Target tracking method of RIS-assisted ISAC system

Abstract

The invention discloses a target tracking method of an RIS-assisted ISAC system. The target tracking method is specifically implemented according to the following steps: step 1, constructing an ISAC system of a BS, an RIS, a vehicle and a user; step 2, under the condition of ensuring communication and perception quality, jointly designing a resource allocation scheme and RIS phase shift, and establishing an optimization problem to minimize an estimation error value; 3, decomposing the optimization problem into two sub-problems by adopting an alternating optimization algorithm, and solving the two sub-problems; 4, for a power optimization problem, converting the power optimization problem into a convex problem by adopting eigenvalue decomposition; for an RIS optimization problem, simplifying cubic and quartic non-convex terms into convex functions; and then optimizing by adopting a semi-definite relaxation technology, and solving a result by utilizing a CVX toolbox. According to the invention, the power distribution scheme of the minimum tracking error PCRB can be obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated sensing and communications, and specifically relates to a target tracking method for RIS-assisted ISAC systems. Background Art

[0002] With the deployment of 5G wireless networks globally, researchers are actively conducting research on future 6G mobile wireless network technologies. Due to the wide application of millimeter-wave technology and large-scale MIMO technology, future wireless communication systems will require higher spectral efficiency and reduced hardware costs. Integrated Sensing and Communications (ISAC), as one of the key technologies for 6G mobile wireless networks, has received increasing attention from researchers in recent years. Sharing the same spectral resources for communication and sensing processes can improve spectral efficiency and reduce hardware costs.

[0003] Traditional target tracking methods mainly rely on single sensing means such as radar and sonar. Radar obtains information such as the distance, speed, and angle of a target by transmitting electromagnetic waves and receiving echoes, and then realizes target tracking. However, introducing a RIS-assisted ISAC system for target tracking can further improve the tracking accuracy and achieve a good performance trade-off between communication and sensing. Summary of the Invention

[0004] The purpose of the present invention is to provide a target tracking method for RIS-assisted ISAC systems, which improves the tracking accuracy of existing target tracking methods.

[0005] The technical solution adopted by the present invention is that the target tracking method for RIS-assisted ISAC systems is specifically implemented according to the following steps:

[0006] Step 1, construct an ISAC system of BS, RIS, vehicle, and user;

[0007] Step 2, under the condition of ensuring communication and sensing quality, jointly design a resource allocation scheme and RIS phase shift, and establish an optimization problem to minimize the estimated error value;

[0008] Step 3, adopt an alternating optimization algorithm to decompose the optimization problem into two sub-problems for solution;

[0009] Step 4, for the power optimization problem, use eigenvalue decomposition to transform it into a convex problem; for the RIS optimization problem, simplify the cubic and quartic non-convex terms into convex functions; then use semi-definite relaxation technology for optimization and solve the result using the CVX toolbox.

[0010] The characteristics of the present invention also lie in:

[0011] The system in Step 1 includes a multi-antenna BS, a target vehicle, k communication users, and a RIS.

[0012] In Step 1, assuming that the CSI is known, the target of the vehicle moves in a straight line, and the BS is equipped with a uniform linear array of N transmit antennas; assuming that the BS also has N receive antennas, the RIS is a uniform planar array composed of M reflection units. The downlink transmission from the BS to the vehicle target is considered in the system. Assuming that the communication signal and the radar signal are transmitted simultaneously for communication and sensing.

[0013] The BS is equipped as a uniform linear array of N transmit antennas and N receive antennas, and the RIS is a uniform planar array composed of M reflection units.

[0014] In Step 2, on the premise of ensuring communication and sensing quality, design a resource allocation scheme and RIS phase shift to achieve the PCRB, and construct an optimization problem with the PCRB as the target:

[0015] Objective function:

[0016] Constraint 1: R c,k ≥Γ c,k ;

[0017] Constraint 2: SINR s ≥Γ s ;

[0018] Constraint 3: |Φ| = diag(e);

[0019] Constraint 4: p min ≤p c,k ≤p max ,p min ≤p s ≤p max ;

[0020] Constraint 5:

[0021] Among them, Constraint 1 is the achievable rate constraint, greater than or equal to the threshold Γ c,k , Constraint 2 is the sensing signal-to-noise ratio constraint, greater than or equal to the threshold Γ s , where Γ c,k 、Γ s is determined by the actual system. Constraint 3 is that the amplitude of the passive RIS is 1, where e is a unit vector. For power allocation, Constraint 4 is to ensure that the sensing power and the communication power are within the effective value range, and Constraint 5 is to ensure that the sum of the allocated powers is the total power.

[0022] In step 3, the strong coupling between variables can be decomposed into a power optimization problem and a RIS optimization problem by using the alternating optimization algorithm.

[0023] In step 3, for the power optimization sub-problem, its objective function and constraints are as follows:

[0024] Objective function:

[0025] Constraint 1: R c,k ≥Γ c,k ;

[0026] Constraint 2: SINR s ≥Γ s ;

[0027] Constraint 3: p min ≤p c,k ≤p max ,p min ≤p s ≤p max ;

[0028] Constraint 4:

[0029] Among them, constraint 1 is the achievable rate constraint, greater than or equal to the threshold Γ c,k , constraint 2 is the sensing signal-to-noise ratio constraint, greater than or equal to the threshold Γ s , where, Γ c,k 、Γ s is determined by the actual system. Constraint 3 is to ensure that the sensing power and communication power are within the effective value range, and constraint 4 is to ensure that the sum of the allocated powers is the total power.

[0030] In step 3, for the RIS phase shift optimization sub-problem, its objective function and constraints are as follows:

[0031] Objective function:

[0032] Constraint 1: R c,k ≥Γ c,k ;

[0033] Constraint 2: SINR s ≥Γ s ;

[0034] Constraint 3: |Φ|=diag(e);

[0035] Among them, constraint 1 is the achievable rate constraint, greater than or equal to the threshold Γ c,k , constraint 2 is the sensing signal-to-noise ratio constraint, greater than or equal to the threshold Γ s, where Γ c,k , Γ s is determined by the actual system, and the constraint condition 3 is that the passive RIS amplitude is 1.

[0036] For the power optimization problem in step 4, eigenvalue decomposition can be used to transform it into a convex problem, simplify the cubic and quartic non-convex terms in the RIS optimization into convex functions, then use the semidefinite relaxation technique for optimization, and finally solve the result through the CVX toolbox.

[0037] The beneficial effects of the present invention are:

[0038] The target tracking method of the RIS-assisted ISAC system of the present invention predicts the posterior Cramer-Rao lower bound of the target according to the feedback information in the tracking recursion period under a limited power budget, establishes an optimization model, then uses the convex relaxation technique and the cyclic minimization method to transform the problem into two convex problems, and gives an effective solution method using the periodic optimization framework to obtain a power allocation scheme that minimizes the tracking error PCRB, thereby improving the target tracking accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is the flowchart of the present invention;

[0040] Figure 2 is the system model diagram of the present invention;

[0041] Figure 3 is the RIS-assisted ISAC scenario model diagram of the present invention;

[0042] Figure 4 is the RIS-assisted ISAC power allocation diagram of the present invention;

[0043] Figure 5 is compared with Figure 4 the RIS-assisted ISAC power allocation diagram;

[0044] Figure 6 is the Pareto diagram of the RIS-assisted ISAC of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0045] The present invention will be described in detail below with reference to the drawings and specific embodiments.

[0046] Example 1

[0047] The target tracking method of the RIS-assisted ISAC system of the present invention, as Figure 1 shown, is specifically implemented according to the following steps:

[0048] Step 1: Construct an ISAC system for BS, RIS, vehicle and user;

[0049] Step 2: Under the condition of ensuring communication and sensing quality, jointly design the resource allocation scheme and RIS phase shift, and establish an optimization problem to minimize the estimation error value, i.e., the PCRB;

[0050] Step 3: Due to the strong coupling between the optimization variables, an alternating optimization algorithm is used to decompose the optimization problem into two sub-problems, namely the power optimization problem and the RIS optimization problem;

[0051] Step 4: For the power optimization problem, eigenvalue decomposition is used to transform it into a convex problem; for the RIS optimization problem, the cubic and quartic non-convex terms are simplified to convex functions; then the semidefinite relaxation (SDR) technique is used for optimization, and the CVX toolbox is used to solve the results.

[0052] The simulation results show that the deployment of RIS can improve the tracking performance and reduce the PCRB. And the performance trade-off between communication and sensing is obtained through the Pareto relationship.

[0053] Embodiment 2

[0054] The target tracking method of the RIS-assisted ISAC system of the present invention, wherein the system in step 1 includes a multi-antenna BS, a target vehicle, k communication users and a RIS. The BS is equipped with a uniform linear array of N transmit antennas and N receive antennas, and the RIS is a uniform planar array composed of M reflection units. It is assumed that the CSI is known, the target of the vehicle moves on a straight line, and the BS is equipped with a uniform linear array of N transmit antennas; it is assumed that the BS also has N receive antennas, and the RIS is a uniform planar array composed of M reflection units. The downlink transmission from the BS to the vehicle target is considered in the system, and it is assumed that the communication signal and the radar signal are transmitted simultaneously for communication and sensing.

[0055] Among them, in step 2, on the premise of ensuring communication and sensing quality, design the resource allocation scheme and RIS phase shift to achieve the PCRB, and construct an optimization problem with the PCRB as the objective:

[0056] Objective function:

[0057] Constraint 1: R c,k ≥Γ c,k ;

[0058] Constraint 2: SINR s ≥Γ s ;

[0059] Constraint 3: |Φ| = diag(e);

[0060] Constraint 4: p min ≤p c,k ≤p max ,pmin ≤ p s ≤ p max ;

[0061] Constraint 5:

[0062] Among them, Constraint 1 is the achievable rate constraint, greater than or equal to the threshold Γ c,k , Constraint 2 is the sensing signal-to-noise ratio constraint, greater than or equal to the threshold Γ s , where Γ c,k , Γ s is determined by the actual system. Constraint 3 is that the passive RIS amplitude is 1, where e is the unit vector. For power allocation, Constraint 4 is to ensure that the sensing power and communication power are within the effective value range, and Constraint 5 is to ensure that the sum of the allocated powers is the total power.

[0063] Example 3

[0064] The target tracking method of the RIS-assisted ISAC system of the present invention. In step 3, for the power optimization sub-problem, its objective function and constraints are as follows:

[0065] Objective function:

[0066] Constraint 1: R c,k ≥ Γ c,k ;

[0067] Constraint 2: SINR s ≥ Γ s ;

[0068] Constraint 3: p min ≤ p c,k ≤ p max , p min ≤ p s ≤ p max ;

[0069] Constraint 4:

[0070] Among them, Constraint 1 is the achievable rate constraint, greater than or equal to the threshold Γ c,k , Constraint 2 is the sensing signal-to-noise ratio constraint, greater than or equal to the threshold Γ s , where, Γ c,k , Γ s is determined by the actual system. Constraint 3 is to ensure that the sensing power and communication power are within the effective value range, and Constraint 4 is to ensure that the sum of the allocated powers is the total power.

[0071] Example 4

[0072] The target tracking method of the RIS-assisted ISAC system of the present invention. In step 3, for the RIS phase shift optimization sub-problem, its objective function and constraints are as follows:

[0073] Objective function:

[0074] Constraint 1: R c,k ≥Γ c,k ;

[0075] Constraint 2: SINR s ≥Γ s ;

[0076] Constraint 3: |Φ| = diag(e);

[0077] Among them, Constraint 1 is the achievable rate constraint, greater than or equal to the threshold Γ c,k , Constraint 2 is the sensing signal-to-noise ratio constraint, greater than or equal to the threshold Γ s , where Γ c,k , Γ s is determined by the actual system, and Constraint 3 is that the passive RIS amplitude is 1.

[0078] Example 5

[0079] As Figure 2 shown, define and as the channels from the BS to the k-th user, from the BS to the RIS, from the BS to the target vehicle, from the RIS to the k-th user, and from the RIS to the target vehicle, respectively. Among them, Specifically, assume that the RIS is a uniform planar array with M reflection units. The RIS phase shift is labeled as and Φ = diag(φ). The transmitted baseband signal is expressed as Then the transmitted signal can be expressed as

[0080]

[0081] where is the transmission precoding matrix.

[0082] The baseband representation of the reflected echo received by the BS is

[0083]

[0084] In the formula, is the array gain factor. p s , μ and τ are the target transmission sensing power, Doppler frequency, and time delay, respectively. represents the receive beamforming matrix. n(t) is a zero-mean, variance-σ 2Additive white Gaussian noise (AWGN).

[0085] The signal received by the k-th user from the BS is

[0086]

[0087] where is the array gain factor. p c,k , μ and τ are the transmitted sensing power, Doppler frequency, and time delay of the target, respectively. represents the receive beamforming matrix. n c (t) is additive white Gaussian noise (AWGN) with zero mean and variance .

[0088] Therefore, the communication signal-to-noise ratio of the k-th user is

[0089]

[0090] The achievable communication rate of the k-th user is

[0091] R k (t) = log2(1 + SNR k ). (5)

[0092] The sensing signal-to-noise ratio of the target vehicle is

[0093]

[0094] Normalize (3) to

[0095]

[0096] Further, the measurement model for distance d n and speed v n is

[0097]

[0098] where f c , c, n τ and n f are the carrier frequency, the speed of light, and Gaussian noise with zero mean and variances and respectively. In addition, the variance of the measurement noise is inversely proportional to the receive signal-to-noise ratio, i.e.,

[0099]

[0100] where For convenience, assume

[0101]

[0102] Among them, is a constant determined by system settings.

[0103] Subsequently, the state evolution model of the target vehicle is derived using geometric analysis. In addition, the state estimation of the target vehicle is obtained using the Extended Kalman Filter (EKF) technique.

[0104] As Figure 3 shown, according to the scenario model diagram, the state evolution model of the target vehicle is derived using geometric analysis, that is

[0105]

[0106] For a moving vehicle target, the motion state vector of the target vehicle at the nth moment is defined as ξ n =[θ n , d n , v n T . The state evolution model can be described as

[0107] ξ n =g(ξ n-1 ) + w n , (12)

[0108] Among them, w n represents the process noise, which is a Gaussian distribution with a mean of zero and a covariance matrix of Υ, where

[0109]

[0110] The non-linear measurement model z n of the target vehicle can be described as

[0111]

[0112] Among them represents the measurement noise, which is a Gaussian distribution with a mean of zero and a covariance matrix of Ψ.

[0113]

[0114] The Jacobian determinants g(·) and h(·) are defined as

[0115]

[0116] Among them,

[0117]

[0118] To obtain good target tracking performance, the EKF method is adopted. The Kalman gain matrix is calculated as

[0119]

[0120] Among them, S n|n-1 = GS n-1 G H + Υ is the predicted MSE matrix. The MSE matrix update process is as follows

[0121] S n = (I - K n H n )S n|n-1 , (20)

[0122] Finally, the state tracking can be expressed as

[0123] ξ n = ξ n|n-1 + K n (z n - h(ξ n|n-1 ). (21)

[0124] Define the Bayesian Fisher information matrix J(ξ n ), the prior information FIMJ P (ξ n ) and the data FIMJ D (ξ n ).

[0125] Specifically, they are calculated as

[0126]

[0127] Therefore, the predicted PCRB can be expressed as

[0128] PCRB = Tr(J -1 (ξ n ). (23).

[0129] Example 6

[0130] In the present invention, an optimization problem is designed to minimize the target tracking error, i.e., PCRB, while ensuring that the user communication sum rate is greater than or equal to a threshold and the target vehicle sensing signal-to-noise ratio is greater than or equal to a threshold. Therefore, the optimization problem is established as:

[0131]

[0132] s.t. R c,k ≥ Γ c,k ,

[0133] SNR s ≥ Γ s ,

[0134] |Φ| = diag(e),

[0135] p min ≤ p c,k ≤ p max , p min ≤ p s ≤ p max ,

[0136]

[0137] where s.t. R c,k ≥ Γ c,k , which is the achievable rate constraint, and its value is greater than or equal to the threshold Γ c,k , SNR s ≥ Γ s , which is the sensing signal-to-noise ratio constraint, and its value is greater than or equal to the threshold Γ s , where Γ c,k 、Γ s is determined by the actual system. |Φ| = diag(e), to ensure that the passive RIS amplitude is 1, and e is a unit vector. For power allocation, p min ≤ p c,k ≤ p max , p min ≤ p s ≤ p max , is to ensure that the sensing power and communication power values are within the effective range. is to ensure that the sum of the allocated powers is the total power.

[0138] Due to the coupling between the variable p n and Φ, the optimization problem (24) is difficult to solve. Therefore, an alternating optimization method is adopted to solve it.

[0139] Update p n , given Φ, thus, the optimization problem is expressed as

[0140]

[0141] s.t. R c,k ≥ Γ c,k ,

[0142] SNR s ≥ Γ s ,

[0143] p min ≤ p c,k ≤ p max , p min ≤ p s ≤ p max ,

[0144]

[0145] For equation (22), there is a relationship

[0146]

[0147] Therefore, there is

[0148] J = p s Q + A, (27)

[0149] where and A = (GS n-1 G H + Υ) -1 . Substitute Q and A into (43) and take the inverse to get

[0150]

[0151] where I is the identity vector. Through eigenvalue decomposition, there is

[0152]

[0153] where Σ is an orthogonal matrix containing eigenvectors, and Λ is a diagonal matrix composed of eigenvalues. Define and

[0154]

[0155] Then the distance PCRB and the angle PCRB can be obtained

[0156]

[0157] where λ is the eigenvalue of Λ, and a ij is the element of a ji in the i-th row and j-th column. So the optimization problem is transformed into

[0158]

[0159] s.t. R c,k ≥ Γ c,k ,

[0160] SNR s ≥ Γ s ,

[0161] p min ≤ p c,k ≤ p max , p min ≤ p s ≤ p max ,

[0162]

[0163] Here, λ ≥ 0. Therefore, the optimization problem can be solved using the CVX toolbox.

[0164] Update Φ, given p n , so the problem is expressed as

[0165]

[0166] s.t. R c,k ≥ Γ c,k ,

[0167] SNR s ≥ Γ s ,

[0168] |Φ| = diag(e),

[0169] Here, A is a constant and is independent of Φ. Define

[0170]

[0171] where

[0172] h3 = [β2a 32 and h4 = γ. Based on the inverse of the block matrix, there is

[0173]

[0174] Just take the elements in the first row and first column and the second row and second column of m. Therefore

[0175]

[0176] By simplifying the trace, there is

[0177]

[0178] Then, substituting α, β1, β2, and γ into Equation (37), the numerator and denominator of the PCRB are obtained as

[0179]

[0180] Subsequently, simplify PCRB n and PCRB d . Among them,

[0181]

[0182] Here, and In addition, (39) can also be expressed as

[0183]

[0184] Among them, ΦGf s = diag(Gf s )φ, and

[0185] Similarly, simplify y and z as follows

[0186]

[0187] Among them, and Therefore, after simplification, we get

[0188]

[0189] In Equation (42), the constants independent of φ are omitted. Similarly, we can obtain

[0190]

[0191] Specifically, by introducing a new auxiliary variable η, the transformed objective can be expressed as

[0192]

[0193] where η has a closed-form solution in each iteration, as follows

[0194]

[0195] Therefore, the objective function is

[0196]

[0197] For We use Then there exists

[0198]

[0199] By using the first-order Taylor expansion and simplifying, we get

[0200]

[0201] Among them, U = [I M jI M , and

[0202] For There exists

[0203]

[0204] Similarly, define

[0205]

[0206] By using the first-order and second-order Taylor expansions, there exist

[0207]

[0208] For cφ * + φ T c H , there exist

[0209]

[0210] Therefore,

[0211]

[0212] After that, define and ||t|| 2 = 1. Simplify the objective function to

[0213] PCRB = Tr(M1V). (54)

[0214] Similarly, for the constraints of the optimization problem, adopt the same simplification method as the objective function, and simplify it to

[0215]

[0216] where and So the optimization problem is transformed into

[0217]

[0218] s.t.(55),(54)

[0219] V m,m = 1, m = 1, 2,..., M + 1

[0220] V > 0,

[0221] Now, the problem (56) is a convex semi-infinite programming problem, and an existing optimization algorithm or solver is used for optimal solution, such as CVX. In addition, it is worth noting that the optimal solution V of the problem (56) * may not be the rank-one solution after relaxation, and it can be solved by Gaussian randomization.

[0222] The power allocation result is as Figures 4 - 5As shown. Among them, the top represents the first communication user, the middle represents the second communication user, and the bottom represents the target vehicle. Figure 4 Shows Γ c,1 = 16 bps / Hz and Γ c,2 = 13 bps / Hz, the variation of power allocation with time, Figure 5 Shows Γ c,1 = 16.5 bps / Hz and Γ c,2 = 13 bps / Hz, the variation of power allocation with time.

[0223] To further verify the performance trade-off between communication and sensing, the Pareto relationship between the user communication rate and the reciprocals of the distance PCRB and the angle PCRB is obtained respectively, as Figure 6 shown. The communication threshold is increased from 15.2 bps / Hz to 16.6 bit / s / Hz. The communication rate increases with the increase of the communication threshold. As the communication rate increases, the sensing ability decreases and the PCRB increases.

Claims

1. The target tracking method of the RIS-assisted ISAC system is characterized by: Follow the steps below to implement it: Step 1: Construct ISAC system of BS, RIS, vehicle and user; Step 2: Under the condition of ensuring communication and perception quality, jointly design the resource allocation scheme and RIS phase shift, and establish an optimization problem to minimize the estimation error value; Step 3: Use the alternating optimization algorithm to decompose the optimization problem into two sub-problems for solution; Step 4: For the power optimization problem, eigenvalue decomposition is used to transform it into a convex problem; for the RIS optimization problem, the cubic and quartic non-convex terms are simplified into convex functions; then the semidefinite relaxation technique is used for optimization, and the CVX toolbox is used to solve the result.

2. The target tracking method of the RIS-assisted ISAC system according to claim 1, characterized in that: The system in step 1 includes a multi-antenna BS, a target vehicle, k communication users and a RIS.

3. The target tracking method of the RIS-assisted ISAC system according to claim 2, characterized in that: In step 1, it is assumed that the CSI is known, the vehicle target moves in a straight line, and the BS is equipped with a uniform linear array of N transmitting antennas; it is assumed that the BS also has N receiving antennas, and the RIS is a uniform planar array composed of M reflection units. The downlink transmission from the BS to the vehicle target is considered in the system, and it is assumed that the communication signal and the radar signal are transmitted simultaneously for communication and perception.

4. The target tracking method of the RIS-assisted ISAC system according to claim 2, characterized in that: The BS is equipped with a uniform linear array of N transmitting antennas and N receiving antennas, and the RIS is a uniform planar array composed of M reflecting units.

5. The target tracking method of the RIS-assisted ISAC system according to claim 1, characterized in that: Under the premise of ensuring the communication and perception quality in step 2, a resource allocation scheme and RIS phase shift are designed to implement PCRB, and an optimization problem is constructed with PCRB as the goal: Objective function: Constraint 1: R c,k ≥Γ c,k ; Constraint 2: SINR s ≥Γ s ; Constraint 3: |Φ| = diag(e); Constraint 4: p min ≤p c,k ≤p max ,p min ≤p s ≤p max ; Constraint 5: The constraint 1 is the achievable rate constraint, which is greater than or equal to the threshold Γ. c,k , constraint 2 is the perceptual signal-to-noise ratio constraint, which is greater than or equal to the threshold Γ s , where Γ c,k , Γ s Determined by the actual system, constraint 3 is that the passive RIS amplitude is 1, where e is a unit vector. For power allocation, constraint 4 is to ensure that the sensing power and communication power are within the valid value range, and constraint 5 is to ensure that the sum of the allocated power is the total power.

6. The target tracking method of the RIS-assisted ISAC system according to claim 1, characterized in that: In step 3, the strong coupling between variables can be solved by using an alternating optimization algorithm to decompose the original optimization problem into two sub-problems: a power optimization problem and a RIS optimization problem.

7. The target tracking method of the RIS-assisted ISAC system according to claim 6, characterized in that: In step 3, for the power optimization sub-problem, its objective function and constraints are as follows: Objective function: Constraint 1: R c,k ≥Γ c,k ; Constraint 2: SINR s ≥Γ s ; Constraint 3: p min ≤p c,k ≤p max ,p min ≤p s ≤p max ; Constraint 4: The constraint 1 is the achievable rate constraint, which is greater than or equal to the threshold Γ. c,k , constraint 2 is the perceptual signal-to-noise ratio constraint, which is greater than or equal to the threshold Γ s , where Γ c,k , Γ s Determined by the actual system, constraint 3 is to ensure that the sensing power and communication power are within the valid value range, and constraint 4 is to ensure that the sum of the allocated power is the total power.

8. The target tracking method of the RIS-assisted ISAC system according to claim 6, characterized in that: In step 3, for the RIS phase shift optimization subproblem, its objective function and constraints are as follows: Objective function: Constraint 1: R c,k ≥Γ c,k ; Constraint 2: SINR s ≥Γ s ; Constraint 3: |Φ| = diag(e); The constraint 1 is the achievable rate constraint, which is greater than or equal to the threshold Γ. c,k , constraint 2 is the perceptual signal-to-noise ratio constraint, which is greater than or equal to the threshold Γ s , where Γ c,k , Γ s Determined by the actual system, constraint 3 is that the passive RIS amplitude is 1.

9. The target tracking method of the RIS-assisted ISAC system according to claim 1, characterized in that: The power optimization problem in step 4 can be converted into a convex problem by using eigenvalue decomposition, and the cubic and quartic non-convex terms in RIS optimization can be simplified into convex functions, and then optimized using semidefinite relaxation technology, and finally the solution is obtained by the CVX toolbox.

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