Communication and sensing integrated waveform design method based on multi-objective particle swarm optimization algorithm

Abstract

This invention discloses a waveform design method for integrated communication and sensing based on a multi-objective particle swarm optimization algorithm, relating to radar detection technology and communication technology. The method includes: constructing a multi-objective optimization problem for waveform parameter design using the subcarrier weight coefficients of the OQAM-OFDM integrated communication and sensing waveform as optimization variables; using the maximization of the sum of the signal-to-noise ratios (SNRs) of all subcarrier communication outputs as the optimization criterion for waveform parameter design; using the maximization of the sum of the SNRs of all range resolution units' sensing outputs as the optimization criterion for waveform parameter design; solving the multi-objective optimization problem for waveform parameter design using the multi-objective particle swarm optimization algorithm to obtain several non-dominated solutions for the subcarrier weight coefficients that simultaneously maximize both the communication output SNR and the sensing output SNR; and forming a Pareto front from these non-dominated solutions. The waveform designed in this invention can effectively resist multipath interference without using cyclic prefixes and blank guard intervals, exhibiting high resource utilization efficiency and excellent sensing and communication performance.

Description

Technical Field

[0001] This invention relates to the fields of radar target detection technology and user communication technology, and more specifically, to a waveform design method for integrated communication and sensing based on a multi-target particle swarm optimization algorithm. Background Technology

[0002] Integrated communication and sensing waveform design is a crucial technology for achieving integrated communication and sensing design. It involves analyzing the communication and sensing capabilities of different waveforms to generate single or composite waveforms, thereby fulfilling the need for fusion waveforms. The goal of integrated communication and sensing waveform design is to find or design suitable waveforms that simultaneously perform high-performance information transmission and target detection functions.

[0003] However, existing integrated communication and sensing waveform design technologies primarily utilize cyclic prefixes or blank guard intervals to resist multipath interference, which faces the problems of wasted spectrum and time resources, as well as the issue of false targets appearing in radar detection due to cyclic prefixes. Furthermore, integrated waveform design often prioritizes the performance indicators of only one aspect—communication (or sensing)—while only requiring the performance indicators of the other to meet certain requirements, resulting in a dilemma where communication and sensing performance cannot be simultaneously optimized. A few multi-objective optimization methods that consider the joint optimization of multiple performance indicators of communication and sensing tend to suffer from insufficient performance robustness due to one-sided consideration of performance indicators. These problems significantly restrict the overall performance improvement of integrated communication and sensing technologies. For example, the published paper "An OFDM System Concept for Joint Radar and Communications Operations, VTC Spring 2009 IEEE 69th Vehicular Technology Conference, Barcelona, ​​Spain, 2009, pp.1-5" uses traditional cyclic prefixes and blank guard intervals to avoid multipath interference and waste of spectrum resources. However, the use of traditional rectangular filters brings disadvantages such as high sidelobes and large out-of-band energy leakage. The published paper "Mutual Information Based OFDM Waveform Design for Integrated Radar-Communication System in Gaussian Mixture Clutter, IEEE Sensors Letters, vol.4, no.1, pp.1-4, Jan.2020" only considers the optimal sensing performance, while only ensuring that the communication performance meets certain requirements, making it difficult to jointly improve and optimize the communication and sensing performance simultaneously. The published paper "Research on Integrated Radar-Communication Design Method Based on OFDM [D]. Xi'an University of Electronic Science and Technology, 2020" only considers the communication throughput when considering communication performance indicators, while ignoring the communication bit error rate, resulting in insufficient robustness of the integrated communication and sensing system.

[0004] In summary, the existing methods have the following three main problems: (1) they are prone to wasting spectrum resources and time resources and creating false targets; (2) single-objective optimization models that take one aspect of performance indicators as optimization targets restrict the overall performance improvement of communication and sensing integration; (3) the few existing waveform optimization methods that jointly consider the optimization of multiple performance indicators of communication and sensing are prone to insufficient robustness of the performance of communication and sensing integration system due to the one-sided consideration of performance indicators. Summary of the Invention

[0005] The present invention provides a waveform design method for integrated communication and sensing based on a multi-objective particle swarm optimization algorithm, which enables the communication performance and sensing performance of the integrated communication and sensing system to be optimized simultaneously.

[0006] The technical solution adopted in this invention is as follows:

[0007] A communication-sensing integrated waveform design method based on a multi-objective particle swarm optimization algorithm includes:

[0008] Using the subcarrier weight coefficients of the OQAM-OFDM integrated communication and sensing waveform as optimization variables, maximizing the sum of the signal-to-noise ratios of all subcarrier communication outputs as the optimization criterion for the waveform parameter design of the communication system, and maximizing the sum of the signal-to-noise ratios of all range resolution units sensing outputs as the optimization criterion for the waveform parameter design of the sensing system, a multi-objective optimization problem for waveform parameter design is constructed.

[0009] The multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization problem of waveform parameter design. Within the feasible region, the subcarrier weight coefficients of the OQAM-OFDM communication-sensing integrated waveform are searched to obtain several non-dominated solutions of the subcarrier weight coefficients that maximize both the communication output signal-to-noise ratio and the sensing output signal-to-noise ratio.

[0010] The non-dominated solution is formed into a Pareto front, such that any point on the Pareto front curve represents a set of waveform parameter configurations that maximize both the communication output signal-to-noise ratio and the sensing output signal-to-noise ratio.

[0011] In a preferred embodiment of the present invention, the communication sensing integrated waveform design method further includes substituting the obtained non-dominated solution into the OQAM-OFDM communication sensing integrated waveform, calculating the communication throughput and communication bit error rate after demodulation of the received signal through the optimal integrated waveform, and obtaining the communication performance under the optimal integrated waveform design.

[0012] In a preferred embodiment of the present invention, the integrated waveform design method for communication and sensing further includes substituting the obtained non-dominated solution into the OQAM-OFDM integrated waveform for communication and sensing, and calculating the integral sidelobe ratio, peak sidelobe ratio and target detection Doppler frequency shift sensitivity after radar pulse compression using the optimal integrated waveform to obtain the sensing performance under the optimal integrated waveform design.

[0013] In a preferred embodiment of the present invention, the formula for calculating the signal-to-noise ratio of the k-th subcarrier communication output is as follows:

[0014]

[0015] Among them, c k,m′ This represents the transmitted signal of the m′th symbol on the k-th subcarrier as a sum of its real and imaginary parts, ω. k,2m′This represents the noise term in the signal demodulation process of the 2m′ symbol on the k-th subcarrier. For the k-th subcarrier channel response In the frequency domain representation, E[·] represents the averaging operation. This indicates the operation of taking the real part.

[0016] In a preferred embodiment of the present invention, the formula for calculating the signal-to-noise ratio of the sensing output of the r-th distance resolution unit is:

[0017]

[0018] Where b represents the weighted target radar cross section coefficient vector obtained through sensing, and S represents the radar system received signal matrix. The variance of Gaussian white noise represents the signal's transmission and processing in the sensing system, and r represents the distance resolution unit number.

[0019] In a preferred embodiment of the present invention, the multi-objective optimization problem for waveform parameter design is:

[0020]

[0021] The optimization variable is the OQAM-OFDM communication sensing integrated waveform subcarrier weighting coefficient α. k k = 1, 2, ..., K, where K represents the total number of subcarriers, R rad This represents the total number of range-resolved units. This represents the signal-to-noise ratio (SNR) of the k-th subcarrier communication system output. This represents the output signal-to-noise ratio of the r-th resolution unit in the sensing system.

[0022] In a preferred embodiment of the present invention, in the multi-objective particle swarm optimization algorithm, through a computational model...

[0023] v s (i+1)=ω(i)·v s (i)+c1·r1·(p s (i)-η s (i))+c2·r2·(p g (i)-η s (i))

[0024] η s (i+1)=η s (i)+v s (i+1)

[0025] The particles are updated iteratively, where v s(i+1) represents the velocity of the s-th particle at step (i+1), ω(·) represents the inertia factor, c1 and c2 represent the learning factors, r1 and r2 represent random numbers distributed on [0,1], and η s (i+1) represents the position of the s-th particle at step (i+1).

[0026] Through calculation model

[0027]

[0028]

[0029] The updated particles are then updated to their individual optimal and global optimal values, where p s (i+1) represents the optimal individual particle at step (i+1), p g (i+1) represents the globally optimal particle at step (i+1), and S is the total number of particles.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] The waveform designed in this invention is a communication and sensing integrated waveform based on OQAM-OFDM, which can effectively resist multipath interference without using cyclic prefix and blank guard interval, saving spectrum, time and energy usage efficiency;

[0032] Simulation experiments demonstrate that the subcarrier weight coefficient configuration method proposed in this invention can improve the target detection probability and reduce the false alarm probability in sensing.

[0033] In terms of communication, this invention can both improve the communication rate and reduce the communication error rate, thus making the integrated system performance robust. Furthermore, the subcarrier weighting coefficients obtained by the multi-objective particle swarm optimization algorithm can simultaneously optimize the signal-to-noise ratio of the communication output and the signal-to-noise ratio of the sensing output, thereby jointly improving the integrated performance. The non-dominated solution set obtained by the multi-objective particle swarm optimization algorithm is adaptable to the needs of various application scenarios, and a set of non-dominated solutions that meets the requirements can be selected in any application scenario.

[0034] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, embodiments of the present invention are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 Flowchart of a multi-objective particle swarm optimization algorithm;

[0037] Figure 2 The Pareto front is the non-dominated solution set of subcarrier weight coefficients obtained by the multi-objective particle swarm optimization algorithm.

[0038] Figure 3 A schematic diagram illustrating the application scenario of integrated communication and sensing waveform design;

[0039] Figure 4 Flowchart of the entire process of OQAM-OFDM integrated waveform design;

[0040] Figure 5 For communication bit error rate performance under optimal waveform design;

[0041] Figure 6 Communication throughput performance under optimal waveform design;

[0042] Figure 7 The performance of sensing integral sidelobe ratio and peak sidelobe ratio under optimal waveform design;

[0043] Figure 8 Perceived Doppler frequency shift tolerance under optimal waveform design. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0045] like Figure 4 As shown, this invention provides a waveform design method for integrated communication and sensing based on a multi-objective particle swarm optimization algorithm, the steps of which are as follows:

[0046] Step 1: Initialize the parameters of the integrated communication and sensing system.

[0047] The signal bandwidth of the integrated communication and sensing system is set to B (affecting the waveform subcarrier spacing), and the sampling frequency is f. s (Affecting the number of sampling points during integrated waveform modulation and demodulation), the number of symbols is 2M, and the number of distance units during the sensing process is R. rad The number of channels during communication is R. com The number of target scattering points is n.

[0048] Step 2, Modeling the Integrated Waveform Design Problem:

[0049] 2.1 Establishing design optimization criteria for waveform parameters of communication systems

[0050] Consider using binary phase shift keying demodulation. The received signal is demodulated to obtain the estimated signal y after demodulation of the k-th (k=1,2,...,K) subcarrier and m-th (m=0,1,...,2M-1) symbol of the communication system. k,m .

[0051] The signal-to-noise ratio (SNR) of the communication system output for each subcarrier is obtained from the demodulated signal estimate and the communication channel parameters.

[0052]

[0053] Among them, c k,m′ This means that the m′ symbol of the k-th subcarrier of the transmitted signal is written as the sum of its real and imaginary parts, ω. k,2m′ This represents the noise term during the demodulation of the 2m′ symbol signal on the k-th subcarrier. For the k-th subcarrier channel response The frequency domain representation. E[·] represents the averaging operation. This indicates the operation of taking the real part.

[0054] Therefore, we can obtain the integrated waveform subcarrier weighting coefficient α. k The optimization criterion for waveform parameter design of communication systems with k = 1, 2, ..., K is to maximize the sum of the signal-to-noise ratios of all subcarrier communication outputs:

[0055]

[0056] 2.2 Establishing design optimization criteria for waveform parameters of the sensing system

[0057] Consider sensing and detecting a single-scattering point target at a certain distance from the integrated communication and sensing base station. The weighted target radar cross-section coefficient vector obtained from the sensing is denoted as b, the radar system's received signal matrix is ​​S, and the Gaussian white noise variance of the signal during transmission and processing in the sensing system is given by... The output signal-to-noise ratio of the r-th distance unit in the sensing region can then be obtained.

[0058]

[0059] Therefore, we can obtain the integrated waveform subcarrier weighting coefficient α. k The optimization criterion for waveform parameters of a sensing system, k = 1, 2, ..., K, is to maximize the sum of the signal-to-noise ratios of the sensing outputs of all distance-resolution units:

[0060]

[0061] 2.3. Based on the waveform parameter design optimization criteria for communication systems and sensing systems, a multi-objective optimization problem for waveform parameter design is formed:

[0062]

[0063] The optimization variable is the OQAM-OFDM communication sensing integrated waveform subcarrier weighting coefficient α. k k = 1, 2, ..., K. K represents the total number of subcarriers, R rad This is an overview of range-resolved units. This represents the signal-to-noise ratio (SNR) of the k-th subcarrier communication system output. This represents the output signal-to-noise ratio of the r-th resolution unit in the sensing system.

[0064] Step 3: Solve the waveform parameter design multi-objective optimization problem based on the multi-objective particle swarm optimization (MOPSO) algorithm.

[0065] The multi-objective optimization problem of waveform parameter design, formed by the waveform parameter design optimization criteria of communication system and sensing system, is shown in Equation (5).

[0066] like Figure 1 As shown, the multi-objective particle swarm optimization (MPS) algorithm searches for the subcarrier weight coefficients of the OQAM-OFDM integrated waveform within the feasible region, forming a series of non-dominated solutions, i.e., the optimal solutions, of the subcarrier weight coefficients that simultaneously maximize both the communication output SNR and the sensing output SNR. The particle update in the multi-objective PMS algorithm follows these rules:

[0067] S particles are updated iteratively according to equations (6) and (7), and then the updated particles are updated to individual optimal and global optimal according to equations (8) and (9).

[0068] v s (i+1)=ω(i)·v s (i)+c1·r1·(p s (i)-η s (i))+c2·r2·(p g (i)-η s (i)) (6)

[0069] η s (i+1)=η s (i)+v s (i+1) (7)

[0070]

[0071]

[0072] In equations (6) and (7), v s (i+1) represents the velocity of the s-th particle at step (i+1), ω(`) represents the inertia factor, c1 and c2 represent the learning factors, r1 and r2 represent random numbers distributed on [0,1], and η s (i+1) represents the position of the s-th particle at step (i+1). At step (i+1), the velocity value v of the s-th particle is first generated using the inertia factor, learning factor, and random variable. s (i+1), then update the position of the generated particle at step (i+1) using the particle's position at step i and its velocity value at step (i+1). Let s = 1, 2, ..., S, and sequentially complete the update of the particle swarm's position and velocity at step (i+1).

[0073] In equations (8) and (9), p s (i+1) represents the optimal individual particle at step (i+1), p g (i+1) represents the globally optimal particle at step (i+1), and S is the total number of particles. By comparing the particle's current position with its individual optimal position at the previous moment, the better position is retained as the individual optimal value of the particle at the current moment. The globally optimal position is then selected from all the individual optimal positions at the current moment and used as the global optimal value at the current moment.

[0074] Record the optimal solutions for all integrated waveform subcarrier weighting coefficients. Plot the communication system output SNR and sensing system output SNR values ​​obtained from each optimal solution on a two-dimensional plane with the communication demodulation output SNR as the x-axis and the sensing radar pulse compression output SNR as the y-axis, forming a Pareto front. Each point on the Pareto front represents a set of optimal solutions for waveform subcarrier weighting coefficients under different schemes, where both the communication output SNR and the sensing output SNR are simultaneously maximized. The Pareto front is shown as follows: Figure 2 As shown.

[0075] Step 4, Communication Demodulation

[0076] Based on the application scenario requirements, a set of non-dominated solutions that meet the requirements is selected from the non-dominated solution set, which is a set of optimal integrated waveform subcarrier weight coefficients. The obtained optimal integrated waveform subcarrier weight coefficients are substituted into the OQAM-OFDM communication sensing integrated waveform, and the communication throughput and communication bit error rate after demodulation of the received signal are calculated through the optimal integrated waveform to obtain the communication performance under the optimal integrated waveform design.

[0077] Step 5: Target Perception

[0078] Based on the application scenario requirements, a set of non-dominated solutions that meet the requirements is selected from the non-dominated solution set, namely a set of optimal integrated waveform subcarrier weighting coefficients. These optimal integrated waveform subcarrier weighting coefficients are then substituted into the OQAM-OFDM communication and sensing integrated waveform. The integrated sidelobe ratio (ISLR), peak sidelobe ratio (PSLR), and target detection Doppler shift sensitivity are calculated using the optimal integrated waveform to obtain the sensing performance under the optimal integrated waveform design.

[0079] By executing steps 1 to 5, we can obtain the integrated waveform design for OQAM-OFDM communication and sensing using the multi-target particle swarm optimization algorithm, as well as the entire process of user communication and target detection under the optimal integrated waveform. The flowchart of the entire waveform design process is as follows: Figure 4 As shown.

[0080] Application scenarios of the method of this invention are as follows: Figure 3 As shown, the following is a computer simulation experiment to demonstrate the method of the present invention:

[0081] The parameters of the integrated communication and sensing system are set as follows: The integrated communication and sensing system uses a single-antenna base station for signal transmission and reception. The target detection distance is 200km from the base station. The signal bandwidth is 50MHz, the sampling frequency is 50MHz, the number of symbols is 4, the number of range resolution units is 65, the number of channels is 6, and the number of scattering points is 1.

[0082] In addition, the number of particles in the multi-objective particle swarm algorithm is set to 300, and the number of iterations is set to 200.

[0083] Substituting the above parameters into the method of this invention, the communication performance under the waveform parameter configuration is obtained as follows: Figure 5 , Figure 6 As shown, the perception performance is as follows Figure 7 , Figure 8 As shown. (Through) Figure 5 , Figure 6 , Figure 7 and Figure 8 It can be seen that by jointly considering communication performance and sensing performance, and taking the communication output signal-to-noise ratio and sensing output signal-to-noise ratio as optimization objectives, the optimal solution of subcarrier weight coefficients obtained by the multi-objective particle swarm optimization algorithm can achieve better communication performance and sensing performance, thus realizing the goal of simultaneous optimization and joint optimization of communication and sensing performance.

[0084] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A communication-sensing integrated waveform design method based on a multi-objective particle swarm optimization algorithm, characterized in that, include: Using the subcarrier weight coefficients of the OQAM-OFDM integrated communication and sensing waveform as optimization variables, maximizing the sum of the signal-to-noise ratios of all subcarrier communication outputs as the optimization criterion for the waveform parameter design of the communication system, and maximizing the sum of the signal-to-noise ratios of all range resolution units sensing outputs as the optimization criterion for the waveform parameter design of the sensing system, a multi-objective optimization problem for waveform parameter design is constructed. The multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization problem of waveform parameter design. Within the feasible region, the subcarrier weight coefficients of the OQAM-OFDM communication-sensing integrated waveform are searched to obtain several non-dominated solutions of the subcarrier weight coefficients that maximize both the communication output signal-to-noise ratio and the sensing output signal-to-noise ratio. The non-dominated solution is formed into a Pareto front, such that any point on the Pareto front curve represents a set of waveform parameter configurations that maximize both the communication output signal-to-noise ratio and the sensing output signal-to-noise ratio. The design method also includes substituting the obtained non-dominated solution into the OQAM-OFDM communication sensing integrated waveform, calculating the communication throughput and communication bit error rate after demodulation of the received signal through the optimal integrated waveform, and obtaining the communication performance under the optimal integrated waveform design. The design method also includes substituting the obtained non-dominated solution into the OQAM-OFDM communication and sensing integrated waveform, and calculating the integral sidelobe ratio, peak sidelobe ratio and target detection Doppler frequency shift sensitivity after radar pulse compression through the optimal integrated waveform to obtain the sensing performance under the optimal integrated waveform design. No. The formula for calculating the signal-to-noise ratio of subcarrier communication output is: in, Indicates the first The subcarrier The transmitted signal of each symbol can be written as the sum of its real and imaginary parts. Indicates the first The subcarrier The noise term in the signal demodulation process of each symbol For the first Subcarrier channel response The frequency domain representation, This indicates the operation of taking the average. This indicates the operation of taking the real part; No. The formula for calculating the signal-to-noise ratio of the sensing output of each distance resolution unit is: in, This represents the weighted target radar cross section coefficient vector obtained through sensing. This represents the radar system's received signal matrix. This represents the variance of Gaussian white noise during signal transmission and processing in the sensing system. Indicates the range resolution unit number.

2. The integrated waveform design method for communication and sensing based on multi-objective particle swarm optimization algorithm according to claim 1, characterized in that, The multi-objective optimization problem for waveform parameter design is as follows: The optimization variable is the OQAM-OFDM communication sensing integrated waveform subcarrier weight coefficient. , Indicates the total number of subcarriers. This represents the total number of range-resolved units. Indicates the first The signal-to-noise ratio of the subcarrier communication system output. Represents the first perceptual system The output signal-to-noise ratio of each resolution unit.

3. The integrated waveform design method for communication and sensing based on multi-objective particle swarm optimization algorithm according to claim 1, characterized in that, In the multi-objective particle swarm optimization algorithm, through the computational model The particles are updated and iterated, whereby... Indicates the first The particle in the first Step speed, Indicates the inertia factor. , Represents the learning factor. , Indicates distribution in Random numbers on the screen Indicates the first The particle in the first The position of the step, Through calculation model The updated particles are then updated to their individual optimal and global optimal values. Indicates the first The optimal individual particle in the step. Indicates the first The globally optimal particle in the step.

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