A method for analyzing diversity multiplexing gain of HAP MIMO radar system

Abstract

The application discloses a kind of HAPMIMO radar system diversity multiplexing gain analysis methods, comprising, the time sample observed to radar receiver is modeled, and the matching filter output of radar and communication transmitting signal is calculated according to time sample model;Matched filter output is obtained and rearranged data according to the correlation of target reflection coefficient;According to the matching filter output of radar subsystem receiver, the detection problem of target hypothesis with and without is constructed: the probability of missed detection of HAPMIMO radar subsystem is calculated;The diversity gain and multiplexing gain of radar are calculated, and the compromise relationship between the diversity gain and multiplexing gain of radar subsystem is obtained;The radar performance of cooperative MIMO radar communication integrated system is evaluated, and the maximum HAPMIMO communication diversity gain and HAPMIMO communication multiplexing gain are extracted from observation data.The radar performance of cooperative MIMO radar communication integrated system is evaluated, and the performance of radar system is improved in cooperation with MIMO communication system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of communication technology, and particularly relates to a HAP MIMO radar system diversity multiplexing gain analysis method. BACKGROUND

[0002] In the past, radar systems and communication systems are usually two independent systems. Radar focuses on target detection, parameter estimation, target tracking and identification, etc. while communication focuses on information transmission and exchange. With the popularization of wireless technology and the improvement of application demand, integrated radar and communication (IRC) system has become an inevitable trend. Most of the existing researches are focused on the interference suppression between radar and communication signals in IRC system, but through appropriate cooperative processing, the integrated system can not only complete the radar and communication tasks well, but also greatly improve the performance of both (Q. He, Z. Wang, J. Hu, R. S. Blum, Performance gains from cooperative MIMO radar and MIMO communication systems[J], IEEE Signal Processing Letters 26(1) (2019) 194-198.).

[0003] In the cooperative integrated system, the echoes from multiple communication transmitters and radar transmitters are used to perform radar tasks, thereby forming a hybrid active-passive (HAP) multiple-input multiple-output (MIMO) radar. Through the cooperation of radar and communication, the corresponding signal processing is adopted at the receiving end, which can significantly improve the radar detection performance and communication quality.

[0004] Due to the difference between radar system and communication system tasks, the corresponding performance indicators are also different (Z. Wei, H. Qu, Y. Wang, et al., Integrated Sensing and Communication Signals Toward 5G-A and 6G: A Survey[J], IEEE Internet of Things Journal, vol. 10, no. 13, pp. 11068-11092, 2023.). In existing integrated research, the detection probability, mean square error of parameter estimation, mutual information, signal-to-noise ratio and ambiguity function can be used as performance indicators of radar system. While the diversity gain and multiplexing gain are used to measure the performance of radar detection and parameter estimation. Therefore, it is necessary to discuss the diversity and multiplexing gain in the integrated framework. The diversity and multiplexing gain are originally used in communication systems to measure communication error probability and data rate, and are later introduced into radar systems to represent radar parameter estimation and target detection performance. Moreover, the diversity and multiplexing gain cannot be optimal at the same time, and there will be a compromise between the two. Therefore, it is very important to discuss the diversity and multiplexing gain of HAPMIMO radar system in the integrated system, and to analyze the compromise between the two on this basis. SUMMARY

[0005] The present application aims at the deficiencies of the background art, and studies the diversity gain and multiplexing gain of HAPMIMO communication system in the cooperative integrated system, and derives the compromise method of radar subsystem diversity gain and multiplexing gain.

[0006] In a first aspect, the present application provides a HAPMIMO radar system diversity multiplexing gain analysis method, comprising:

[0007] Modeling the time samples observed by the radar receiver, the time sample model includes target reflection path and direct path signals transmitted by the radar, and target reflection path and direct path signals transmitted by the communication;

[0008] According to the time sample model, the matched filter output of the radar and communication transmission signals is calculated;

[0009] Obtain the matched filter output and rearrange the data according to the correlation of the target reflection coefficient;

[0010] According to the matched filter output of the radar subsystem receiver, construct the detection problem with and without target hypothesis:

[0011] Calculate the missed detection probability of HAPMIMO radar subsystem;

[0012] The diversity gain and multiplexing gain of the radar are calculated to obtain a compromise between the diversity gain and multiplexing gain of the radar subsystem, including calculating the diversity gain according to the missed detection probability, calculating the maximum a posteriori probability estimation according to the matched filter output, then calculating the minimum mean square error of the parameter estimation of the HAPMIMO radar, and then calculating the multiplexing gain according to the minimum mean square estimation error

[0013] The radar performance of the cooperative MIMO radar communication integrated system is evaluated according to the compromise curve, and the maximum HAPMIMO communication diversity gain and HAPMIMO communication multiplexing gain are extracted from the observation data.

[0014] Further, the time sample observed by the radar receiver is modeled, and the time sample model includes target reflection path and direct path signals of radar transmission and target reflection path and direct path signals of communication transmission, and includes:

[0015] Obtaining receiver data of a plurality of radar subsystems;

[0016] The nth R (n R =1,...,N R ) radar receiver of the HAPMIMO radar subsystem in the integrated system obtains the (l=1,...,L) time sample point as

[0017]

[0018] Wherein, the first two terms are target reflection path and direct path signals from radar transmission, and the third and fourth terms are target reflection path and direct path signals from communication transmission, and denote the time delay of the corresponding path, and denote the corresponding path coefficient, is the noise at the receiving end of the radar subsystem.

[0019] Further, the matched filter output of the radar and communication transmission signals is calculated according to the time sample model, including:

[0020] The signal received by the nth R radar receiver of the radar subsystem is matched filtered with all M R +M C radar and communication transmission signals, and the output of the lth matched filter is

[0021]

[0022] Wherein and are matrix E, and the first element of

[0023]

[0024] where 1 M×1 is an M x 1 all-one matrix.

[0025] Further, the matched filter output is obtained and rearranged according to the correlation of the target reflection coefficients, and specifically includes:

[0026] The matched filter output obtained is rearranged according to the correlation of the target reflection coefficients to obtain the nth φ (1≤n φ ≤N φ ) group of the first data.

[0027]

[0028] where N φ represents the total number of groups of independent reflection coefficients, represents the number of completely correlated reflection coefficients contained in the nth φ group.

[0029] Further, the M R +M R matched filter outputs corresponding to the N C receivers of the radar subsystem after rearrangement according to the reflection coefficient correlation are represented in vector form:

[0030] r R = α d + α t + w R ,

[0031] where represents the target reflection path signal, represents the direct path signal, w R obeys a Gaussian distribution with a mean of 0 and a covariance matrix

[0032] Further, the matched filter output of the radar subsystem receiver is used to construct the detection problem with and without target hypothesis, and specifically includes:

[0033] The detection problem of the radar subsystem is constructed

[0034] H0: r R = α d + wR

[0035] H1:r R = a d + a t + w R .

[0036] where H1 represents a hypothesis with target, and H0 represents a hypothesis without target.

[0037] Further, the false alarm probability of the HAPMIMO radar subsystem is calculated, and specifically includes:

[0038] False alarm probability:

[0039]

[0040] where η represents a detection threshold, and T represents a detection statistic.

[0041] Further, the maximum HAPMIMO communication diversity gain and HAPMIMO communication multiplexing gain are extracted from the observation data according to the optimal compromise curve, and specifically includes:

[0042] The diversity gain of the HAPMIMO radar is calculated as:

[0043]

[0044] The multiplexing gain r of the radar subsystem is calculated r

[0045]

[0046] The compromise relationship between the diversity gain and the multiplexing gain of the radar subsystem is obtained as

[0047] d r r r = N R (M R + M C ),

[0048] where d r is the diversity gain of the radar, r r is the multiplexing gain of the radar, and satisfies 1≤d r ≤N R (M R + M C ) and 1≤r r ≤N R (M R + M C ).

[0049] Further, the maximum a posteriori probability estimation is calculated according to the matched filter output, then the minimum mean square error of the parameter estimation of the HAPMIMO radar is calculated, and then the multiplexing gain is calculated according to the minimum mean square estimation error, and the specific steps include:

[0050] Based on the maximum a posteriori probability estimation, the observation r R d +α t +w R , the received signal vector r R is directly estimated α t ;

[0051] The following maximization problem is established:

[0052]

[0053] Wherein,

[0054]

[0055]

[0056] is the covariance matrix of α t , and the dimension is

[0057] According to the analysis of , rank(C) = N φ ;

[0058] Transform to obtain

[0059] The minimum mean square error of the parameter estimation obtained by the MAP estimator is

[0060]

[0061] Wherein,

[0062]

[0063]

[0064] Obtain

[0065] Transform to obtain the minimum mean square error of the new parameter estimation

[0066]

[0067] According to the multiplexing gain of the HAPMIMO radar subsystem

[0068] ​

[0069] wherein C0 is the covariance matrix for all radar target paths uncorrelated case t .

[0070] The HAP MIMO radar system diversity multiplexing gain analysis method provided by the application can obtain the diversity gain, multiplexing gain and compromise relationship between the two of the radar system.

[0071] The technical solution provided by the application is used to evaluate the radar performance of the cooperative MIMO radar communication integrated system.

[0072] The analysis method can be used in cooperation with the MIMO communication system, so that the performance of the radar system can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0073] The drawings described herein are used to provide a further understanding of the embodiments of the application, and form a part of the application, and do not constitute a limitation on the embodiments of the application. In the drawings:

[0074] Figure 1 The radar missed detection probability P miss and the signal-to-noise ratio under different antenna configurations in the HAP MIMO radar system diversity multiplexing gain analysis method provided by the exemplary embodiment of the application.

[0075] Figure 2 The radar estimation error and noise variance ratio and signal-to-noise ratio under different target reflection coefficient correlation conditions in the HAP MIMO radar system diversity multiplexing gain analysis method provided by the exemplary embodiment of the application.

[0076] Figure 3 The compromise curve of the HAP MIMO radar diversity gain and multiplexing gain under different antenna configurations in the HAP MIMO radar system diversity multiplexing gain analysis method provided by the exemplary embodiment of the application. DETAILED DESCRIPTION

[0077] The exemplary embodiments will be described in detail hereinafter, and the examples are shown in the drawings.

[0078] Explanation of symbols / terminology, in order to facilitate description, the following definitions are first made:

[0079] (·) T denotes transposition, (·) H is the conjugate transpose, Diag{·} represents the block diagonal line, E{·} is the mathematical expectation, H(·) is the information entropy, Pr{A} represents the probability of event A occurring, det(·) represents the determinant, I is an identity matrix.

[0080] The technical concept of the present application is:

[0081] The diversity gain, multiplexing gain and compromise calculation method of the HAPMIMO radar system in the cooperative integration system obtained by analysis calculation can be used to evaluate the radar performance of the cooperative MIMO radar communication integration system.

[0082] The present application provides a HAPMIMO radar system diversity multiplexing gain analysis method, which aims to solve the above technical problems of the prior art.

[0083] Embodiment 1: The present application provides a HAPMIMO radar system diversity multiplexing gain analysis method, which will be described below in combination with the accompanying drawings, and the accompanying drawings in the embodiment of the present application are more convenient to understand in English and data, wherein, including specific analysis and calculation as follows:

[0084] In the cooperative IRC system, the MIMO radar is composed of M R transmitters and N R receivers, and the MIMO communication system is composed of M C transmitters and N C receivers. R (m R =1,...,M R ) radar transmitters and the M C (m C =1,...,M C ) communication transmitters are respectively and All transceivers are placed at different positions in space, wherein E R and E C represent the transmit power of radar and communication respectively, E is the total transmit power of the IRC system, β R is the power ratio allocated to the radar, then M R E R =Eβ R , M C E C =E(1-β R ).

[0085] Therefore, for the HAPMIMO radar subsystem in the IRC system, the n R (n R =1,...,N RThe (l=1,...,L)th time sample point received by the radar receiver is

[0086]

[0087] The first two items represent the target reflection path and direct path signals emitted by radar, while the third and fourth items represent the target reflection path and direct path signals emitted by communication systems. and This indicates the delay of the corresponding path. and Indicates the corresponding path coefficients. This refers to the noise at the receiver of the radar subsystem.

[0088] All radar signals and communication signals satisfy orthogonality, and the nth radar subsystem... R The signal received by each receiver and all M R +M C Matched filtering is performed on the radar and communication transmission signals to obtain the first... The output of each matched filter is

[0089]

[0090] in and These are matrices E, and The Each element.

[0091]

[0092] Among them 1 M×1 It is an M×1 dimensional matrix of all 1s.

[0093] The direct path emission coefficient is known, the target reflection path coefficient follows a random distribution, and there are a large number of radar receiving antennas. Some antennas are close enough that their reflection coefficients are correlated, while others are far enough apart that their reflection coefficients are independent. Let N... φ This indicates the number of groups of independent reflection coefficients. Indicates the nth φ The number of relevant reflection coefficients within the group, i.e.

[0094]

[0095] The matched filter outputs obtained are rearranged according to the correlation of the target reflection coefficients to obtain the nth... φ ,(1≤n φ ≤N φ Group ) The data is

[0096]

[0097] wherein

[0098] The signal vector of the radar receiving end is

[0099] r R = α d + α t + w R , (5)

[0100] wherein represents a target reflection path signal, represents a direct path signal, w R obeys a Gaussian distribution, whose mean value is 0, and whose covariance matrix is

[0101] The present application calculates the diversity gain and multiplexing gain of the HAP MIMO radar subsystem in the IRC system, and the tradeoff between the two gains from the following steps:

[0102] Step 1: according to the obtained time sample point of the lth (l = 1,..., L) of the nth (n = 1,..., N) radar receiver in the integrated system, the HAP MIMO radar subsystem is obtained as R (n R = 1,..., N R ) radar receiver

[0103]

[0104] Step 2: according to the obtained output of the lth (l = 1,..., L) matching filter is

[0105]

[0106] Step 3: according to the obtained rearrangement of the correlation of the target reflection coefficient, the lth (l = 1,..., L) matching filter output data of the nth (1 ≤ n ≤ N) group is obtained as φ φ φ

[0107]

[0108] Step 4: according to the obtained vector form of the M + M matching filter output corresponding to the radar subsystem N receiver after rearrangement according to the correlation of the reflection coefficient R R C

[0109] r R = α​​​​​​​​d + a t + w R ,

[0110] Step 5: Construct the detection problem of the radar subsystem

[0111] H0: r R = a d + w R

[0112] H1: r R = a d + a t + w R .

[0113] where H1 represents the hypothesis of having a target, and H0 represents the hypothesis of not having a target.

[0114] Step 6: Calculate the missed detection probability of the HAPMIMO radar subsystem

[0115]

[0116] where η represents the detection threshold, and T represents the detection statistic.

[0117] Step 7: Calculate the diversity gain of the HAPMIMO radar as

[0118]

[0119] Step 8: Calculate the multiplexing gain r of the radar subsystem r

[0120]

[0121] Step 9: Obtain the trade-off relationship between the diversity gain and the multiplexing gain of the radar subsystem as

[0122] d r r r = N R (M R + M C ),

[0123] where d r is the diversity gain of the radar, r r is the multiplexing gain of the radar, and satisfies 1 ≤ d r ≤ N R (M R + M C ) and 1 ≤ r r ≤ N R (M R + M C ).

[0124] Since in a cooperative IRC system, the radar receiver can decode and reconstruct the communication signal carrying information through cooperation (C. D. Richmond, P. Basu, R. E. Learned, J. Vian, A. Worthen, M. Lockard, Performance bounds on cooperative radar and communication systems operation, in: Proceedings of the 2016 IEEE Radar Conference (RadarConf), 2016, pp. 1-6), the radar can utilize the target echoes from both radar and communication transmitters to perform its tasks, forming a HAP MIMO radar. The target detection and parameter estimation performance of the radar system can be measured by the diversity gain and multiplexing gain (M. Majd, M. Radmard, M. M. Chitgarha, A. Farina, M. Nayebi, M. Bastani, Diversity multiplexing tradeoff in MIMO radars, IET Radar, Sonar & Navigation 11 (4) (2017) 691-700.), defined as:

[0125]

[0126] and

[0127]

[0128] where P miss is the probability of missed detection of the radar, C represents the covariance matrix of a t defined in the first formula, C0is the covariance matrix of a t corresponding to the case where all radar target paths are uncorrelated, and MMSE(C) represents the estimation error when the covariance matrix of a t is C.

[0129] According to the HAP MIMO radar receive signal model, the target detection problem can be established as

[0130]

[0131] where H1represents the hypothesis of having a target, and H0represents the hypothesis of not having a target.

[0132] Therefore, under the two hypotheses, the received signal vector r R obeys a Gaussian distribution, and the probability density functions thereof are respectively

[0133]

[0134] and

[0135]

[0136] where represents the covariance matrix of

[0137] The log-likelihood ratio is

[0138]

[0139] where the first term and are independent of r R , which can be included in the detection threshold, then the detection statistic T R can be obtained. Thus, the detection problem described in can be transformed into

[0140]

[0141] where η denotes the detection threshold.

[0142] In the case of high signal-to-noise ratio (SNR), we have

[0143]

[0144] Thus, from we can see that the terms related to the target path within the same subset add up in a coherent manner, while the results from different subsets add up in a non-coherent manner. It is worth noting that under the H0 hypothesis, can be regarded as Gaussian noise with variance Thus, we can obtain that, for a given false alarm probability, the threshold η is independent of E.

[0145] Under the H1 hypothesis, since we define

[0146]

[0147] where has a complex Gaussian distribution with mean 0 and variance 1. In the case of high signal-to-noise ratio (SNR), we have

[0148]

[0149] Define then we know that is independent of E. Let The formula can be rewritten as

[0150] ​

[0151] make

[0152]

[0153] It is represented as The linear combination of n follows a chi-square distribution with 2 degrees of freedom, and for different n φ They are independent. Therefore, the cumulative distribution function of T can be obtained as (Moschopoulos. Panagis, Canada. WB, The distribution function of a linear combination of chi-squares, in: Computers and Mathematics with Applications, Vol. 10, 1984, pp. 383–386).

[0154]

[0155] in,

[0156]

[0157]

[0158]

[0159] (χ i ) r =χ i (χ i +1)…(χ i +r-1), (22)

[0160]

[0161] Γ(·) represents the gamma distribution, and ν i express The degrees of freedom, satisfying ν i =2. Therefore, and s=N φ When E→∞, T→0 + When T→0, we can obtain the equation. + The integral is

[0162]

[0163] When T→0 + When j=0 is considered, other terms are ignored because h j It is an exponential function of T, such that

[0164]

[0165] According to and Available

[0166]

[0167] Wherein is a non-zero constant.

[0168] Therefore, the probability of missing detection is

[0169]

[0170] When SNR→∞ According to T→0 + , the cumulative distribution function formula is

[0171]

[0172] So the diversity gain of HAPMIMO radar in cooperative system is:

[0173]

[0174] The multiplexing gain of radar system is inversely proportional to the estimation error. Assuming that the maximum a posteriori probability (MAP) estimator is used to estimate the target parameters in the formula, the multiplexing gain is calculated. Since all target parameters are contained in α t , α R is directly estimated by observing the received signal vector r t . Based on the maximum a posteriori probability (MAP) estimation, the following maximization problem should be solved

[0175]

[0176] Wherein,

[0177]

[0178]

[0179] is the covariance matrix of α t , with dimension According to the formula, rank(C) = N φ . According to

[0180]

[0181] Therefore, the minimum mean square error (MMSE) of the parameter estimation obtained by the MAP estimator is

[0182]

[0183] Let

[0184]

[0185]

[0186] It can be seen that Therefore, the formula can be further expressed as

[0187]

[0188] The multiplexing gain of the radar system is inversely proportional to the estimation error. In addition, when all target paths are completely uncorrelated (completely independent), the multiplexing gain is equal to 1. When the signal-to-noise ratio is high, the second term in the formula is much larger than the first term, so the estimation error can be expressed as

[0189]

[0190] According to the definition of the multiplexing gain of the HAPMIMO radar subsystem, it can be obtained that

[0191]

[0192] According to the formula, the diversity gain can be obtained as d r = N φ The multiplexing gain of the system is Therefore, while achieving higher diversity gain, the achievable multiplexing gain will decrease, which indicates that there is a trade-off relationship between the diversity gain and the multiplexing gain of the HAPMIMO radar. Therefore, the trade-off relationship between the diversity gain and the multiplexing gain of the HAPMIMO radar subsystem is

[0193] d r r r = N R (M R + M C ), (39)

[0194] Based on the diversity gain, the multiplexing gain, and the trade-off between the two of the HAPMIMO radar and the RAMIMO communication subsystem in the integrated system of cooperative MIMO radar and MIMO communication, the results are shown in Figures 1-3 , where the simulation parameters are set as follows:

[0195] Three antenna configurations are considered: the first configuration is M R = 1, N R = 3, M C = 1, N C = 3; the second configuration is M R = 2, NR =2,M C =2,N C =2; the third configuration is M R =3,N R =2,M C =3,N C =2. For these three configurations, consider the case where the reflection coefficients of all radar targets are uncorrelated, i.e., the number of uncorrelated subsets is N. φ =N R (M R +M C In addition, N in the second configuration was also considered. φ =2 and N φ The case where N = 4, and the third configuration where N φ =3 and N φ The case where the value is 7. Assume the radar receiver noise is additive white Gaussian noise with a variance of 1.

[0196] Figure 1 Let P be the radar missed detection probability. miss The relationship with signal-to-noise ratio (SNR). To illustrate the relationship between radar diversity / multiplexing gain and antenna configuration, the simplest antenna configuration M is also considered. R =1, N R =1, M C =1 and N C =1. The slopes of the curves at high signal-to-noise ratios are 6.1, 7.31, 1.95, 4.04, 9.93, 2.92, 6.8, and 2.01, respectively. These values ​​are consistent with the theoretical values ​​obtained in step 7, i.e., d r =N φ .

[0197] Figure 2 The relationship between the ratio of radar estimation error to noise variance and signal-to-noise ratio (SNR) under different conditions was plotted. The results show that at high SNR values, the estimation error is related to the uncorrelated subset N. φ The quantity is directly proportional to the theoretical value obtained in step 8.

[0198] Figure 3 This is a trade-off curve for diversity multiplexing in a cooperative IRC system's HAPMIMO radar. The intersection point of the optimal trade-off curves on the X-axis is N. R (M R +M C This indicates that the maximum achievable subset is equal to the number of irrelevant target reflection coefficient subsets given by the radar miss probability in step 16. The curve at N... R (M R +M C) with the y-axis, which is the maximum diversity gain, as shown in step 9. In addition, increasing the diversity gain results in a loss of multiplexing gain, and vice versa. As the number of transmit M R or receive antennas N R increases, the radar system has more degrees of freedom, and the entire tradeoff curve shifts to the right, meaning that both the multiplexing gain and the diversity gain increase.

[0199] In several embodiments provided by the present application, it should be understood that the disclosed method can be implemented in other ways. For example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, a plurality of modules or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0200] Those skilled in the art should understand that the embodiments of the present application can be provided as a method or a device. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0201] It should be understood that the present application is not limited to the precise construction that has been described above and illustrated in the accompanying drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the application is limited only by the claims that follow.

Claims

1. A method for HAP MIMO radar system diversity multiplexing gain analysis, characterized by, The method comprises: modeling time samples observed by a radar receiver, the time sample model comprising radar transmitted target reflected path and direct path signals, and communication transmitted target reflected path and direct path signals; calculating matched filter outputs of radar and communication transmitted signals according to the time sample model; obtaining the matched filter outputs and rearranging data according to correlation of target reflection coefficients; constructing detection problems with and without target hypotheses according to matched filter outputs of radar subsystem receivers; calculating a missed detection probability of the HAP MIMO radar subsystem; calculating a diversity gain and a multiplexing gain of the radar, and obtaining a trade-off relationship between the diversity gain and the multiplexing gain of the radar subsystem; comprising: calculating the diversity gain according to the missed detection probability, calculating a maximum a posteriori probability estimation according to the matched filter outputs, then calculating a minimum mean square error of parameter estimation of the HAP MIMO radar, and then calculating the multiplexing gain according to the minimum mean square error; evaluating radar performance of a cooperative MIMO radar communication integrated system according to the trade-off curve and extracting maximum HAP MIMO communication diversity gain and HAP MIMO communication multiplexing gain from observation data.

2. The HAP MIMO radar system diversity multiplexing gain analysis method of claim 1, wherein, The modeling of time samples observed by a radar receiver, the time sample model comprising radar transmitted target reflected path and direct path signals, and communication transmitted target reflected path and direct path signals comprises: obtaining receiver data of a plurality of radar subsystems; The first radar receiver of the HAP MIMO radar subsystem in an integrated system gets the first time sample point as ； wherein , ; In the cooperative MIMO radar-communication integrated system, the MIMO radar consists of transmitters and receive antennas, the MIMO communication system consists of transmit antennas and receive antennas, the signals transmitted at the th radar transmitter and the th communication transmit antenna are and respectively, all the transmit and receive antennas are placed at different spatial locations, where and denote the transmit powers of radar and communication respectively, where, , , and denote the transmit powers of radar and communication respectively. where the first two terms are the target-reflected and direct-path signals from the radar transmission, and the third and fourth terms are the target-reflected and direct-path signals from the communication transmission, and denote the time delays of the respective paths, and denote the respective path coefficients, is the noise at the receive end of the radar subsystem.

3. The method of claim 2, wherein the HAP MIMO radar system diversity multiplexing gain is analyzed by, The calculating of matched filter outputs of radar and communication transmitted signals according to the time sample model comprises: The radar subsystem The signal received by each receiver is related to all Matched filtering is performed on the radar and communication transmission signals to obtain the first... The output of each matched filter is: ； wherein , , , and are the first , and elements of the matrices , respectively; ； ； wherein is a full 1 matrix.

4. The method of claim 3, wherein, The obtaining of the matched filter outputs and the rearranging of data according to correlation of target reflection coefficients comprises: The matched filter output obtained is rearranged according to the correlation of the target reflection coefficient to obtain the first group of the first data ； wherein, , , represents the total number of groups of independent reflection coefficients, represents the number of fully correlated reflection coefficients contained within the group.

5. The method of claim 4, wherein, Further comprising: Radar subsystems rearranged by reflectivity coefficient correlation Corresponding to The output of the matched filter for each receiver is represented in vector form: ； wherein denotes a target reflected path signal, denotes a direct path signal, , , obeys a Gaussian distribution with mean 0 and covariance matrix .

6. The method of claim 5, wherein, The constructing of detection problems with and without target hypotheses according to matched filter outputs of radar subsystem receivers comprises: constructing detection problems of radar subsystems ； wherein represents a hypothesis with a target, represents a hypothesis without a target.

7. The method of claim 6, wherein the HAP MIMO radar system diversity multiplexing gain is analyzed by, The calculating of a missed detection probability of the HAP MIMO radar subsystem comprises: the missed detection probability: ； wherein denotes a detection threshold, denotes a detection statistic.

8. The method of claim 7, wherein, The extracting of maximum HAP MIMO communication diversity gain from observation data according to the trade-off curve comprises: the diversity gain of the HAP MIMO radar is calculated as: 。 9. The HAP MIMO radar system diversity multiplexing gain analysis method of claim 8, wherein, The calculating of a maximum a posteriori probability estimation according to the matched filter outputs, then calculating a minimum mean square error of parameter estimation of the HAP MIMO radar, and then calculating the multiplexing gain according to the minimum mean square error comprises: Based on maximum a posteriori estimation, the received signal vector in the observation is directly estimated ; the following maximization problem is established: ； wherein, ； ； is the covariance matrix of , dimension ; According to Analysis shows that, ; transform , obtaining ; the minimum mean square error of parameter estimation obtained by the MAP estimator is: ； wherein, ； ； acquisition ; the minimum mean square error of the new parameter estimation obtained by the transformation is: ； the multiplexing gain of the HAP MIMO radar subsystem is ； wherein is the covariance matrix of the radar target paths under the assumption of no correlation between all radar target paths. is the covariance matrix of the radar target paths under the assumption of no correlation between all radar target paths.

10. The HAP MIMO radar system diversity multiplexing gain analysis method of claim 9, wherein, comprising: the trade-off relationship between the diversity gain and the multiplexing gain of the radar subsystem is obtained as ； wherein is the diversity gain of the radar, is the multiplexing gain of the radar, satisfying and .

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