A MIMO-OFDM Target Detection Method and System Considering Clutter Interference

Abstract

This invention discloses a MIMO-OFDM target detection method and system considering clutter interference. Addressing the characteristics of multiple symbols and multiple carriers on an OFDM frame, this invention first combines all received symbols on a subcarrier, dividing all subcarriers into several groups. For each subcarrier and one symbol, a received signal model is established to indicate whether the target is present or absent. Then, using the generalized likelihood ratio test, the clutter covariance matrix of each subcarrier group and the maximum likelihood estimate of the target response channel are calculated. Next, based on the maximum likelihood estimate of the unknown parameters of each subcarrier group, the test statistics for all subcarriers on the OFDM frame are calculated. Finally, based on the detection threshold obtained through offline training, the presence of the target is determined by comparing it with the test statistics. This invention considers the statistical characteristics of clutter interference in MIMO-OFDM target detection, achieving a higher correct detection probability compared to traditional energy detectors, and can achieve ideal results on a hardware platform.

Description

Technical Field

[0001] This invention belongs to the field of integrated signal processing, estimation and detection of communication and sensing, and particularly relates to a multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) target detection method that takes into account clutter interference. Background Technology

[0002] Communication-sensing integration is considered a key innovative technology for future mobile communication systems. Compared to traditional independent communication and sensing subsystem designs, communication-sensing integration significantly improves spectrum and energy efficiency while reducing hardware and signaling costs by integrating sensing and communication into a single system. Unlike the joint design of communication-sensing functions, "sensing-assisted communication" and "communication-assisted sensing" are also important research directions in communication-sensing integration.

[0003] Sensing capabilities can be implemented based on existing communication frameworks, known as "communication-assisted sensing." Current work on "communication-assisted sensing" primarily focuses on MIMO systems, Orthogonal Frequency Division Multiplexing (OFDM) systems, and MIMO-OFDM systems. However, these works do not consider the presence of environmental clutter, and MIMO-OFDM sensing that takes clutter interference into account is rarely studied in the literature. Furthermore, existing integrated communication and sensing systems assume that the radar has prior information such as channel and covariance matrices, failing to consider interference in real-world scenarios where there is no prior information exchange, severely limiting their detection performance in practical situations. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a MIMO-OFDM target detection method and system that takes into account clutter interference by utilizing clutter information in MIMO-OFDM systems, so as to solve the technical problems of poor target detection performance due to lack of prior information and low practicality of traditional single-carrier target detection.

[0005] Technical Solution: To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0006] A MIMO-OFDM target detection method considering clutter interference includes the following steps:

[0007] Step 1: For the k-th subcarrier and m-th symbol of the OFDM frame, establish the received signal model under two assumptions: target non-existence (H0) and target presence (H1); for all subcarriers on the resource grid, divide them into N groups using a grouping method. g Group subcarriers, first N g -1 group contains The remaining subcarriers are divided into the Nth subcarrier. g Group.

[0008] Step 2: For the i-th subcarrier received signal model established in Step 1, calculate the clutter plus noise covariance matrix R under assumption H0 based on the generalized likelihood ratio test method. i，0 Assume that the channel matrix H of the k-th subcarrier under H1 is... k With clutter plus noise covariance matrix R i，1 The maximum likelihood estimate is obtained by iterating through all subcarrier groups to get N. g The unknown parameters under the group subcarrier are estimated and substituted into the generalized likelihood ratio test formula to obtain the test statistic for the entire OFDM frame.

[0009] Step 3: Based on the Monte Carlo experiment, the detection threshold under the corresponding false alarm probability is obtained by offline training method. The detection threshold is compared with the test statistic to determine whether the target exists.

[0010] Furthermore, step 1 includes the following steps:

[0011] Step 1.1: Receive signal on the k-th subcarrier and the m-th OFDM receive symbol. in and These represent the transmitted signal on the k-th subcarrier, the m-th OFDM symbol, the target response channel, and clutter plus noise, respectively. It follows a pattern with a mean of 0 and a covariance matrix of . The complex Gaussian distribution, N T N represents the number of transmitting antennas. R This indicates the number of receiving antennas; the received signal, under assumptions H0 and H1, can be expressed as:

[0012]

[0013] Where M represents the number of symbols in an OFDM frame. The received signal of all symbols on the k-th subcarrier can be represented as:

[0014] Step 1.2, for N on OFDM frames s Consider dividing the N subcarriers into N subcarriers. g Groups, each group contains There are subcarriers, of which This indicates a floor operation; the last group of subcarriers contains... There are i subcarriers; the received symbol corresponding to the i-th subcarrier group can be represented as:

[0015]

[0016] Furthermore, step 2 includes the following steps:

[0017] Step 2.1: For the i-th group of subcarriers, obtain the estimated values ​​of the unknown parameters based on maximum likelihood estimation under assumptions H0 and H1 respectively. in and They represent the received signals respectively. The probability density function under H0 and H1; Θ i，0 With Θ i，1 These represent the unknown parameters of the i-th subcarrier group under H0 and H1, respectively; This represents the estimated value of the clutter plus noise covariance matrix under H0. This represents the estimated value of the clutter plus noise covariance matrix under H1. and target response channel estimation value Estimated covariance matrix of clutter plus noise under H0 It is given by the following formula:

[0018]

[0019] in

[0020]

[0021]

[0022] Target response channel estimation under H1 Covariance matrix estimate of clutter plus noise They are given by the following formulas respectively:

[0023]

[0024]

[0025] Step 2.2: Substituting the estimated values ​​of the unknown parameters under hypotheses H0 and H1, and applying the generalized likelihood ratio test method to all subcarriers, the following detector can be obtained:

[0026]

[0027] in and Let H0 and H1 represent the probability density functions of the received signal Y in the entire OFDM frame, respectively. Right now and These represent the received signals after obtaining the estimated values ​​of the unknown parameters. The probability density functions under H0 and H1, i = 1, 2, ..., N g ΛG (Y) and γ represent the test statistic and threshold, respectively. If Λ G If (Y) is greater than γ, then the object exists; if Λ G If (Y) is less than γ, then the target object is determined to not exist.

[0028] Furthermore, step 3 includes the following steps:

[0029] Step 3.1: Obtain the detection threshold γ using an offline training method: given the false alarm probability P FA Given the total number of OFDM frames L, the transmit signal for each frame is randomly generated assuming the target does not exist. and the corresponding received signal Calculate Λ G (Y) and Λ G (Y) is saved to an array T. thres In the middle, after L training cycles, the array T thres Sort in descending order, take T. thres The Middle One value is used as the detection threshold γ.

[0030] Step 3.2: Apply the obtained detection threshold to object detection in the corresponding environment. If the threshold is greater than the threshold, the target object is determined to exist; if the threshold is less than the threshold, it is determined to not exist.

[0031] Preferably, in the method, the number of subcarrier packets N g Based on the detection performance, as the number of subcarriers in each group increases, the detection performance first increases and then decreases. The number of groups corresponding to the highest detection performance is selected.

[0032] Based on the same inventive concept, this invention provides a MIMO-OFDM target detection system considering clutter interference, comprising: a subcarrier grouping module, used to establish a received signal model for the k-th subcarrier and m-th symbol of an OFDM frame under two assumptions, namely, target absence (H0) and target presence (H1); and to divide all subcarriers on the resource grid into N subcarriers using a grouping method. g Group subcarriers, first N g -1 group contains The remaining subcarriers are divided into the Nth subcarrier. g Group; Parameter estimation module, used to calculate the clutter plus noise covariance matrix R under assumption H0 for the established i-th group of subcarrier received signal model based on the generalized likelihood ratio test method. i，0 Assume that the channel matrix H of the k-th subcarrier under H1 is... k With clutter plus noise covariance matrix R i，1 The maximum likelihood estimate is obtained by iterating through all subcarrier groups to obtain N. gThe unknown parameters under the group subcarrier are estimated and substituted into the generalized likelihood ratio test formula to obtain the test statistic for the entire OFDM frame; the offline training module is used to obtain the detection threshold under the corresponding false alarm probability based on the Monte Carlo experiment and the offline training method; and the detection judgment module is used to compare the detection threshold with the test statistic obtained by the parameter estimation module to determine whether the target exists.

[0033] Based on the same inventive concept, the present invention provides a computer system including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded onto the processor, it implements the steps of the MIMO-OFDM target detection method considering clutter interference.

[0034] Beneficial Effects: Compared with existing technologies, this invention has the following advantages: For the characteristics of multiple symbols and multiple carriers on an OFDM frame, this invention first establishes two received signal models on a subcarrier and a symbol, including models for when the target object is absent and when it is present; for the characteristics of multiple carriers on the resource grid, a grouping method is used to divide all subcarriers into several groups, each group including multiple subcarriers, thereby obtaining higher parameter estimation accuracy through more received signal samples; using the generalized likelihood ratio test method, the clutter covariance matrix and target response channel of each group of subcarriers are estimated; considering all subcarrier groups, the test statistics on the entire resource grid are calculated; finally, addressing the difficulty in obtaining the closed-form solution of the detection threshold, an offline training algorithm is proposed to obtain the detection threshold; this invention extends the traditional single-carrier-based target detection method to target detection considering clutter interference in MIMO-OFDM systems; by estimating the clutter covariance matrix and target response channel, object detection with high correct detection probability is achieved, and ideal results can be achieved on the hardware platform. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of a MIMO-OFDM-based sensing system under clutter interference according to the method of the present invention.

[0036] Figure 2 A flowchart of an algorithm for a MIMO-OFDM target detection method considering clutter interference, provided by the present invention;

[0037] Figure 3 This is a graph showing the comparison of the detection performance of the method of the present invention under different subcarrier grouping schemes;

[0038] Figure 4 This is a simulation diagram comparing the performance of the method of the present invention with other detection algorithms;

[0039] Figure 5 This is a schematic diagram of the platform implementation comparing the performance of the method of the present invention with other detection algorithms;

[0040] Figure 6 This is a diagram of the MIMO-OFDM-based target detection hardware platform used in the experiments of this invention. Detailed Implementation

[0041] To better understand the purpose, structure, and function of this invention, the technical solution of this invention will be described in further detail below with reference to the accompanying drawings.

[0042] This invention discloses a MIMO-OFDM target detection method considering clutter interference. Taking into account the multi-symbol and multi-carrier characteristics of an OFDM frame, the method first combines all received symbols on a subcarrier and divides all subcarriers into several groups, each group containing multiple subcarriers. For each subcarrier and each symbol in the OFDM frame, a received signal model is established under two assumptions: target non-existence and target presence. Then, for the subcarrier received signal model, the generalized likelihood ratio test method is used to calculate the maximum likelihood estimate of the clutter covariance matrix and the target response channel for each group of subcarriers. Next, based on the maximum likelihood estimate of the unknown parameters of each group of subcarriers, the test statistics for all subcarriers in the OFDM frame are calculated. Finally, based on the detection threshold obtained by the offline training method under the corresponding false alarm probability, the detection threshold is compared with the test statistics to determine whether the target exists.

[0043] Figure 1 This is a schematic diagram of a MIMO-OFDM-based sensing system in a cluttered environment, as shown in the embodiment, wherein the transmitter is configured with N T One antenna, receiver configured with N R The antenna transmits a signal, which is then scattered by scattering objects in the environment, generating clutter and noise that is superimposed on the signal. The receiver then uses this clutter and noise to detect the target. For example... Figure 2 As shown, the detection threshold in the current environment is obtained through offline training, given a false alarm probability P. FA Given the total number of OFDM frames L, the transmit signal for each frame is randomly generated assuming the target does not exist. and the corresponding received signal Calculate Λ G (Y) and Λ G (Y) is saved to an array T. threa In the middle, after L training cycles, the array T threa Sort in descending order, take T. threa The Middle A value is used as the detection threshold. During real-time detection, if the value is greater than the threshold, the object is considered to exist; if the value is less than the threshold, the object is considered not to exist.

[0044] The method for obtaining the target detection threshold of MIMO-OFDM considering clutter interference provided by this invention includes the following steps:

[0045] 1) Received signal on the k-th subcarrier and the m-th OFDM receive symbol in and These represent the transmitted signal on the k-th subcarrier, the m-th OFDM symbol, the target response channel, and clutter plus noise, respectively. It follows a pattern with a mean of 0 and a covariance matrix of . The complex Gaussian distribution, N T N represents the number of transmitting antennas. R This indicates the number of receiving antennas; the received signal, under assumptions H0 and H1, can be expressed as:

[0046]

[0047] Where M represents the number of symbols in an OFDM frame. The received signal of all symbols on the k-th subcarrier can be represented as:

[0048] 2) For N on OFDM frames s Consider dividing the N subcarriers into N subcarriers. g Groups, each group contains There are subcarriers, of which This indicates a floor operation; the last group of subcarriers contains... There are i subcarriers; the received symbol corresponding to the i-th subcarrier group can be represented as:

[0049]

[0050] 3) For the i-th subcarrier group, estimates of the unknown parameters are obtained based on maximum likelihood estimation under assumptions H0 and H1, respectively. in and They represent the received signals respectively. The probability density function under H0 and H1; Θ i，0 With Θ i，1 These represent the unknown parameters of the i-th subcarrier group under H0 and H1, respectively; This represents the estimated value of the clutter plus noise covariance matrix under H0. This represents the estimated value of the clutter plus noise covariance matrix under H1. and target response channel estimation value

[0051] Estimated covariance matrix of clutter plus noise under H0 It is given by the following formula:

[0052]

[0053] in

[0054]

[0055]

[0056] Target response channel estimation under H1 Covariance matrix estimate of clutter plus noise They are given by the following formulas respectively:

[0057]

[0058]

[0059] 4) Substituting the estimated values ​​of the unknown parameters under hypotheses H0 and H1, and applying the generalized likelihood ratio test method to all subcarriers, we can obtain the following detector:

[0060]

[0061] in and Let H0 and H1 represent the probability density functions of the received signal Y in the entire OFDM frame, respectively. Right now Λ G (Y) and γ represent the test statistic and threshold, respectively. If Λ G If (Y) is greater than γ, then the object exists; if Λ G If (Y) is less than γ, then the target object is determined to not exist.

[0062] 5) The detection threshold γ is obtained using an offline training method: given the false alarm probability P FA Given the total number of OFDM frames L, the transmit signal for each frame is randomly generated assuming the target does not exist. and the corresponding received signal Calculate Λ a (Y) and Λ G (Y) is saved to an array T. thres In the middle, after L training cycles, the array T thres Sort in descending order, take T. thres The Middle One value is used as the detection threshold γ.

[0063] 6) Apply the obtained detection threshold to object detection in this environment. If the threshold is greater than the threshold, the target object is determined to exist; if the threshold is less than the threshold, it is determined to not exist.

[0064] After calculating the threshold, the clutter-based MIMO-OFDM target detection method provided by this invention can be implemented, specifically including the following steps:

[0065] Signal modeling steps: For the k-th subcarrier and m-th symbol of the OFDM frame, establish the received signal model under two assumptions: target non-existence (H0) and target presence (H1); for all subcarriers on the resource grid, divide them into N groups using a grouping method. g Group subcarriers, first N g -1 group contains The remaining subcarriers are divided into the Nth subcarrier. g The specific steps are the same as steps 1)-2) in the above construction method;

[0066] Parameter estimation step: For the i-th subcarrier received signal model established in the signal modeling step, calculate the clutter plus noise covariance matrix R under assumption H0 based on the generalized likelihood ratio test method. i，0、 Assume the channel matrix H of the k-th subcarrier under H1 k With clutter plus noise covariance matrix R i The maximum likelihood estimate of 1. By iterating through all subcarrier groups, N is obtained. g The unknown parameters under the group subcarrier are estimated and substituted into the generalized likelihood ratio test formula to obtain the test statistic for the entire OFDM frame. The specific steps are the same as steps 3)-4) in the above construction method.

[0067] Target detection steps: Based on the Monte Carlo experiment, an offline training method is used to obtain the detection threshold under the corresponding false alarm probability. The detection threshold is compared with the test statistic to determine whether the target exists. During real-time detection, if the test statistic is greater than the threshold, the object is judged to exist; if it is less than the threshold, the object is judged not to exist. The specific steps are the same as steps 5)-6) in the above construction method.

[0068] Figure 3 To compare the detection performance of the method of the present invention under different subcarrier grouping schemes, receiver operating characteristic (ROC) curves were plotted on... Figure 3 In this context, each point on the curve corresponds to a given false alarm probability and a correct detection probability at a given detection threshold. A resource block (RB) refers to 12 consecutive subcarriers. From... Figure 3 It can be observed that as the number of subcarriers in each group increases, the detection performance initially improves, then declines. Although each group has more subcarriers (i.e., a smaller N), the detection performance remains relatively stable. g This means that more received signal can be used to improve the quality of clutter plus noise covariance estimation, but due to the frequency selectivity of the clutter response channel, it can also lead to a decrease in detection performance. Therefore, N needs to be carefully selected. g The value is used to ensure detection performance. From Figure 3 As can be seen, under this channel environment, each group containing 12 RBs can achieve the best detection performance.

[0069] Figure 4 The figure shows the simulation results of the proposed method, the traditional energy detector, and the estimator-correlator with the best performance. As can be seen from the figure, the proposed method is superior to the traditional energy detector and is close to the performance of the optimal detector. Figure 5 The figure shows the platform experimental results of the method of the present invention, the traditional energy detector, and the estimator-correlator with the best performance. As can be seen from the figure, the hardware platform experimental results are similar to the simulation results, which proves the effectiveness and practicality of the provided method. Figure 6 This is the 2×2 MIMO-OFDM-based target detection hardware platform used in this invention.

[0070] Based on the same inventive concept, this invention discloses a MIMO-OFDM target detection system considering clutter interference, comprising: a subcarrier grouping module, used to establish a received signal model for the k-th subcarrier and m-th symbol of an OFDM frame under two assumptions, namely, target absence (H0) and target presence (H1); and to divide all subcarriers on the resource grid into N subcarriers using a grouping method. g Group subcarriers, first N g -1 group contains The remaining subcarriers are divided into the Nth subcarrier. g Group; Parameter estimation module, used to calculate the clutter plus noise covariance matrix R under assumption H0 for the established i-th group of subcarrier received signal model based on the generalized likelihood ratio test method. i，0 Assume that the channel matrix H of the k-th subcarrier under H1 is... k With clutter plus noise covariance matrix R i，1 The maximum likelihood estimate is obtained by iterating through all subcarrier groups to obtain N. g The unknown parameters under the subcarrier group are estimated and substituted into the generalized likelihood ratio test formula to obtain the test statistic for the entire OFDM frame; an offline training module is used to obtain the detection threshold under the corresponding false alarm probability based on Monte Carlo experiments and an offline training method; and a detection judgment module is used to compare the detection threshold with the test statistic obtained by the parameter estimation module to determine whether the target exists. For specific implementation details, please refer to the method section above.

[0071] Based on the same inventive concept, this invention discloses a computer system including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded onto the processor, it implements the steps of the MIMO-OFDM target detection method considering clutter interference.

[0072] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

Claims

1. A MIMO-OFDM target detection method considering clutter interference, characterized in that, Includes the following steps: Step 1: For the k-th subcarrier and m-th symbol of the OFDM frame, establish the received signal model under two scenarios: target absence (H0) and target presence (H1). For all subcarriers on the resource grid, divide them into N groups using a grouping method. g Group subcarriers, first N g -1 group contains The remaining subcarriers are divided into the Nth subcarrier. g Group; Step 2: For the i-th subcarrier received signal model established in Step 1, calculate the clutter plus noise covariance matrix R under case H0 based on the generalized likelihood ratio test method. i,0 In case H1, the channel matrix H of the k-th subcarrier k With clutter plus noise covariance matrix R i,1 The maximum likelihood estimate is obtained by iterating through all subcarrier groups to obtain N. g The unknown parameters under the group subcarrier are estimated and substituted into the generalized likelihood ratio test formula to obtain the test statistic for the entire OFDM frame; Step 3: Based on the Monte Carlo experiment, the detection threshold under the corresponding false alarm probability is obtained by offline training method, and the detection threshold is compared with the test statistic to determine whether the target exists. Step 1 includes the following steps: Step 1.1: Receive signal on the k-th subcarrier and the m-th OFDM receive symbol. in and These represent the transmitted signal on the k-th subcarrier, the m-th OFDM symbol, the target response channel, and clutter plus noise, respectively. It follows a pattern with a mean of 0 and a covariance matrix of . The complex Gaussian distribution, N T N represents the number of transmitting antennas. R Indicates the number of receiving antennas; the received signal is represented as follows in cases H0 and H1: Where M represents the number of symbols in an OFDM frame, and the received signal of all symbols on the k-th subcarrier is represented as... Step 1.2, for N on OFDM frames s Divide the N subcarriers into N g Groups, each group contains There are subcarriers, of which This indicates a floor operation; the last group of subcarriers contains... There are i subcarriers; the received symbol corresponding to the i-th subcarrier packet is represented as: Step 2 includes the following steps: Step 2.1: For the i-th group of subcarriers, obtain the estimated values ​​of the unknown parameters based on maximum likelihood estimation under cases H0 and H1 respectively. in and They represent the received signals respectively. The probability density function under H0 and H1; Θ i,0 With Θ i,1 These represent the unknown parameters of the i-th subcarrier group under H0 and H1, respectively; This represents the estimated value of the clutter plus noise covariance matrix under H0. This represents the estimated value of the clutter plus noise covariance matrix under H1. and target response channel estimation value Estimated covariance matrix of clutter plus noise under H0 It is given by the following formula: in Target response channel estimation under H1 Covariance matrix estimate of clutter plus noise They are given by the following formulas respectively: Step 2.2: Substitute the estimated values ​​of the unknown parameters for cases H0 and H1, and apply the generalized likelihood ratio test method to all subcarriers to obtain the following detector: in and Let H0 and H1 represent the probability density functions of the received signal Y in the entire OFDM frame, respectively. Right now and These represent the received signals after obtaining the estimated values ​​of the unknown parameters. The probability density function under H0 and H1, i = 1, 2, ..., N g Λ G (Y) and γ represent the test statistic and threshold, respectively. If Λ G If (Y) is greater than γ, then the object exists; if Λ G If (Y) is less than γ, then the target object is determined to not exist.

2. The MIMO-OFDM target detection method considering clutter interference according to claim 1, characterized in that, Step 3 includes the following steps: Step 3.1: Obtain the detection threshold γ using an offline training method: given the false alarm probability P FA Given the total number of OFDM frames L, the transmit signal for each frame is randomly generated assuming the target does not exist. and the corresponding received signal Calculate Λ G (Y) and Λ G (Y) is saved to an array T. thres In the middle, after L training cycles, the array T thres Sort in descending order, take T. thres The Middle One value is used as the detection threshold γ; Step 3.2: Apply the obtained detection threshold to object detection in the corresponding environment. If the threshold is greater than the threshold, the target object is determined to exist; if the threshold is less than the threshold, it is determined to not exist.

3. The MIMO-OFDM target detection method considering clutter interference according to claim 1, characterized in that, Number of subcarrier packets N g Based on the detection performance, as the number of subcarriers in each group increases, the detection performance first increases and then decreases. The number of groups corresponding to the highest detection performance is selected.

4. A MIMO-OFDM target detection system considering clutter interference, characterized in that, include: The subcarrier grouping module is used to establish a received signal model for the k-th subcarrier and m-th symbol of an OFDM frame, under two scenarios: target absence (H0) and target presence (H1). It also divides all subcarriers on the resource grid into N groups using a grouping method. g Group subcarriers, first N g -1 group contains The remaining subcarriers are divided into the Nth subcarrier. g Group; The parameter estimation module is used to calculate the clutter plus noise covariance matrix R for case H0 based on the generalized likelihood ratio test method for the established i-th subcarrier received signal model. i,0 In case H1, the channel matrix H of the k-th subcarrier k With clutter plus noise covariance matrix R i,1 The maximum likelihood estimate is obtained by iterating through all subcarrier groups to obtain N. g The unknown parameters under the group subcarrier are estimated and substituted into the generalized likelihood ratio test formula to obtain the test statistic for the entire OFDM frame; The offline training module is used to obtain the detection threshold under the corresponding false alarm probability based on Monte Carlo experiments and offline training methods. And a detection and judgment module, which compares the detection threshold with the test statistic obtained by the parameter estimation module to determine whether the target exists; In the subcarrier grouping module, the received signal on the k-th subcarrier and the m-th OFDM receive symbol is recorded. in and These represent the transmitted signal on the k-th subcarrier, the m-th OFDM symbol, the target response channel, and clutter plus noise, respectively. It follows a pattern with a mean of 0 and a covariance matrix of . The complex Gaussian distribution, N T N represents the number of transmitting antennas. R Indicates the number of receiving antennas; the received signal is represented as follows in cases H0 and H1: Where M represents the number of symbols in an OFDM frame, and the received signal of all symbols on the k-th subcarrier is represented as... For N on OFDM frames s Divide the N subcarriers into N g Groups, each group contains There are subcarriers, of which This indicates a floor operation; the last group of subcarriers contains... There are i subcarriers; the received symbol corresponding to the i-th subcarrier packet is represented as: In the parameter estimation module, for the i-th group of subcarriers, the estimated values ​​of the unknown parameters are obtained based on maximum likelihood estimation under cases H0 and H1, respectively. in and They represent the received signals respectively. The probability density function under H0 and H1; Θ i,0 With Θ i,1 These represent the unknown parameters of the i-th subcarrier group under H0 and H1, respectively; This represents the estimated value of the clutter plus noise covariance matrix under H0. This represents the estimated value of the clutter plus noise covariance matrix under H1. and target response channel estimation value Estimated covariance matrix of clutter plus noise under H0 It is given by the following formula: in Target response channel estimation under H1 Covariance matrix estimate of clutter plus noise They are given by the following formulas respectively: Substituting the estimated values ​​of the unknown parameters for cases H0 and H1, and applying the generalized likelihood ratio test method to all subcarriers, we obtain the following detector: in and Let H0 and H1 represent the probability density functions of the received signal Y in the entire OFDM frame, respectively. Right now and These represent the received signals after obtaining the estimated values ​​of the unknown parameters. The probability density function under H0 and H1, i = 1, 2, ..., N g Λ G (Y) and γ represent the test statistic and threshold, respectively. If Λ G If (Y) is greater than γ, then the object exists; if Λ G If (Y) is less than γ, then the target object is determined to not exist.

5. The MIMO-OFDM target detection system considering clutter interference according to claim 4, characterized in that, In the offline training module, given a false alarm probability P FA Given the total number of OFDM frames L, the transmit signal for each frame is randomly generated assuming the target does not exist. and the corresponding received signal Calculate Λ G (Y) and Λ G (Y) is saved to an array T. thres In the middle, after L training cycles, the array T thres Sort in descending order, take T. thres The Middle One value is used as the detection threshold γ.

6. A computer system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is loaded into the processor, it implements the steps of a MIMO-OFDM target detection method considering clutter interference as described in any one of claims 1-3.

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