Integrated signal design method for radar communication based on receiving-end spatial modulation

Abstract

The present invention belongs to the field of signal processing technology, and relates to a radar communication integrated signal design method based on receiving end spatial modulation. The present invention first designs a transmit covariance matrix, and simultaneously realizes spatial power allocation to radar targets and communication receiving antennas, and obtains a beamforming weight vector by rank-one decomposition of the transmit covariance matrix. The weight vector concentrates power to the desired communication receiving antenna, while suppressing power leakage to the remaining receiving antennas. Weight vectors with corresponding antenna power directivity can be designed for all receiving antennas, and these weight vectors constitute a beamforming weight vector set. All orthogonal transmit waveforms are transmitted using the weight vectors in the set. After receiving the signal, the communication receiving end uses a matched filter to demodulate and obtain the receiving antenna index corresponding to each orthogonal waveform. The matrix composed of all receiving antenna indexes is used as a communication symbol, and the information bits can be mapped to the communication symbols to realize communication information embedding.

Description

Technical Field

[0001] The invention belongs to the technical field of signal processing and relates to a radar communication integrated signal design method based on receiving end spatial modulation. Background Art

[0002] In today's society, the number of radar and communication system devices has increased dramatically, and the application scenarios have become more extensive, which has made wireless spectrum resources increasingly scarce and the hardware cost of equipment increased. There are many similarities between modern radar systems and communication systems, which provides feasible conditions for realizing radar communication integration. In order to better utilize spectrum resources and control hardware costs, radar communication integration has become a key research direction for many experts and scholars. The implementation directions of radar communication integration are mainly divided into radar-centric, communication-centric, and radar communication joint design. The implementation methods can be mainly divided into beamforming technology, integrated waveform design, radar communication resource allocation, etc.

[0003] Beamforming technology is a core technology in the field of array signal processing. It can realize the spatial power allocation of the transmitted signal, so it plays an important role in phased array radar, multiple-input multiple-output (MIMO) radar and modern MIMO wireless communication. The application of beamforming technology in the radar field is to filter the signal in the spatial domain to enhance the signal in the desired direction and suppress the signal in the undesired direction. The application in the communication field is to use the channel information to pre-transform the transmitted signal, thereby eliminating the crosstalk between multiple data streams, adapting to channel fading, and improving the system transmission performance.

[0004] There are many traditional radar communication integrated signal design schemes, but they often focus on the frequency domain and time domain, and do not consider the signal design in the spatial domain. Considering the spatial diversity and spatial multiplexing characteristics of the MIMO system, the spatial information of the antenna array can also become a medium for the transmission of communication information. In MIMO communication, there is already a receiving-end spatial modulation technology, that is, the power allocation of receiving antennas in different channels is controlled by beamforming technology. Different receiving antenna selections can constitute communication symbols, thereby realizing the embedding of communication information. Traditional receiving-end spatial modulation uses spatial information to only realize the communication function. In order to improve the spectrum and system utilization, it is necessary to design a radar communication integrated signal that uses the spatial domain to achieve the purpose of integrating radar detection and downlink communication. Summary of the invention

[0005] In view of the above existing problems, the purpose of the present invention is to provide a radar communication integrated signal design method based on linear constrained power allocation receiving end spatial modulation, which realizes the integration of radar detection and downlink communication on the basis of MIMO radar, and the radar and communication spectrum are shared and the equipment is shared. By designing the beamforming weight vector to allocate spatial power to the radar target and the communication receiving antenna, the spatial modulation of the communication receiving end is effectively realized while ensuring the radar detection performance.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A radar communication integrated signal design method based on receiving end spatial modulation is proposed. Consider a MIMO radar communication integrated system, which includes N t An integrated transmitting array with N elements, r The radar receiving array has N elements and c The communication receiving array has array elements. The channel information between the communication receiver and the transmitting array is accurately estimated and known by the transmitting end. The following steps are included:

[0008] S1. Design the emission covariance matrix:

[0009] For N c Root communication receiving antenna, need to solve N c The transmit covariance matrix w n N c ×1-dimensional beamforming weight vector. Transmit covariance matrix W n Control the power of the transmitted signal to be concentrated on the receiving antenna n, but N c The radar patterns corresponding to the transmit covariance matrices all satisfy the same constraints. Considering the transmit power is P0, the radar expected pattern is a focused beam, and the expected main lobe pointing is θ r , the pattern is in the side lobe area The internal demand is less than the preset sidelobe level p. Then the demand solution N c The optimization problem is as follows:

[0010]

[0011] stdiag(G H W n G)=f n

[0012]

[0013] tr(W n )=P0

[0014]

[0015] rank(W n )=1

[0016] The above problem is a non-convex optimization problem and can be solved by ignoring the rank-one constraint. H is the conjugate transpose, (·) T is the transpose, and diag(·) means taking the diagonal elements of the matrix and arranging them into column vectors. α and ε are the power coefficients of the radar target and the desired communication receiving antenna, respectively. The matrix G = [a(θ r ) H H ], where a(θ r ) is the desired main lobe direction θ r The corresponding steering vector, H is N c ×N t dimensional channel matrix. n =[f r，n f c，n ] T is the power allocation vector, where f r，n = [α] controls the radar target power, f c，n =[0 … ε … 0] controls the communication receiving antenna power, the nth element is ε, and the rest are 0. Radar pattern sidelobe area Existence L s Angle sampling, a(θ k ) is the sampling angle θ k The corresponding steering vector. tr(·) means finding the trace of the matrix, ≥ means semi-positive definite, and rank(·) means finding the rank of the matrix.

[0017] S2, rank-one decomposition to construct a set of beamforming weight vectors:

[0018] N c The transmit covariance matrix W n , n=1,…,N c Perform rank-one decomposition to obtain N c The beamforming weight vector w n , with W n For example, first, the matrix W n Perform singular value decomposition to obtain:

[0019]

[0020] Where U is the matrix of singular vectors, Λ is N t Singular Values The real diagonal matrix is ​​formed by taking the first column of the matrix Λ and taking the square root of the first element to obtain the vector Q:

[0021]

[0022] Then the beamforming weight vector w n It can be expressed as:

[0023] w n =Uσ

[0024] Perform N c After sub-rank one decomposition, the beamforming weight vector set is constructed Each weight vector contributes the same amount within the radar main lobe, but the power points to different communication receiving antennas.

[0025] S3. Construct a communication dictionary using the beamforming weight vector set:

[0026] Consider a MIMO radar transmitting L orthogonal waveforms, from the set of transmission weight vectors Select the nth weight vector w from n As the transmission weight vector w of the lth waveform n，l , then the receiving antenna index vector corresponding to the lth waveform is:

[0027]

[0028] where h n , n=1,…,N r is the nth row of the channel matrix H. The L receive antenna index vectors constitute the receive antenna index matrix S m , which satisfies:

[0029]

[0030] Consider the number of symbols in the communication dictionary to be M b , then the actual communication symbol is the receiving antenna index matrix S m , m=1,…,M b , communication dictionary The number of symbols must meet The maximum transmission rate is bits per second, where Represents the largest integer not greater than the parameter, f PRF is the pulse repetition frequency.

[0031] S4, integrated signal transmission and reception:

[0032] MIMO radar transmits L orthogonal waveforms φ l (t), n = 1…L, these orthogonal waveforms satisfy the time orthogonality. The beamforming weight vector of the lth orthogonal waveform is w n，l , which satisfies ∈ represents the vector is included in the set. Then N t The ×1-dimensional transmitted signal s(t) is:

[0033]

[0034] Assume that there are Q far-field radar targets located in direction θ q , g = 1, ..., Q, and is located within the radar main lobe. Then the radar receiving array receives N r ×1-dimensional signal x r (t) is:

[0035]

[0036] where β q is the reflection coefficient of the g-th radar target, a(θ q ) and b(θ q ) are the radar transmitting and receiving arrays at angle θ q The guiding vector, vector n r (t) represents radar receiving noise. Since the MIMO radar system structure is not changed, the received signal processing and target detection are performed according to the traditional MIMO radar signal processing process.

[0037] N received at the communication receiver c ×1-dimensional signal x c (t) is:

[0038]

[0039] in is the channel matrix. Without loss of generality, we consider the channel to be a flat Rayleigh fading channel, n c (t) is the Gaussian noise of the communication process.

[0040] S5. Communication symbol detection:

[0041] Using the transmitted L orthogonal waveforms φ l (t), n = 1 ... L for the received signal x of the communication receiver c (t) Perform L times of matched filtering, then the output y of the lth channel c，l for:

[0042]

[0043] where n MF，l is the noise n c (t) passes through the output of the lth matched filter. The results of L times matched filtering constitute the matrix Y c :

[0044] Y c =[y c，1 … y c，L ]

[0045] Considering that communication symbols are transmitted with equal probability, maximum likelihood detection can be used for communication symbol detection. The detection criteria are as follows:

[0046]

[0047] in is the estimated symbol index, ||·|| F Represents the F-norm of the matrix.

[0048] The beneficial effect of the present invention is that a radar communication integrated signal design method based on linear constrained power allocation and receiving end spatial modulation is provided. With MIMO radar as the center, spatial power allocation is performed to radar targets and communication receiving antennas through beamforming technology, while meeting the radar's expected radiation pattern and the power requirements of the receiving end spatial modulation. Communication information is embedded in the spatial domain while ensuring that the MIMO radar system structure remains unchanged, ultimately realizing spectrum sharing and equipment sharing for radar detection and downlink communication, and improving spectrum utilization and system utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flowchart of the process of implementing the present invention;

[0050] Figure 2 A schematic diagram of the radar communication integrated system model constructed for the present invention;

[0051] Figure 3 The radar emission pattern designed by the present invention;

[0052] Figure 4 This is the relationship curve between symbol error rate and signal-to-noise ratio under receiving antenna index matrix modulation. DETAILED DESCRIPTION

[0053] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0054] Example 1

[0055] The purpose of this embodiment is to simulate and obtain a communication dictionary based on linear constrained power allocation receiving end spatial modulation radar communication integration, draw the focused beam radar pattern corresponding to the communication space symbol in the communication dictionary, and analyze the radar pattern performance of the present invention. The embodiment of the signal design method based on linear constrained power allocation receiving end spatial modulation radar communication integration is shown in the attached figure. Figure 1 The schematic diagram of the radar communication integrated system model constructed by the present invention is shown in the attached Figure 2 In this embodiment, the simulation conditions are set to transmit power P0 = 20 dBm, a uniform linear array with a half-wavelength array element spacing as the transmit array, and the number of transmit array elements Nt = 20. The channel between the transmitting array and the communication receiving array is a flat Rayleigh fading channel, and the number of communication receiving array elements is N. c =4, then there are 4 communication symbols in the communication dictionary, that is, there are beamforming weight vectors w1, ..., w4. The radar expected pattern is a focused beam, with the main lobe pointing to θ r =0°, sidelobe level p=-15dB.

[0056] The normalized directional diagrams corresponding to the four communication space symbols are shown in the attached figure. Figure 2 As shown. It can be found that the four beamforming weight vectors obtained by the algorithm proposed in the present invention are subject to the same focused beam radar pattern constraints, so the contributions within the radar main lobe range are equal, and the desired focused beams are formed within the main lobe range, while the side lobe levels are controlled at a predetermined level. Therefore, the radar transmission pattern obtained by the present invention meets expectations, and the radar waveform is still used, so the radar detection performance is not affected by the communication function.

[0057] Example 2

[0058] The purpose of this embodiment is to verify the feasibility of the communication modulation scheme proposed by the present invention and to draw a curve of the relationship between the symbol error rate and the signal-to-noise ratio. Figure 1 The schematic diagram of the radar communication integrated system model constructed by the present invention is shown in the attached Figure 2 The simulation conditions are set as the transmission power P0 = 20dBm, the uniform linear array with a half-wavelength array element spacing as the transmission array, and the number of array elements in the transmission array is N l = 20. Number of communication receiving antennas N c They are equal to 2, 4, and 8 respectively, that is, each communication symbol can carry 1, 2, and 3 bits of information respectively. Considering that the communication symbols are transmitted with equal probability, the number of Monte Carlo experiments is 100,000. Without loss of generality, it is assumed that the channel between the transmitting array and each communication user satisfies an independent and identically distributed complex Gaussian random distribution, that is, the channel is a flat Rayleigh fading channel.

[0059] The relationship curve between the false coincidence rate and the signal-to-noise ratio corresponding to the communication modulation scheme proposed in the present invention is shown in the attached figure. Figure 3 As shown. It can be found that as the signal-to-noise ratio increases, the symbol error rate gradually decreases. Under the same signal-to-noise ratio, the more information a symbol carries, the higher the symbol error rate. The simulation proves the feasibility of embedding communication information in the radar waveform of the present invention. The present invention simultaneously takes advantage of the MIMO radar and achieves the purpose of spatial modulation at the receiving end, realizing radar and communication spectrum sharing and equipment sharing.

Claims

1. Radar communication integrated signal design method based on receiving end spatial modulation, used for MIMO radar communication integrated system, defining the system including N t An integrated transmitting array with N elements, r The radar receiving array has N elements and c A communication receiving array of array elements; The channel information between the communication receiver and the transmitting array is accurately estimated and known by the transmitting end; characterized in that, The signal design method comprises the following steps: S1. Obtain N by solving the following optimization problem c The transmit covariance matrix W n ,n=1,…,N c : in(·) H is the conjugate transpose, (·) T is transposed, diag(·) means taking the diagonal elements of the matrix side by side as a column vector; α and ε are the power coefficients of the radar target and the desired communication receiving antenna respectively; the matrix G = [a(θ r )H H ], where a(θ r ) is the desired main lobe direction θ r The corresponding steering vector, H is N c ×N t dimensional channel matrix; f n =[f r,n f c,n ] T is the power allocation vector, where f r,n = [α] controls the radar target power, f c,n =[0 ... ε ... 0] controls the communication receiving antenna power, its nth element is ε, and the rest are 0; P0 is the transmit power; ρ is the preset sidelobe level; radar pattern sidelobe area Existence L s Angle sampling, a(θ k ) is the sampling angle θ k The corresponding steering vector; tr(·) means finding the trace of the matrix, ≥ means semi-positive definite, and rank(·) means finding the rank of the matrix; S2, the obtained N c The transmit covariance matrix W n ,n=1,...,N c Perform rank-one decomposition to obtain N c The beamforming weight vector w n , for the matrix W n Perform singular value decomposition to obtain: Where U is the matrix of singular vectors, Λ is N t Singular Values The real diagonal matrix is ​​formed; take the first column of the matrix Λ and take the square root of the first element to get the vector σ: Then the beamforming weight vector w n It is expressed as: In n =Uσ Perform N c After sub-rank one decomposition, the beamforming weight vector set is constructed Each weight vector contributes the same amount within the radar main lobe, but the power points to different communication receiving antennas; S3. Construct a communication dictionary using the beamforming weight vector set: Define MIMO radar to transmit L orthogonal waveforms, from the set of transmission weight vectors Select the nth weight vector w from n As the transmission weight vector w of the lth waveform n,l , then the receiving antenna index vector corresponding to the lth waveform is: where h n is the nth row of the channel matrix H, n=1,...,N r ; L receive antenna index vectors constitute the receive antenna index matrix S m , which satisfies: Define the number of symbols in the communication dictionary as M b , then the actual communication symbol is the receiving antenna index matrix S m ,m=1,...,M b , communication dictionary The number of symbols must meet The maximum transmission rate is bits per second, where Represents the largest integer not greater than the parameter, f PRF is the pulse repetition frequency; S4. Generate N t The ×1-dimensional transmitted signal s(t) is: Among them, φ l (t) is the L orthogonal waveforms emitted by the MIMO radar, l = 1...L, and defines a total of Q far-field radar targets located in the direction θ q ,q=1,...,Q, and is located within the radar main lobe; then the radar receiving array receives N r ×1-dimensional signal x r (t) is: where β q is the reflection coefficient of the qth radar target, a(θ q ) and b(θ q ) are the radar transmitting and receiving arrays at angle θ q The guiding vector, vector n r (t) represents radar receiving noise; N received at the communication receiver c ×1-dimensional signal x c (t) is: in is the channel matrix, n c (t) is the Gaussian noise of the communication process; S5, using the transmitted L orthogonal waveforms φ l (t) The received signal x of the communication receiver c (t) Perform L times of matched filtering, then the output y of the lth channel c,l for: Where n MF,l is the noise n c (t) Through the output of the lth matched filter; the results of L times matched filtering constitute the matrix Y c : AND c =[and c,1 ... and c,L ] Define the communication symbols to be transmitted with equal probability, then use maximum likelihood detection for communication symbol detection, the detection criteria are as follows: in is the estimated symbol index, ||·|| F Represents the F-norm of the matrix.

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