A communication and sensing integrated waveform design and parameter estimation method

Abstract

The present application relates to a kind of communication perception integrated waveform design and parameter estimation method, through the multicarrier frequency modulation of baseband signal, over-sampling is carried out in receiving end, to complete the communication task of user and the perception task of target.In terms of communication, the waveform design can adapt wideband multipath fading channel, ensure that communication rate reaches almost the same rate as orthogonal frequency division multiplexing system, in the aspect of perception, distance parameter estimation can be carried out according to received signal in the case where unknown communication symbol, so that the waveform has passive perception ability, and design under the premise of guaranteeing communication performance, as far as possible improve the precision and resolution of perception parameter estimation.

Description

TECHNICAL FIELD

[0001] The present application relates to a communication and perception integrated waveform design and parameter estimation method, belonging to the technical field of wireless communication and radar perception. BACKGROUND

[0002] With the development of the fifth generation mobile communication, a large number of intelligent devices need to access the communication network, such as robots, autonomous driving, virtual reality, etc., which all need high-speed communication services and high-resolution perception services. At the same time, high-speed, high-reliability, low-delay communication requirements and high-precision, high-resolution perception requirements require a large amount of spectrum resources, and sharing spectrum between communication and perception is one of the important means to meet the communication and perception requirements. In fact, in addition to the use of frequency bands, there is a common use in hardware architecture, time domain waveform and beamforming, etc. Both communication systems and radar perception systems use radio frequency signals, which have a high degree of similarity. "F. Liu, C. Masouros, A. P. Petropulu, H. Griffiths and L. Hanzo, "Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead," IEEE Trans. Commun., vol. 68, no. 6, pp. 3834-3862, June 2020." introduces that in the future wireless system, combining the two into a communication and perception integrated system is one of the important research contents of the next generation mobile communication, through the mutual integration of communication and perception, the performance of the whole system can be improved, and the hardware cost and complexity are greatly reduced.

[0003] The prior art "W. Zhou, R. Zhang, G. Chen and W. Wu, "Integrated Sensing and Communication Waveform Design: A Survey," IEEE OpenJ. Commun. Society, vol. 3, pp. 1930-1949, Oct. 2022." introduces the fusion of communication and sensing, in the three dimensions of space, time and frequency, there are beam design, waveform design and carrier design respectively, for realizing the corresponding communication and sensing requirements, and the time domain waveform design is the most basic and important problem. Prior art such as radar waveform-centered design embeds communication symbols between radar pulses or modulates on radar signals, but often has a low communication rate and is difficult to adapt to multipath fading channels. Or communication waveform-centered design develops the sensing ability of orthogonal frequency division multiplexing (OFDM) waveform itself, optimizes waveform indicators such as peak-to-average ratio to adapt to sensing channels and sensing algorithms, and uses matched filtering or carrier phase analysis for ranging and speed measurement, but its accuracy and resolution are directly determined by the OFDM symbol length, which cannot be broken through by algorithm or physical means. At the same time, more and more communication and sensing integrated scenarios have the demand for passive sensing, such as distributed communication and sensing integrated systems, which need to estimate the target distance without knowing the communication symbol, which poses a greater challenge to OFDM waveforms. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a communication and sensing integrated waveform design and parameter estimation method, which can adapt to wideband multipath fading channels in communication, ensure that the communication rate reaches almost the same rate as the OFDM system, in sensing, can estimate the distance parameter according to the received signal without knowing the communication symbol, so that the waveform has passive sensing ability, and at the same time, under the premise of ensuring communication performance, improve the accuracy and resolution of parameter estimation as much as possible.

[0005] The present application adopts the following technical solutions to solve the above technical problems: the present application proposes a communication and sensing integrated waveform design and parameter estimation method, which performs the following steps: a base station transmits a time domain continuous signal modulated by multicarrier frequency conversion, a communication user receives and demodulates the baseband communication signal, and at the same time, the base station receives the echo signal from the sensing target to estimate the distance parameter between the base station and the sensing target;

[0006] Step A. The base station implements multicarrier frequency conversion modulation on the baseband signal to be transmitted to form a time domain continuous signal for transmission, which is used for user communication and target sensing;

[0007] Step B. Establishing a communication channel model conforming to wideband wireless multipath fading and a perception channel model mainly with line-of-sight path;

[0008] Step C. A communication user receives a time-domain continuous signal sent by a base station, and demodulates the received signal according to the communication channel model;

[0009] Step D. The base station oversamples the echo signal of a perceived target received, and estimates the parameters of the perceived target according to the perception channel model.

[0010] As a preferred technical solution of the present application: the step A includes the following steps A1 to A2:

[0011] Step A1. Assuming that the base station serves U communication users and perceives K perception targets, for the u-th communication user, the base station sends a baseband signal f u (m), m = 1, 2, …, M, according to the following formula:

[0012]

[0013] Design f u (m) corresponding to the time-domain continuous signal s u (t), wherein u = 1, 2, …, U, T represents the duration of s u (t), ρ represents the frequency variation parameter corresponding to all subcarriers, and satisfies p is a predetermined positive integer, and j represents an imaginary unit satisfying j 2 = -1;

[0014] Step A2. Take the part of s u (t) with a tail length of L CP as a cyclic prefix added to the head of s u (t), to constitute a time-domain continuous signal x u (t') after multicarrier frequency modulation as follows:

[0015]

[0016] Wherein L CP is the length of the cyclic prefix.

[0017] As a preferred technical solution of the present application: in the step B, a communication channel model h u (t) conforming to wideband wireless multipath fading between the base station and the u-th communication user is established:

[0018]

[0019] Wherein, Lu denotes the number of paths contained in the communication channel between the base station and the u-th communication user, l = 1, 2, …, L u , δ(t) denotes the unit impulse function, α u,l and τ u,l respectively denote the gain and time delay of the l-th path of the u-th communication user;

[0020] A LoS path-based perception channel model g k (t) is established between the base station and the k-th perception target:

[0021] g k (t) = β k δ(t - v k ),

[0022] where k = 1, 2, …, K, β k and v k respectively denote the gain and time delay of the perception channel, and c denotes the speed of light.

[0023] As a preferred technical solution of the present application: in step C, the u-th communication user performs the following steps C1 to C5:

[0024] Step C1. The received signal y u (t”) of the u-th communication user is as follows:

[0025]

[0026] The u-th communication user samples the received signal y u (t”) at M + 2μ sampling points as follows:

[0027]

[0028] where μ denotes the number of discrete sampling points corresponding to the signal prefix, and σ u,l denotes the sampling point index of the time delay of the l-th path between the base station and the u-th communication user;

[0029] Step C2. For y n (n), first remove the sampling results of the first μ sampling points, then sequentially select the sampling results of M sampling points, and update y n (n), i.e. 1≤n≤M;

[0030] Step C3. The u-th communication user solves the equivalent channel matrix H of the communication channel according to the communication channel model, where denotes the shift position exchange matrix as follows: ​

[0031]

[0032] Step C4. Define signal vector and Then, the two vectors satisfy y u = H u s u , according to s u = H u -1 y u , the communication user can obtain the estimation of the transmitted signal

[0033] Step C5. The communication user obtains the estimation of the baseband signal according to the following formula:

[0034]

[0035] The estimation is the demodulation result of the baseband signal.

[0036] As a preferred technical solution of the present application, the step D includes the following steps D1 to D6:

[0037] Step D1. The base station receives the echo signals from the K sensing targets, removes the signal prefix, and sets the maximum sensing delay as Then, the signal from T s to T is intercepted, and the signal is taken as the start at T s , and z(t) is obtained as follows:

[0038]

[0039] Step D2. Define The base station performs intermediate frequency processing on z(t), multiplies it with , and obtains z s (t) as follows:

[0040]

[0041] Step D3. The base station performs oversampling on z s (t) according to a preset sampling point number Q greater than M, and obtains z s (n') as follows:

[0042]

[0043] and is further updated according to the following formula:

[0044]

[0045] ​Wherein, the preset value of Q satisfies the Nyquist theorem, that is, the sampling rate is greater than twice the signal frequency:

[0046]

[0047] Wherein, Indicates the upward rounding operation;

[0048] Step D4. The base station performs discrete Fourier transform on z s (n') to obtain the corresponding signal spectrum as follows:

[0049]

[0050] Step D5. The base station sequentially analyzes F(q) in order, first obtains the frequency point value of the first non-zero spectrum value F(q), and then sequentially obtains K-1 frequency points with an amplitude increase exceeding the preset amplitude threshold compared with the adjacent last spectrum value, a total of K frequency points a k ,k=1,2,…,K;

[0051] Step D6. According to the relationship between d k and v k According to the following formula:

[0052]

[0053] The distance estimation between the base station and the sensing target corresponding to the frequency point a k is calculated and obtained That is, the distance parameter estimation of K sensing targets is completed. The communication and sensing integrated waveform design and parameter estimation method provided by the present application has the following technical effects compared with the prior art:

[0054] (1) The communication and sensing integrated waveform design and parameter estimation method provided by the present application completes the communication task of users and the sensing task of targets by performing multi-carrier frequency modulation on the baseband signal and oversampling at the receiving end. In terms of communication, the waveform design can adapt to wideband multipath fading channels and ensure that the communication rate reaches almost the same rate as the OFDM system. In terms of sensing, the distance parameter estimation can be performed according to the received signal without knowing the communication symbol, so that the waveform has passive sensing capability, adapts to passive sensing scenarios such as multi-base station cooperative sensing, and improves the precision and resolution of the sensing parameter estimation as much as possible under the premise of ensuring the communication performance.

[0055] ​(2) The communication and perception integrated waveform design and parameter estimation method provided by the application, in the aspect of perception, the perception performance of the waveform is related to the symbol duration, the linear frequency change rate and the oversampling rate, the perception performance can be improved from multiple dimensions, and the noise sensitivity is low; and the waveform designed by the application can still adapt to complex wideband multipath fading channels for communication on the basis of completing distance estimation, obtain almost the same communication spectral efficiency and communication rate as the OFDM system, combine the communication advantages of OFDM and the perception advantages of frequency-modulated continuous wave radar, and obtain better communication and perception performance. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 The simulation experimental result schematic diagram about the communication performance in the embodiment of the application is shown in the figure.

[0057] Figure 2 The simulation experimental result schematic diagram about the single-target perception algorithm in the embodiment of the application is shown in the figure.

[0058] Figure 3 The simulation experimental result schematic diagram about the multi-target perception algorithm in the embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0059] The specific embodiment of the application will be further described in detail in combination with the drawings of the specification.

[0060] The communication and perception integrated waveform design and parameter estimation method used by the application is specifically executed as follows in practical application: the base station sends a time-domain continuous signal modulated by multicarrier frequency conversion, the communication user receives and demodulates, and the base station receives the echo signal from the perception target to complete the distance parameter estimation between the base station and the perception target.

[0061] Step A. The following steps A1 to A2 are designed and executed: the base station implements multicarrier frequency conversion modulation on the baseband signal to be sent, to form a time-domain continuous signal for sending, for user communication and target perception.

[0062] Step A1. Assuming that the base station serves U communication users and perceives K perception targets, for the u-th communication user, the baseband signal f u (m) on M orthogonal subcarriers is modulated as follows:

[0063]

[0064] The time-domain continuous signal s u (t) corresponding to f u (m) is designed, wherein u=1, 2, …, U, and T represents the duration of s u (t). ρ represents a frequency variation parameter corresponding to all subcarriers, and satisfies p is a preset positive integer, j represents an imaginary unit satisfying j 2 = -1.

[0065] Step A2. Take s u (t) Tail length L CP Part, as a cyclic prefix added to the head of s u (t) to constitute a time-domain continuous signal x u (t') after multicarrier frequency modulation as follows:

[0066]

[0067] Where L CP is the length of the cyclic prefix.

[0068] Step B. Establish a wideband wireless multipath fading communication channel model h u (t) between the base station and the u-th communication user:

[0069]

[0070] Where L u represents the number of paths contained in the communication channel between the base station and the u-th communication user, l = 1, 2, …, L u , δ(t) represents the unit impulse function, α u,l and τ u,l represent the gain and time delay of the l-th path of the u-th communication user, respectively.

[0071] Establish a line-of-sight path-based perception channel model g k (t) between the base station and the k-th perception target:

[0072] g k (t) = β k δ(t-v k ),#(4)

[0073] Where k = 1, 2, …, K, β k and v k represent the gain and time delay of the perception channel, respectively, and the distance between the base station and the k-th perception target is c represents the speed of light. It should be noted that the communication user and the perception target can be the same device or two different devices.

[0074] Step C. The communication user receives the time-domain continuous signal sent by the base station, and demodulates the received signal according to the communication channel model.

[0075] The above step C in practical application, the user terminal executes the following step C1 to step C5.

[0076] Step C1. The received signal y u (t") is sampled as follows:

[0077]

[0078] The u-th communication user samples the received signal y u (t") as follows:

[0079]

[0080] Wherein, μ represents the number of discrete sampling points corresponding to the signal prefix, σ u,l represents the sampling point index of the l-th path delay between the base station and the u-th communication user;

[0081] Step C2. For y u (n), first remove the sampling results of the first μ sampling points, then sequentially select the sampling results of M sampling points, and update y u (n), that is, 1≤n≤M;

[0082] Step C3. The u-th communication user solves the equivalent channel matrix H Wherein, represents the shift position exchange matrix as follows:

[0083]

[0084] Step C4. Define the signal vector and Then the two vectors satisfy y u = H u s u , according to s u = H u -1 y u , the communication user can obtain the estimation of the transmitted signal

[0085] Step C5. The communication user obtains the estimation of the baseband signal as follows:

[0086]

[0087] The estimation is used as the demodulation result of the baseband signal;

[0088] ​Step D. The base station oversamples the received echo signal of the sensing target and estimates the parameters of the sensing target according to the sensing channel model.

[0089] In practical applications, the above step D is specifically designed to be executed as follows: steps D1 to D6.

[0090] Step D1. The base station receives echo signals from K sensing targets, removes the signal prefix, and sets the maximum sensing delay to [value missing]. Then extract from T s The signal up to time T, and T s Taking the time as the start of the signal, z(t) is obtained as follows:

[0091]

[0092] Step D2. Definition The base station performs intermediate frequency processing on z(t) and combines it with... Multiply to obtain z s (t) is as follows:

[0093]

[0094] Step D3. The base station performs sampling on z according to a preset number of sampling points Q greater than M. s (t) is oversampled to obtain z s (n') is as follows:

[0095]

[0096] And further update according to the following formula;

[0097]

[0098] The preset value of Q should satisfy the Nyquist theorem, that is, the sampling rate is greater than twice the signal frequency.

[0099]

[0100] in, This indicates the rounding up operation;

[0101] Step D4. The base station targets z s Performing a Discrete Fourier Transform on (n') yields the corresponding signal spectrum as follows:

[0102]

[0103] Step D5. The base station sequentially analyzes F(q) in order, first obtains the frequency point value of the first non-zero spectrum value F(q), and then sequentially obtains K-1 frequency points with an amplitude increase exceeding a preset amplitude threshold compared with the adjacent last spectrum value, totaling K frequency points a k , k = 1, 2, …, K

[0104] Step D6. According to the relationship between d k and v k According to the following formula:

[0105]

[0106] The distance estimate between the base station and the sensing target corresponding to the frequency point a k is calculated That is, the distance parameter estimation of the K sensing targets is completed.

[0107] The communication and sensing integrated waveform design and parameter estimation method proposed in the present application is applied in practice. In terms of communication, assuming a total of 1024 subcarriers, the s(t) duration is 2.5x10 -5 seconds, the subcarrier interval is 40 kHz, the bandwidth is 40 MHz, the baseband signal adopts Quadrature Phase Shift Keying (QPSK), there are three non-line-of-sight paths between the communication user and the base station, and the gain amplitude of each path is uniformly distributed in [0, 2π], σ u,1 , σ u,2 and σ u,3 are 60, 100 and 160 respectively, and the corresponding μ value is 160. In terms of sensing, for a single target sensing scenario, the sensing target and the base station are assumed to be 1 km apart, and the base station performs two times oversampling. For a multi-target sensing scenario, the sensing targets and the base station are assumed to be 1 km, 4 km and 5 km apart, and the base station performs six times oversampling, and p is 5. The entire communication demodulation algorithm and sensing ranging algorithm are as follows.

[0108] 1: Input: f u (m), h u (t), g k (t).

[0109] 2: Modulate the baseband signal according to formula (1), calculate s u (t).

[0110] 3: Add a cyclic prefix according to formula (2), calculate the transmitted signal x u (t).

[0111] 4: According to formula (5), the communication user receives the signal to obtain y​u (t”)。

[0112] 5: According to the formula (6), a communication user samples a received signal to obtain y u (n).

[0113] 6: According to the formula (8), baseband demodulation is performed to obtain

[0114] 7: According to the formula (9), a base station receives an echo signal to obtain z(t).

[0115] 8: According to the formula (10), intermediate frequency processing is performed on a received signal to obtain z s (t).

[0116] 9: According to the formula (11), Q-point oversampling is performed on a received signal to obtain z s (n').

[0117] 10: According to the formula (14), Q-point discrete Fourier transform is performed on r s (n) to obtain a time delay spectrum F(q).

[0118] 11: A frequency point of the time delay spectrum is found out, and according to the formula (15), a distance estimate

[0119] 12: Output: and

[0120] Actual application-specific simulation experiment results are shown in Figure 1 , Figure 2 , Figure 3 . Among them, Figure 1 is a simulation experiment result about communication performance in the embodiment of the application. Figure 1 The solid line, the dashed line and the dotted line in the figure respectively represent the bit error rate and the signal-to-noise ratio (SNR) relationship of single carrier QPSK under a single path channel, QPSK-OFDM under a multi-path channel and the waveform designed in the application under a multi-path channel. As shown in Figure 1 , it can be seen that the communication performance of the waveform designed in the application is similar to that of single carrier QPSK under a single path channel, and is rapidly improved with the SNR from 0 to 20 dB, but the SNR requirement is increased by about 6 dB under the same performance. The performance of QPSK-OFDM under a multi-path channel is almost linearly improved from 0 to 20 dB. Before 13 dB, the communication performance of QPSK-OFDM is slightly higher than that of the waveform designed in the application, and after that, the waveform designed in the application is superior to QPSK-OFDM, and the gap is rapidly improved.

[0121] As shown in Figure 2The figure shows the simulation results of the perception algorithm in this embodiment of the invention. The figure is the time delay spectrum for single-target perception when the SNR is 10dB. Figure 2 As shown, a very obvious broadband signal can be seen. Take the first one with an amplitude greater than 1.5 and record its index a. Then, according to equation (15), the distance measurement can be completed.

[0122] like Figure 3 The figure shows the simulation results of the perception algorithm in this embodiment of the invention. The figure shows the time delay spectrum for multi-target perception when the SNR is 10dB. Figure 3 As shown, the signal of the first sensing target among the three sensing targets is independent, while the time delay spectra of the other two sensing targets overlap due to their close proximity. In this case, the three frequency points a with an amplitude increase greater than 4 are selected. k k = 1, 2, 3, 4. Similarly, according to equation (15), the distance measurement of the three sensing targets can be completed.

[0123] Furthermore, the integrated waveform design and parameter estimation method for communication and sensing proposed in this invention has advantages in multi-base station collaborative sensing due to its passive sensing characteristics. Base stations can estimate distance parameters even without knowing the specific baseband information. For collaborative sensing of equilateral triangle base stations in a cellular network architecture, it can combine the location information and received signals of the three base stations to pinpoint the specific location of the sensing target without beamforming angle measurement, achieving both broadband communication and sensing positioning in low-frequency bands such as Sub6G. Specifically, each base station in the equilateral triangle can transmit the waveform designed in this invention at three different times, while the other two base stations receive the echo signal from the sensing target. The sum of the distances between the sensing target and the two base stations is calculated using the aforementioned sensing processing method. Combined with the fixed geometric relationship of the three base stations, the specific coordinates of the sensing target can be determined.

[0124] In summary, this invention proposes an integrated waveform design and parameter estimation method for communication and sensing. By performing multi-carrier frequency conversion modulation on the baseband signal and oversampling at the receiving end, it accomplishes both user communication and target sensing tasks. In terms of communication, this waveform design can adapt to broadband multipath fading channels, ensuring a communication rate almost identical to that of OFDM systems. In terms of sensing, it can perform distance parameter estimation based on the received signal even without knowing the communication symbols, giving the waveform passive sensing capabilities. Furthermore, the design maximizes the accuracy and resolution of sensing parameter estimation while ensuring communication performance.

[0125] In addition, the sensing performance of the designed waveform is related to the symbol duration, the linear frequency change rate and the oversampling rate, the sensing performance can be improved from multiple dimensions, and the noise sensitivity is low; and the passive sensing ability of the designed waveform is suitable for passive sensing scenes such as multi-base station cooperative sensing, and the positioning of the sensing target can be completed in a low frequency band in multi-base station cooperative sensing.

[0126] The embodiments of the present application are described in detail above in combination with the drawings, but the present application is not limited to the above-described embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the purpose of the present application.

Claims

1. A waveform design and parameter estimation method for communication and sensing integration, characterized in that: Wireless communication and target sensing are accomplished by performing the following steps, and target parameters are estimated: Step A. The base station performs multicarrier frequency modulation on the baseband signal to be transmitted to form a time-domain continuous signal for transmission, for user communication and target sensing; Step B. A communication channel model conforming to wideband wireless multipath fading and a sensing channel model mainly with line-of-sight paths are established; Step C. The communication user receives the time-domain continuous signal transmitted by the base station, and demodulates the received signal according to the communication channel model; Step D. The base station oversamples the echo signal of the sensed target, and estimates the parameters of the sensing target according to the sensing channel model. 2.The method of claim 1, wherein: The step A includes the following steps A1 to A2: Step A1. Assuming that the base station serves U communication users and senses K sensing targets, for the u-th communication user, the transmit baseband signal f u (m), m = 1, 2,..., M, is given by the following equation: Design f u (m) corresponding time-domain continuous signal s u (t), where u = 1, 2, …, U, T represents the duration of s u (t), ρ represents a frequency variation parameter corresponding to all subcarriers, and satisfies p is a predetermined positive integer, and j represents an imaginary unit satisfying j 2 = -1. Step A2. Take s u (t) Tail duration is L CP The part is added as a loop prefix to s u The header of (t) constitutes the time-domain continuous signal x after multi-carrier frequency conversion modulation. u (t') is as follows: where L CP is the length of the cyclic prefix. 3.The method of claim 1, wherein: In step B, a communication channel model h of the base station and the u-th communication user in compliance with the wideband wireless multipath fading is established u (t): wherein L u represents the number of paths contained in the communication channel between the base station and the u-th communication user, l = 1, 2,..., L u , δ(t) represents a unit impulse function, α u,l and τ u,l respectively represent the gain and the time delay of the l-th path of the u-th communication user; Establishing a line-of-sight path-based perception channel model g between a base station and the kth perception target k (t): g k (t) = β k δ(t-v k ), where k = 1, 2,..., K, β k and v k denote the gain and delay of the sensing channel, respectively, then the distance between the base station and the kth sensing target is c denotes the speed of light. 4.The method of claim 3, wherein: In the step C, the u-th communication user performs the following steps C1 to C5: Step Cl. Received signal y of the u-th communicating user u (t") is as follows: The u-th communication user samples the received signal y u (t") as follows: Wherein, μ represents the number of discrete sampling points corresponding to the signal prefix, σ u,l represents the sampling point index of the path delay of the base station and the u-th communication user. Step C2. For y u (n), first remove the sampling results of the first μ sampling points, then sequentially select the sampling results of M sampling points, and update y u (n), that is, 1 ≤ n ≤ M; Step C3. The u-th communication user solves the equivalent channel matrix of the communication channel according to the communication channel model wherein, The shift matrix P is given by Step C4. Defining the signal vector and then the two vectors satisfy y u = H u s u , according to s u = H u -1 y u , the communication user can obtain an estimate of the transmitted signal Step C5. The communication user updates the parameters according to the following formula: Obtaining an estimate of a baseband signal m = 1, 2,..., M, which estimate is the demodulated result of the baseband signal.

5. The communication and sensing integrated waveform design and parameter estimation method according to claim 3, characterized in that: The step D includes the following steps D1 to D6: Step D1. The base station receives the echo signals from the K sensing targets and removes the signal prefix, setting the maximum sensing delay as The signal from T s to T is again intercepted and the signal is taken as starting at T s and z(t) is obtained as follows: Step D2. Definition The base station performs intermediate frequency processing on z(t) and combines it with... Multiply to obtain z s (t) is as follows: Step D3. The base station oversamples z s (t) by a preset sampling point number Q greater than M, to obtain z s (n') as follows: And further updated according to the following formula: Wherein, the preset value of Q satisfies the Nyquist theorem, that is, the sampling rate is greater than twice the signal frequency: wherein denotes a rounding up operation; Step D4. The base station performs a discrete Fourier transform on z s (n') to obtain the corresponding signal spectrum as follows: Step D5. The base station sequentially analyzes for F(q), first obtains the frequency point value of the first non-zero spectrum value F(q), and then sequentially obtains K-1 frequency points with an amplitude exceeding a preset amplitude threshold compared with the adjacent last spectrum value, totaling K frequency points a k k = 1, 2, …, K; Step D6. According to d k and v k between the relationships In the following equation: The distance parameter of the K perception targets is estimated k The distance estimation between the corresponding perception targets That is, the distance parameter estimation of the K perception targets is completed.

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