Target Fusion Sensing Method Based on Superimposed Communication Sensing Integrated Waveform

Abstract

The present invention discloses a target fusion perception method based on a superimposed communication and sensing integrated waveform. By generating and transmitting a multi-carrier superimposed integrated waveform, the communication signals of the multi-carriers are linearly superimposed with the radar signals in the symbol domain, and through power allocation and signal reception and processing, the perception ability of the new waveform is improved without loss of communication performance; it includes: transmitting integrated signals, estimating target parameters based on IM-OFDM echoes, estimating target parameters based on radar signal echoes, and fusing and estimating target parameters based on the minimum mean square error criterion. Adopting the technical solution of the present invention can reduce perception errors, improve perception accuracy, and at the same time achieve the same or even higher data transmission rate and bit error rate as the original communication system, and the hardware implementation is convenient, the operation is flexible and reliable, and the cost is low.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless mobile communication, relates to integrated sensing and communication (ISAC) technology, and particularly relates to a target fusion sensing method based on a superimposed communication sensing integrated waveform, which is applied to target fusion sensing based on a superimposed communication sensing integrated transmission waveform at a single-antenna transceiver. Through a signal processing algorithm for the reflected echo of the integrated waveform, the perception accuracy of targets in the environment is improved without affecting the communication performance of the system, and the robustness of target perception in mobile communication scenarios such as vehicle-to-everything (V2X) can be enhanced. Background Art

[0002] In recent years, with the exponential growth of data services and communication devices, the low-frequency band of wireless communication has been approaching saturation. In the research of next-generation mobile communication systems, both academia and industry tend to use higher-frequency bands such as millimeter waves to increase the communication bandwidth and capacity to meet the transmission of higher-speed and massive data. In existing mobile scenarios such as vehicle-to-everything (V2X), high-frequency radars are widely used to provide relatively robust and immediate sensing information, such as millimeter-wave radars and even higher-frequency radars. Based on the development of communication and sensing technologies and service requirements, the conflict of spectrum resources and the like between the two will become an inevitable problem. Since radar and communication systems have similar hardware devices, such as antennas, radio frequency transceiver modules, digital signal processing modules, etc., the integrated sensing and communication (ISAC) technology considering co-design has become a research hotspot in academia and industry in recent years.

[0003] Waveform design is one of the core technologies of integrated sensing and communication. With the development of communication and radar technologies and the continuous increase of integration requirements, the degree of integration between communication and sensing has been deepening, from the simple coexistence of the two in waveforms to the sharing and collaborative optimization of waveforms. An important way of dual-functional integration of communication and sensing is to make full use of communication signals based on existing wireless communication modulation methods and protocol architectures to realize the perception of targets in the environment while data is transmitted. Due to its high spectral efficiency and strong anti-interference ability, the OFDM waveform has been widely used in the research of waveform design for dual-functional integration of communication and sensing. Existing methods include using methods such as Fourier transform and subspace algorithms to process the OFDM signal echo to extract information such as the distance and speed of targets in the environment; other methods are based on the trade-off between communication and sensing performance, establishing an online optimization problem to obtain the best sensing waveform, thereby theoretically ensuring the perception accuracy.

[0004] However, existing communication sensing methods still have some limitations: due to the randomness of communication signals themselves, it is often difficult to perfectly meet the low cross-correlation characteristics between transmitted waveforms at different times, thus reducing the accuracy of echo estimation; optimization-based methods have high requirements for computing resources and rely on the state information of communication channels. In high-speed mobile scenarios such as vehicle-to-everything (V2X) networks, it may be difficult to obtain the rapidly changing channel state information in a timely and accurate manner, thus affecting the solution of optimization problems. In summary, existing methods for target sensing, especially in mobile communication scenarios, often suffer from insufficient sensing accuracy, excessive complexity, or seriously affecting the performance of normal communication, making them difficult to be practically applied. Summary of the Invention

[0005] The present invention proposes a target fusion sensing method based on a superimposed communication sensing integrated waveform, which is applied to target fusion sensing in an environment of single-antenna transceiver active sensing based on a superimposed communication sensing integrated waveform. By generating and transmitting a multi-carrier superimposed integrated waveform, the communication signals of multiple carriers are linearly superimposed with radar signals in the symbol domain, and through reasonable power allocation and signal reception and processing, the sensing ability of the new waveform is improved without sacrificing communication performance, thereby achieving the technical effect of reducing sensing error and improving sensing accuracy.

[0006] The method provided by the present invention can robustly reduce the error of target parameter estimation by reasonably processing the reflected echo, thereby improving the sensing performance of the mobile communication system. In specific applications, the multi-carrier superimposed integrated waveform generated by the present invention can support various modulation methods and parameter settings in a relatively complex mobile communication system, realizing downlink communication transmission and dynamic target detection with high energy efficiency and low complexity.

[0007] To achieve the above object, the present invention designs a new method for fusing and estimating target parameters, generates a multi-carrier superimposed communication sensing integrated waveform, designs a corresponding signal processing algorithm based on the superimposed communication sensing integrated waveform, and can enhance the radio frequency sensing ability for complex mobile communication systems. The target fusion sensing method of the present invention includes transmitting an integrated signal, estimating target parameters based on the echo of IM-OFDM (Index Modulation-Orthogonal Frequency Division Multiplexing), estimating target parameters based on the radar signal echo, and a target parameter fusion estimation algorithm based on the minimum mean square error criterion. Among them, the estimation based on the IM-OFDM echo uses an improved MUSAIC algorithm, and the estimation based on the radar echo uses a matched filtering method.

[0008] This target fusion sensing method includes the following steps:

[0009] 1) The transmitting end superimposes two existing waveforms (communication signal and radar signal) according to the bit sequence to be transmitted. By superimposing the communication signal and the radar signal in the symbol domain, a new integrated communication and sensing superimposed waveform is generated. After modulation, it is sent out by the transmitting antenna;

[0010] 2) The integrated communication and sensing superimposed waveform signal sent by the transmitting antenna is reflected by the target to be sensed in the mobile communication system environment, and the echo reaches the receiving antenna. Based on the known IM-OFDM waveform at the transmitting end, the reflected echo is processed, and an improved MUSIC (Multiple Signal Classification) algorithm is used to obtain the estimated values of the target's radial distance and the target's moving radial velocity;

[0011] 3) Based on the known radar waveform at the transmitting end, the reflected echo is processed, and the method of matched filtering is used to obtain the estimated values of the target's radial distance and radial motion velocity;

[0012] 4) Based on the minimum mean square error criterion, the estimated values of the target parameters (i.e., radial distance and radial motion velocity) in the first two steps are weighted and fused to obtain the final estimated values.

[0013] Through the above steps, the accuracy of target parameter estimation, that is, target sensing, can be improved, and the sensing accuracy improvement of the integrated transmission waveform based on multi-carrier communication and sensing superimposition can be realized.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0015] The present invention provides a new waveform design scheme for radio frequency sensing method. By linearly superimposing and power allocating the high-energy efficiency index modulation communication signal and the high-sensing accuracy radar signal in the symbol domain, the sensing accuracy can be further improved on the basis of the existing waveform, the error of the distance-velocity estimation of the target in the environment can be reduced, and at the same time, the data transmission rate and bit error rate equivalent to or even higher than those of the original communication system can be achieved. And compared with the existing scheme, there is no obvious increase in complexity, no need for immediate optimization and solution, the modification to the existing communication protocol and radar system is small, and the hardware implementation is convenient, the operation is flexible and reliable, and the cost is low.

[0016] The target fusion sensing method based on the superimposed communication and sensing integrated waveform provided by the present invention has the following advantages:

[0017] 1) This method comprehensively considers the sensing and communication performance, and uses the high spectral efficiency and high energy efficiency of the IM-OFDM signal. It can not only estimate the target parameters, but also ensure that the communication ability of the superimposed waveform is not affected and is not lower than the existing OFDM scheme;

[0018] 2) By utilizing the good autocorrelation characteristics of radar signals and the orthogonality between two signals, the distance-velocity estimation of the perceived target is respectively realized based on the echoes of IM-OFDM signals and radar signals, ensuring the feasibility of the two parts of the estimation algorithms.

[0019] 3) The method of weighted fusion of the two parts of the estimation can further improve the perception accuracy and robustness, and enhance the perception ability in the statistical sense.

[0020] 4) Based on the performance trade-off between communication and perception for power allocation, the solution to the problem of real-time waveform optimization at the transmitter end in the existing methods is avoided, thereby reducing the complexity of system implementation. At the same time, the modification to the original system is not significant, and the existing communication and radar systems can be fully utilized. Description of the Drawings

[0021] Figure 1 It is a block diagram of the transceiver system for communication and perception integration in the present invention.

[0022] Figure 2 It is a block diagram of the multi-carrier superposition integrated transmitter waveform and signal processing flow in the present invention.

[0023] Figure 3 It is a block diagram of the target fusion perception algorithm based on the superposition integrated transmission waveform in the present invention. Detailed Embodiment

[0024] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0025] Refer to Figure 1 As shown, the waveform provided by the present invention is sent by the transmitting end base station. The communication signal and radar signal superimposed in the symbol domain of the transmitting antenna form a new transmission waveform. After being modulated by using the existing modulation method, it reaches the receiving end (user) via the wireless channel of the communication system. The receiving end detects and demodulates the signal (waveform), restores the transmitted bit sequence, realizes the normal transmission of data, and ensures the due communication performance. It can be verified that the performance of the communication side is not affected. Therefore, the present invention can improve the performance of the perception side while ensuring the communication performance. The transmitted signal is reflected by the target in the environment, and the echo reaches the receiving antenna at the transmitting end, and is further processed to extract the target parameters in the environment. Specifically, when implemented, the waveform of the reflected echo can be applied to the mobile user system including information transmission, supporting multiple modulation methods and parameter settings, and realizing downlink communication transmission and dynamic target detection with high energy efficiency and low complexity.

[0026] Based on the generated superimposed waveform, the present invention designs a cooperative signal processing and information extraction algorithm, which includes signal transmission, target parameter estimation based on IM-OFDM echoes, target parameter estimation based on radar signal echoes, and target parameter fusion estimation based on the minimum mean square error criterion. The overall signal processing flow is as shown in Figure 2 shown, and the specific steps are as follows:

[0027] S10: The transmitter generates a communication-sensing superimposed integrated waveform according to the bit sequence to be transmitted, and after modulation, it is sent out by the transmitting antenna;

[0028] S20: The signal reaches the receiving antenna after being reflected by the target in the environment. Based on the known IM-OFDM waveform at the transmitter, the echo is processed, and an improved MUSIC algorithm is used to obtain the estimated values of the target's radial distance and radial velocity;

[0029] S30: Based on the known radar waveform at the transmitter, the echo is processed, and the method of matched filtering is used to obtain the estimated values of the target's radial distance and radial motion velocity; (can be executed in parallel with S20)

[0030] S40: Based on the minimum mean square error criterion, the target parameter estimated values in S20 and S30 are weighted and fused to obtain the final estimated values.

[0031] In step S10: The waveform generation module at the transmitter needs to generate the information-carrying communication signal and radar signal respectively and superimpose them to generate an integrated waveform, and then send it out from the transmitting antenna. This step includes the following processes S11~S14:

[0032] S11: According to the information bit sequence to be transmitted, the transmitter generates an IM-OFDM symbol sequence, that is, a communication signal, by means of index modulation;

[0033] S12: The transmitter generates an M sequence (a kind of pseudo-random sequence) that is 1 less than the number of subcarriers of the IM-OFDM signal through a shift register as the radar signal;

[0034] S13: Refer to the transmitter part in Figure 2 , and based on the theoretically feasible power allocation ratio, the communication signal and the radar signal are linearly superimposed in the symbol domain of the baseband at the transmitter to obtain a superimposed waveform;

[0035] S14: The superimposed waveform is sent out by the transmitting antenna through process modules such as IFFT (Inverse Fast Fourier Transformation), baseband modulation, and upconversion.

[0036] In step S20: For the reflected echo of the superimposed waveform signal, the orthogonality between the IM-OFDM signal and the radar signal is utilized for separate processing. For the IM-OFDM echo, an improved MUSIC algorithm is adopted to obtain the estimated values of the target radial distance and radial velocity. The improved MUSIC estimation method includes the following processes S21 to S25:

[0037] S21: Down-convert and sample the received signal (reflected echo) to obtain the baseband time-domain signal; perform FFT (Fast Fourier Transformation) on the baseband time-domain signal to transform it into the frequency domain, obtaining the received signal in the frequency domain, i.e., the multi-carrier frequency-domain signal where N s is the number of symbols in a signal pulse, and P is the number of pulses in a frame of signal sequence;

[0038] S22: Communication information compensation:

[0039] According to the known communication information of the transmitted communication signal, i.e., the IM-OFDM signal, from Figure 3 divide the multi-carrier frequency-domain signal obtained at the receiving end by the IM-OFDM signal element by element and carrier by carrier to obtain the compensated signal where

[0040] S23: Decoherence processing: Perform time-domain smoothing on the signal compensated in S22. The sliding window length is M. Arrange the preprocessed data at the k to k + M - 1 moments of the nth OFDM symbol of each pulse in a row where k = 0, 1, 2,... N c -M to form a new signal array;

[0041] S24: Subspace projection: Utilize the signal subspace-based super-resolution processing method in signal array processing to achieve super-resolution estimation of the target distance and target moving speed.

[0042] Specifically, for the covariance matrix of each signal obtained in S23 where and perform eigenvalue decomposition on the covariance matrix Among them, the covariance matrix of MN p -N t the eigenvectors corresponding to the smallest eigenvalues form the noise subspace Thus, construct the spatial spectrum function where a(R q , V l) is the response vector of the range-velocity grid (R q , V l ), which is related to the range and velocity of the target.

[0043] S25: The range and velocity corresponding to the spectral peak of the spatial spectrum function are the estimated values of the target range and target moving velocity based on the IM-OFDM signal echo

[0044] In step S30: Based on the known radar waveform at the transmitting end, the reflected echo is processed, and the method of matched filtering is used to obtain the estimated values of the target radial range and radial moving velocity; this step includes the following processes S31 to S33:

[0045] S31: Divide the range-velocity dynamic range into N R ×N V grids, and each grid represents a pair of range-velocity estimated values (R q , V l ), q = 1, 2,..., N R , l = 1, 2,..., N V , where N R , N V are the maximum number of grids in the range dimension and velocity dimension respectively, representing the resolution of range and velocity; the range-velocity dynamic range is the range between the minimum range and the maximum range that the radar can estimate. For each range-velocity grid, calculate its compensation vector for the reflected echo signal vector where () * represents the conjugate operation.

[0046] S32: For the signal (multi-carrier frequency domain signal) obtained by sampling and transforming to the frequency domain in S21, compensate it with the compensation vector at each range-velocity grid in S31, that is, multiply, and then perform a correlation operation with the original transmitted radar signal x rad to obtain the estimated power spectrum at each grid where D(x rad ) is the diagonal matrix form with the diagonal elements being the original radar signal x rad .

[0047] S33: The range and velocity corresponding to the spectral peak of the estimated power spectrum at each grid are the estimated values of the target range and velocity based on the radar signal echo

[0048] In step S40: Based on the minimum mean square error criterion, the target parameter estimated values in S20 and S30 are weighted and fused to obtain the final estimated value to reduce the estimation error and improve the stability. This step includes the following processes S41 to S43:

[0049] S41: Calculate the weights W of the two parts of the parameter fusion estimation according to the mean square error history records of the estimation values of the two parts of S20 and S30. rad and W com , where and respectively represent the mean square errors of the estimation methods used in the S2 and S3 stages, which can be obtained through off-line measurement.

[0050] S42: According to the optimal weights of S41, perform linear weighted fusion on the two parts of the estimation values obtained from S20 and S30 to obtain the final target radial distance - velocity estimation values, the final target radial distance estimation value and the radial velocity estimation value can be expressed as: are respectively the target distance and velocity estimation values based on the IM - OFDM echo obtained in S2, are respectively the target distance and velocity estimation values based on the radar signal echo obtained in S3.

[0051] The present invention provides a target fusion perception method based on a multi - carrier superposition communication - sensing integrated transmission waveform. The transmission waveform utilizes the high energy efficiency brought by index modulation to achieve lossless communication rate and reliability; by superimposing dedicated radar signals for sensing and corresponding echo signal processing algorithms, it ensures a robust improvement in target sensing accuracy. The target sensing method provided by the present invention makes full use of existing communication / radar systems for supplementation and coordination, without involving re - design of waveforms and online optimization solutions, and is a communication - sensing integration implementation method applicable to dynamic scenarios, which can meet the practical requirements of high energy efficiency, low complexity, and low implementation cost.

[0052] It should be noted that the purpose of publishing the embodiments is to help further understand the present invention. However, those skilled in the art can understand that various substitutions and modifications are possible without departing from the present invention and the appended claims. Therefore, the present invention should not be limited to the content disclosed in the embodiments, and the scope of protection claimed by the present invention is defined by the scope of the claims.

Claims

1. A target fusion perception method based on a superimposed communication and sensing integrated waveform, characterized in that, By generating and transmitting a multi-carrier superposition integrated waveform, the communication signals of multiple carriers and radar signals are linearly superimposed in the symbol domain, and through power allocation and signal reception and processing, the perception ability of the new waveform is improved without sacrificing communication performance; It includes: transmitting integrated signals, target parameter estimation based on IM-OFDM echoes, target parameter estimation based on radar signal echoes, and target parameter fusion estimation based on the minimum mean square error criterion; the steps are as follows: 1) The transmitting end generates a new communication perception superposition integrated waveform by superimposing communication signals and radar signals according to the bit sequence to be transmitted, and after modulation, it is sent out by the transmitting antenna; Specifically, based on the power allocation ratio, the communication signal and the radar signal are linearly superimposed in the symbol domain of the baseband at the transmitting end to obtain the communication perception superposition integrated waveform; 2) The communication perception superposition integrated waveform signal is reflected by the target to be perceived in the mobile communication system environment, and the echo reaches the receiving antenna; the reflected echo is processed based on the IM-OFDM waveform at the transmitting end to obtain the estimated values of the target radial distance and the radial velocity of the target movement; it includes: S21: The reflected echo is down-converted and sampled to obtain a baseband time-domain signal; the baseband time-domain signal is subjected to a fast Fourier transform FFT to the frequency domain to obtain a frequency-domain received signal, that is, a multi-carrier frequency-domain signal; S22: Perform communication information compensation; according to the communication information, that is, the IM-OFDM signal, the multi-carrier frequency-domain signal obtained at the receiving end is divided element by element and carrier by carrier with the IM-OFDM signal to obtain a compensated signal; S23: Perform decoherence processing: perform time-domain smoothing processing on the signal compensated in S22 to form a new signal array; S24: Perform subspace projection: use a signal subspace-based super-resolution processing method to perform super-resolution estimation of the target distance and the target movement speed; S25: The distance and speed corresponding to the spectral peak of the spatial spectrum function are the estimated values of the target distance and the target movement speed based on the IM-OFDM signal echo; 3) The reflected echo is processed based on the radar waveform at the transmitting end, and the method of matched filtering is used to obtain the estimated values of the target radial distance and the radial movement speed; it includes the following processes S31 to S33: S31: Divide the distance-velocity dynamic range, and each grid point divided represents a pair of distance-velocity estimated values; the distance-velocity dynamic range is the range between the minimum distance and the maximum distance that the radar can estimate; for each distance-velocity grid point, calculate its compensation vector for the reflected echo signal vector; S32: Multiply the compensation vectors of each grid point in S31 with the multi-carrier frequency-domain signal obtained in S21 respectively for compensation, and then perform correlation operations with the original radar signal respectively to obtain the power spectrum at each grid point; S33: The distance and speed corresponding to the spectral peak of the estimated power spectrum at each grid point are the estimated values of the target distance and speed based on the radar signal echo; 4) The estimated values of the target parameters of the radial distance and the radial movement speed are weighted and fused to obtain the final estimated value; it includes the following processes S41 to S43: S41: Calculate the corresponding optimal weight of the parameter fusion estimation based on the mean square error of the estimated values obtained in steps 2) and 3). S42: Perform weighted fusion on the estimated values obtained in steps 2) and 3) according to the optimal weight to obtain the final target radial distance - velocity estimated value. Through the above steps, the target fusion perception based on the superimposed communication - sensing integrated waveform is realized.

2. The target fusion perception method based on a superimposed communication and sensing integrated waveform according to claim 1, characterized in that, Steps 2) and 3) are executed in parallel.

3. The target fusion perception method based on a superimposed communication and sensing integrated waveform according to claim 1, characterized in that, Specifically, step 2) uses the improved Multiple Signal Classification (MUSIC) algorithm to estimate based on the IM - OFDM echo, and obtains the estimated values of the target radial distance and the radial velocity of the target movement.

4. The target fusion perception method based on a superimposed communication and sensing integrated waveform according to claim 3, characterized in that, In step 2), the processes S21 - S25 included in the improved MUSIC estimation method are specifically: In S21, the baseband time-domain signal is subjected to fast Fourier transform (FFT) to transform it into the frequency domain, and the received signal in the frequency domain is denoted as n s = 1, 2, …, N s , p = 1, 2, …, P, where N s is the number of symbols of a signal pulse, and P is the number of pulses within a frame of signal sequence; In S22, according to the communication information of the known transmitted communication signal, i.e., the IM-OFDM signal, the multi-carrier frequency domain signal obtained at the receiving end is divided element by element and carrier by carrier by the IM-OFDM signal, and the compensated signal is denoted as wherein In S23, the specific time-domain smoothing process is as follows: take the sliding window length as M, arrange the preprocessed data at the k to k+M-1 moments of the nth OFDM symbol of each pulse in a row, denoted as where k = 0, 1, 2, … N c -M, to form a new signal array; In S24, specifically, the covariance matrix of each signal is obtained : Among them And perform eigen - decomposition on the covariance matrix , where the covariance matrix has MN p - N t eigen - vectors corresponding to the smallest eigenvalues that form the noise subspace Construct the spatial spectrum function from this where a(R q , V l ) is the response vector of the range - velocity grid point (R q , V l ).

5. The target fusion perception method based on a superimposed communication and sensing integrated waveform according to claim 1, characterized in that, Specifically, step 4) performs linear weighted fusion on the estimated values of the radial distance and the radial movement velocity based on the minimum mean square error criterion to obtain the final estimated value.

6. The target fusion perception method based on a superimposed communication and sensing integrated waveform according to claim 1, characterized in that, Step 1) includes the following processes: S11: According to the information bit sequence to be transmitted, the transmitter generates an IM - OFDM symbol sequence, that is, the communication signal, through the method of index modulation. S12: Generate a pseudo - random sequence M sequence with one less sub - carrier number than the IM - OFDM signal through the shift register at the transmitter as the radar signal. S13: Based on the power allocation ratio, the transmitter linearly superimposes the communication signal and the radar signal in the symbol domain of the baseband at the transmitter to obtain the superimposed waveform. S14: The superimposed waveform is sent out by the transmitter antenna through the processes of inverse fast Fourier transform modulation, baseband modulation, and up - conversion.

7. The target fusion perception method based on the superimposed communication and sensing integrated waveform according to claim 1, wherein, In step 3), specifically in S31, the distance-velocity dynamic range is divided into N R ×N V lattice points, and each lattice point represents a pair of distance-velocity estimated values (R q , V l ), where q = 1, 2, …, N R , l = 1, 2, …, N V , and N R , N V are the maximum number of lattice points in the distance dimension and the velocity dimension, respectively.

8. The target fusion perception method based on the superimposed communication and sensing integrated waveform according to claim 7, wherein, In S32, perform the correlation operation to obtain the estimated power spectrum at each grid point, expressed as: where a(R q ,V l ) is the response vector for the range-velocity bin (R q ,V l ).

9. The target fusion perception method based on the superimposed communication and sensing integrated waveform according to claim 8, wherein, In S41, the weights of the two parts of the parameter fusion estimation are denoted as W rad and W com , respectively: where and respectively represent the mean square errors used for target estimation based on radar signals and IM-OFDM signals, and are obtained through offline measurement.

10. The target fusion perception method based on the superimposed communication and sensing integrated waveform according to claim 9, wherein, In S42, linear weighted fusion is performed to obtain the final estimated value of the target radial distance - velocity, expressed as: Based on the estimated values of the target distance and velocity of the IM - OFDM echo respectively, which are the estimated values of the target distance and velocity based on the radar signal echo respectively.

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