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CN116962119A - High-precision parameter estimation method based on novel digital-analog hybrid precoder - Google Patents

High-precision parameter estimation method based on novel digital-analog hybrid precoder Download PDF

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CN116962119A
CN116962119A CN202310800286.0A CN202310800286A CN116962119A CN 116962119 A CN116962119 A CN 116962119A CN 202310800286 A CN202310800286 A CN 202310800286A CN 116962119 A CN116962119 A CN 116962119A
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radar
steps
received signal
time
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石运梅
杨鸿银
黄逸
唐小伟
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Tongji University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/28—Details of pulse systems
    • G01S7/285—Receivers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/35—Details of non-pulse systems
    • G01S7/352—Receivers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • G01S7/4004—Means for monitoring or calibrating of parts of a radar system
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • G01S7/4004—Means for monitoring or calibrating of parts of a radar system
    • G01S7/4021—Means for monitoring or calibrating of parts of a radar system of receivers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • G01S7/4004—Means for monitoring or calibrating of parts of a radar system
    • G01S7/4026—Antenna boresight
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • G01S7/4004—Means for monitoring or calibrating of parts of a radar system
    • G01S7/4039—Means for monitoring or calibrating of parts of a radar system of sensor or antenna obstruction, e.g. dirt- or ice-coating
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Electromagnetism (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention provides a high-precision parameter estimation method based on a novel digital-analog hybrid precoder, which comprises the steps of firstly considering a sense-through integrated millimeter wave communication system based on orthogonal time-frequency air conditioning to construct an orthogonal time-frequency air conditioning signal model; secondly, constructing a single-antenna radar received signal model and a communication received signal model based on multi-user multi-beam, and obtaining a received signal through time-frequency domain and delay-Doppler domain conversion; a delay-Doppler domain matched filtering structure is designed again, and the reflection signals of each user are separated at a radar end and decomposed into single user angle correction sub-problems; finally, a digital-analog hybrid architecture radar receiver is set, low-dimensional observation data are output, angle error estimation is achieved through a space smoothing technology, and parameter estimation precision and spectrum efficiency of the general sense integrated system are improved. The intelligent network vehicle communication perception system can be used for intelligent network vehicle communication perception integrated application, automatic driving, traffic control and other scenes.

Description

High-precision parameter estimation method based on novel digital-analog hybrid precoder
Technical Field
The invention relates to a high-precision parameter estimation method based on a novel digital-analog hybrid precoder.
Background
Communication perception integrated systems have been widely studied in future millimeter wave or even terahertz communications. With the development of next generation wireless applications, both communication systems and radar systems are seeking more spectrum resources to support gigabit data transmission and estimation of channel state, thus achieving an effective combination of both communication and perception functions has become a research hotspot. The sense-of-general integrated system is considered as an important enabling technology for application to high frequency bands such as millimeter waves, such as automatic driving and traffic control, but the performance of millimeter wave communication is greatly affected by the "pencil-like" beamforming. Although many beam alignment algorithms are proposed, most of the beam alignment algorithms are realized by searching optimal solutions, which causes problems of large computational complexity, pilot frequency overhead and the like, and cannot meet the accurate and timely tracking requirements in a dynamic environment. At the same time, its performance is also affected by the number of discrete search dots.
To improve the performance of ISAC applications, the sparsity of millimeter wave channels may be utilized to expedite beam alignment. Based on this, previous work has proposed estimating complex channel parameters with compressed sensing methods that, while achieving efficient beam alignment, force the assumption that the instantaneous channel is unchanged throughout the beam alignment process. In response to this disadvantage, many beam modification methods are presented for improving communication and perceptual performance, but these methods do not make full use of perceptual information to improve communication performance. Furthermore, these methods are all based on OFDM modulation, which, although particularly advantageous in many applications, is insensitive to doppler frequency offset and the orthogonality of OFDM is no longer true in highly dynamic scenarios.
Based on the above-mentioned problems, orthogonal time-frequency space modulation is proposed to solve the problem, and OTFS modulates data symbols into delay-doppler (DD) domain, so that the data symbols are extended to the whole time-frequency domain and OTFS waveforms can fully utilize the diversity of the whole time-frequency domain, thereby having high reduction capability in a high dynamic environment. Compared with OFDM modulation, OTFS modulation can obtain better error rate in vehicle application, and in addition, the DD domain modulation mechanism of OTFS enables direct separation of data symbols and channel parameters, which perfectly meets the perception requirement of an ISAC system. Based on this characteristic of OTFS, L.Gaudio, M.Kobayashi et al studied parameter estimation in single antenna and MIMO scenarios, respectively, indicating that an ISAC system based on OTFS can achieve improvement in communication rate and parameter estimation accuracy. Quality of service (QoS) based on OTFS-ISAC applications is greatly affected by timely and accurate user state tracking, but most research is focused on all-digital radar receiver architecture. This results in higher energy consumption and hardware costs, and furthermore it is not practical to equip each antenna with a radio frequency link.
Disclosure of Invention
The invention provides a high-precision parameter estimation method based on a novel digital-analog hybrid precoder, which comprises the steps of firstly considering a general sense integrated millimeter wave communication system based on orthogonal time-frequency air conditioning to construct an orthogonal time-frequency air conditioning signal model; secondly, constructing a single-antenna radar received signal model and a communication received signal model based on multi-user multi-beam, and obtaining a received signal through time-frequency domain and delay-Doppler domain conversion; a delay-Doppler domain matched filtering structure is designed again, and the reflection signals of each user are separated at a radar end and decomposed into single user angle correction sub-problems; finally, a digital-analog hybrid architecture radar receiver is set, low-dimensional observation data are output, angle error estimation is achieved through a space smoothing technology, and parameter estimation precision and spectrum efficiency of the general sense integrated system are improved.
The implementation mode is as follows: the method comprises the following steps:
(1) Constructing a signal model based on orthogonal time-frequency space modulation;
(2) Constructing a radar receiving signal model and a communication receiving signal model;
(3) Designing a DD domain matched filtering structure based on the parameter coarse estimation and a digital-analog hybrid architecture;
(4) Based on the low-dimensional observation data output by the digital-analog hybrid structure and the space degree of freedom provided by the large-scale array, a virtual covariance matrix is constructed, and the angle error is estimated by using a space-smoothed MUSIC algorithm, so that the beam correction and the high-precision user state parameter estimation under the dynamic scene are realized.
The implementation mode is as follows: description of the embodiments principle
Taking an OTFS-ISAC system with millimeter wave bands into consideration, constructing an OTFS signal model, a radar received signal model and a communication received signal model, adopting a more practical time delay-Doppler domain matched filtering method, and constructing an OTFS-based parameter estimation model by utilizing the conversion relation between a time frequency domain and a time delay-Doppler domain; a brand new digital-analog hybrid architecture consisting of a phase shift network and an antenna selection matrix is designed and used for outputting low-dimensional observation data; based on the output observation data, the spatial degree of freedom provided by the large-scale array is fully utilized, and the angle correction is realized by using a brand new virtual guide vector and spatial smoothing technology; through the process, the constructed OTFS signal model, radar receiving signal model and communication receiving signal model fully utilize the full time frequency domain characteristic of the OTFS and the communication perception integrated characteristic of the ISAC system, and utilize the mapping relation of signals in the time frequency domain and the time delay-Doppler domain to construct a high-precision parameter estimation model; meanwhile, a digital-analog hybrid precoder (comprising a phase shift network and an antenna selection matrix) with a nested array structure is designed, low-dimensional observation data is output, the spatial degree of freedom provided by the nested array structure is fully utilized, and a MUSIC method based on spatial smoothing is applied to realize high-precision parameter estimation.
The invention focuses on a more practical OTFS-ISAC system, combines with a digital-analog hybrid architecture design, researches the problem of beam correction in a high dynamic environment, and improves the parameter estimation precision and the communication spectrum efficiency of a communication sense integrated system. Based on the acquisition of the initial user state rough estimation, a more practical millimeter wave integrated system based on a digital-analog hybrid architecture is considered. Based on the above, a novel design scheme of a digital-analog hybrid precoder composed of a phase shift network and an antenna selection matrix is provided, and based on a rough estimation result of channel parameters and low-dimensional structured observation data, angle errors are estimated through a spatial smoothing MUSIC algorithm, so that the estimation precision of radar end parameters is further improved. Based on the corrected angle estimation result, a beam correction scheme and a user state update strategy are provided, so that the communication spectrum efficiency and radar parameter estimation precision of the integrated system are improved. The intelligent network vehicle communication perception system can be used for intelligent network vehicle communication perception integrated application, automatic driving, traffic control and other scenes.
Drawings
Fig. 1 illustrates a multi-user beam tracking scenario under an OTFS-based unified system of sense of general.
Fig. 2 is a diagram of a digital-analog hybrid architecture.
FIG. 3 is a graph of the frequency efficiency and the signal to noise ratio of the no-beam gain under the condition of different coarse parameter estimation errors E of multiple users.
Fig. 4 is a multiuser state estimation mean square error diagram.
Fig. 5 is a graph of the number of different sub-array antennas versus frequency efficiency.
Fig. 6 is a graph of the mean square error of different sub-array antenna numbers versus different coarse estimation error angles.
Detailed Description
The method selects the communication system based on OTFS-ISAC to carry out multi-user state estimation and beam correction, and establishes an OTFS signal model, a radar perception model and a communication model by utilizing the diversity of OTFS signals in the whole time-frequency domain and the characteristics of the ISAC system communication and perception integration. Further, a novel digital-analog hybrid architecture consisting of a phase shift network and an antenna selection matrix is proposed. The virtual covariance matrix is constructed by fully utilizing the angle degree of freedom provided by the large-scale array and applying the space smoothing constraint to realize parameter estimation. According to the technical scheme, the wave beam correction and the state parameter estimation with low power consumption, high precision and high frequency spectrum efficiency are realized through the digital-analog hybrid architecture based on the OTFS-ISAC.
Fig. 1 illustrates a multi-user beam tracking scenario under OTFS-based pass-through system: a scene of a considered sense-of-general integrated multi-user beam correction is depicted.
Fig. 2 is a diagram of a digital-analog hybrid architecture: an overall framework and system design for a digital-analog hybrid architecture including a phase shift network and an antenna selection matrix is shown.
FIG. 3 is a graph of the frequency efficiency and the signal to noise ratio of the no-beam gain under the estimation error E of different coarse parameters of multiple users: the spectrum efficiency relation under the estimation errors of different coarse parameters of multiple users is shown, and the result shows that the spectrum efficiency with angle correction is better under the condition of high signal-to-noise ratio, and the gain is more obvious when the error is larger.
Fig. 4 multiuser state estimation mean square error diagram: the simulation results of the state estimation under different coarse parameter errors are depicted, including the mean square error of angle, distance and speed estimation, wherein (a) is multi-user angle mean square error, (b) is distance mean square error, and (c) is speed mean square error.
Fig. 5 is a graph of the number of different sub-array antennas versus frequency efficiency.
Fig. 6 is a graph of the mean square error of different sub-array antenna numbers versus different coarse estimation error angles.
The invention will now be described in detail with reference to the drawings and specific examples.
(1) Constructing a signal model based on orthogonal time-frequency space modulation;
(2) Constructing a radar receiving signal model and a communication receiving signal model;
(3) Designing a DD domain matched filtering structure based on the parameter coarse estimation and a digital-analog hybrid architecture;
(4) Based on the low-dimensional observation data output by the digital-analog hybrid structure and the space degree of freedom provided by the large-scale array, a virtual covariance matrix is constructed, and the angle error is estimated by using a space-smoothed MUSIC algorithm, so that the beam correction and the high-precision user state parameter estimation under the dynamic scene are realized.
Examples
Step 1. Establishment of an orthogonal time-frequency space modulation (OTFS) Signal model
The method comprises the following specific steps:
step (1.1), combining with OTFS-ISAC system scene of millimeter wave frequency band, base station and radar receiver are deployed in system together, wherein the base station has N t With N antennas, radar receiver r An antenna. Assume that the transmitted data symbol is { x } k,l K=0, 1, …, N-1, l=0, 1, …, M-1.N, M denote the number of slots and subcarriers, respectively. Mapping data in a Doppler Delay Domain (DD) to a time-frequency (TF) domain by a two-dimensional inverse-octyl finite Fourier transform, denoted as
Where n=0, 1, …, N-1, m=0, 1, …, M-1.
Step (1.2), converting the time-frequency domain sampling signal into time domain to obtain continuous time signal
Wherein g tx (T), T, Δf respectively represent the transmission impulse shape, single symbol duration and subcarrier frequency difference step (1.3) to obtain a noise-free received signal as
Step (1.4) passing through a matched filter g at the receiving end rx (t), calculating to obtain an output result Y (t, f), and sampling by t=nT, f=mΔf to obtain the following time-frequency domain sampling signals
Wherein H is n,m [n′,m′]Representing channel response in time-frequency domain, in particular
Wherein g tx (t) and g rx The cross-ambiguity function of (t) is defined as
Step (1.5) transforming the time-frequency domain sampling signal Y (n, m) to DD domain by octyl finite Fourier transform to obtain
Wherein g k′,k [l′,l]Represents x k′,l′ And x k,l Is expressed as the intersymbol interference coefficient of (C)Wherein the method comprises the steps ofIs defined as
Step (1.6) by setting g tx (t) and g rx (T) is a rectangular impulse of length T,can be simplified into
Step 2, constructing a radar receiving signal model and a communication receiving signal model
Step 2.1 construction of a radar reception Signal model
The method comprises the following specific steps:
step (2.1.1), as shown in fig. 1, considering a multi-user scenario, the multi-beam time domain OTFS signal transmitted by the base station is represented as
s(t)＝[s 1 (t),s 2 (t),…,s P (t)] T
Wherein s is p (t) carrying data information of p users. Setting wave beam forming matrix at base station end Based on the above-mentioned transmitting end signal processing, the transmitting signal can be expressed as
Step (2.1.2), the reflected signal received at the radar is
Wherein the method comprises the steps of
θ p Representing the angle between p users and the base station;
β p ,respectively representing radar reflection coefficient, loop propagation delay and Doppler frequency offset.
Step (2.1.3) of applying a mixing matrixAdding noise z (t) and mapping to DD domain by SFFT conversion, the received signal can be expressed as
Wherein the method comprises the steps of
And G k′,k [l′,l]Representing additive gaussian noise vectors and vector interference parameters in the DD domain, specifically expressed as
Wherein the method comprises the steps of
Step (2.1.4), considering millimeter wave large-scale multiple-input multiple-output scene, utilizing orthogonal characteristic of steering vector and precoding matrix, the received signal can be finally expressed as
Wherein the method comprises the steps of Representing the transmit beamforming gain of p users;
for loop delay and Doppler shift pairs>A function.
Step 2.2 communication received Signal model construction
The method comprises the following specific steps:
step (2.2.1), consider a downlink communication model, assuming that all users are spatially separated and equipped with a single antenna. The impulse response of p users under a line-of-sight time-varying channel can be modeled as
α p ,τ p And v p Representing channel attenuation parameters, propagation delay and doppler spread, respectively.
Step (2.2.2), based on the channel model, the received signal of p-user can be expressed as
Where w (t) represents additive noise conforming to a gaussian distribution.
Step (2.2.3), similar to the radar received signal model, after matched filtering and SFFT transformation, the received signal of p user can be expressed as
Wherein the method comprises the steps ofAnd->Representing the corresponding channel gain and noise in the DD domain.
Step 3, DD domain matched filtering structure based on parameter coarse estimation and digital-analog mixed architecture design
Step 3.1DD domain matched filter structure design
The method comprises the following specific steps:
step (3.1.1) based on time delayAnd Doppler shift->A more realistic DD domain matched filter structure is considered, equivalently converting the original parameter correction problem into a plurality of sub-problems.
Step (3.1.2) with N RF X MN size matrix type rewriting receiving signal
Wherein the method comprises the steps ofCan be regarded as x p Delay and doppler shift filtered data symbols; />Is DD domain noise.
Step (3.1.3) based on the given time delayAnd Doppler shift->Is able to pass the radar through a matched filter +.>Decomposing the reflected signal of p-users, expressed as
Wherein, defineAnd G is t,p Representing the signal to noise ratio gain of an ideal matched filter. η (eta) p And the cross correction term representing the estimation parameter true value and the corresponding rough estimation.
Step 3.2 design of nested array structure digital-analog hybrid architecture
The method comprises the following specific steps:
step (3.2.1) for fine tuning the angle estimation of p-usersA novel digital-analog hybrid radar receiver is provided, wherein the analog network has the following special structure
Wherein the method comprises the steps ofIs a diagonal matrix, is composed of adjustable phase offsets, wherein N r Indicating the number of antennas, the nth r The individual elements are-> Fixed setting for filters for taking radar observations from N r Reduced to N RF . The architecture of the hybrid matrix is shown in fig. 2.
Step (3.2.2), based on the above arrangement, the received signal at the radar side can be expressed as
Step (3.2.3) of using a diagonal matrixThe direction of the offset p user beam points in a particular direction
Step (3.2.4), settingWherein phi is p Representing the angle of incidence of the received signal and defining an angle estimation error +.>Is available in the form of
Step (3.2.5), since e is typically very small, is obtained by Taylor expansion approximation (a) and a small approximation (b) for cos (e) and sin (e)
Step (3.2.6), so that the received steering vector of p users after passing through the phase shift network is Wherein-> Based on this, a fine-tuned angle estimate can be obtained>Is that
Step (3.2.7) of obtaining an angle estimation error termBased on the nested array we construct the antenna selection matrix W such that the measured output values produce an imaginary double-layer nested array structure. As shown in the following figure (3), a digital-analog hybrid architecture network of the proposed nested array architecture is depicted. Thus, N is RF The received signal represented by the x MN matrix is
Step (3.2.8), N RF The radio frequency chains are divided into N R F 1 And N RF2 An inner group and an outer group, and N is selected from all antenna elements RF2 (N RF1 +1) non-repeating subarrays, each group containing n sub Antenna elements, get Further, Y r,p Can be expressed as covariance matrix of (2)
Wherein the method comprises the steps of
Step (3.2.9) ofGiven b (phi) p ) Can obtain +.>The first and second parts of (a) are
Thus, the first and second substrates are bonded together,can be expressed as
Wherein the method comprises the steps of
A step (3.2.10) of based on the small-scale process, the limited antenna number condition, andwith a characteristic generally greater than 1, radar can obtain +.>Based on the aboveIn principle, the covariance matrix can be rewritten as
Wherein the method comprises the steps of
And 4, constructing a virtual covariance matrix based on the low-dimensional observation data output by the digital-analog hybrid structure and the spatial degree of freedom provided by the large-scale array, and estimating an angle error by using a MUSIC algorithm with spatial smoothing, so as to realize beam correction and high-precision user state parameter estimation in a dynamic scene.
The method comprises the following specific steps:
step (4.1), fully utilizing the increased spatial degrees of freedom (DOFs) of the nested arrays, improving the accuracy of angle estimation based on a differential co-array technology, and setting N RF1 ＝N RF2 ＝N RF And vectorizing covariance matrix R, expressed as
Wherein the method comprises the steps ofCan be treated as +.>Virtual steering vectors of dimensions.
Step (4.2), constructing a new oneVector b of orientation of dimensions eff (φ p ) For removing the remaining repeat items, thus remaining +.>Determining that items can be classified, wherein-> Based on this, the corresponding element is removed from the vectorized covariance matrix and b is selected in the same way eff (ψ p ) The observation vector is
Wherein e eff Is divided byThe term is an all 0 vector of 1.
Step (4.3), splitting the observation vector r eff Is thatA subset of (i) th, where the i-th subset is expressed as
Wherein r is eff,i And b eff,i (ψ p ) Respectively r eff And b eff (ψ p ) From the firstTo the firstSlicing of items, e eff,i Is a vector in which all elements except the ith element is 1 are 0.
Step (4.4), definitionSolving the average value to construct a space smoothing matrix
And (4.5) combining the obtained space smoothing matrix, and calculating the angle estimation error of the p users by applying a MUSIC algorithm based on subspaces.

Claims (6)

1. A high-precision parameter estimation method based on a novel digital-analog hybrid precoder is characterized in that firstly, a general sense integrated millimeter wave communication system based on orthogonal time-frequency air conditioning is considered to construct an orthogonal time-frequency air conditioning signal model; secondly, constructing a single-antenna radar received signal model and a communication received signal model based on multi-user multi-beam, and obtaining a received signal through time-frequency domain and delay-Doppler domain conversion; a delay-Doppler domain matched filtering structure is designed again, and the reflection signals of each user are separated at a radar end and decomposed into single user angle correction sub-problems; finally, a digital-analog hybrid architecture radar receiver is set, low-dimensional observation data are output, angle error estimation is achieved through a space smoothing technology, and parameter estimation precision and spectrum efficiency of the general sense integrated system are improved.
2. The method of claim 1, comprising the steps of:
step (1) constructing a signal model based on orthogonal time-frequency space modulation;
step (2) constructing a radar receiving signal model and a communication receiving signal model;
step (3) designing a DD domain matched filtering structure based on the parameter coarse estimation and a digital-analog hybrid architecture;
and (4) constructing a virtual covariance matrix based on the low-dimensional observation data output by the digital-analog hybrid structure and the spatial degree of freedom provided by the large-scale array, and estimating an angle error by using a space-smoothed MUSIC algorithm, so that beam correction and high-precision user state parameter estimation under a dynamic scene are realized.
3. The method of claim 2, wherein the step (1) includes:
step (1.1), combining with OTFS-ISAC system scene of millimeter wave frequency band, base station and radar receiver are deployed in system together, wherein the base station has N t With N antennas, radar receiver r An antenna; assume that the transmitted data symbol is { x } k,l K=0, 1, …, N-1, l=0, 1, …, M-1; n, M represent the number of time slots and subcarriers respectively; mapping data in a Doppler Delay Domain (DD) to a time-frequency (TF) domain by a two-dimensional inverse-octyl finite Fourier transform, denoted as
Where n=0, 1, …, N-1, m=0, 1, …, M-1;
step (1.2), converting the time-frequency domain sampling signal into time domain to obtain continuous time signal
Wherein g tx (T), T, Δf respectively represent the transmission impulse shape, single symbol duration and subcarrier frequency difference step (1.3) to obtain a noise-free received signal as
Step (1.4) passing through a matched filter g at the receiving end rx (t), calculating to obtain an output result Y (t, f), and sampling by t=nT, f=mΔf to obtain the following time-frequency domain sampling signals
Wherein H is n,m [n′,m′]Representing channel response in time-frequency domain, in particular
Wherein g tx (t) and g rx The cross-ambiguity function of (t) is defined as
Step (1.5) transforming the time-frequency domain sampling signal Y (n, m) to DD domain by octyl finite Fourier transform to obtain
Wherein g k′,k [l′,l]Represents x k′,l′ And x k,l Is expressed as the intersymbol interference coefficient of (C)Wherein the method comprises the steps ofIs defined as
Step (1.6) by setting g tx (t) and g rx (T) is a rectangular impulse of length T,is simplified into
4. The method according to claim 2, wherein the step (2) includes:
the radar receiving signal model is constructed in the step (2.1), and the specific steps are as follows:
step (2.1.1), considering a multi-user scenario, the multi-beam time domain OTFS signal transmitted by the base station is expressed as
s(t)＝[s 1 (t)，s 2 (t)，...，s P (t)] T
Wherein s is p (t) carrying data information of p users; setting wave beam forming matrix at base station end Based on the above-mentioned transmitting end signal processing, the transmitting signal is expressed as
Step (2.1.2), the reflected signal received at the radar is
Wherein the method comprises the steps of
θ p Representing the angle between p users and the base station;
respectively representing radar reflection coefficients, loop propagation delay and Doppler frequency offset;
step (2.1.3) of applying a mixing matrixAdding noise z (t) and mapping to DD domain by SFFT conversion, and then the received signal is expressed as
Wherein the method comprises the steps of
And G k′,k [l′,l]Representing additive gaussian noise vectors and vector interference parameters in the DD domain, specifically expressed as
Wherein the method comprises the steps of
Step (2.1.4), taking millimeter wave large-scale multiple-input multiple-output scene into consideration, and using orthogonal characteristics of a steering vector and a precoding matrix, the received signal is expressed as
Wherein the method comprises the steps of Representing the transmit beamforming gain of p users;
for loop delay and Doppler shift pairs>A function;
step (2.2) of constructing a communication receiving signal model, which comprises the following specific steps:
step (2.2.1), modeling the impulse response of p users in a line-of-sight time-varying channel as if they were, taking into account the downlink communication model, assuming that all users are spatially separated and equipped with a single antenna
α p ,τ p And v p Respectively representing channel attenuation parameters, propagation delay and Doppler spread;
step (2.2.2), based on the channel model, the received signal of p user is expressed as
Wherein w (t) represents additive noise conforming to a gaussian distribution;
step (2.2.3), similar to the radar received signal model, the received signal of p user after SFFT conversion is expressed as
Wherein the method comprises the steps ofAnd->Representing the corresponding channel gain and noise in the DD domain.
5. The method according to claim 2, wherein the step (3) includes:
step (3.1) DD domain matched filtering structure design, which comprises the following specific steps:
step (3.1.1) based on time delayAnd Doppler shift->Taking into account a DD domain matched filter structure, equivalently converting an original parameter correction problem into a plurality of sub-problems;
step (3.1.2) with N RF X MN size matrix type rewriting receiving signal
Wherein the method comprises the steps ofIs regarded as x p Delay and doppler shift filtered data symbols; />Is DD domain noise;
step (3.1.3) based on the given time delayAnd Doppler shift->Is a rough estimate of (1), the radar can pass through a matched filterDecomposing the reflected signal of p-users, expressed as
Wherein, defineAnd G is t,p Representing the signal to noise ratio gain of an ideal matched filter; η (eta) p A cross correction term representing the true value of the estimated parameter and the corresponding rough estimation;
step (3.2) design of a nested array structure digital-analog hybrid architecture, which comprises the following specific steps:
step (3.2.1) for fine tuning the angle estimation of p-usersA novel digital-analog hybrid radar receiver is provided, wherein the analog network has the following special structure
Wherein the method comprises the steps ofIs a diagonal matrix, is composed of adjustable phase offsets, wherein N r Indicating the number of antennas, the nth r The individual elements are-> Fixed setting for filters for taking radar observations from N r Reduced to N RF ；
Step (3.2.2) of representing the received signal at the radar end as based on the above arrangement
Step (3.2.3) of using a diagonal matrixThe direction of the offset p user beam points in a particular direction
Step (3.2.4), settingWherein phi is p Representing the angle of incidence of the received signal and defining an angle estimation error +.>Obtaining the product
Step (3.2.5) of obtaining the Taylor expansion approximation (a) and the small approximation (b) for cos (E) and sin (E)
Step (3.2.6), the received steering vector of p users after passing through the phase shift network is Wherein-> Obtaining a fine-tuned angle estimate +.>Is that
Step (3.2.7) of obtaining an angle estimation error termBased on the nested array, an antenna selection matrix W is constructed so that a measurement output value generates an imaginary double-layer nested array structure; by N RF The received signal represented by the x MN matrix is
Step (3.2.8), N RF The radio frequency chains are divided into N RF1 And N RF2 An inner group and an outer group, and N is selected from all antenna elements RF2 (N RF1 +1) non-repeating subarrays, each group containing n sub Antenna elements, get Further, Y r,p Is expressed as covariance matrix of (2)
Wherein the method comprises the steps of
Step (3.2.9) ofGiven b (phi) p ) Respectively obtain->The first and second parts of (a) are
Represented as
Wherein the method comprises the steps of
A step (3.2.10) of based on the small-scale process, the limited antenna number condition, andcharacteristic of generally greater than 1, radar obtains +.>Is rewritten as the signal-to-noise ratio gain of (2)
Wherein the method comprises the steps of
6. The method according to claim 1, wherein the step (4) specifically includes:
step (4.1), fully utilizing the increased spatial degrees of freedom (DOFs) of the nested arrays, improving the accuracy of angle estimation based on a differential co-array technology, and setting N RF1 ＝N RF2 ＝N RF And vectorizing covariance matrix R, expressed as
Wherein the method comprises the steps ofTreated as +.>Virtual steering vectors of dimensions;
step (4.2), constructing a new oneVector b of orientation of dimensions eff (φ p ) For removing the remaining repeat items, thus remaining +.>Determining that items can be classified, wherein-> Removing the corresponding element from the vectorized covariance matrix and selecting b in the same way eff (ψ p ) The observation vector is
Wherein e eff Is divided byAll 0 vectors with entries 1;
step (4.3), splitting the observation vector r eff Is thatA subset of (i) th, where the i-th subset is expressed as
Wherein r is eff,i And b eff,i (ψ p ) Respectively r eff And b eff (ψ p ) From the firstTo the firstSlicing of items, e eff,i Is a vector in which all elements except the ith element is 1 are 0;
step (4.4), definitionSolving the average value to construct a space smoothing matrix
And (4.5) combining the obtained space smoothing matrix, and calculating the angle estimation error of the p users by applying a MUSIC algorithm based on subspaces.
CN202310800286.0A 2023-06-30 2023-06-30 High-precision parameter estimation method based on novel digital-analog hybrid precoder Pending CN116962119A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117872264A (en) * 2023-12-07 2024-04-12 南方科技大学 Vehicle positioning method, device, electronic equipment and computer readable storage medium

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117872264A (en) * 2023-12-07 2024-04-12 南方科技大学 Vehicle positioning method, device, electronic equipment and computer readable storage medium

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