Non-zero mean clutter neutron space signal adaptive detection method and system

Abstract

The invention provides a non-zero mean clutter neutron space signal adaptive detection method and system. The method comprises the following steps: firstly, jointly estimating a mean vector and a covariance matrix of clutters by using to-be-detected data and auxiliary data; then, on the basis of the subspace signal model, deriving a detection statistic by utilizing a generalized likelihood ratio criterion; and finally, comparing the detection statistics with a preset threshold to complete target detection. According to the method, the non-zero mean value of the clutter is explicitly estimated and compensated, so that the adaptive detector keeps a false alarm rate characteristic for a clutter covariance matrix and also has a false alarm characteristic for the non-zero mean value of the clutter in a non-zero mean value clutter environment, and the detection probability is remarkably improved; the technical problem that a traditional subspace detector fails in the environment is solved.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and in particular to an adaptive detection method and system for non-zero mean clutter neutron space signals. Background Technology

[0002] Multichannel adaptive signal detection plays an irreplaceable role in fields such as radar and sonar. Traditional adaptive detectors, such as Kelly's Generalized Likelihood Ratio Test (GLRT), Adaptive Matched Filter (AMF), and De Maio's Rao detector (DMRao), are mostly based on the assumption that clutter follows a zero-mean complex Gaussian distribution. For cases where the signal exists in a known subspace rather than a single steering vector (i.e., subspace signal), researchers have also proposed corresponding detectors such as Subspace-based GLRT (SGLRT), Subspace-based AMF (SAMF), and Subspace-based Rao (SRao).

[0003] However, in practical applications, clutter often exhibits non-zero mean characteristics. For example, in hyperspectral imaging, the average spectral radiation of background targets is modulated by factors such as illumination and atmospheric transport, resulting in a non-zero mean. In Space-Time Adaptive Processing (STAP), discrete clutter, interference, or interfering targets located in the sidelobes can all cause the data to have a non-zero mean. In such non-zero mean clutter environments, if traditional detectors based on the zero-mean assumption are still used, their constant false alarm rate (CFAR) characteristic will be lost, and the probability of detection (PD) will drop sharply.

[0004] While some existing research has begun to focus on target detection in non-zero mean clutter—for example, Frontera-Pons et al. analyzed the robustness of existing detectors to clutter mean mismatch—these works primarily concentrate on rank-1 signal models and lack effective schemes for detecting subspace signals in non-zero mean clutter. Subspace signal models are more general than rank-1 signal models and can describe a wider range of signal types, such as helicopter target echoes and polarized radar echoes. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the prior art by providing an adaptive detection method and system for subspace signals in non-zero mean clutter, thereby overcoming the performance degradation of existing subspace signal detection technologies in non-zero mean clutter environments.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: This invention provides an adaptive detection method for non-zero mean clutter neutron space signals, comprising the following steps: S1. Obtain the radar-received data vector of the target unit. ,as well as A data vector from a pure clutter environment, related to the unit to be detected. Independent and identically distributed auxiliary data vectors , Data vector of the unit to be detected and auxiliary data vectors The dimensions are all ; S2, Based on the data vector of the unit to be detected and the auxiliary data vector , Calculate the clutter mean vector The estimated value and the first scattering matrix Second scattering matrix ; S3. Based on the subspace signal model, construct the detection statistics of the adaptive detector. ; S4. Based on the detection statistics Based on the statistical characteristics and the preset false alarm probability value PFA, the detection threshold is calculated. ; S5. The detection statistics are... With the detection threshold If a comparison is made, If so, it is determined that a target exists in the unit to be detected; if If so, it is determined that no target exists; S6. For a given effective output signal-to-noise ratio and the detection threshold Output the predicted detection probability .

[0007] Furthermore, in S2, the clutter mean vector The estimated value The calculation formula is: .

[0008] Furthermore, in S2, the first scattering matrix Second scattering matrix The calculation formula is: ; ; in, To ensure the second scattering matrix The positive definiteness of .

[0009] Furthermore, the detection statistics in S3 for: ; in, for A known signal subspace matrix of dimension ; Let be the dimension of the subspace; This refers to the number of sensor channels; Represents the determinant of a matrix; This indicates the Hermitian conjugate transpose.

[0010] Furthermore, in S4, the detection threshold Based on the false alarm probability value (PFA) preset in the system, it is determined offline as follows: ; in, Represents the binomial coefficient; that is, from Selected from the samples The probability of a sample.

[0011] Furthermore, in S6, the predicted detection probability for: ; in, ; ; ; ; in, Represents the cumulative distribution function; Represents random variables In hypothesis testing The probability density function under; To achieve an effective signal-to-noise ratio; The signal component in the data to be detected; express factorial.

[0012] Furthermore, an adaptive detection system for non-zero mean clutter neutron space signals, implemented using the aforementioned adaptive detection method for non-zero mean clutter neutron space signals, further includes: Data acquisition module: used to acquire the data vector of the unit to be detected. and auxiliary data vectors , ; Parameter estimation module: Used to calculate the estimated value of the clutter mean vector. and the first scattering matrix Second scattering matrix ; Detection Statistic Calculation Module: Used to calculate detection statistics. ; Target decision module: used to compare statistics with detection thresholds Compare and output the final detection results.

[0013] The beneficial effects of this invention are as follows: Highly targeted: This invention is the first to specifically address the problem of subspace signal detection in non-zero mean clutter, proposing an effective solution and filling the gap in the prior art; Superior performance: By explicitly estimating and compensating for clutter mean, the detector proposed in this invention can maintain near-optimal detection performance in non-zero mean clutter environments, and its detection probability is significantly higher than that of traditional subspace detectors such as SGLRT and SAMF. CFAR characteristics: This invention derives the probability distribution of detection statistics under pure clutter conditions and gives a closed-form expression for the false alarm probability, enabling the detector to maintain a constant false alarm rate by setting a fixed threshold, which is crucial for practical radar systems. Good robustness: The detector proposed in this invention is not sensitive to the fluctuation of non-zero mean power of clutter and can work stably in environments with different intensities of non-zero mean clutter, while the performance of traditional detectors deteriorates sharply as the mean energy of clutter increases. Theoretically complete: This invention not only proposes the detector structure, but also completes the theoretical analysis of its statistical performance, revealing the loss of degrees of freedom and signal-to-noise ratio under non-zero mean conditions, providing theoretical guidance for detector design and performance prediction. Attached Figure Description

[0014] Figure 1 A flowchart of an adaptive detection method for non-zero mean clutter neutron space signals; Figure 2 This is a comparison chart of the detection performance of the detector of the present invention with that of traditional SGLRT and SAMF detectors under different signal-to-noise ratios (SCRs). Figure 3The graphs show the detection performance of the detector of this invention under different non-zero mean clutter powers. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] Please see Figure 1 An adaptive detection method for non-zero mean clutter neutron space signals includes the following steps: S1. Obtain the radar-received data vector of the target unit. ,as well as A data vector from a pure clutter environment, related to the unit to be detected. Independent and identically distributed auxiliary data vectors , Data vector of the unit to be detected and auxiliary data vectors The dimensions are all ; S2, Based on the data vector of the unit to be detected and the auxiliary data vector , Calculate the clutter mean vector The estimated value and the first scattering matrix Second scattering matrix ; S3. Based on the subspace signal model, construct the detection statistics of the adaptive detector. ; S4. Based on the detection statistics Based on the statistical characteristics and the preset false alarm probability value PFA, the detection threshold is calculated. ; S5. The detection statistics are... With the detection threshold If a comparison is made, If so, it is determined that a target exists in the unit to be detected; if If so, it is determined that no target exists; S6. For a given effective output signal-to-noise ratio and the detection threshold Output the predicted detection probability .

[0017] clutter mean vector The estimated value The calculation formula is: .

[0018] The first scattering matrix Second scattering matrix The calculation formula is: ; ; in, To ensure the second scattering matrix The positive definiteness of .

[0019] The detection statistics in S3 for: ; in, for A known signal subspace matrix of dimension ; Let be the dimension of the subspace; This refers to the number of sensor channels; Represents the determinant of a matrix; This indicates the Hermitian conjugate transpose.

[0020] In S4, the detection threshold Based on the false alarm probability value (PFA) preset in the system, it is determined offline as follows: ; in, Represents the binomial coefficient; that is, from Selected from the samples The probability of a sample.

[0021] In S6, the predicted detection probability for: ; in, ; ; ; ; in, Represents the cumulative distribution function; Represents random variables In hypothesis testing The probability density function under; To achieve an effective signal-to-noise ratio; The signal component in the data to be detected; express factorial.

[0022] An adaptive detection system for non-zero mean clutter neutron space signals, implemented using an adaptive detection method for non-zero mean clutter neutron space signals, further includes: Data acquisition module: used to acquire the data vector of the unit to be detected. and auxiliary data vectors , ; Parameter estimation module: Used to calculate the estimated value of the clutter mean vector. and the first scattering matrix Second scattering matrix ; Detection Statistic Calculation Module: Used to calculate detection statistics. ; Target decision module: used to compare statistics with detection thresholds Compare and output the final detection results.

[0023] Simulation experiment: To verify the effectiveness of this invention, a simulation experiment was conducted. The simulation conditions were set as follows: number of array elements. Subspace dimension =3, number of training samples =24, false alarm probability PFA=10⁻³. The clutter covariance matrix adopts an exponential decay model, and the clutter mean vector is a specific steering vector. The results are as follows: Figure 2 As shown, the detector proposed in this invention has significantly better detection performance than traditional SGLRT and SAMF detectors. Especially in the high signal-to-noise ratio (SCR) region, traditional detectors suffer from poor performance due to the influence of non-zero mean, while the performance of the detector proposed in this invention continues to improve.

[0024] Figure 2 In this context, MC represents the Monte Carlo simulation result, and TH represents the theoretical result. Figure 3 The performance of each detector is further demonstrated as the non-zero mean clutter power ξ varies. It can be seen that the detection probability of the detector proposed in this invention hardly changes with the non-zero mean clutter power ξ, exhibiting excellent robustness; while the performance of the traditional detector decreases significantly with increasing non-zero mean clutter power ξ. This demonstrates the effectiveness of this invention in suppressing non-zero mean clutter.

[0025] Among them, non-zero mean clutter power .

[0026] The embodiments described above are merely illustrative of implementation methods of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be defined by the appended claims.

Claims

1. An adaptive detection method for non-zero mean clutter neutron space signals, characterized in that, Includes the following steps: S1. Obtain the radar-received data vector of the target unit. ,as well as A data vector from a pure clutter environment, related to the unit to be detected. Independent and identically distributed auxiliary data vectors , Data vector of the unit to be detected and auxiliary data vectors The dimensions are all ; S2, Based on the data vector of the unit to be detected and the auxiliary data vector , Calculate the clutter mean vector The estimated value and the first scattering matrix Second scattering matrix ; S3. Based on the subspace signal model, construct the detection statistics of the adaptive detector. ; S4. Based on the detection statistics Based on the statistical characteristics and the preset false alarm probability value PFA, the detection threshold is calculated. ; S5. The detection statistics are... With the detection threshold If a comparison is made, If so, it is determined that a target exists in the unit to be detected; if If so, it is determined that no target exists; S6. For a given effective output signal-to-noise ratio and the detection threshold Output the predicted detection probability .

2. The adaptive detection method for non-zero mean clutter neutron space signals according to claim 1: In S2, the clutter mean vector The estimated value The calculation formula is: 。 3. An adaptive detection method for non-zero mean clutter neutron space signals according to claim 2: In S2, the first scattering matrix Second scattering matrix The calculation formula is: ； ； in, To ensure the second scattering matrix The positive definiteness of .

4. The adaptive detection method for non-zero mean clutter neutron space signals according to claim 3, characterized in that, The detection statistics in S3 for: ； in, for A known signal subspace matrix of dimension ; Let be the dimension of the subspace; This refers to the number of sensor channels; Represents the determinant of a matrix; This indicates the Hermitian conjugate transpose.

5. The adaptive detection method for non-zero mean clutter neutron space signals according to claim 4, characterized in that: In S4, the detection threshold Based on the false alarm probability value (PFA) preset in the system, it is determined offline as follows: ； in, Represents the binomial coefficient; that is, from Selected from the samples The probability of a sample.

6. The adaptive detection method for non-zero mean clutter neutron space signals according to claim 5, characterized in that, In S6, the predicted detection probability for: ； in, ； ； ； ； in, Represents the cumulative distribution function; Represents random variables In hypothesis testing The probability density function under; To achieve an effective signal-to-noise ratio; The signal component in the data to be detected; express factorial.

7. An adaptive detection system for non-zero mean clutter neutron space signals, characterized in that, This method employs an adaptive detection method for non-zero mean clutter neutron space signals as described in any one of claims 1 to 6, and further includes: Data acquisition module: used to acquire the data vector of the unit to be detected. and auxiliary data vectors , ; Parameter estimation module: Used to calculate the estimated value of the clutter mean vector. and the first scattering matrix Second scattering matrix ; Detection Statistic Calculation Module: Used to calculate detection statistics. ; Target decision module: used to compare statistics with detection thresholds Compare and output the final detection results.

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