CN111432341B - Environment self-adaptive positioning method - Google Patents

Abstract

The invention belongs to the field of electronics, communication and automatic control, relates to user positioning based on wireless signals, and particularly relates to an environment self-adaptive positioning method. In the method, a receiver measures surrounding environment information including a natural geographic environment, an artificial geographic environment, weather and a receiver platform state in real time during movement to form an environment vector and match the environment vector with a known environment vector to determine a current environment identifier; the receiver simultaneously measures the signal information of each active signal source and represents the current signal state with discrete values. And determining the current environment classification category by integrating the environment identifier and the signal source signal state value, matching the current environment classification category with the known environment category, and determining the current environment category. And the receiver executes a positioning method associated with the matched environment category to realize positioning. The invention enables the receiver to select the optimal effective signal source according to the environment, and adopts the optimal positioning method by comprehensively considering the advantages and the disadvantages of various feasible positioning methods, thereby improving the effectiveness and the precision of positioning.

Description

Environment self-adaptive positioning method

Technical Field

The invention belongs to the field of electronics, communication and automatic control, relates to user positioning based on wireless signals, and particularly relates to an environment self-adaptive positioning method.

Background

The current major navigation positioning technologies include navigation positioning based on Global Navigation Satellite System (GNSS), cellular network base stations, Wi-Fi access points, bluetooth, ZigBee, emerging wireless emission sources, and the like. Each technology has its own advantages in terms of performance parameters such as accuracy, complexity, robustness, cost, and the like, but its own disadvantages also limit its wide application in different occasions, and in different environments, the effectiveness of resources will limit the effectiveness of different navigation positioning methods, so that it is difficult for a receiver to ensure stable positioning performance in the case of only one positioning technology. However, if the carrier receiver can select an optimal effective signal source according to the environment and adopt an optimal positioning method by comprehensively considering the advantages and disadvantages of various feasible positioning methods, the effectiveness and the accuracy of positioning can be improved. The current receiver only fixedly adopts one positioning technology or adopts several positioning technologies aiming at several environments with obvious difference indoors and outdoors, but has no self-adaptability aiming at different environments, and the number of signal sources and information fusion methods which can participate in positioning at the same time are limited, and the positioning precision of the receiver cannot be enhanced by fully utilizing the available signal sources and information fusion technologies. With the development of electronic information technology, the calculation and storage capabilities of the receiver will be continuously improved, and the receiver will have the capability of fully utilizing the existing signal source for navigation and positioning. Meanwhile, in order to adapt to navigation positioning with high precision and high flexibility, the receiver also has the capability of automatically adjusting a positioning method and a signal source participating in positioning. Therefore, according to the environment type, the precision and the stability of the navigation positioning of the carrier receiver can be improved by adopting the effective multi-signal source to realize the self-adaptive intelligent navigation positioning.

Disclosure of Invention

In order to solve the problems and ensure stable high-precision navigation positioning in different environment occasions, the invention provides an environment self-adaptive positioning method.

The technical scheme of the invention is as follows:

an environment self-adaptive positioning method comprises the following specific steps:

step (1) of generating an environmental vector

The receiver installed in the moving carrier platform senses and classifies the external environment: with A_iRepresents an environment attribute, where subscript i is an attribute identification and 1 ≦ i ≦ N_A，N_AIs the number of attributes describing the environment; attribute A_iIs composed of a plurality of parameters p_i,kK is 1,2, …, N_iIn which N is_iIs to describe the attribute A_iThe number of parameters of (2); attribute value I_AiWill be determined by a combination of different ranges of values of a plurality of parameters, theta_i,k,l≤p_i,k≤θ_i,k,uThat is to say that,

I_Ai＝e_i,jif, if

e_i,j∈N⁺，j∈{1,2,…,N_i,vAnd (c) the step of (c) in which,

and

is a parameter p_i,kThe upper and lower bounds of (1); n is a radical of⁺Is a set of positive integers; n is a radical of_i,vIs attribute A_iThe number of values; if i₁≠i₂Allowing

Taking an attribute value from each environment attribute to form a vector which represents an environment state;

classifying the external environment by adopting a facet classification method: each environment attribute is a surface, and the environment attribute value is a category contained in each surface; for the carrier platform state, grading the measured values according to the influence of the carrier platform state on navigation positioning, wherein each grade is an attribute value, namely a category; the category information contained in each category of environment attribute forms an attribute vector E_i，

From each attribute vector E_iOne element in each of them to form a vector V_e,lCalled context vector, representing a context state, i.e. context

Wherein l is an environment vector identification; a certain type of environment can be represented through the combination of the corresponding types of objects in each facet;

step (2) matching environmental vectors

The environment database is composed of a plurality of environment vectors V_e,iComposition, i ═ 1,2, …, N_eIn which N is_eIs the total number of context vectors; when the receiver perceives the environment, an environment measurement vector V is generated based on the range of measured environment attribute values_mI.e. by

The receiver will measure the obtained V_mIs compared and matched with each element of the vector in the selected context database, i.e. each element of the vector is compared and matched with each element of the vector in the selected context database

Wherein the content of the first and second substances,

is V_mAnd V_e,lThe matching result of the ith element, V (i) represents the ith element in V, namely attribute A_iA value of (d); then one matching vector phi_lThe generation is as follows:

weight vector W_a＝{w_i|i＝1,2,…,N_AIs used to adjust the matching result, where w_iIs a weighted value, i.e.

If ω is_l≥γ_mThen V is_e,lTo match the context vector, where W_aAnd a threshold value gamma_mWill depend on the application;

step (3) layering signal information

Layering signal characteristic information: the signal information obtained from each signal source is divided into three layers: the information of the first layer is directly obtained from the measurement of the signal, including average signal power and standard deviation, signal kurtosis, signal skewness and signal autocorrelation coefficient; the information of the second layer is obtained by indirect measurement of signals and comprises average excess delay, average root mean square delay spread, average multipath power variation range, average multipath number, phase, Doppler frequency shift and arrival direction; the third layer information is obtained by utilizing the information carried by the signal to calculate, and comprises the propagation time of the signal source reaching the receiver, the pseudo range and the related information of the signal source; when the positioning model is trained offline by using the multilayer information, the speed, the acceleration and the position coordinates of the receiver are used as the third layer of information;

for each signal source, the signal information characteristic is represented by a discrete integer value determined by a plurality of parameter measurements for different information layers; suppose a signal source s_iThe first layer and the second layer information are respectively composed of signal parameters r_i,1,j，j＝1,2,…,N_r1And r_i,2,j，j＝1,2,…,N_r2Describing that a combination of ranges of measured values of a parameter represents a signal condition information, expressed by a non-negative integer, i.e.

I_si＝b_i,k,b_i,k∈N⁰，k∈{1,2,…,N_i,kGet it out if

And is

Wherein N is_r1And N_r2The number of parameters describing the information of the first layer and the second layer of the signal respectively;

and

is a signal parameter r_i,t,jT is 1, 2; n is a radical of⁰Set of non-negative integers, N_i,kIs representative of the signal source s_iThe number of discrete values of signal states; b_i,1Is not identical to 0, if I_si＝b_i,1Then, it represents the signal source spring s_iInvalid; if k is₁≠k₂Then, then

But if i₁≠i₂，

Can be equal to

Consider all signal sources, set b_i,k|i＝1,2,…,N_sDenotes all signal source signal information, where N_sIs the number of effective signal sources;

step (4), environment classification and matching

Defining environment classification categories: an environment classification category C_iDefined as a vector whose elements are an identification of the environment vector and discrete values expressing information about the signal characteristics of the respective signal source, i.e.

Wherein, I_d,vIs the identification of the context vector and,

is representative of the signal source s_iAn integer value of signal characteristic information; different context classes may have the same context vector identification; positioning scheme F_iAnd environment class C_iMake an association, i.e. environment class C_iUsing a positioning scheme F_iTo determine a matching environment class C_iTime, location scheme F_iThe positioning is realized by the receiver;

the environmental and signal information data will be pre-analyzed to form an environmental classification database and continuously updated with new data: the receiver firstly performs environment matching to determine the current environment, namely, determines an environment identifier, then measures signal parameters of each signal source, determines discrete values of signal characteristic information of each signal source according to the range of the measured value of the signal parameter, and then forms an environment type measured value C_m(ii) a Is provided with C_iFor an environment category vector in the database, the measured environment category vector C is obtained if the following conditions are met_mWill be reacted with C_iMatching:

C_m(k)＝C_i(k) if C is present_i(k)≠0,k＝1,2,…,N_s+1

Defining a set of N_d×N_dDimension matrix P_k,k＝1,2,…,N_dIn which N is_d＝N_s+ 1; matrix P_kElement p in (1)^(k) _i,jSatisfy the requirement of

Using a matrix P_kPerforming the following operation to vector the environment class C_iConversion into diagonal matrix

Wherein, I₁Is a number N_dDimension vector, all elements of which are 1, i.e.

If it is not

Then C is_mAnd C_iMatching, wherein for vectors

Root mean square of

Is defined as

Then the matched context class vector C_iAssociated positioning scheme F_iThe positioning is realized by the receiver;

step (5) of comprehensively determining and positioning scheme by multiple environment category vectors

First for each positioning scheme F_iAnd the average positioning error is recorded as ε_iAdapted to the parameter gamma_iIs defined as gamma_i＝1/ε_i(ii) a Suppose there is N_mIndividual environment class vector and C_mMatch, 1 is less than or equal to N_m≤N_cIn which N is_cIs the total number of environment category vectors; the matching context class vector is labeled C_j，j∈{1,2,…,N_cAnd form a matching direction quantity set M_c；

For each positioning scheme F associated with a matching environment category vector_kThe receiver calculates its mean fitness value ρ_k：

Having a minimum of rho_kPositioning scheme F of values_kWill be performed by the receiver to achieve positioning.

The invention has the beneficial effects that: the invention enables the receiver to select the optimal effective signal source according to the environment, and adopts the optimal positioning method by comprehensively considering the advantages and the disadvantages of various feasible positioning methods, thereby improving the effectiveness and the precision of positioning.

Detailed Description

The following further describes the specific embodiments of the present invention in combination with the technical solutions.

The invention relates to an environment self-adaptive positioning method, which comprises the following specific steps:

step (1) environmental vector generation

Moving vehicles, such as unmanned vehicles, will constantly change their environment while in motion. A receiver installed in the vehicle will sense and classify the external environment. The external environmental factors include natural geographic environment, artificial geographic environment, weather, and receiver platform status. Since the position determined by the general positioning method is the coordinate of the center point of the antenna, the carrier moving platform is also considered as an external environment relative to the antenna, and the moving state of the carrier moving platform influences the determination of the coordinate of the antenna.

The natural geographic environment includes terrain and topographical attributes. The landform is divided into plain, hilly mountain, valley, mountain and canyon; the landform is vacant,

Ground, shrubs, crop land, forest, water surface, and the like. The artificial geographic environment includes general building and space-constrained building attributes. The buildings are divided into low, medium and high buildings and various buildings formed by combining single points, bars and blocks; and the space-limited buildings separately place buildings, overpasses, viaducts, overpasses, street-crossing underground passages, tunnels, underground parking lots and the like. The weather is divided into sunny days, cloudy and non-foggy days, cloudy and foggy days, rainy days, snowy days and the like. The motion state of the receiver platform is composed of attribute variables of speed, acceleration, energy, attitude, storage capacity, computing capacity and target task.

The environment consists of a number of the above factors, each of which contains a number of attributes. Each environment attribute value will be determined by a plurality of parameters describing the attribute, each attribute value being represented by a positive integer. Selecting a discrete integer value from each attribute of each factor to form an environment vector, so that each type of external environment is represented by a vector V, and V is { V ═ V }₁,v₂,v₃,…,v_NIn which v is_iRepresenting an environment attribute variable value. Thus, an ambient state will be represented by a vector of integers. The specific process is as follows:

with A_iRepresents an environment attribute, where subscript i is an attribute identification and 1 ≦ i ≦ N_A，N_AIs the number of attributes describing the environment. Attribute A_iIs composed of a plurality of parameters p_i,kK is 1,2, …, N_iIn which N is_iIs to describe the attribute A_iThe number of parameters (c). These attribute parameters p_i,kThe value of (c) will affect the effectiveness of the positioning method. Attribute value I_AiWill be determined by a combination of different ranges of values of a plurality of parameters, theta_i,k,l≤p_i,k≤θ_i,k,uI.e. by

I_Ai＝e_i,jIf, if

e_i,j∈N⁺，j∈{1,2,…,N_i,vAnd (c) the step of (c) in which,

and

is a parameter p_i,kThe upper and lower bounds of (1); n is a radical of⁺Is a set of positive integers; n is a radical of_i,,vIs attribute A_iThe number of values. e.g. of the type_i,jIs a positive integer, e for different environments_i,jThe values will be different. The values of the different attributes may be the same, that is if i₁≠i₂Allowing

An attribute value is taken from each environment attribute, and the attribute values form a vector, and the vector represents an environment state. The attribute value assignment process is referred to as an attribute discretization process.

And classifying the external environment by adopting a facet classification method so as to meet the requirement of classification dynamic expandability and improve the class matching efficiency. And for the navigation and positioning external environment, dividing the plane according to the attributes contained in the natural geographic environment, the artificial geographic environment and the weather environment and various state variables of the carrier platform. Each context attribute is considered to be a face, and the context attribute values are the categories contained in each face.

For the carrier platform state, the attribute value of each attribute is determined by the corresponding measured value, and the measured values are graded according to the influence of the measured values on navigation positioning, wherein each grade is an attribute value, namely a category. The category information contained in each category of environment attribute (each facet) can form an attribute vector E_i，E_i＝{e_i,1,e_i,2,e_i,3,...,e_i,Ni,v}，i＝1,2,…,N_A. An environment state will be composed of a set of attribute values e_i,j,i＝1,2,…,N_AIn expression, each attribute value describes a property of an attribute in the environment. From each attribute vector E_iOne element in each of them, and a vector V is formed by these elements_e,lCalled context vector, to represent a context state, i.e.

Where l is the context vector identification. A certain class of environment can be represented by a combination of the corresponding categories in each facet (one category is selected for each facet).

Step (2) environmental vector matching

The environment database is composed of a plurality of environment vectors V_e,iComposition, i ═ 1,2, …, N_eIn which N is_eIs the total number of context vectors. When the receiver perceives the environment, an environment measurement vector V may be generated based on the range of measured environment attribute values_mI.e. by

In order to decide on the positioning method, the receiver needs to match the measured environment vector with the known environment vectors stored in the database. The receiver will measure the obtained V_mIs compared with each element of the vector in the selected context database, i.e. each element of the vector is compared with each element of the vector in the selected context database

Wherein the content of the first and second substances,

is V_mAnd V_e,lThe matching result of the ith element, V (i), represents the ith element in V, namely the attribute A_iThe value of (c). Then, one matching vector Φ_lWill be generated as:

when matching context vectors, each attribute may have a different weight in determining the matching context vector and the final positioning method for different applications, and thus oneWeight vector W_a＝{w_i|i＝1,2,…,N_AWill be used to adjust the match result, i.e.

If ω is_l≥γ_mThen V is_e,lTo match the context vector, where W_aAnd gamma_mWill depend on the application.

Step (3) signal information layering

The signal characteristics are context dependent but do not have a one-to-one correspondence. The environment is the same, but the signal characteristics may be different. In addition, the environment categories are different, but the signal characteristic information may be the same, although the probability is smaller. Therefore, the receiver should consider the external environment and the signal information of the signal source together to determine the optimal positioning method, i.e. merge the environment information and the signal information to provide a feature description of the whole "positioning environment".

The signal characteristic information is first layered. The signal information obtained from each signal source is divided into three layers: the information of the first layer is directly obtained from the measurement of the signal, including average signal power and standard deviation, signal kurtosis, signal skewness and signal autocorrelation coefficient; the information of the second layer is obtained by indirect measurement of signals and comprises average excess delay, average root mean square delay spread, average multipath power variation range, average multipath number, phase, Doppler frequency shift and arrival direction; the third layer information is calculated by using information carried by the signal, and comprises propagation time of the signal source reaching the receiver, pseudo range, signal source related information and the like. When the positioning model is trained off line by using the multi-layer information, the speed, the acceleration and the position coordinates of the receiver are taken as the third layer information.

For each signal source, the signal information characteristic is represented by a discrete integer value, and the integer value is determined by a plurality of parameter measurement values of different information layers, namely, the signal information characteristic of each signal source is determined by a plurality of parameter measurement values. Suppose a signal source s_iFirst layer and second layer information separationIs determined by the signal parameter r_i,1,j，j＝1,2,…,N_r1And r_i,2,j，j＝1,2,…,N_r2It is stated that a combination of these parameter measurement value ranges will represent a signal state information, expressed by a non-negative integer, i.e.

I_si＝b_i,k,b_i,k∈N⁰，k∈{1,2,…,N_i,kGet it out if

And is

Wherein N is_r1And N_r2The number of parameters describing the first and second layer information of the signal, respectively.

And

is a signal parameter r_i,t,jT is 1, 2. N is a radical of⁰Set of non-negative integers, N_i,kIs representative of the signal source s_iNumber of discrete values of signal state. b_i,1If I is 0_si＝b_i,1Then, it represents the signal source spring s_iInvalid; if k is₁≠k₂Then, then

But if i₁≠i₂，

Can be equal to

Consider all signal sources, set b_i,k|i＝1,2,…,N_sDenotes all signal source signal information, where N_sIs the number of valid signal sources. Synthetic context vector sum set { b_i,kThe receiver will decide the positioning scheme to be performed, see step (4) and step (5). The specific value ranges of the signal parameters used to describe a given signal condition will be determined by the performance obtained by the active positioning scheme, the signal measurement empirical data, and the corresponding scheme.

Step (4) environmental classification and matching

An environment classification category is first defined. An environment classification category C_iDefined as a vector whose elements are an identification of the environment vector and discrete values expressing information about the signal characteristics of the respective signal source, i.e.

Wherein I_d,vIs the identification of the context vector and,

is representative of the signal source s_iInteger values of signal characteristic information. Different context classes may have the same context vector identification. Positioning scheme F_iAnd environment class C_iPerforming correlation to determine matching environment class C_iTime, location scheme F_iWill be performed by the receiver to achieve positioning.

The environmental and signal information data will be pre-analyzed to form an environmental classification database and continuously updated with new data. To determine the positioning scheme, the receiver first performs environment matching to determine the current environment, i.e. to determine the environment identifier, then measures the signal parameters of each signal source, determines the discrete value of the signal characteristic information of each signal source according to the range of the measured value of the signal parameter, and then forms the measured value C of the environment type_m. Finally, the following procedure is performed to determine the positioning scheme. Is provided with C_iFor an environment category vector in the database, the measured environment category vector C is obtained if the following conditions are met_mWill be reacted with C_iMatching:

C_m(k)＝C_i(k) if C is present_i(k)≠0,k＝1,2,…,N_s+1。

Defining a set of N_d×N_dDimension matrix P_k,k＝1,2,…,N_dIn which N is_d＝N_s+1. Matrix P_kElement p in (1)^(k) _i,jSatisfy the requirement of

Using a matrix P_kPerforming the following operation to vector the environment class C_iConversion into diagonal matrix

Wherein I₁Is a number N_dDimension vector, all elements of which are 1, i.e.

If it is not

Then C is_mAnd C_iMatching, wherein for vectors

Root mean square of

Is defined as

Thus, the matched environment class vector C_iAssociated positioning scheme F_iWill be performed by the receiver to achieve positioning.Note that there may be more than one context class vector and C_mMatching, and integrating the matched environment category vectors to determine a positioning scheme, wherein the process is shown in step (5):

step (5) multi-environment category vector comprehensive decision positioning scheme

First for each positioning scheme F_iAnd the average positioning error is recorded as ε_iAdapted to the parameter gamma_iIs defined as gamma_i＝1/ε_i. Suppose there is N_mIndividual environment class vector and C_mMatch, 1 is less than or equal to N_m≤N_cIn which N is_cIs the total number of context category vectors. These matching context category vectors are labeled C_j，j∈{1,2,…,N_cAnd form a matching direction quantity set M_c。

For each location method F associated with a matching environment category vector_k(note: possibly more than one matching environment category vector is associated with the same positioning method), the receiver calculates its mean fitness value ρ_k

Having a minimum of rho_kPositioning scheme F of values_kWill be performed by the receiver to achieve positioning.

Thus, the external environment including signal source information is classified, so that the receiver can form an environment type measured value after sensing the current environment and match the measured value with the known environment type, and then a corresponding positioning method is adopted to achieve the optimal positioning performance in the given environment.

Claims (1)

1. An environment self-adaptive positioning method is characterized by comprising the following specific steps:

step (1) of generating an environmental vector

The receiver installed in the moving carrier platform senses and classifies the external environment: with A_iRepresents an environment attribute, where subscript i is an attribute identification and 1 ≦ i ≦ N_A，N_AIs the number of attributes describing the environment; attribute A_iIs composed of a plurality of parameters p_i,kK is 1,2, …, N_iIn which N is_iIs to describe the attribute A_iThe number of parameters of (2); attribute value I_AiWill be determined by a combination of different ranges of values of a plurality of parameters, theta_i,k,l≤p_i,k≤θ_i,k,uThat is to say that,

I_Ai＝e_i,jif, if

e_i,j∈N⁺，j∈{1,2,…,N_i,vAnd (c) the step of (c) in which,

and

is a parameter p_i,kThe upper and lower bounds of (1); n is a radical of⁺Is a set of positive integers; n is a radical of_i,vIs attribute A_iThe number of values; if i₁≠i₂Allowing

Taking an attribute value from each environment attribute to form a vector which represents an environment state;

classifying the external environment by adopting a facet classification method: each environment attribute is a surface, and the environment attribute value is a category contained in each surface; for the carrier platform state, grading the measured values according to the influence of the carrier platform state on navigation positioning, wherein each grade is an attribute value, namely a category; the category information contained in each category of environment attribute forms an attribute vector E_i，

From each attribute vector E_iOne element in each of them to form a vector V_e,lCalled context vector, representing a context state, i.e. context

Wherein l is an environment vector identification; a certain type of environment can be represented through the combination of the corresponding types of objects in each facet;

step (2) matching environmental vectors

The environment database is composed of a plurality of environment vectors V_e,iComposition, i ═ 1,2, …, N_eIn which N is_eIs the total number of context vectors; when the receiver perceives the environment, an environment measurement vector V is generated based on the range of measured environment attribute values_mI.e. by

The receiver will measure the obtained V_mIs compared and matched with each element of the vector in the selected context database, i.e. each element of the vector is compared and matched with each element of the vector in the selected context database

Wherein the content of the first and second substances,

is V_mAnd V_e,lThe matching result of the ith element, V (i) represents the ith element in V, namely attribute A_iA value of (d); then one matching vector phi_lThe generation is as follows:

weight vector W_a＝{w_i|i＝1,2,…,N_AIs used to adjust the matching result, where w_iIs a weighted value, i.e.

If ω is_l≥γ_mThen V is_e,lTo match the context vector, where W_aAnd a threshold value gamma_mWill depend on the application;

step (3) layering signal information

Layering signal characteristic information: the signal information obtained from each signal source is divided into three layers: the information of the first layer is directly obtained from the measurement of the signal, including average signal power and standard deviation, signal kurtosis, signal skewness and signal autocorrelation coefficient; the information of the second layer is obtained by indirect measurement of signals and comprises average excess delay, average root mean square delay spread, average multipath power variation range, average multipath number, phase, Doppler frequency shift and arrival direction; the third layer information is obtained by utilizing the information carried by the signal to calculate, and comprises the propagation time of the signal source reaching the receiver, the pseudo range and the related information of the signal source; when the positioning model is trained offline by using the multilayer information, the speed, the acceleration and the position coordinates of the receiver are used as the third layer of information;

for each signal source, the signal information characteristic is represented by a discrete integer value determined by a plurality of parameter measurements for different information layers; suppose a signal source s_iThe first layer and the second layer information are respectively composed of signal parameters r_i,1,j，j＝1,2,…,N_r1And r_i,2,j，j＝1,2,…,N_r2Describing that a combination of ranges of measured values of a parameter represents a signal condition information, expressed by a non-negative integer, i.e. I_si＝b_i,k,b_i,k∈N⁰，k∈{1,2,…,N_i,kGet it out if

And is

Wherein N is_r1And N_r2The number of parameters describing the information of the first layer and the second layer of the signal respectively;

and

is a signal parameter r_i,t,jT is 1, 2; n is a radical of⁰Set of non-negative integers, N_i,kIs representative of the signal source s_iThe number of discrete values of signal states; b_i,1If I is 0_si＝b_i,1Then, it represents the signal source spring s_iInvalid; if k is₁≠k₂Then, then

But if i₁≠i₂，

Can be equal to

Consider all signal sources, set b_i,k|i＝1,2,…,N_sDenotes all signal source signal information, where N_sIs the number of effective signal sources;

step (4), environment classification and matching

Defining environment classification categories: an environment classification category C_iDefined as a vector whose elements are an identification of the environment vector and discrete values expressing information about the signal characteristics of the respective signal source, i.e.

Wherein，I_d,vIs the identification of the context vector and,

is representative of the signal source s_iAn integer value of signal characteristic information; different context classes may have the same context vector identification; positioning scheme F_iAnd environment class C_iMake an association, i.e. environment class C_iUsing a positioning scheme F_iTo determine a matching environment class C_iTime, location scheme F_iThe positioning is realized by the receiver;

the environmental and signal information data will be pre-analyzed to form an environmental classification database and continuously updated with new data: the receiver firstly performs environment matching to determine the current environment, namely, determines an environment identifier, then measures signal parameters of each signal source, determines discrete values of signal characteristic information of each signal source according to the range of the measured value of the signal parameter, and then forms an environment type measured value C_m(ii) a Is provided with C_iFor an environment category vector in the database, the measured environment category vector C is obtained if the following conditions are met_mWill be reacted with C_iMatching:

C_m(k)＝C_i(k) if C is present_i(k)≠0,k＝1,2,…,N_s+1

Defining a set of N_d×N_dDimension matrix P_k,k＝1,2,…,N_dIn which N is_d＝N_s+ 1; matrix P_kElement p in (1)^(k) _i,jSatisfy the requirement of

Using a matrix P_kPerforming the following operation to vector the environment class C_iConversion into diagonal matrix

Wherein, I₁Is a number N_dDimension vector, all elements of which are 1, i.e.

If it is not

Then C is_mAnd C_iMatching, wherein for vectors

Root mean square of

Is defined as

Then the matched context class vector C_iAssociated positioning scheme F_iThe positioning is realized by the receiver;

step (5) of comprehensively determining and positioning scheme by multiple environment category vectors

First for each positioning scheme F_iAnd the average positioning error is recorded as ε_iAdapted to the parameter gamma_iIs defined as gamma_i＝1/ε_i(ii) a Suppose there is N_mIndividual environment class vector and C_mMatch, 1 is less than or equal to N_m≤N_cIn which N is_cIs the total number of environment category vectors; the matching context class vector is labeled C_j，j∈{1,2,…,N_cAnd form a matching direction quantity set M_c；

For each positioning scheme F associated with a matching environment category vector_kThe receiver calculates its mean fitness value ρ_k：

Having a minimum of rho_kPositioning scheme F of values_kWill be performed by the receiver to achieve positioning.