WO2024082475A1 - 基于毫米波通感一体化无人设备的自主导航系统及方法 - Google Patents
基于毫米波通感一体化无人设备的自主导航系统及方法 Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/024—Guidance services
Definitions
- the present invention belongs to the technical field of unmanned system control and mobile communication, and mainly relates to an autonomous navigation system and method based on millimeter wave synaesthesia integrated unmanned equipment.
- GPS global positioning system
- the present invention is aimed at the problem that the real-time high-precision positioning of unmanned systems in the prior art is still defective, and provides an autonomous navigation system and method based on millimeter-wave synaesthesia integrated unmanned equipment, including at least one intelligent unmanned system and at least one mobile communication base station.
- the mobile communication base station sends signals to all users, and the signals include but are not limited to millimeter-wave signals; the intelligent unmanned system is arranged in the unmanned equipment, and at least includes an inertial navigation positioning system; the intelligent unmanned system receives the millimeter-wave signals from the mobile communication base station in real time, While decoding the base station identity information and location information in the signal frame header, the relative position between the intelligent unmanned system and the base station is estimated based on the signal's departure angle, arrival angle and arrival delay. Based on this position, the accumulated error of its own inertial navigation positioning system is corrected, and its own position is determined based on inertial navigation positioning and millimeter wave positioning to achieve autonomous navigation.
- the present invention enables the unmanned system to utilize millimeter wave signals in free space to achieve high-precision positioning and autonomous navigation without affecting the mobile communication system.
- the technical solution adopted by the present invention is: an autonomous navigation system based on millimeter wave synaesthesia integrated unmanned equipment, including at least one intelligent unmanned system and at least one mobile communication base station,
- the mobile communication base station sends signals to all users, including but not limited to millimeter wave signals;
- the intelligent unmanned system is arranged in the unmanned equipment and at least includes an inertial navigation positioning system; the intelligent unmanned system receives the millimeter wave signal from the mobile communication base station in real time, decodes the base station identity information and position information in the signal frame header, and estimates the relative position between the intelligent unmanned system and the base station according to the signal's departure angle, arrival angle and arrival delay. Based on this position, the accumulated error of its own inertial navigation positioning system is corrected, and its own position is determined according to the inertial navigation positioning and millimeter wave positioning to achieve autonomous navigation.
- the intelligent unmanned system includes unmanned equipment, a power system, a millimeter wave sensing system and an inertial navigation system.
- the power system is used to provide power support for the intelligent unmanned system, including a motor, a battery, a power amplifier module and a mobile control module.
- the unmanned equipment is connected to the motor, the battery is connected to the motor and the power amplifier module respectively, and the power amplifier module is connected to the mobile control module;
- the millimeter wave sensing system is used to receive and process the millimeter wave signals of the mobile communication base station and feedback navigation correction, including a millimeter wave signal receiving module, a millimeter wave signal processing module and a navigation correction module, and the millimeter wave signal processing module is respectively connected to the millimeter wave signal receiving module and the navigation correction module.
- the inertial navigation positioning system includes an inertial navigation module, an inertial navigation information processing module and an inertial navigation positioning module, and the inertial navigation information processing module is respectively connected to the inertial navigation module and the inertial navigation positioning module, and the inertial navigation module is also respectively connected to the mobile control module and the navigation correction module.
- the unmanned equipment includes but is not limited to a drone, an unmanned vehicle or an intelligent robot.
- the mobile communication base station is composed of a radio frequency communication transmitting antenna, a communication signal processing system and a radio frequency communication receiving antenna.
- the communication signal processing system is respectively connected to the radio frequency communication transmitting antenna and the radio frequency communication receiving antenna, and the communication signal processing system is also connected to a gateway.
- the intelligent unmanned system receives information broadcast by the sensor and estimates its own position information based on the sensor's identity information, position information, and the direction and delay of the radio frequency signal.
- a threshold is set for the cumulative error in the inertial navigation positioning system.
- the intelligent unmanned system stops running, and the cumulative error is positively correlated with time.
- an autonomous navigation method based on millimeter wave synaesthesia integrated unmanned equipment comprising the following steps:
- the intelligent unmanned system starts from the initial point and obtains the coordinates of the initial point.
- the inertial navigation positioning system monitors and calculates the direction, speed and attitude information of the intelligent unmanned system during movement, compares it with the position of the initial point, and guides the positioning and navigation of the intelligent unmanned system.
- the intelligent unmanned system jointly processes the millimeter wave signal to extract the channel state information, and uses the radio frequency positioning algorithm to extract at least the departure angle, arrival angle and delay information of the millimeter wave signal from the channel state information, thereby estimating the relative position information of the mobile communication base station relative to the intelligent unmanned system;
- step S3 the MUS I C radio frequency positioning algorithm is used to extract the departure angle, arrival angle and delay information of the millimeter wave signal from the channel state information, and the step of calculating the arrival angle using the MUS I C algorithm specifically includes:
- S34 Calculate the spectrum function and obtain the estimated value of the arrival angle by finding the peak value.
- the spatial spectrum function will obtain an extreme point. Based on the extreme point, the arrival angle of the signal is obtained.
- the intelligent unmanned system can receive information broadcast by sensors in the environment, and estimate its own location based on the identity information and location of the sensors, and the direction and delay of the radio frequency signals.
- the present invention can realize the autonomous navigation of the intelligent unmanned aerial vehicle system without the need for human remote control and route correction.
- the combined work of the inertial navigation positioning module and the millimeter wave sensing system can enable the intelligent unmanned aerial vehicle system to reach the destination accurately and at high speed.
- the present invention is based on the existing mobile communication system, does not require the construction of additional communication links and does not affect the existing communication system. It has low cost, high feasibility and reliability. Specifically:
- the present invention is based on the existing radio frequency mobile communication system, with high signal transmission rate and low bit error rate.
- the intelligent unmanned system only partially decodes the millimeter wave signal in the free space without the need to build a new communication link, which greatly reduces the cost and improves the reliability.
- the intelligent unmanned system extracts the arrival angle and signal delay from the millimeter wave signal, thereby performing high-precision positioning and correction of the inertial navigation system, which has higher accuracy than existing GPS positioning technologies.
- the system in the invention can realize more functions by adding new modules to the intelligent unmanned system on the basis of normal operation of the communication link, and has good scalability;
- FIG1 is a schematic diagram of a scene of an autonomous navigation method based on millimeter wave synaesthesia integrated unmanned equipment according to the present invention
- FIG. 2 is a block diagram of an intelligent unmanned system in an autonomous navigation system of the present invention
- FIG3 is a structural block diagram of a mobile communication base station in the autonomous navigation system of the present invention.
- the autonomous navigation system based on the millimeter wave synaesthesia integrated unmanned equipment includes one or more intelligent unmanned systems and one or more mobile communication base stations, wherein the mobile communication base station is composed of a radio frequency communication transmitting antenna, a communication signal processing system and a radio frequency communication receiving antenna, as shown in FIG3.
- the radio frequency communication transmitting antenna is connected to the communication signal processing system
- the communication signal processing system is connected to the radio frequency communication receiving antenna
- the communication signal processing system is also connected to the gateway.
- the mobile communication base station sends signals to all users, not just to the intelligent unmanned system service.
- the signals include but are not limited to millimeter wave signals. Therefore, the intelligent unmanned system only uses the millimeter wave signals in the environment for positioning and autonomous navigation.
- the intelligent unmanned system includes unmanned equipment, a power system, a millimeter wave sensing system and an inertial navigation positioning system. As shown in FIG2 , the intelligent unmanned system can be a series of unmanned systems such as drones, unmanned vehicles, and intelligent robots.
- the power system is used to provide power support for the intelligent unmanned system, including a motor, a battery, a power amplifier module, and a mobile control module.
- the unmanned equipment is connected to the motor, and the battery is connected to the motor and the power amplifier module respectively, and the power amplifier module is connected to the mobile control module.
- the millimeter wave sensing system is used to receive and process the millimeter wave signal of the mobile communication base station and feedback navigation correction, including a millimeter wave signal receiving module, a millimeter wave signal processing module, and a navigation correction module.
- the millimeter wave signal processing module is respectively connected to the millimeter wave signal receiving module and the navigation correction module.
- the inertial navigation positioning system includes an inertial navigation module, an inertial navigation information processing module, and an inertial navigation positioning module.
- the inertial navigation information processing module is respectively connected to the inertial navigation module and the inertial navigation positioning module.
- the inertial navigation module is also respectively connected to the mobile control module and the navigation correction module.
- the intelligent unmanned system receives millimeter wave signals from mobile communication base stations in real time, decodes the base station identity information and location information in the signal frame header, and estimates the relative position between the intelligent unmanned system and the base station based on the signal's departure angle, arrival angle and arrival delay. Based on this position, it corrects the accumulated error of its own inertial navigation system, and determines its own position based on inertial navigation positioning and millimeter wave positioning to achieve autonomous navigation.
- a threshold value can be set for the cumulative error e(t).
- e(t) reaches the threshold value, the intelligent unmanned system will deviate from the original route and arrive at the wrong location. Therefore, the intelligent unmanned system can be set to stop running at this time to prevent further deviation from the route.
- the e(t) is positively correlated with time.
- the autonomous navigation system of this embodiment can realize the autonomous navigation of the intelligent unmanned system without the need for human remote control and route correction.
- the existing mobile communication system there is no need to build additional communication links and does not affect the existing communication system. It has low cost, high feasibility and reliability, enabling the intelligent unmanned system to reach the destination accurately and at a high speed, solving the problem of defects in the real-time high-precision positioning of unmanned systems in the existing technology. It is highly practical and is an emerging autonomous navigation product in the future.
- the autonomous navigation method based on millimeter wave synaesthesia integrated unmanned equipment includes the following steps:
- Step S1 The intelligent unmanned system starts from the initial point, the coordinates of which are known.
- the intelligent unmanned system relies on its own inertial navigation positioning system to monitor and calculate the direction, speed, posture and other information of the intelligent unmanned system during movement, and compares it with the position of the initial point, thereby achieving preliminary positioning and navigation of the intelligent unmanned system.
- the inertial navigation positioning system has a cumulative error e(t).
- a threshold is set for the cumulative error e(t).
- e(t) is positively correlated with time. When e(t) reaches a certain threshold, the intelligent unmanned system will deviate from the original route and arrive at the wrong location.
- Step S2 The intelligent unmanned system collects millimeter wave signals in free space, decodes the signals, obtains the base station identity information and location information in the signal frame header, and decodes the signals from different base stations. The information is categorized and stored for further processing.
- Step S3 Process the signal information from the same base station within a period of time to extract the channel state information (CSI), and then use the radio frequency positioning algorithm to extract the millimeter wave signal's departure angle, arrival angle, delay and other information from the CSI, so as to estimate the base station's position relative to the intelligent unmanned system.
- CSI channel state information
- the algorithm steps for extracting information such as angle of arrival (AoA) from CSI through the MUSIC RF positioning algorithm are as follows:
- the eigenvector corresponding to the largest eigenvalue equal to the number of signals is regarded as the signal subspace, and the eigenvectors corresponding to the remaining eigenvalues are regarded as the noise subspace.
- the correlation matrix of the received signal can be expressed as R X , and its expression is
- RS is the covariance matrix of the signal.
- A is the direction matrix, which is a Vandermonde matrix.
- the covariance matrix is a Hermitian matrix
- the eigenvalues of the matrix are all real numbers, and since the eigenvalues of the covariance matrix are all greater than 0, that is, the matrix is positive definite, it can be inferred that RS is semi-positive definite.
- the rank of ASA H is n
- the number of eigenvalues and eigenvectors corresponding to the signal space is n, which are ⁇ 1 , ⁇ 2 , ⁇ 3 ,..., ⁇ n ⁇ and ⁇ v 1 ,v 2 ,v 3 ,...,v n ⁇ respectively
- Mn the number of eigenvalues and eigenvectors corresponding to the noise space
- ⁇ n+1 , ⁇ n+2 , ⁇ n+3 ,..., ⁇ M ⁇ is the smallest set of eigenvalues among all the eigenvalues of the covariance matrix, and it approaches zero, that is
- ⁇ ( ⁇ ) is the phase difference matrix of the receiving antenna array. From the definition of the spatial spectrum function, it can be seen that when ⁇ ( ⁇ ) and En are orthogonal, P( ⁇ ) will have a spectrum peak, and the ⁇ value corresponding to the spectrum peak is the estimated value of the signal arrival direction.
- Step S4 Comprehensively analyze the positioning information obtained by the inertial navigation system and the millimeter wave sensing system, estimate the accurate position of the current intelligent unmanned system, and then the navigation correction module in the millimeter wave sensing system performs error correction on the inertial navigation module of the intelligent unmanned system.
- the reason is that millimeter wave positioning also has certain errors, and the errors are different depending on the channel conditions.
- the intelligent unmanned system needs to calculate the positioning confidence interval of the inertial navigation system and the positioning confidence interval of the millimeter wave sensing system according to the actual situation, and jointly analyze to obtain a more accurate positioning result as the correction basis for the inertial navigation module.
- intelligent unmanned systems can also receive information broadcast by sensors in the environment and estimate their own location based on the sensor's identity information, location, direction and delay of the radio frequency signal.
- Step S5 Determine its own position based on inertial navigation positioning and millimeter wave positioning to achieve autonomous navigation.
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Abstract
本发明公开了一种基于毫米波通感一体化无人设备的自主导航系统及方法，包括至少一个智能无人系统和至少一个移动通信基站，所述移动通信基站面向所有用户发送信号，信号包括但不限于毫米波信号；所述智能无人系统设于无人设备中，至少包括惯导定位系统；智能无人系统实时接收来自移动通信基站的毫米波信号，解码信号帧头中基站身份信息和位置信息的同时，根据信号的出射角、到达角和到达时延，估计出智能无人系统与基站间的相对位置，依据此位置，对自身惯导定位系统的累积误差进行校正，并且根据惯导定位和毫米波定位，确定自身位置，实现自主导航，充分利用自由空间中的毫米波信号，在不影响移动通信系统的前提下，实现高精度定位和自主导航。
Description
本发明属于无人系统控制和移动通信的技术领域，主要涉及了一种基于毫米波通感一体化无人设备的自主导航系统及方法。
随着无人机、无人车、智能机器人等智能无人系统应用场景的拓展，人们对于无人系统的智能化需求不断提升。传统无人系统需要人为遥控，并且操作者需要实时观察无人系统的位置，来调整无人系统的移动轨迹。惯导系统的出现，使无人系统有了自主导航的能力。高精度的惯导系统可以实时记录无人系统的运行状态，从而绘制无人系统的运行路线进而实现对无人系统的实时定位。但是惯导系统存在累积误差，随着无人系统的移动，该误差值会不断增加。现有的研究中，人们会在无人系统中安装全球定位系统(GPS)来修正惯导系统的误差，但是在单个无人系统里安装GPS，增加了无人系统的成本。此外，GPS的定位精度较低，无法满足无人系统实时高精度定位的需求。
在现有的毫米波移动通信系统中，由于大规模多发多收系统(Mass iveMI MO)的应用，以及毫米波波束具有一定的指向性，通信感知一体化成为了研究热点。随着物联网技术的发展，被放置各个场景中的小型传感器也被纳入了毫米波移动通信系统中，而这些技术都为无人系统的自动化控制和导航提供了客观条件。
发明内容
本发明正是针对现有技术中无人系统实时高精度定位尚有缺陷的问题，提供一种基于毫米波通感一体化无人设备的自主导航系统及方法，包括至少一个智能无人系统和至少一个移动通信基站，所述移动通信基站面向所有用户发送信号，信号包括但不限于毫米波信号；所述智能无人系统设于无人设备中，至少包括惯导定位系统；智能无人系统实时接收来自移动通信基站的毫米波信号，
解码信号帧头中基站身份信息和位置信息的同时，根据信号的出射角、到达角和到达时延，估计出智能无人系统与基站间的相对位置，依据此位置，对自身惯导定位系统的累积误差进行校正，并且根据惯导定位和毫米波定位，确定自身位置，实现自主导航。本发明使无人系统可以利用自由空间中的毫米波信号，在不影响移动通信系统的前提下，实现高精度定位和自主导航。
为了实现上述目的，本发明采取的技术方案是：基于毫米波通感一体化无人设备的自主导航系统，包括至少一个智能无人系统和至少一个移动通信基站，
所述移动通信基站面向所有用户发送信号，所述信号包括但不限于毫米波信号；
所述智能无人系统设于无人设备中，至少包括惯导定位系统；智能无人系统实时接收来自移动通信基站的毫米波信号，解码信号帧头中基站身份信息和位置信息的同时，根据信号的出射角、到达角和到达时延，估计出智能无人系统与基站间的相对位置，依据此位置，对自身惯导定位系统的累积误差进行校正，并且根据惯导定位和毫米波定位，确定自身位置，实现自主导航。
作为本发明的一种改进，所述智能无人系统包括无人设备、动力系统、毫米波感知系统和惯导系统，
所述动力系统用于为智能无人系统提供动力支持，包括电机、电池、功放模块及移动控制模块，所述无人设备与电机相连接，电池分别与电机和功放模块相连接，功放模块与移动控制模块相连接；
所述毫米波感知系统用于接收和处理移动通信基站的毫米波信号，并反馈导航校正，包括毫米波信号接收模块、毫米波信号处理模块及导航校正模块，所述毫米波信号处理模块分别与毫米波信号接收模块和导航校正模块相连接。所述惯导定位系统包括惯导导航模块、惯导信息处理模块及惯导定位模块，所述惯导信息处理模块分别与惯导导航模块和惯导定位模块相连接，惯导导航模块还分别与移动控制模块和导航校正模块相连接。
作为本发明的一种改进，所述无人设备包括但不限于无人机、无人车或智能机器人。
作为本发明的一种改进，所述移动通信基站由射频通信发射天线、通信信号处理系统及射频通信接收天线组成，所述通信信号处理系统分别与射频通信发射天线和射频通信接收天线相连接，通信信号处理系统还连接到网关。
作为本发明的又一种改进，还包括传感器，所述智能无人系统接收传感器广播中的信息，根据传感器的身份信息、位置信息、射频信号的方向和时延，估测自身位置信息。
作为本发明的又一种改进，对惯导定位系统中的累积误差设定阙值，当到达阙值时，智能无人系统停止运行，所述累积误差与时间呈正相关。
为了实现上述目的，本发明还采取的技术方案是：基于毫米波通感一体化无人设备的自主导航方法，包括如下步骤：
S1：智能无人系统从初始点出发，获取初始点坐标，惯导定位系统通过监测和计算智能无人系统在移动中的行进方向、速率和姿态信息，与初始点位置相比对，指导智能无人系统的定位和导航；
S2：智能无人系统行进过程中，收集移动通信基站在自由空间中发出的毫米波信号，并对信号进行解码，获取毫米波信号帧头中的基站身份信息和位置信息；
S3：智能无人系统对毫米波信号进行联合处理，提取出信道状态信息，利用射频定位算法，从信道状态信息中至少提取出毫米波信号的出射角、到达角和时延信息，从而估计出移动通信基站相对于智能无人系统的相对位置信息；
S4：综合分析惯导定位系统和毫米波感知系统获取到的定位信息，估计出当前智能无人系统的准确位置，然后由毫米波感知系统中的导航校正模块，对智能无人系统的惯导导航模块进行误差校正
作为本发明的一种改进，所述步骤S3中，利用MUS I C射频定位算法，从信道状态信息中提取出毫米波信号的出射角、到达角和时延信息，所述利用MUS I C算法计算到达角的步骤具体包括：
S31：计算不同时域的多个接收信号向量的协方差矩阵；
S32：对协方差矩阵进行特征值分解；
S33：按特征值的大小顺序，把与信号个数相等的最大特征值对应的特征向量看成信号子空间，把剩下的特征值对应的特征向量看成噪声子空间；
S34：计算谱函数，通过寻求峰值来得到到达角的估计值，对信号到达角进行遍历搜索时，当搜索到的角度与某个信号的到达角相同时，空间谱函数就会得到一个极值点，依据极值点，获取信号的到达角。
作为本发明的又一种改进，所述智能无人系统能接收环境中传感器广播的信息，根据传感器的身份信息、所在位置，射频信号的方向和时延，估测自身所在位置。
与现有技术相比较，本案的有益效果为：
(1)与传统的智能无人机系统相比，本发明可以实现智能无人系统的自主导航，而不需要人为的遥控和路线修正，惯导定位模块和毫米波感知系统的联合工作，可以使智能无人系统精准高速地到达目的地。
(2)与现有的智能无人系统导航技术相比，本发明基于现有的移动通信系统，不需要搭建额外的通信链路且不影响现有通信系统，成本低、可实现性和可靠性高，具体为：
A.本发明基于现有的射频移动通信系统，信号传输速率高且误码率低，智能无人系统只解码对自由空间中的毫米波信号进行部分解码，而无需额外搭建新的通信链路，大大降低成本，提高了可靠性。
B.本发明中，智能无人系统从毫米波信号中提取到达角和信号时延，从而进行高精度定位和惯导系统的修正，相比现有的GPS定位等技术，具有更高的准确度.
C.该发明中系统可以在通信链路正常工作的基础上，通过给智能无人系统添加新的模块以实现更多的功能，具有良好的延展性；
D.整个系统的复杂度较低，智能无人系统和移动通信基站在现有技术下都比较容易实现。
图1是本发明基于毫米波通感一体化无人设备的自主导航方法的场景示意图；
图2是本发明自主导航系统中智能无人系统的结构框图；
图3是本发明自主导航系统中移动通信基站的结构框图。
下面结合附图和具体实施方式，进一步阐明本发明，应理解下述具体实施方式仅用于说明本发明而不用于限制本发明的范围。
实施例1
基于毫米波通感一体化无人设备的自主导航系统，包括一个或多个智能无人系统和一个或多个移动通信基站，其中，移动通信基站由射频通信发射天线、通信信号处理系统和射频通信接收天线组成，具体如图3所示。射频通信发射天线连接到通信信号处理系统，通信信号处理系统连接到射频通信接收天线，通信信号处理系统还连接到网关。移动通信基站面向所有用户发送信号，而非仅面向智能无人系统服务的，信号包括但不限于毫米波信号，因而智能无人系统只是利用了环境中的毫米波信号，进行定位和自主导航。
所述智能无人系统包括无人设备、动力系统、毫米波感知系统和惯导定位系统，如图2所示，智能无人系统可以是无人机、无人车、智能机器人等一系列无人系统，动力系统用于为智能无人系统提供动力支持，包括电机、电池、功放模块及移动控制模块，无人设备与电机相连接，电池分别与电机和功放模块相连接，功放模块与移动控制模块相连接。毫米波感知系统用于接收和处理移动通信基站的毫米波信号，并反馈导航校正，包括毫米波信号接收模块、毫米波信号处理模块及导航校正模块，所述毫米波信号处理模块分别与毫米波信号接收模块和导航校正模块相连接。惯导定位系统包括惯导导航模块、惯导信息处理模块及惯导定位模块，所述惯导信息处理模块分别与惯导导航模块和惯导定位模块相连接，惯导导航模块还分别与移动控制模块和导航校正模块相连接。
智能无人系统实时接收来自移动通信基站的毫米波信号，解码信号帧头中基站身份信息和位置信息的同时，根据信号的出射角、到达角和到达时延，估计出智能无人系统与基站间的相对位置，依据此位置，对自身惯导定位系统的累积误差进行校正，并且根据惯导定位和毫米波定位，确定自身位置，实现自主导航。
惯导定位系统存在累积误差e(t)，因而可以为累积误差e(t)设定阙值，当e(t)达到阈值后，智能无人系统会偏离原有路线，到达错误的地点，因而此时可设定智能无人系统停止运行，以防止更加偏离路线，所述e(t)与时间呈正相关。
本实施例的自主导航系统可以实现智能无人系统的自主导航，而不需要人为的遥控和路线修正，同时，基于现有的移动通信系统，不需要搭建额外的通信链路且不影响现有通信系统，成本低、可实现性和可靠性高，使智能无人系统精准高速地到达目的地，解决了现有技术中无人系统实时高精度定位尚有缺陷的问题，实用性强，是未来新兴的自主导航产品。
实施例2
基于毫米波通感一体化无人设备的自主导航方法，如图1所示，包括如下步骤：
步骤S1：智能无人系统从初始点出发，初始点坐标已知。智能无人系统依靠的自身惯导定位系统，通过监测和计算智能无人系统在移动中的行进方向、速率和姿态等信息，与初始点位置相比对，从而实现智能无人系统初步的定位和导航。
该惯导定位系统存在累积误差e(t)，对累积误差e(t)设定阙值，e(t)与时间呈正相关，当e(t)达到一定阈值后，智能无人系统会偏离原有路线，从而到达错误的地点。
步骤S2：智能无人系统收集自由空间中的毫米波信号，并对信号进行解码，获取信号帧头中的基站身份信息和位置信息，同时对来自不同基站的信号解码
信息进行分类和存储，以进行下一步的处理。
步骤S3：对一段时间内来自同一基站的信号信息进行处理，提取出信道状态信息(CSI)，然后利用射频定位算法，从CSI中提取出毫米波信号的出射角、到达角和时延等信息，从而估计出基站相对于智能无人系统的方位等。
在实际系统中，我们可以根据状况的不同，选择不同的射频定位算法。本实施例中，通过MUSIC射频定位算法，从CSI中提取出到达角(AoA)等信息的算法步骤示例如下：
1、计算不同时域的多个接收信号向量的协方差矩阵；
2、对协方差矩阵进行特征值分解；
3、按特征值的大小顺序，把与信号个数相等的最大特征值对应的特征向量看成信号子空间，把剩下的特征值对应的特征向量看成噪声子空间。
4、计算谱函数，通过寻求峰值来得到到达角的估计值。对信号AoA进行遍历搜索时，当搜索到的角度与某个信号的AoA相同时，空间谱函数就会得到一个极值点，依据极值点，我们可以获取信号的AoA。
假设接收端有M根天线，接收信号为X，则接收信号的相关矩阵可以被表示为RX，它的表达式为
RX＝E[XXH]＝AE[SSH]AH+E[NNH]＝ARSAH+σ2I
其中RS是信号的协方差矩阵。A为方向矩阵，是范徳蒙德矩阵。我们对RS做奇异值分解，可得到特征值{λ1,λ2,λ3,...,λM}和特征向量{v1,v2,v3,...,vM}。
由于协方差矩阵是厄尔米特矩阵,所以矩阵的特征值均为实数,并且由于协方差矩阵的特征值均大于0,即矩阵是正定的,所以可推出RS是半正定的。根据ASAH的秩为n可知,信号空间对应的特征值和特征向量的个数为n,分别为{λ1,λ2,λ3,...,λn}和{v1,v2,v3,...,vn}，而噪声空间对应的特征值和特征向量的个数则为M-n，分别为{λn+1,λn+2,λn+3,...,λM}和{vn+1,vn+2,vn+3,...,vM}。由于噪声能量相对于信号能量要小很多,因此,{λn+1,λn+2,λn+3,...,λM}是协方差矩阵所有特征值中一组最小的特征值,且趋近于零，即
λn+1＝λn+2＝...＝λM＝σ2≈0
由于信号空间与噪声空间是正交的,因此信号空间方向矩阵A的列向量与噪声空间的特征向量{vn+1,vn+2,vn+3,...,vM}也是正交的，而A的列向量与信号的到达方向一一对应,根据此性质,便可以利用噪声空间的特征向量来求解信号的到达方向。构建噪声矩阵En＝{vn+1,vn+2,vn+3,...,vM},然后定义空间谱函数P(θ)
其中α(θ)是接收天线阵列的相位差矩阵。由空间谱函数的定义可以看出,当α(θ)和En正交时，P(θ)会出现一个谱峰,谱峰对应的θ值便是信号到达方向的估计值。
步骤S4：综合分析惯导定位系统和毫米波感知系统获取到的定位信息，估计出当前智能无人系统的准确位置，然后由毫米波感知系统中的导航校正模块，对智能无人系统的惯导导航模块进行误差校正。综合分析惯导定位系统和毫米波感知系统获取到的定位信息，估计出当前智能无人系统的准确位置，其原因在于毫米波定位也存在一定的误差，根据信道状况的不同，其误差也不同。智能无人系统需要根据实际情况，推算出惯导定位系统的定位置信区间和毫米波感知系统的定位置信区间，并联合分析得到一个更加精确的定位结果，作为惯导导航模块的修正依据。
除了移动通信基站外，智能无人系统也可以接收环境中的传感器广播的信息，根据传感器的身份信息、所在位置，射频信号的方向和时延，估测自身所在位置。
步骤S5：根据惯导定位和毫米波定位，确定自身位置，实现自主导航。
假设无人系统初始位置为l0,t时刻位置为l(t),惯导系统刷新间隔为τ，t时刻无人系统位移为st(τ),则有关系式需要说明的是，以上内容仅仅说明了本发明的技术思想，不能以此限定本发明的保护范围，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰均落入本发明权利要求书的保护范围之内。
Claims (10)
- 基于毫米波通感一体化无人设备的自主导航系统，其特征在于：包括至少一个智能无人系统和至少一个移动通信基站，所述移动通信基站面向所有用户发送信号，所述信号包括但不限于毫米波信号；所述智能无人系统设于无人设备中，至少包括惯导定位系统；智能无人系统实时接收来自移动通信基站的毫米波信号，解码信号帧头中基站身份信息和位置信息的同时，根据信号的出射角、到达角和到达时延，估计出智能无人系统与基站间的相对位置，依据此位置，对自身惯导定位系统的累积误差进行校正，并且根据惯导定位和毫米波定位，确定自身位置，实现自主导航。
- 如权利要求1所述基于毫米波通感一体化无人设备的自主导航系统，其特征在于：所述智能无人系统包括无人设备、动力系统、毫米波感知系统和惯导系统，所述动力系统用于为智能无人系统提供动力支持，包括电机、电池、功放模块及移动控制模块，所述无人设备与电机相连接，电池分别与电机和功放模块相连接，功放模块与移动控制模块相连接；所述毫米波感知系统用于接收和处理移动通信基站的毫米波信号，并反馈导航校正，包括毫米波信号接收模块、毫米波信号处理模块及导航校正模块，所述毫米波信号处理模块分别与毫米波信号接收模块和导航校正模块相连接。所述惯导定位系统包括惯导导航模块、惯导信息处理模块及惯导定位模块，所述惯导信息处理模块分别与惯导导航模块和惯导定位模块相连接，惯导导航模块还分别与移动控制模块和导航校正模块相连接。
- 如权利要求2所述基于毫米波通感一体化无人设备的自主导航系统，其特征在于：所述无人设备包括但不限于无人机、无人车或智能机器人。
- 如权利要求2或3所述基于毫米波通感一体化无人设备的自主导航系统，其特征在于：所述移动通信基站由射频通信发射天线、通信信号处理系统及射频 通信接收天线组成，所述通信信号处理系统分别与射频通信发射天线和射频通信接收天线相连接，通信信号处理系统还连接到网关。
- 如权利要求4所述基于毫米波通感一体化无人设备的自主导航系统，其特征在于：还包括传感器，所述智能无人系统接收传感器广播中的信息，根据传感器的身份信息、位置信息、射频信号的方向和时延，估测自身位置信息。
- 如权利要求5所述基于毫米波通感一体化无人设备的自主导航系统，其特征在于：对惯导定位系统中的累积误差设定阙值，当到达阙值时，智能无人系统停止运行，所述累积误差与时间呈正相关。
- 如权利要求1所述基于毫米波通感一体化无人设备的自主导航方法，其特征在于，包括如下步骤：S1：智能无人系统从初始点出发，获取初始点坐标，惯导定位系统通过监测和计算智能无人系统在移动中的行进方向、速率和姿态信息，与初始点位置相比对，指导智能无人系统的定位和导航；S2：智能无人系统行进过程中，收集移动通信基站在自由空间中发出的毫米波信号，并对信号进行解码，获取毫米波信号帧头中的基站身份信息和位置信息；S3：智能无人系统对毫米波信号进行联合处理，提取出信道状态信息，利用射频定位算法，从信道状态信息中至少提取出毫米波信号的出射角、到达角和时延信息，从而估计出移动通信基站相对于智能无人系统的相对位置信息；S4：综合分析惯导定位系统和毫米波感知系统获取到的定位信息，估计出当前智能无人系统的准确位置，然后由毫米波感知系统中的导航校正模块，对智能无人系统的惯导导航模块进行误差校正；S5：根据惯导定位和毫米波定位，确定自身位置，实现自主导航。
- 如权利要求7所述基于毫米波通感一体化无人设备的自主导航方法，其特征在于：所述步骤S3中，利用MUSIC射频定位算法，从信道状态信息中提取出毫 米波信号的出射角、到达角和时延信息，所述利用MUSIC算法计算到达角的步骤具体包括：S31：计算不同时域的多个接收信号向量的协方差矩阵；S32：对协方差矩阵进行特征值分解；S33：按特征值的大小顺序，把与信号个数相等的最大特征值对应的特征向量看成信号子空间，把剩下的特征值对应的特征向量看成噪声子空间；S34：计算谱函数，通过寻求峰值来得到到达角的估计值，对信号到达角进行遍历搜索时，当搜索到的角度与某个信号的到达角相同时，空间谱函数就会得到一个极值点，依据极值点，获取信号的到达角。
- 如权利要求7或8所述基于毫米波通感一体化无人设备的自主导航方法，其特征在于：所述步骤S4中，惯导定位系统和毫米波感知系统获取的智能无人系统位置分别为l(t)和lRF(t)，惯导定位系统和毫米波感知系统定位的误差和ERF为半径的圆形区域范围，，以两个位置为中心，取两个定位误差范围的交集，得到智能无人系统的更新位置。
- 如权利要求9所述基于毫米波通感一体化无人设备的自主导航方法，其特征在于：所述智能无人系统能接收环境中传感器广播的信息，根据传感器的身份信息、所在位置，射频信号的方向和时延，估测自身所在位置。
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