CN113960578A - Time-of-arrival non-line-of-sight error elimination method, system, device and storage medium - Google Patents
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Abstract
The invention belongs to the technical field of communication, and discloses a method, a system, equipment and a readable storage medium for eliminating arrival time non-line-of-sight errors, wherein an arrival time observation sequence, non-line-of-sight environmental characteristics of the current environment, non-line-of-sight error-free arrival time estimation values and non-line-of-sight error estimation values of arrival time observation vectors of initial sampling moments in the arrival time observation sequence are obtained, a state transition matrix of a preset Kalman filter is determined, an error elimination Kalman filter is obtained, and the method, the system, the equipment and the readable storage medium aim at the arrival time observation vectors of all the sampling moments: inputting the non-line-of-sight error arrival time estimation value and the non-line-of-sight error estimation value of the arrival time observation vector of the last sampling moment and the arrival time observation vector of the current sampling moment into an error elimination Kalman filter to obtain the non-line-of-sight error arrival time estimation value and the non-line-of-sight error estimation value of the arrival time observation vector of each sampling moment in the arrival time observation sequence. The non-line-of-sight error does not need to be identified, the problems of false alarm and missing report do not exist, and the precision of the estimated value of the arrival time is effectively improved.
Description
Technical Field
The invention belongs to the technical field of communication, and relates to a method, a system and equipment for eliminating time of arrival non-line-of-sight errors and a readable storage medium.
Background
The 5G communication technology is spreading, and it uses a completely new technology and network framework, including large bandwidth, Massive MIMO, Flexible-OFDM, millimeter wave communication, indoor base station, etc. The large bandwidth can improve the distance resolution of the positioning technology based on the arrival time, and the indoor base station can bring opportunity for positioning the indoor terminal. However, since a non-line-of-sight error exists in a non-line-of-sight environment where there is a blockage, such as indoors, and the like, and the non-line-of-sight error affects the accuracy of the 5G positioning parameter arrival time, in order to ensure the accuracy of 5G positioning, it is necessary to eliminate the non-line-of-sight error and improve the accuracy of estimating the 5G positioning parameter arrival time.
As in the chinese patent application: CN105445699A, a distance measurement method and system for eliminating non-line-of-sight errors are provided, the distance measurement error elimination method includes the following steps: firstly, establishing an error database; secondly, identifying non-line-of-sight errors; and thirdly, eliminating errors by a K nearest neighbor method. However, the non-line-of-sight error needs to be identified, and the problems of false alarm and false alarm exist. In addition, the method uses a K neighbor method, the calculation amount of the K neighbor algorithm for the data set with large sample capacity such as non-line-of-sight errors is large, the selection of the K value is obtained by depending on experience, so that under-fitting of a non-line-of-sight elimination model is easily caused, and the non-line-of-sight error elimination effect is poor.
Disclosure of Invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and to provide a method, system, device and readable storage medium for eliminating time-of-arrival non-line-of-sight errors.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
in a first aspect of the present invention, a method for eliminating non-line-of-sight errors in arrival time includes the following steps:
acquiring an arrival time observation sequence, non-line-of-sight environment characteristics of the current environment, and a non-line-of-sight error-free arrival time estimation value and a non-line-of-sight error estimation value of an arrival time observation vector of an initial sampling moment in the arrival time observation sequence;
determining a preset state transition matrix of a Kalman filter according to the arrival time observation sequence and the non-line-of-sight environment characteristics of the current environment to obtain an error elimination Kalman filter;
traversing the arrival time observation vectors of all sampling moments in the arrival time observation sequence according to the sequence of the sampling moments, and performing the following steps on the arrival time observation vectors of all the sampling moments: inputting the non-line-of-sight error arrival time estimation value and the non-line-of-sight error estimation value of the arrival time observation vector of the last sampling moment and the arrival time observation vector of the current sampling moment into an error elimination Kalman filter to obtain the non-line-of-sight error arrival time estimation value and the non-line-of-sight error estimation value of the arrival time observation vector of each sampling moment in the arrival time observation sequence.
The arrival time non-line-of-sight error elimination method is further improved as follows:
the non-line-of-sight environmental characteristics include a cellular network number of paths of arrival and a cellular network signal reflection coefficient of the reflector.
The specific method for determining the state transition matrix of the preset Kalman filter according to the arrival time observation sequence and the non-line-of-sight environment characteristics of the current environment is as follows:
according to the non-line-of-sight environment characteristics of the current environment, the process coefficient lambda and the state transition matrix are subjected to
Assigning any two of the a, b, c and d parameters;
two unassigned parameters of the a, b, c, d parameters of the state transition matrix F are obtained by the following formula:
wherein, E [ Y]Mean of the observed sequence of arrival times, DY]For the variance of the observed sequence of times of arrival,
the variance of the system measurement error caused by the cellular network system itself.
According to the non-line-of-sight environment characteristics of the current environment, the process coefficient lambda and the state transition matrix are subjected to
The specific method for assigning any two of the parameters a, b, c and d is as follows:
randomly initializing a plurality of non-line-of-sight environmental characteristics;
respectively carrying out a simulation test of arrival time non-line-of-sight error elimination aiming at each non-line-of-sight environment characteristic to obtain a process coefficient simulation value and a parameter simulation value of a state transition matrix F under each non-line-of-sight environment characteristic;
and assigning any two parameters of the process coefficient lambda and the a, b, c and d parameters of the state transition matrix F according to the process coefficient simulation value and the parameter simulation value of the state transition matrix F under the non-line-of-sight environment characteristics and the non-line-of-sight environment characteristics of the current environment.
The preset Kalman filter is as follows:
K′k＝FKk-1FT+Q
Gk＝FK′kCT(CK′kCT+R)-1
Kk＝K′k-F-1GkCK′k
wherein F is a state transition matrix; r is a covariance matrix of observation noise vectors; q is a covariance matrix of the process noise vector; c ═ 1,1]；YkAn observation vector of arrival time at the kth sampling moment;
the state vector is a state vector of the kth-1 sampling moment, and the state vector comprises a non-line-of-sight error-free arrival time estimation value and a non-line-of-sight error estimation value of an arrival time observation vector;
is the state vector of the kth sampling moment; kk-1An estimation error covariance matrix at the k-1 th sampling moment; kkAn estimation error covariance matrix at the kth sampling moment; alpha is alphakIs new; k'kA prediction error covariance matrix at the kth sampling moment;
is the predicted state vector at the kth sampling instant.
In a second aspect of the present invention, a time-of-arrival non-line-of-sight error cancellation system includes:
the acquisition module is used for acquiring the arrival time observation sequence, the non-line-of-sight environment characteristics of the current environment and non-line-of-sight error-free arrival time estimation value and non-line-of-sight error estimation value of the arrival time observation vector of the initial sampling moment in the arrival time observation sequence;
the matrix determination module is used for determining a state transition matrix of a preset Kalman filter according to the arrival time observation sequence and the non-line-of-sight environment characteristics of the current environment to obtain an error elimination Kalman filter;
the elimination module is used for traversing the arrival time observation vector of each sampling time in the arrival time observation sequence according to the sequence of the sampling times, and performing the following steps on the arrival time observation vector of each sampling time: inputting the non-line-of-sight error arrival time estimation value and the non-line-of-sight error estimation value of the arrival time observation vector of the last sampling moment and the arrival time observation vector of the current sampling moment into an error elimination Kalman filter to obtain the non-line-of-sight error arrival time estimation value and the non-line-of-sight error estimation value of the arrival time observation vector of each sampling moment in the arrival time observation sequence.
The arrival time non-line-of-sight error elimination system of the invention is further improved in that:
the specific method for determining the state transition matrix of the preset Kalman filter according to the arrival time observation sequence and the non-line-of-sight environment characteristics of the current environment is as follows:
according to the non-line-of-sight environment characteristics of the current environment, the process coefficient lambda and the state transition matrix are subjected to
Assigning any two of the a, b, c and d parameters;
two unassigned parameters of the a, b, c, d parameters of the state transition matrix F are obtained by the following formula:
wherein, E [ Y]Mean of the observed sequence of arrival times, DY]For the variance of the observed sequence of times of arrival,
the variance of the system measurement error caused by the cellular network system itself.
According to the non-line-of-sight environment characteristics of the current environment, the process coefficient lambda and the state transition matrix are subjected to
The specific method for assigning any two of the parameters a, b, c and d is as follows:
randomly initializing a plurality of non-line-of-sight environmental characteristics;
respectively carrying out a simulation test of arrival time non-line-of-sight error elimination aiming at each non-line-of-sight environment characteristic to obtain a process coefficient simulation value and a parameter simulation value of a state transition matrix F under each non-line-of-sight environment characteristic;
and assigning any two parameters of the process coefficient lambda and the a, b, c and d parameters of the state transition matrix F according to the process coefficient simulation value and the parameter simulation value of the state transition matrix F under the non-line-of-sight environment characteristics and the non-line-of-sight environment characteristics of the current environment.
In a third aspect of the present invention, a computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the time-of-arrival non-line-of-sight error elimination method when executing the computer program.
In a fourth aspect of the present invention, a computer-readable storage medium stores a computer program, which when executed by a processor implements the steps of the above-described time-of-arrival non-line-of-sight error cancellation method.
Compared with the prior art, the invention has the following beneficial effects:
the invention relates to a method for eliminating arrival time non-line-of-sight errors, which determines a state transition matrix of a preset Kalman filter according to an arrival time observation sequence and non-line-of-sight environmental characteristics of the current environment so as to obtain an error elimination Kalman filter, then realizes the non-line-of-sight error elimination of an arrival time observation vector of the current sampling time through the error elimination Kalman filter based on a non-line-of-sight error arrival time estimation value and a non-line-of-sight error estimation value of an arrival time observation vector of the last sampling time, further obtains a non-line-of-sight error arrival time estimation value of the arrival time observation vector of each sampling time, realizes the non-line-of-sight error elimination of the arrival time observation vector, does not need to identify the non-line-of-sight errors, does not have the problems of false alarm and missing report, has small calculated amount, and is easy to realize. Meanwhile, the state transition matrix is determined based on the arrival time observation sequence and the non-line-of-sight environmental characteristics of the current environment, so that the error elimination Kalman filter is more suitable for the actual network environment, and the accuracy of the arrival time estimation value is effectively improved.
Drawings
FIG. 1 is a block diagram of a method for eliminating time-of-arrival non-line-of-sight errors in accordance with the present invention;
FIG. 2 is a block diagram of a system for eliminating time-of-arrival non-line-of-sight errors according to the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
First, related art terms related to the present invention are introduced:
cellular network wireless positioning: the cellular network is 4G, 5G and the like based on wireless positioning technology of the cellular network.
Arrival time: the time refers to the air interface propagation time of a wireless signal from a sending end to a receiving end, and is an important positioning parameter in 5G positioning.
NLOS: Non-Line of Sight, refers to Non-Line-of-Sight propagation of wireless signals.
Non-line-of-sight error: if the electromagnetic wave propagates between the terminal and the base station in a non-line-of-sight manner, an additional time delay, i.e., a non-line-of-sight error, occurs in the time of arrival measurement.
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1, in an embodiment of the present invention, an arrival time non-line-of-sight error elimination method is provided, where an arrival time observation sequence is processed by using a pre-constructed error elimination kalman filter, an arrival time estimation value without an NLOS error and an NLOS error estimation value are respectively used as state variables of a system, and an influence of the NLOS error on the arrival time estimation value is finally eliminated.
S1: and acquiring an arrival time observation sequence, non-line-of-sight environment characteristics of the current environment, and a non-line-of-sight error-free arrival time estimation value and a non-line-of-sight error estimation value of an arrival time observation vector of the initial sampling moment in the arrival time observation sequence.
Specifically, the arrival time observation sequence is obtained by collecting an arrival time observation vector on site, and generally includes arrival time observation vectors of a plurality of sampling moments, and the time between adjacent sampling moments is sampling time Δ t.
In a line-of-sight cellular wireless network environment, the time-of-arrival observation vector can be expressed as:
Y＝τ0+ε
wherein, tau0Is the line-of-sight propagation time of electromagnetic waves between the terminal and the base station, and epsilon is the system measurement error caused by the cellular network system.
For non-line-of-sight channel environments, the time-of-arrival observation vector may be expressed as:
Y＝τ0+ε+β
where β is an additional time delay caused by non-line of sight. In the line-of-sight environment, the error source is mainly the system measurement error epsilon caused by the cellular network system itself, where beta is 0. In a non-line-of-sight propagation environment, the main source of errors is the non-line-of-sight-induced additional delay beta, and the system measurement errors epsilon caused by the cellular network system per se are easily eliminated by following a gaussian distribution with the mean value of 0, so that the non-line-of-sight errors, namely the non-line-of-sight-induced additional delay beta, are mainly eliminated in estimation.
Specifically, the non-line-of-sight environmental characteristics are environmental variables which generally have a large influence on the non-line-of-sight error, and in this embodiment, the non-line-of-sight environmental characteristics include the number of cellular network arrival paths and the cellular network signal reflection coefficient of a reflector, and the influence of the environment on the non-line-of-sight error can be better reflected by selecting the two variables.
S2: and determining a preset state transition matrix of the Kalman filter according to the arrival time observation sequence and the non-line-of-sight environment characteristics of the current environment to obtain the error elimination Kalman filter.
Specifically, in this embodiment, non-line-of-sight error elimination for the arrival time observation vector is implemented based on a pre-constructed error elimination kalman filter, and therefore, a construction process of the error elimination kalman filter is described below.
Firstly, taking an estimated time of arrival without NLOS error and an estimated NLOS error as state variables of a Kalman filter, wherein a state data vector is as follows:
wherein,
is the state vector at the kth sampling instant; x is the number of1kThe estimated value of the arrival time without NLOS error at the kth sampling moment;
is the state vector of the k-1 sampling moment; x is the number of2kIs the NLOS error estimate at the kth sampling instant.
Is a state transition matrix, the parameters a, b, c, d are constants; u shapekThe process noise vector can be obtained by pre-measurement in practical use, and the covariance matrix of the process noise vector
Wherein I is an identity matrix.
The observation equation of the kalman filter is:
wherein, YkAn observation vector of arrival time at the kth sampling moment; c ═ 1,1]Is an observation matrix; vkFor observing noise vectors, realThe actual use time can be obtained by pre-measurement, and the covariance matrix is
The specific expression of the kalman filter, i.e., the recursion process, is:
K′k＝FKk-1FT+Q
Gk＝FK′kCT(CK′kCT+R)-1
Kk＝K′k-F-1GkCK′k
wherein, Kk-1An estimation error covariance matrix at the k-1 th sampling moment; kkFor the estimated error covariance matrix at the kth sampling instant, K0The preset value is generally set according to simulation tests or historical experience;
a predicted state vector for the kth sampling instant; kk' is the prediction error covariance matrix at the kth sampling instant; gkRepresents a Kalman gain; alpha is alphakThe innovation is the difference between the observed value and the predicted observed value.
In order to determine the state transition matrix of the kalman filter when in use, in the present embodiment, an inference process is provided that first follows equation Xk+1＝FXk+UkThe following can be obtained:
from a non-line-of-sight error model Y ═ τ0And + epsilon + beta shows that under the non-line-of-sight channel environment, the arrival time observation vector is the sum of the true value of the arrival time, the system measurement error caused by the cellular network system and the NLOS error. Wherein the real value of the arrival time is constant, the mean value is equal to itself, the variance is 0, the mean value of the system measurement error caused by the cellular network system itself is 0, and the variance is
The mean and variance of the NLOS error are both greater than 0. This gives:
substituting the above formula into the formula
In the above step, the following results are obtained:
wherein, E [ Y]And D [ Y ]]Respectively mean value and variance of the observation vector of the arrival time;
variance of system measurement errors caused for the cellular network system itself; λ is the process coefficient. As can be seen from the above equation, the state transition matrix F can be obtained as long as the parameters a, b, c, and d satisfy the above equation on the premise that a ≠ 1 and c ≠ 0.
Specifically, in this embodiment, the specific method for determining the state transition matrix of the preset kalman filter according to the arrival time observation sequence and the non-line-of-sight environment characteristic of the current environment includes:
according to the non-line-of-sight environment characteristics of the current environment, the process coefficient lambda and the state transition matrix are subjected to
Assigning any two of the a, b, c and d parameters;
two unassigned parameters of the a, b, c, d parameters of the state transition matrix F are obtained by the following formula:
wherein, E [ Y]Mean of the observed sequence of arrival times, DY]For the variance of the observed sequence of times of arrival,
the variance of the system measurement error caused by the cellular network system itself.
Wherein, the process coefficient lambda and the state transition matrix are processed according to the non-line-of-sight environment characteristics of the current environment
The specific method for assigning any two of the parameters a, b, c and d is as follows:
randomly initializing a plurality of non-line-of-sight environmental characteristics;
respectively carrying out a simulation test of arrival time non-line-of-sight error elimination aiming at each non-line-of-sight environment characteristic to obtain a process coefficient simulation value and a parameter simulation value of a state transition matrix F under each non-line-of-sight environment characteristic;
and assigning any two parameters of the process coefficient lambda and the a, b, c and d parameters of the state transition matrix F according to the process coefficient simulation value and the parameter simulation value of the state transition matrix F under the non-line-of-sight environment characteristics and the non-line-of-sight environment characteristics of the current environment.
In this embodiment, the kalman filter that determines the parameters of the state transition matrix is referred to as an error elimination kalman filter, and thus the error elimination kalman filter is constructed.
S3: traversing the arrival time observation vectors of all sampling moments in the arrival time observation sequence according to the sequence of the sampling moments, and performing the following steps on the arrival time observation vectors of all the sampling moments: inputting the non-line-of-sight error arrival time estimation value and the non-line-of-sight error estimation value of the arrival time observation vector of the last sampling moment and the arrival time observation vector of the current sampling moment into an error elimination Kalman filter to obtain the non-line-of-sight error arrival time estimation value and the non-line-of-sight error estimation value of the arrival time observation vector of each sampling moment in the arrival time observation sequence.
The non-line-of-sight error-free arrival time estimation value of the arrival time observation vector is the arrival time estimation value of the arrival time observation vector with the non-line-of-sight error eliminated.
Specifically, based on the continuity of the sampling times, in this embodiment, the arrival time observation vector of each sampling time in the arrival time observation sequence is traversed according to the sequence of the sampling times, and according to the non-line-of-sight error arrival time estimation value and the non-line-of-sight error estimation value of the arrival time observation vector of the previous sampling time, the non-line-of-sight error elimination of the arrival time observation vector of the current sampling time is realized through the error elimination kalman filter, so as to obtain the non-line-of-sight error arrival time estimation value of the arrival time observation vector of each sampling time.
In summary, according to the arrival time non-line-of-sight error elimination method of the present invention, a state transition matrix of a preset kalman filter is determined according to an arrival time observation sequence and non-line-of-sight environmental characteristics of a current environment, so as to obtain an error elimination kalman filter, and then based on a non-line-of-sight error arrival time estimation value and a non-line-of-sight error estimation value of an arrival time observation vector of a previous sampling time, the non-line-of-sight error elimination of the arrival time observation vector of the current sampling time is realized through the error elimination kalman filter, so as to obtain a non-line-of-sight error arrival time estimation value of the arrival time observation vector of each sampling time, so as to realize the non-line-of-sight error elimination of the arrival time observation vector, without identifying the non-line-of-sight error, without the problems of false alarm and missed report, and the calculation amount is small, and is easy to implement. Meanwhile, the state transition matrix is determined based on the arrival time observation sequence and the non-line-of-sight environmental characteristics of the current environment, so that the error elimination Kalman filter is more suitable for the actual network environment, and the accuracy of the arrival time estimation value is effectively improved.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details of non-careless mistakes in the embodiment of the apparatus, please refer to the embodiment of the method of the present invention.
Referring to fig. 2, in a further embodiment of the present invention, an arrival time non-line-of-sight error elimination system is provided, which can be used to implement the above arrival time non-line-of-sight error elimination method.
The acquisition module is used for acquiring the arrival time observation sequence, the non-line-of-sight environmental characteristics of the current environment and a non-line-of-sight error-free arrival time estimation value and a non-line-of-sight error estimation value of an arrival time observation vector of an initial sampling moment in the arrival time observation sequence; the matrix determination module is used for determining a state transition matrix of a preset Kalman filter according to the arrival time observation sequence and the non-line-of-sight environment characteristics of the current environment to obtain an error elimination Kalman filter; the elimination module is used for traversing the arrival time observation vector of each sampling time in the arrival time observation sequence according to the sequence of the sampling time, and performing the following steps on the arrival time observation vector of each sampling time: inputting the non-line-of-sight error arrival time estimation value and the non-line-of-sight error estimation value of the arrival time observation vector of the last sampling moment and the arrival time observation vector of the current sampling moment into an error elimination Kalman filter to obtain the non-line-of-sight error arrival time estimation value and the non-line-of-sight error estimation value of the arrival time observation vector of each sampling moment in the arrival time observation sequence.
Preferably, the matrix determination module determines the specific method of the state transition matrix of the preset kalman filter according to the arrival time observation sequence and the non-line-of-sight environment characteristic of the current environmentThe method comprises the following steps: according to the non-line-of-sight environment characteristics of the current environment, the process coefficient lambda and the state transition matrix are subjected to
Assigning any two of the a, b, c and d parameters;
two unassigned parameters of the a, b, c, d parameters of the state transition matrix F are obtained by the following formula:
wherein, E [ Y]Mean of the observed sequence of arrival times, DY]For the variance of the observed sequence of times of arrival,
the variance of the system measurement error caused by the cellular network system itself.
Preferably, the matrix determination module is used for determining the process coefficient lambda and the state transition matrix according to the non-line-of-sight environment characteristics of the current environment
The specific method for assigning any two of the parameters a, b, c and d is as follows:
randomly initializing a plurality of non-line-of-sight environmental characteristics;
respectively carrying out a simulation test of arrival time non-line-of-sight error elimination aiming at each non-line-of-sight environment characteristic to obtain a process coefficient simulation value and a parameter simulation value of a state transition matrix F under each non-line-of-sight environment characteristic;
and assigning any two parameters of the process coefficient lambda and the a, b, c and d parameters of the state transition matrix F according to the process coefficient simulation value and the parameter simulation value of the state transition matrix F under the non-line-of-sight environment characteristics and the non-line-of-sight environment characteristics of the current environment.
In yet another embodiment of the present invention, a computer device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is specifically adapted to load and execute one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function; the processor described in the embodiments of the present invention may be used for the operation of the time of arrival non-line-of-sight error cancellation method.
In yet another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a computer device and is used for storing programs and data. It is understood that the computer readable storage medium herein can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to perform the corresponding steps of the time-of-arrival non-line-of-sight error cancellation method in the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A method for eliminating non-line-of-sight errors of arrival time is characterized by comprising the following steps:
acquiring an arrival time observation sequence, non-line-of-sight environment characteristics of the current environment, and a non-line-of-sight error-free arrival time estimation value and a non-line-of-sight error estimation value of an arrival time observation vector of an initial sampling moment in the arrival time observation sequence;
determining a preset state transition matrix of a Kalman filter according to the arrival time observation sequence and the non-line-of-sight environment characteristics of the current environment to obtain an error elimination Kalman filter;
traversing the arrival time observation vectors of all sampling moments in the arrival time observation sequence according to the sequence of the sampling moments, and performing the following steps on the arrival time observation vectors of all the sampling moments: inputting the non-line-of-sight error arrival time estimation value and the non-line-of-sight error estimation value of the arrival time observation vector of the last sampling moment and the arrival time observation vector of the current sampling moment into an error elimination Kalman filter to obtain the non-line-of-sight error arrival time estimation value and the non-line-of-sight error estimation value of the arrival time observation vector of each sampling moment in the arrival time observation sequence.
2. The time-of-arrival non-line-of-sight error cancellation method of claim 1, wherein the non-line-of-sight environmental characteristics include a cellular network arrival path number and a cellular network signal reflection coefficient of a reflector.
3. The method for eliminating the time-of-arrival non-line-of-sight error according to claim 1, wherein the specific method for determining the state transition matrix of the preset kalman filter according to the time-of-arrival observation sequence and the non-line-of-sight environmental characteristics of the current environment comprises:
according to the non-line-of-sight environment characteristics of the current environment, the process coefficient lambda and the state transition matrix are subjected to
Assigning any two of the a, b, c and d parameters;
two unassigned parameters of the a, b, c, d parameters of the state transition matrix F are obtained by the following formula:
wherein, E [ Y]Mean of the observed sequence of arrival times, DY]Variance, σ, of the observed sequence for time of arrival0 2The variance of the system measurement error caused by the cellular network system itself.
4. The method according to claim 3, wherein the non-line-of-sight error elimination is performed according to a non-line-of-sight environment characteristic of a current environment with respect to a process coefficient λ and a state transition matrix
The specific method for assigning any two of the parameters a, b, c and d is as follows:
randomly initializing a plurality of non-line-of-sight environmental characteristics;
respectively carrying out a simulation test of arrival time non-line-of-sight error elimination aiming at each non-line-of-sight environment characteristic to obtain a process coefficient simulation value and a parameter simulation value of a state transition matrix F under each non-line-of-sight environment characteristic;
and assigning any two parameters of the process coefficient lambda and the a, b, c and d parameters of the state transition matrix F according to the process coefficient simulation value and the parameter simulation value of the state transition matrix F under the non-line-of-sight environment characteristics and the non-line-of-sight environment characteristics of the current environment.
5. The method according to claim 1, wherein the predetermined kalman filter is:
K′k＝FKk-1FT+Q
Gk＝FK′kCT(CK′kCT+R)-1
Kk＝K′k-F-1GkCK′k
wherein F is a state transition matrix; r is a covariance matrix of observation noise vectors; q is a covariance matrix of the process noise vector; c ═ 1,1]；YkAn observation vector of arrival time at the kth sampling moment;
the state vector is a state vector of the kth-1 sampling moment, and the state vector comprises a non-line-of-sight error-free arrival time estimation value and a non-line-of-sight error estimation value of an arrival time observation vector;
is the state vector of the kth sampling moment; kk-1An estimation error covariance matrix at the k-1 th sampling moment; kkEstimation error co-processing for the kth sampling instantA variance matrix; alpha is alphakIs new; k'kA prediction error covariance matrix at the kth sampling moment;
is the predicted state vector at the kth sampling instant.
6. A time-of-arrival non-line-of-sight error cancellation system, comprising:
the acquisition module is used for acquiring the arrival time observation sequence, the non-line-of-sight environment characteristics of the current environment and non-line-of-sight error-free arrival time estimation value and non-line-of-sight error estimation value of the arrival time observation vector of the initial sampling moment in the arrival time observation sequence;
the matrix determination module is used for determining a state transition matrix of a preset Kalman filter according to the arrival time observation sequence and the non-line-of-sight environment characteristics of the current environment to obtain an error elimination Kalman filter;
the elimination module is used for traversing the arrival time observation vector of each sampling time in the arrival time observation sequence according to the sequence of the sampling times, and performing the following steps on the arrival time observation vector of each sampling time: inputting the non-line-of-sight error arrival time estimation value and the non-line-of-sight error estimation value of the arrival time observation vector of the last sampling moment and the arrival time observation vector of the current sampling moment into an error elimination Kalman filter to obtain the non-line-of-sight error arrival time estimation value and the non-line-of-sight error estimation value of the arrival time observation vector of each sampling moment in the arrival time observation sequence.
7. The system for eliminating time-of-arrival non-line-of-sight errors according to claim 6, wherein the specific method for determining the state transition matrix of the predetermined kalman filter according to the time-of-arrival observation sequence and the non-line-of-sight environmental characteristics of the current environment comprises:
according to the non-line-of-sight environment characteristics of the current environment, the process coefficient lambda and the state transition matrix are subjected to
Assigning any two of the a, b, c and d parameters;
two unassigned parameters of the a, b, c, d parameters of the state transition matrix F are obtained by the following formula:
wherein, E [ Y]Mean of the observed sequence of arrival times, DY]For the variance of the observed sequence of times of arrival,
the variance of the system measurement error caused by the cellular network system itself.
8. The system according to claim 7, wherein the process coefficient λ and the state transition matrix are adjusted according to the non-line-of-sight environment characteristics of the current environment
The specific method for assigning any two of the parameters a, b, c and d is as follows:
randomly initializing a plurality of non-line-of-sight environmental characteristics;
respectively carrying out a simulation test of arrival time non-line-of-sight error elimination aiming at each non-line-of-sight environment characteristic to obtain a process coefficient simulation value and a parameter simulation value of a state transition matrix F under each non-line-of-sight environment characteristic;
and assigning any two parameters of the process coefficient lambda and the a, b, c and d parameters of the state transition matrix F according to the process coefficient simulation value and the parameter simulation value of the state transition matrix F under the non-line-of-sight environment characteristics and the non-line-of-sight environment characteristics of the current environment.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the time of arrival non-line of sight error cancellation method according to any one of claims 1 to 5.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the time-of-arrival non-line-of-sight error cancellation method according to any one of claims 1 to 5.
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