CN116202528A - Real-time positioning method based on self-adaptive weight coefficient space - Google Patents
Real-time positioning method based on self-adaptive weight coefficient space 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
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
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Abstract
A real-time positioning method based on a self-adaptive weight coefficient space. The method comprises the following specific steps: establishing an array signal receiving model; applying a virtual subarray-based spatial smoothing method to the array signal receiving model; establishing a weight coefficient space according to the signal covariance matrix; performing angle estimation and position calculation based on a subspace algorithm; establishing a position error function according to the real position result and the estimated position result; and establishing a weight coefficient space self-adaptive adjustment feedback mechanism according to the position error function. The invention has the beneficial effects that: the method starts from defining the weight coefficient space, estimates the position error by setting the position error function, and compares the position error with the error threshold value, so that the weight coefficient space parameter is dynamically adjusted, the weight coefficient space parameter self-adaption can be realized, and the indoor positioning accuracy can be improved.
Description
Technical Field
The invention relates to the technical field of indoor positioning, in particular to a real-time positioning method based on a self-adaptive weight coefficient space.
Background
In an angle of arrival (AOA) positioning algorithm in indoor positioning, an angle estimation algorithm theory based on feature space decomposition is continuously developed, such as a multiple signal classification algorithm, a signal estimation algorithm based on rotation factor invariance, and the like. The resolution of these algorithms is high, but in multipath environments, coherent signals can confuse the algorithm estimates and thus the positioning results of the positioning algorithm. Therefore, the spatial smoothing theory is established, and is mainly used for coherent decomposition of signals, so that more accurate position information is obtained. The conventional spatial smoothing technique is classified into forward spatial smoothing, backward spatial smoothing and forward-backward spatial smoothing. All three types can be used for carrying out decoherence on coherent signals to a certain extent, but parameter self-adaption cannot be realized, and parameters cannot be dynamically adjusted according to positioning result evaluation results.
Disclosure of Invention
Aiming at the defects and shortcomings in the prior art, the invention provides a real-time positioning method based on a self-adaptive weight coefficient space.
In order to achieve the above purpose, the invention adopts the following technical scheme: a real-time positioning method based on self-adaptive weight coefficient space comprises the following specific steps: establishing an array signal receiving model; applying a virtual subarray-based spatial smoothing method to the array signal receiving model; establishing a weight coefficient space according to the signal covariance matrix; performing angle estimation and position calculation based on a subspace algorithm; establishing a position error function according to the real position result and the estimated position result; and establishing a weight coefficient space self-adaptive adjustment feedback mechanism according to the position error function.
Further, the method for establishing the array signal receiving model specifically comprises the following steps: considering that M far-field narrowband signals are incident on a certain linear array in space, the array antenna is composed of M array elements, wherein the number of the array elements is equal to the number of channels, namely, the signals received by each array element are sent to a central processor through respective transmission channels, so that the signals can be represented by using the following composite envelope form:
under the assumption of a narrow-band far-field source, the following formula exists: />
Any array element receiving signal expression can be written:
the signals received by m array elements at a specific moment are arranged into a column vector, and each array element in the array is assumed to be homopolar and is not influenced by factors such as inconsistent channels, mutual coupling and the like, and the expression of the vector of the signals received by any array element is as follows: x (t) =as (t) +n (t).
Further, the method for applying the spatial smoothing method based on the virtual subarray to the array signal receiving model specifically comprises the following steps: in the linear array, the total number of array elements is m, and if each n array elements are divided into a group of virtual subspaces, the subspace matrix is v= [1,2,3 … …, m-n+1]Thus, the received signal vector expression based on the virtual subspace is as follows: x is X v (t)＝AU (v-1) S(t)+N v (t) may be expressed using the following formula:
and the signal covariance matrix based on the v-th sub-array is expressed as follows: r's' V ＝AU (v-1) SU H(v-1) A H σ 2 I。
Further, the establishing a weight coefficient space according to the signal covariance matrix specifically includes: after the signal covariance matrix calculation is finished, signal covariance matrices based on different subarrays are obtained, and the matrices can be expressed as follows: r= [ R ]' 1 ,R′ 2 ,R′ 3 ,……,R′ v ]Thus, a weight coefficient space is established based on the signal covariance array, and the formula is expressed as follows: k=k 1 ,K 2 ,K 3 ,……,K v Thus, the covariance matrix is recalculated from the weight coefficient space, as represented by the following formula:
further, the angle estimation and the position calculation based on the subspace algorithm are specifically as follows: the signal covariance matrix is subjected to characteristic decomposition, and can be expressed by the following formula:
because the signal subspace and the noise subspace of the large feature vector composition in the feature decomposition are equal, the following can be performed:
thus, there is a unique nonsingular matrix W such that +.>
And the above structure holds for both sub-arrays in the signal subspace, so the following can be made: />
Wherein Ω represents V S1 And V S2 The rotation of the two sub-arrays is not a change, and therefore the signal subspace relationship for the two sub-arrays is as follows: v (V) S2 ＝V S1 W -1 ΩW＝V S1 And psi, wherein if the rotation invariant relation matrix psi can be obtained, the incident angle theta of the signal can be obtained, and the position information of the source can be obtained according to the incident angle, and the formula is expressed as follows: x=z * tan(θ)。
Further, the establishing a position error function according to the real position result and the estimated position result specifically includes: assuming that the true distance between the source and the base station is x * Thus, a position error function is established according to the true distance and the estimated distance, and the position error function is expressed by the following formula:
further, the establishing a weight coefficient space adaptive adjustment feedback mechanism according to the position error function specifically includes: judging whether the space parameters of the weight coefficients need to be adjusted according to the position error function: if the position error meets the error threshold requirement, the space parameter of the weight coefficient does not need to be dynamically adjusted; if the error threshold is not met, the weight coefficient space parameter needs to be dynamically adjusted again to carry out iterative calculation until the error threshold requirement is met.
After the technical scheme is adopted, the invention has the beneficial effects that: the method starts from defining the weight coefficient space, estimates the position error by setting the position error function, and compares the position error with the error threshold value, so that the weight coefficient space parameter is dynamically adjusted, the weight coefficient space parameter self-adaption can be realized, and the indoor positioning accuracy can be improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
Fig. 1 is a schematic flow chart of the present invention.
Fig. 2 is a schematic diagram of spatial smoothing of a virtual sub-array in an algorithm of the present invention.
Detailed Description
Referring to fig. 1-2, the technical scheme adopted in the specific embodiment is as follows: the method comprises the following specific steps:
s1, establishing an array signal receiving model:
considering that M far-field narrowband signals are incident on a certain linear array in space, the array antenna is composed of M array elements, wherein the number of the array elements is equal to the number of channels, namely the signals received by each array element are sent to a central processor through respective transmission channels,
thus, the signal can be represented using a complex envelope form as follows:
wherein u is i (t) is the amplitude of the received signal, phi (t) is the phase of the received signal, w 0 Is the frequency of the received signal;
under the assumption of a narrow-band far-field source, the following formula exists:
any array element receiving signal expression can be written:
wherein i is any array element, i=1, 2, … … m, g ij Expressed as the gain of the ith element to the jth signal, n i (t) represents the noise of the ith element at time t, τ ij The time delay for the jth signal to reach the ith element is denoted as the reference element.
2) The signals received by m array elements at a specific moment are arranged into a column vector, and each array element in the array is assumed to be homopolar and is not influenced by factors such as inconsistent channels, mutual coupling and the like, and the expression of the vector of the signals received by any array element is as follows: x (t) =as (t) +n (t), where X (t) is an mx1-dimensional snapshot data vector, N (t) is an mx1-dimensional noise data vector, S (t) is an mx1-dimensional spatial signal vector, and a is an mxm-dimensional signal steering vector matrix.
S2, applying a space smoothing method based on a virtual subarray to the array signal receiving model:
in the linear array, the total number of array elements is m, and if each n array elements are divided into a group of virtual subspaces, the subspace matrix is v= [1,2,3 … …, m-n+1]Thus, the received signal vector expression based on the virtual subspace is as follows: x is X v (t)＝AU (v-1) S(t)+N v (t), wherein U v To the power of k for an M x M diagonal matrix,
the following formula can be used to represent:
and the signal covariance matrix based on the v-th sub-array is expressed as follows: r is R v ＝AU (v-1) SU H(v-1) A H +σ 2 I，
And can be rewritten as: r's' V ＝AU (v-1) SU H(v-1) A H σ 2 I, wherein σ 2 I represents a noise signal matrix.
S3, establishing a weight coefficient space according to the signal covariance matrix:
after the signal covariance matrix calculation is finished, signal covariance matrices based on different subarrays are obtained, and the matrices can be expressed as follows: r= [ R ]' 1 ,R′ 2 ,R′ 3 ,……,R′ v ]Wherein v represents a subspace matrix and v= [1,2,3 … …, m-n+1]，
Thus, a weight coefficient space is established based on the signal covariance array, and the formula is expressed as follows: k=k 1 ,K 2 ,K 3 ,……,K v Wherein K is v Representing the weight coefficient space coefficient of the v th subarray, and
thus, the covariance matrix is recalculated from the weight coefficient space, as represented by the following formula:
wherein K is T Representing a transposed operation on the weight coefficient space K. />
S4, angle estimation and position calculation are carried out based on a subspace algorithm:
the signal covariance matrix obtained in the step S3 is subjected to characteristic decomposition, and can be expressed by the following formula:
wherein R is S A signal subspace consisting of feature vectors corresponding to large feature values, R N A noise subspace composed of feature vectors corresponding to small feature values,
because the signal subspace and the noise subspace of the large feature vector composition in the feature decomposition are equal, the following can be performed:
where span represents the generation subspace,
thus, there is a unique non-singular matrix W such that
And the above structure holds for both sub-arrays in the signal subspace,
the following can be made:
wherein Ω represents V S1 And V S2 The rotation of the two subarrays is constant,
thus, for two childrenThe signal subspace relationship of the array is as follows: v (V) S2 ＝V S1 W -1 ΩW＝V S1 And psi, wherein if the rotation invariant relation matrix psi can be obtained, the incident angle theta of the signal can be obtained, and the position information of the source can be obtained according to the incident angle, and the formula is expressed as follows: x=z * tan (θ), where Z represents the known source height and x represents the calculated distance between the source and the base station.
S5, establishing a position error function according to the real position result and the estimated position result:
assuming that the true distance between the source and the base station is x * Thus, a position error function is established according to the true distance and the estimated distance, and the position error function is expressed by the following formula:
wherein, the value range of F is 0% -100%.
S6, establishing a weight coefficient space self-adaptive adjustment feedback mechanism according to the position error function:
judging whether the space parameters of the weight coefficients need to be adjusted according to the position error function:
if the position error meets the error threshold requirement, the space parameter of the weight coefficient does not need to be dynamically adjusted;
if the error threshold is not met, the weight coefficient space parameter needs to be dynamically adjusted again to carry out iterative calculation until the error threshold requirement is met.
The adjusting method comprises the following steps:
the spatial combination of the signal covariance matrix after the spatial smoothing is applied and the corresponding weight coefficient is established to form a joint matrix, which can be expressed by the following formula: r' = [ K 1 :R′ 1 ,K 2 :R′ 2 ,K 3 :R′ 3 ,……,K v ：R′ V ]，
Next, the joint matrices are arranged in ascending order according to the covariance matrix, expressed by the following formula: r "= [ K f :R′ min ，……，K h ：R‘ max ]Wherein K is f And K h Is R min And R is max A corresponding covariance matrix.
Aiming at the space dynamic adjustment of the weight coefficient, mainly aiming at K h Adjusting based on the doubling method and the division, namely, adjusting K h Finding a proper interval by continuously expanding twice or continuously shrinking twice, finding an optimal value in the interval, and correspondingly adjusting other parameters to meet the requirement
And (3) obtaining the product.
The working principle of the invention is as follows: firstly, an array signal receiving model is established according to array signal characteristics, then a space smoothing technology processing is carried out on a signal covariance matrix based on a virtual subarray, and as the dimensions of the virtual subarray are the same, a weight coefficient space initial value of the same dimension is established according to the dimensions of the virtual subarray and the signal covariance matrix subjected to space smoothing technology processing is carried out according to the weight coefficient space, a signal covariance matrix subjected to weight coefficient space processing is obtained, then angle estimation is carried out based on a subspace algorithm and position resolving is carried out, for example, angle estimation is carried out by using a rotation invariant subspace (ESPRIT) algorithm, then a position error function is established according to an estimated position result and a true position result, whether the error reaches a set error threshold value is estimated, if the error threshold value result reaches the set error, the final position is determined, if the error threshold value result does not reach the set error, the weight coefficient space is calculated in an iterative manner through adjusting the parameter proportion until the error threshold value result reaches the set error.
The foregoing is merely illustrative of the present invention and not restrictive, and other modifications and equivalents thereof may occur to those skilled in the art without departing from the spirit and scope of the present invention.
Claims (7)
1. A real-time positioning method based on a self-adaptive weight coefficient space is characterized by comprising the following steps of: the method comprises the following specific steps:
s1, establishing an array signal receiving model;
s2, applying a space smoothing method based on a virtual subarray to the array signal receiving model;
s3, establishing a weight coefficient space according to the signal covariance matrix;
s4, angle estimation and position calculation are carried out based on a subspace algorithm;
s5, establishing a position error function according to the real position result and the estimated position result;
s6, establishing a weight coefficient space self-adaptive adjustment feedback mechanism according to the position error function.
2. The real-time positioning method based on the adaptive weight coefficient space according to claim 1, wherein the method comprises the following steps: the specific steps of S1 are as follows:
1) Considering that M far-field narrowband signals are incident on a certain linear array in space, the array antenna is composed of M array elements, wherein the number of the array elements is equal to the number of channels, namely, the signals received by each array element are sent to a central processor through respective transmission channels, so that the signals can be represented by using the following composite envelope form:
under the assumption of a narrow-band far-field source, the following formula exists:
any array element receiving signal expression can be written: />
2) The signals received by m array elements at a specific moment are arranged into a column vector, and each array element in the array is assumed to be homopolar and is not influenced by factors such as inconsistent channels, mutual coupling and the like, and the expression of the vector of the signals received by any array element is as follows: x (t) =as (t) +n (t).
3. The real-time positioning method based on the adaptive weight coefficient space according to claim 1, wherein the method comprises the following steps: in the linear array, the total number of array elements is m, and each n array elements are divided into a group of virtual subspaces, so that the subspace matrix is v= [1,2,3 … …, m-n+1];
thus, the received signal vector expression based on the virtual subspace is as follows: x is X v (t)＝AU (v-1) S(t)+N v (t)；
The following formula can be used to represent:
and the signal covariance matrix based on the v-th sub-array is expressed as follows: r is R v ＝AU (v-1) SU H(v-1) A H +σ 2 I, and can be rewritten as: r's' V ＝AU (v-1) SU H(v-1) A H σ 2 I。
4. The real-time positioning method based on the adaptive weight coefficient space according to claim 1, wherein the method comprises the following steps: the step S3 is specifically as follows: after the signal covariance matrix calculation is finished, signal covariance matrices based on different subarrays are obtained, and the matrices can be expressed as follows: r= [ R ]' 1 ,R′ 2 ,R′ 3 ,……,R′ v ]；
Thus, a weight coefficient space is established based on the signal covariance array, and the formula is expressed as follows: k=k 1 ,K 2 ,K 3 ,……,K v ；
The covariance matrix is recalculated from the weight coefficient space as shown in the following formula:
5. real-time determination based on adaptive weight coefficient space as recited in claim 1A bit method, characterized by: the step S4 specifically comprises the following steps: the signal covariance matrix is subjected to characteristic decomposition, and can be expressed by the following formula:
because the signal subspace and the noise subspace of the large feature vector composition in the feature decomposition are equal, the following can be performed:
thus, there is a unique non-singular matrix W such that
The structure is established for two subarrays in the signal subspace;
the following can be made:
wherein Ω represents V S1 And V S2 The rotation of the two subarrays is unchanged;
thus, the signal subspace relationship for the two subarrays is as follows: v (V) S2 ＝V S1 W -1 ΩW＝V S1 And psi, wherein if the rotation invariant relation matrix psi can be obtained, the incident angle theta of the signal can be obtained, and the position information of the source can be obtained according to the incident angle, and the formula is expressed as follows: x=z * tan(θ)。
6. The real-time positioning method based on the adaptive weight coefficient space according to claim 1, wherein the method comprises the following steps: the step S5 specifically comprises the following steps: assuming that the true distance between the source and the base station is x * Thus, a position error function is established according to the true distance and the estimated distance, and the position error function is expressed by the following formula:
7. the real-time positioning method based on the adaptive weight coefficient space according to claim 1, wherein the method comprises the following steps: the step S6 specifically comprises the following steps: judging whether the space parameters of the weight coefficients need to be adjusted according to the position error function:
if the position error meets the error threshold requirement, the space parameter of the weight coefficient does not need to be dynamically adjusted;
if the error threshold is not met, the weight coefficient space parameter needs to be dynamically adjusted again to carry out iterative calculation until the error threshold requirement is met.
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| CN117812057A (en) * | 2024-03-01 | 2024-04-02 | 福建南亿智能科技有限公司 | Access control equipment based on digital cloud intercom system and control method thereof |
| CN117812057B (en) * | 2024-03-01 | 2024-05-10 | 福建南亿智能科技有限公司 | Access control equipment based on digital cloud intercom system and control method thereof |
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| CN117812057A (en) * | 2024-03-01 | 2024-04-02 | 福建南亿智能科技有限公司 | Access control equipment based on digital cloud intercom system and control method thereof |
| CN117812057B (en) * | 2024-03-01 | 2024-05-10 | 福建南亿智能科技有限公司 | Access control equipment based on digital cloud intercom system and control method thereof |
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