive direction detector (GADD) based on the generalized likelihood ratio criterion under different auxiliary data amounts is shown, where the simulation conditions are: false alarm probability P fa =0.001, the number of radar detection system channels M = 12, the signal subspace dimension Q = 6, and the amount of data to be measured K p =10, auxiliary data volume K s From Q-2=4 to 3M=36, the signal-to-noise ratio is α H α×p H H H(γR) -1 Hp=22dB, where γR is the noise covariance matrix in the data to be measured. Figure 3 It can be seen that GADD requires the amount of auxiliary data K s It is only valid when it is greater than the observation dimension M and is s ≥2M can guarantee a detection probability of more than 80%; compared with GADD, the method proposed in this invention has the following advantages: s It is effective when it is greater than the signal subspace dimension Q, and when M≤K s When the detection probability is less than 2M, the detection probability is increased by more than 20%. When the detection probability is 80%, the method proposed in this invention has a significant impact on the auxiliary data volume K. s The demand has decreased When the amount of auxiliary data is sufficient, such as K s When the detection rate is ≥3M, the performance of GADD is consistent with that of the method proposed in the present invention, and both achieve 100% detection probability.

[0104] Figure 4 The comparison diagram of the detection probability of the technical solution of the present invention and the existing GADD under different signal-to-noise ratios is shown, where the simulation conditions are: false alarm probability P fa =0.001, the number of radar detection system channels M = 12, the signal subspace dimension Q = 6, and the amount of data to be measured K p =10, auxiliary data volume K s =2M=24, the signal-to-noise ratio increases from 10dB to 30dB at equal intervals. Figure 4 It can be seen from the figure that under different signal-to-noise ratios, the method proposed in the present invention has an advantage over GADD in terms of detection probability. Equivalently, when achieving the same detection probability, the method proposed in the present invention requires a lower signal-to-noise ratio: Figure 4 As shown in the figure, the required SNR of the proposed method is reduced by about 1.7dB to achieve an 80% detection probability. When the SNR is large, such as when the SNR is ≥ 25dB, the performance of GADD and the proposed method is consistent, and both can achieve a 100% detection probability.

[0105] An application embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor performs the steps of a radar distributed target detection method based on data dimensionality reduction.

[0106] An application embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of a radar distributed target detection method based on data dimensionality reduction.

[0107] An application embodiment of the present invention provides an information data processing terminal, which includes a radar distributed target detection system based on data dimensionality reduction.

[0108] The present invention can be applied to all non-contact, high-precision target detection fields, such as

[0109] ① Military field: Currently, all military radars need to detect targets and then perform early warning, tracking, navigation, fire control and other tasks according to different tactical requirements.

[0110] ② Civil aviation field: The present invention can be used to detect aircraft for air traffic control, and can also detect unidentified flying objects such as birds and drones around airports for early warning.

[0111] ③ Transportation: This invention can be used to detect vehicles and pedestrians, and further monitor traffic flow, traffic accidents, and congestion. It can also be used in intelligent driving, helping intelligent driving systems to promptly detect nearby vehicles, pedestrians, and other targets.

[0112] ④ Medical field: The present invention can be used to detect abnormal tissues such as tumors and nodules in the human body.

[0113] The present invention can also be used for meteorological monitoring to predict weather, for environmental protection to monitor forest fires, for industrial production positioning robots, etc.

[0114] In summary, the target detection technology involved in the present invention plays an important role in many fields, and the application fields of this technology will continue to expand and deepen.

[0115] The technical effects obtained by the embodiments of the present invention are obtained through Monte Carlo simulation. Whether it is the existing technology or the technology of the present invention, the detection process involves implicit operations such as finding eigenvalues ​​and eigenvectors of matrices, so it is impossible to draw a conclusion on which is better through theoretical analysis. Monte Carlo simulation is a numerical calculation method based on probability and statistical theory. In a statistical sense, Monte Carlo simulation is equivalent to theoretical analysis and is also a commonly used numerical analysis method in the industry. Therefore, independent and repeated Monte Carlo simulations can be used to simulate complex detection processes and evaluate the detection probabilities of the existing technology and the technology of the present invention under the same environment.

[0116] Figure 3 and Figure 4 The curve shows the comparison results obtained through Monte Carlo simulation. The comparison results are true and reliable, and can clearly demonstrate the performance advantages and broader application range of the technology of the present invention compared with the existing technology.

[0117] The following are two industrial application examples of radar distributed target detection methods based on data dimensionality reduction:

[0118] 1. Application in marine vessel monitoring system

[0119] In marine environments, radar is widely used to monitor and track ships. However, due to the presence of sea waves and clutter signals, traditional radar detection systems often face problems such as signal-noise confusion and high false alarm probability. Using the method of the present invention, the original radar echo can be reduced to the signal subspace through data dimensionality reduction to remove redundant information. On this basis, the Wald criterion is used to accurately detect the target of interest (such as a ship). By reducing the influence of Gaussian colored noise, the system can improve the sensitivity to weak signals from ships and reduce the false alarm probability in complex sea conditions.

[0120] Actual Results: In a specific maritime application, this radar-based distributed target detection system effectively reduced the false alarm rate from 8% to 1.5% with the original system, while increasing ship detection accuracy by 15%. The system is particularly well-suited for marine surveillance, maritime search and rescue, and national defense security.

[0121] 2. Application in airport runway foreign object detection system

[0122] Foreign Object Debris (FOD) detection on runways is crucial at large airports. Traditional detection systems are often affected by ambient noise and weather conditions, resulting in low detection accuracy. The radar distributed target detection method of the present invention effectively addresses these challenges. It uses data dimensionality reduction techniques to reduce the dimensionality of detection data, filtering out background noise in complex orthogonal signal subspaces. It also improves the system's detection accuracy for foreign object signals by estimating the power mismatch of Gaussian colored noise. Furthermore, the Wald criterion is used to calculate detection statistics, enabling efficient target detection and false alarm control.

[0123] Actual Results: In a trial application at an international airport, the system successfully detected foreign objects less than 2 centimeters in diameter, improving detection accuracy by approximately 20% compared to traditional systems and significantly reducing the false alarm rate to less than 0.8%. The system has broad application prospects in aviation safety, transportation facility management, and other fields.

[0124] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware portion can be implemented using dedicated logic; the software portion can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art will appreciate that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0125] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A radar distributed target detection method based on data dimensionality reduction, characterized in that: include: S1: Get radar in K p The data to be tested sampled in the continuous units to be tested The signal subspace to which the steering vector of the unit to be detected belongs is represented by the column-full rank matrix Zhang Cheng, the coordinate vector of the steering vector in the signal subspace is Under the target assumption H1, the target signal amplitude vector is Where M is the number of radar channels or data model dimension, Q is the signal subspace dimension, represents a complex matrix of dimension m×n, (·) * indicates conjugation; S2: Get the K of the radar from the unit under test s Auxiliary data sampled in adjacent cells S3: The data to be tested X p and auxiliary data X s Are projected into the signal subspace for data dimensionality reduction, and new test data are obtained respectively. and new auxiliary data S4: Under the target hypothesis H1, according to the new test data Y p and new auxiliary data Y s The joint probability density function f1(Y p ,Y s ) and the signal subspace matrix H, solve the estimated value of the power mismatch γ of the Gaussian colored noise in the measured data and the auxiliary data Solve the coordinate vector of the target signal in the signal subspace spanned by the matrix H in the measured data Estimated value of Solve the conjugate amplitude vector of the target signal in the measured data Estimated value of Solve the covariance matrix of the new Gaussian colored noise Estimated value of S5: Conjugate the target signal to the estimated amplitude vector Column vectorization and storage in the relevant parameter vector The estimated value of the Gaussian colored noise power mismatch between the measured data and the auxiliary data is The estimated value of the coordinate vector of the target signal in the measured data in the signal subspace spanned by the matrix H New covariance matrix estimate of Gaussian colored noise Vectorize the columns and save them in the redundant parameter vector In the equation: r and the redundant parameter vector θ s The columns are vectorized and stored in the parameter vector middle; S6: Calculate the detection statistic t based on the Wald criterion according to the parameter vector θ Wald ; S7: According to the false alarm probability P fa Determine the detection threshold T Wald ; S8: Comparison of the test statistic t based on the Wald criterion Wald and the detection threshold T Wald The size relationship is used to determine whether there is a target signal of interest in the data to be tested.

2. The radar distributed target detection method based on data dimensionality reduction according to claim 1, characterized in that: In step S3, the data to be tested X p and auxiliary data X s All are projected into the signal subspace for data dimensionality reduction to obtain the new test data Y p and new auxiliary data Y s They are: Y p =(H H H) -1 H H X p and U s =(H H H) -1 H H X s ,in(·) H represents the conjugate transpose of the matrix, (·) -1 Represents the inversion of a reversible matrix.

3. The radar distributed target detection method based on data dimensionality reduction according to claim 1, characterized in that: The estimated value of the Gaussian colored noise power mismatch γ between the measured data and the auxiliary data in step S4 for: It is a multivariate equation of one variable The only positive solution of , where N = min{K p ,Q},ε1,ε2,……,ε N is the new test data Y p Hermitian inner product matrix after quasi-whitening The non-zero eigenvalues ​​of , and ε1≥ε2≥…≥ε N , is the new auxiliary data U s The sampling covariance matrix of .

4. The radar distributed target detection method based on data dimensionality reduction according to claim 1, wherein: The estimated value of the coordinate vector p of the target signal in the data to be measured in the signal subspace spanned by the matrix H in step S4 for: yes The eigenvector corresponding to the largest eigenvalue of Indicates dimension K s The unit matrix of the target signal; the estimated value of the conjugate amplitude vector α in the measured data for:

5. The radar distributed target detection method based on data dimensionality reduction according to claim 1, wherein: The estimated value of the covariance matrix Σ of the new Gaussian colored noise in step S4 is for:

6. The radar distributed target detection method based on data dimensionality reduction according to claim 1, characterized in that: The relevant parameter vector θ in step S5 r for: Redundant parameter vector θ s for: Where vec(·) means to vectorize the matrix columns, (·) T represents the transpose; the parameter vector θ is:

7. The radar distributed target detection method based on data dimensionality reduction according to claim 1, wherein: The detection statistic t based on the Wald criterion in step S6 Wald The expression is: in i,j∈{r,s}, E[·] means to find the expectation, ln(·) means the natural logarithm function, f1(Y p ,Y s ) is the new test data Y p and new auxiliary data Y s Joint probability density function under the target hypothesis H1.

8. The radar distributed target detection method based on data dimensionality reduction according to claim 1, wherein: In step S7, according to the false alarm probability P fa Determine the detection threshold T Wald The specific steps are: S71: Under the targetless hypothesis H0, calculate the test statistic t based on the Wald criterion Wald ; S72: Execute M on step S71 c The Monte Carlo simulation is performed and the calculated detection statistic t based on the Wald criterion is used. Wald Stored in column vectors Among them represents a real matrix of dimension m×n; S73: Vector V Wald The elements in V are rearranged in ascending order. Wald Middle The element is the false alarm probability P fa The corresponding detection threshold T Wald ,in Indicates rounding down.

9. The radar distributed target detection method based on data dimensionality reduction according to claim 1, wherein: In step S8, the detection statistic y based on the Wald criterion is compared. Wald and the detection threshold T Wald The specific process of judging whether there is a target signal of interest in the data to be tested is as follows: If t Wald >T Wald , then the target signal of interest exists; if t Wald ≤T Wald , then the target signal of interest does not exist.

10. A radar distributed target detection system based on data dimensionality reduction for implementing the radar distributed target detection method based on data dimensionality reduction according to any one of claims 1 to 9, characterized in that: include: Data acquisition module: used to obtain the data to be tested and auxiliary data; Parameter acquisition module: used to obtain the subspace column full rank matrix to which the steering vector of the unit to be detected belongs and the required false alarm probability; Data dimensionality reduction module: used to project the test data and auxiliary data into the signal subspace to perform data dimensionality reduction, so as to obtain new test data and new auxiliary data respectively; Parameter calculation module: used to calculate the sampling covariance matrix of the new auxiliary data using the new data to be measured, the new auxiliary data and the signal subspace matrix, calculate the power mismatch of the Gaussian colored noise in the data to be measured and the auxiliary data, calculate the coordinate vector of the target signal in the data to be measured in the subspace spanned by the signal subspace matrix, calculate the amplitude vector of the target signal in the data to be measured, and calculate the covariance matrix of the new Gaussian colored noise; Detection statistic calculation module: used to calculate the detection statistic based on the Wald criterion using the results output by the parameter calculation module; Detection threshold calculation module: used to calculate the detection threshold based on the false alarm probability; Target detection output module: used to compare the detection statistics and the detection threshold, and output the judgment result of whether the target signal of interest exists in the data to be tested.

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