CN114585082B - Wireless positioning method, device and storage medium of electric power Internet of things equipment - Google Patents
Wireless positioning method, device and storage medium of electric power Internet of things equipment Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention discloses a wireless positioning method, a wireless positioning device and a storage medium of electric power internet of things equipment, and relates to the technical field of internet of things equipment communication. The wireless positioning method of the electric power internet of things equipment comprises the following steps: periodically acquiring wireless positioning signals sent by at least three positioning base stations; according to the wireless positioning signals acquired in each period, channel estimation is respectively carried out on the corresponding positioning base stations; according to the channel estimation result, calculating the distance difference between the Internet of things equipment and different positioning base stations; performing distance difference data Kalman filtering processing based on the distance differences obtained in a plurality of periods; and calculating the actual position of the Internet of things equipment according to the distance difference data after Kalman filtering processing. The method can be used for realizing accurate positioning of the nodes of the Internet of things.
Description
Technical Field
The invention relates to the technical field of electric power internet of things communication, in particular to a wireless positioning method, a wireless positioning device and a storage medium of electric power internet of things equipment.
Background
The electric power internet of things connects various power grid terminal devices to perform information exchange and communication, and surrounds each department of each link of an electric power system, so that intelligent identification, positioning, tracking, monitoring and management are realized, infrastructure resources of electric power and a communication system can be effectively integrated, the utilization efficiency of intelligent power grid facilities is improved, and technical support is provided for a power grid.
In the electric power Internet of things, high-precision positioning of node equipment has important significance. Through the high-precision positioning technology, the position information of equipment or personnel and even the moving track can be obtained in real time, the fault handling capacity and emergency handling capacity of the power system are improved, the working efficiency is improved, and the personnel and equipment losses are reduced. For example, when equipment faults occur, the position of abnormal equipment can be found in time through a positioning technology, and rapid fault handling is facilitated. In the inspection system, the working condition of inspection personnel can be tracked in real time. For example, by checking the inspection route, specifying the inspection time of the inspection point, and the like, it can be judged whether the inspection personnel inspect according to the requirement, and the visualization and the intellectualization of the inspection process are realized. When an emergency occurs, the management and control center can master the real-time positions of personnel and equipment at the first time, schedule and control the equipment quickly in time, and provide road guidance assistance for management personnel.
The electric power internet of things equipment has special industrial requirements for positioning technology. Different from the application scene of the conventional internet of things positioning technology, the electric power internet of things equipment is limited by different degrees of factors due to the special use environment and hardware characteristics of the electric power internet of things equipment. For example, since it is difficult to secure openness in a node arrangement scenario, channel conditions and communication conditions tend to be very complex and diverse. In addition, in a field environment, the nodes are usually powered by batteries, have limited energy and are not easy to supplement, and a positioning algorithm is needed to save enough power. These characteristics all present challenges to the location technology of the power internet of things.
The accuracy of wireless positioning is strongly dependent on the measurement accuracy of the propagation delay of the ranging signal. In complex channel environments, non-line-of-sight (Non Line of Sight, NLOS) propagation of the ranging signal can result in large ranging errors, thereby reducing positioning accuracy. Identifying and compensating NLOS range errors is a key technique to improve positioning accuracy.
There are many existing positioning technologies, such as those based on WiFi, bluetooth, zigbee, UWB, RFID, and the like. But the technology is mainly suitable for indoor positioning, and the coverage area is limited, so that the positioning requirement of the electric power internet of things equipment in a large range is difficult to meet. Moreover, such techniques often require the deployment of more nodes, which is costly.
Time of arrival (TOA) based positioning methods are methods that calculate the distance by measuring the propagation time of a wireless signal to obtain the final position, which requires three or more base stations with known positions at the same time. An observed time difference of arrival (OTDOA) positioning method is to perform positioning according to the time difference of propagation of wireless signals from a terminal to a plurality of base stations. The ODTOA method has higher positioning accuracy than the cell ID-based method, but is susceptible to environmental influences. In a complex ranging environment, the probability of signal propagation blocked by non-line-of-sight NLOS obstacles is greatly increased, and signals are difficult to effectively detect because of power reduction caused by obstacle blocking, so that larger NLOS ranging errors are generated. For example, in suburban and rural open areas, the positioning accuracy can reach ten meters, but in urban areas, due to the large number of buildings, radio wave signals are difficult to reach the terminal directly from the base station, and generally, the positioning accuracy can be affected by complex refraction or reflection, and the positioning accuracy is about tens of meters to hundreds of meters.
Disclosure of Invention
The invention aims to provide a wireless positioning method, a wireless positioning device and a storage medium for electric power Internet of things equipment, which can realize accurate positioning of Internet of things nodes. The technical scheme adopted by the invention is as follows.
In one aspect, the present invention provides a wireless positioning method for an electric power internet of things device, including:
periodically acquiring wireless positioning signals sent by at least three positioning base stations;
according to the wireless positioning signals acquired in each period, channel estimation is respectively carried out on the corresponding positioning base stations;
According to the channel estimation result, calculating the distance difference between the Internet of things equipment and different positioning base stations;
performing distance difference data Kalman filtering processing based on the distance differences obtained in a plurality of periods;
And calculating the actual position of the Internet of things equipment according to the distance difference data after Kalman filtering processing.
Optionally, the calculating the distance difference between the internet of things device and different positioning base stations according to the channel estimation result includes:
according to the channel result, calculating the propagation delay of each positioning base station in the combination of any three positioning base stations;
Calculating the time delay difference between the other two positioning base stations and the positioning base station serving as the reference by taking the propagation time delay of any one of the positioning base stations in the combination as the reference;
And calculating a corresponding distance difference according to the time delay difference to obtain the distance difference between the Internet of things equipment and the reference positioning base station and the distance difference between the Internet of things equipment and the other two positioning base stations respectively.
Optionally, the distance difference calculation formula is:
dij＝c×τij
Wherein d ij represents the difference between the distance from the internet of things device to the positioning base station i and the distance from the internet of things device to the positioning base station j; c represents the propagation velocity of radio waves in the air; τ ij＝τi-τjτij represents the delay difference between the delay τ i of positioning base station i and the delay τ j of positioning base station j.
The above process of channel estimation and obtaining the propagation delay of the base station according to the channel estimation result can adopt the prior art.
Optionally, the distance difference data kalman filtering processing based on the distance differences obtained in a plurality of periods includes:
after the wireless positioning signals are acquired in each period, calculating to obtain distance difference data based on the wireless positioning signals in the period, and performing Kalman filtering processing on the obtained distance difference data once based on a pre-constructed distance difference measurement model to obtain an updated real distance difference estimated value;
After each Kalman filtering process, judging whether the Kalman filtering algorithm is converged, if so, taking the currently obtained true distance difference estimated value as the true distance difference, and if not, continuing the distance difference calculation and the Kalman filtering process after the wireless positioning signal is acquired in the next period.
In the invention, whether the Kalman filtering algorithm converges or not can be based on the fact that iterative calculation reaches the set times, or the variation of the real distance difference estimated value updated for a plurality of times is smaller than a preset threshold, the result representing Kalman filtering tends to be stable, namely, the stable and accurate distance difference estimated value can be obtained.
Optionally, the pre-constructed distance difference measurement model is:
dij(k)＝cijxij(k)+vij(k)
Wherein x ij(k)＝aijxij(k-1)+wij (k-1)
In the above formula, d ij (k) represents the difference between the distance from the internet of things device corresponding to the kth period to the positioning base station i and the distance from the internet of things device corresponding to the kth period to the positioning base station j; c ij represents a measurement coefficient; v ij (k) represents the measurement error, which is zero in mean and zero in varianceIs white gaussian noise; a ij denotes a state transition coefficient; w ij (k-1) represents the excitation signal corresponding to the (k-1) th period, and the average value is zero and the variance is/>Is white gaussian noise; x ij (k) represents a true value of a distance difference between a distance from the internet of things device corresponding to the kth period to the positioning base station i and a distance from the internet of things device to the positioning base station j;
the primary Kalman filtering is performed by updating the estimated value of the true value of the distance difference according to the following formula
In the method, in the process of the invention,An estimated value representing the true value of the distance difference corresponding to the kth period, b ij (k) represents the measurement weighting coefficient, expressed as the formula:
representing the variance of the measurement error,/> The one-step covariance estimation value updated when the k-th period is subjected to Kalman filtering is expressed as the formula:
p ij (k-1) represents the updated covariance coefficient when the k-1 th cycle performs the kalman filter, Representing the variance of the excitation signal w ij.
Optionally, the step of performing the kalman filter process in each cycle includes:
updating a covariance one-step estimation value based on the covariance coefficient updated in the previous period of Kalman filtering processing;
Updating the measurement weighting coefficient based on the updated covariance one-step estimation value;
Updating the covariance coefficient based on the updated covariance one-step estimation value and the measurement weighting coefficient;
And updating the estimated value of the distance difference true value based on the updated measurement weighting coefficient, the estimated value of the distance difference true value obtained by the Kalman filtering process of the previous period and the distance difference obtained by the calculation of the current period.
Optionally, the calculating the actual position of the internet of things device according to the distance difference data after the kalman filtering process includes:
Judging whether the number of the positioning base stations of the wireless positioning signal transmitting end is equal to 3 or more than 3;
If the number of the positioning base stations is equal to 3, calculating the position of the Internet of things equipment according to the obtained 2 distance difference data;
if the number of the positioning base stations is more than 3, grouping the positioning base stations, and determining 2 distance difference data corresponding to each positioning base station combination;
aiming at each positioning base station combination, respectively calculating the position of the equipment of the Internet of things according to the corresponding distance difference;
determining the positioning base station combination with the optimal layout according to a preset optimal layout selection strategy according to the position relation between each positioning base station in each positioning base station combination and the Internet of things equipment;
And taking the position of the Internet of things equipment obtained by combining and calculating according to the optimal positioning base station as the actual position of the Internet of things equipment.
In the above scheme, the prior art can be adopted for resolving the position of the internet of things equipment according to the distance difference.
Optionally, the determining, according to the positional relationship between each positioning base station in each positioning base station combination and the internet of things device and the preset optimal layout selection policy, the positioning base station combination with the optimal layout includes:
For any positioning base station combination, calculating the included angle of a connecting line between the position point of the Internet of things equipment and the position point of each base station in the combination according to the calculated position of the Internet of things equipment, and determining the maximum included angle of the connecting line to obtain the maximum included angle corresponding to each positioning base station combination;
And comparing the maximum included angles corresponding to all the positioning base station combinations, selecting the positioning base station combination with the minimum maximum included angle, and taking the positioning base station combination as the positioning base station combination with the optimal layout.
Optionally, the positioning base station is a positioning base station with a positioning function or an internet of things device with a positioning function, and the positioning function is that the positioning base station can position self-position information and can send wireless positioning signals;
The wireless positioning signal is an ODTOA wireless positioning signal.
In a second aspect, the present invention provides a wireless positioning device for an electric power internet of things device, including:
The positioning signal acquisition module is configured to periodically acquire wireless positioning signals sent by at least three positioning base stations;
The channel estimation module is configured to perform channel estimation respectively for each positioning base station according to the wireless positioning signals acquired in each period;
The distance difference calculation module is configured to calculate the distance difference between the Internet of things equipment and different positioning base stations according to the channel estimation result;
the Kalman filtering module is configured to perform distance difference data Kalman filtering processing based on the distance differences obtained in a plurality of periods;
and the position determining module is configured to calculate the actual position of the Internet of things equipment according to the distance difference data after the Kalman filtering processing.
Optionally, the channel estimation module calculates a distance difference between the internet of things device and different positioning base stations according to a channel estimation result, including:
according to the channel result, calculating the propagation delay of each positioning base station in the combination of any three positioning base stations;
Calculating the time delay difference between the other two positioning base stations and the positioning base station serving as the reference by taking the propagation time delay of any one of the positioning base stations in the combination as the reference;
And calculating a corresponding distance difference according to the time delay difference to obtain the distance difference between the Internet of things equipment and the reference positioning base station and the distance difference between the Internet of things equipment and the other two positioning base stations respectively.
Optionally, the kalman filtering module performs a distance difference data kalman filtering process based on the distance differences obtained in a plurality of periods, including:
After the wireless positioning signals are acquired in each period, after the distance difference is calculated based on the wireless positioning signals in the period, kalman filtering processing is carried out once based on a pre-constructed distance difference measurement model, and an updated real distance difference estimated value is obtained;
After each Kalman filtering process, judging whether the Kalman filtering algorithm is converged, if so, taking the currently obtained true distance difference estimated value as the true distance difference, and if not, continuing the distance difference calculation and the Kalman filtering process after the wireless positioning signal is acquired in the next period.
In a third aspect, the present invention provides an electric power internet of things device, which includes the wireless positioning device described in the second aspect, and the wireless positioning device is used for determining an actual position of the electric power internet of things device.
In a fourth aspect, the present invention provides a computer readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the wireless positioning method of the electric power internet of things device according to the first aspect.
Advantageous effects
According to the wireless positioning method of the electric power internet of things equipment, the distance difference measured value from the node to the positioning base station is obtained through channel estimation, and then the Kalman filtering is used for filtering the distance difference, so that the distance measurement error can be effectively filtered, and the positioning accuracy is improved.
On the basis of obtaining a plurality of accurate estimates of distance differences, in order to eliminate the negative influence of the non-ideal positioning base station layout on positioning accuracy, the invention selects three positioning base stations with optimal layout from all positioning base stations through a specific optimal layout selection strategy, thereby obtaining the most accurate node positioning result.
The method does not require time synchronization between the node of the Internet of things and the positioning base station, and is easy to deploy; and the algorithm is simple to implement and has a larger cost advantage.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of a wireless positioning method of an electric power Internet of things device according to the present invention;
fig. 2 is a schematic diagram of deployment of a peripheral positioning base station of an internet of things device;
FIG. 3 is a schematic diagram of a positioning principle based on OTDOA;
Fig. 4 is a schematic diagram of the optimal layout selection principle of the positioning base station.
Detailed Description
Further description is provided below in connection with the drawings and the specific embodiments.
The technical conception of the invention is as follows: in order to realize accurate positioning of the Internet of things equipment, firstly, pseudo-range estimation between the Internet of things equipment to be positioned and a plurality of positioning base station nodes is obtained by adopting methods such as channel estimation, pseudo-range estimation and the like; secondly, through modeling the range error, a Kalman filtering technology is adopted to filter the range result, NLOS range error is filtered, and positioning accuracy is improved. And for the situation that more than 3 base stations are distributed in the environment where the Internet of things equipment is located, selecting the positioning result determined by the base station combination with the optimal layout as a final positioning result.
Example 1
The embodiment introduces a wireless positioning method of electric power internet of things equipment, which comprises the following steps:
periodically acquiring wireless positioning signals sent by at least three positioning base stations;
according to the wireless positioning signals acquired in each period, channel estimation is respectively carried out on the corresponding positioning base stations;
According to the channel estimation result, calculating the distance difference between the Internet of things equipment and different positioning base stations;
performing distance difference data Kalman filtering processing based on the distance differences obtained in a plurality of periods;
And calculating the actual position of the Internet of things equipment according to the distance difference data after Kalman filtering processing.
The following description of the specific implementation process of the wireless positioning method of the electric power internet of things device in this embodiment refers to the following steps shown in fig. 1.
1. Acquisition of wireless positioning signals
To realize the positioning of the internet of things equipment, a plurality of positioning base stations should be deployed in advance in the area where the equipment is located. The positioning base station is a special base station, and needs to know the accurate position information of the positioning base station, and needs to send positioning auxiliary information including the accurate position information of the positioning base station to the internet of things nodes in the area through a proper information transmission mode, namely needs to be capable of sending wireless positioning signals. For a certain target area, the distribution of the positioning base stations should enable all the nodes of the internet of things in the area to receive at least wireless positioning signals sent by three positioning base stations. In this embodiment, the wireless positioning signal sent by the positioning base station may be an ODTOA-based wireless positioning signal, and the ODTOA wireless positioning signal and the transceiving technology may refer to the prior art.
Referring to fig. 2, there are multiple internet of things device nodes in the target area, and four positioning base stations, namely, a "positioning base station 1" to a "positioning base station 4", are deployed in total, so that any one of the internet of things device nodes can receive positioning signals of at least three positioning base stations.
For any one of the nodes of the internet of things, when there are not enough three positioning base stations in the area, the node of the internet of things which has obtained the accurate position information of the node of the internet of things can be used as the positioning base station to expand the service range of the high-precision positioning service.
For any one of the nodes of the Internet of things in the target area where enough positioning base stations are deployed, when positioning is needed, periodically intercepting and acquiring at least 3 positioning base stations to send out wireless positioning signals.
2. Channel estimation and distance difference calculation
After each interception obtains wireless positioning signals sent by a plurality of positioning base stations, channel estimation is carried out according to the wireless positioning signals sent by each positioning base station, and then the difference between the distances from the equipment node of the Internet of things to the corresponding positioning base stations is calculated according to the channel estimation result. The method for channel estimation by using the positioning signal can refer to the prior art.
When the distance difference between the positioning base station and the equipment node of the Internet of things is calculated, firstly, the propagation delay of the positioning base station is determined according to the channel estimation result. As shown in fig. 3, for the internet of things node N 1 and three positioning base stations-base station 1, base station 2, base station 3. According to the result of channel estimation, node N 1 measures propagation delays to three positioning base stations as τ 1、τ2 and τ 3, respectively. When the distance difference is calculated, the delay difference τ 21＝τ2-τ1 between the base station 2 and the base station 1 and the delay difference τ 31＝τ3-τ1 between the base station 3 and the base station 1 are calculated based on the propagation delay τ 1 of the base station 1. The delay difference is then converted into a distance difference, i.e. the distance from node N 1 to base station 2, and the distance from node N 1 to base station 1, which differ by a value d 21＝c×τ21, similarly d 31＝c×τ31, where c represents the propagation velocity of the radio wave in the air.
The above can be deduced, for any internet of things device, the calculation formula of the distance difference between the internet of things device and different positioning base stations is as follows:
dij＝c×τij
Wherein d ij represents the difference between the distance from the internet of things device to the positioning base station i and the distance from the internet of things device to the positioning base station j, τ ij＝τi-τjτij represents the delay difference between the delay τ i of the positioning base station i and the delay τ j of the positioning base station j.
According to the above embodiment, each internet of things device capable of receiving at least 3 wireless positioning signals may calculate at least 2 distance difference data.
3. Kalman filtering of distance difference data
And compensating the distance measurement error according to the link attenuation condition of the obstacle in a typical power scene, so that the positioning accuracy can be improved. In order to filter out the error of the estimated value of the distance difference obtained in the second step, the present embodiment performs a kalman filter process on the estimated distance difference.
Kalman filtering is a linear filter that uses a linear system state equation to optimally estimate the system state by inputting and outputting observed data through the system. And the estimation of the state variable is completed through the estimated value of the last moment and the observed value of the current moment. The Kalman filtering can be applied to any dynamic system containing uncertain information, effectively resists the interference of noise and takes the state variable as the optimal estimation, so that the Kalman filtering method is very suitable for filtering noise signals in ranging in a positioning method, and improves the ranging precision so as to improve the positioning precision.
The technical concept of the Kalman filtering of the distance difference in the embodiment is as follows: after the wireless positioning signals are acquired in each period, calculating to obtain distance difference data based on the wireless positioning signals acquired in the period, and then performing Kalman filtering processing on the obtained distance difference data once based on a pre-constructed distance difference measurement model to obtain an updated real distance difference estimated value;
After each Kalman filtering process, judging whether the Kalman filtering algorithm is converged, if so, taking the currently obtained true distance difference estimated value as the true distance difference, and if not, continuing the distance difference calculation and the Kalman filtering process after the wireless positioning signal is acquired in the next period.
The basis for whether the Kalman filtering algorithm converges can be that iterative calculation reaches the set times, or the variation of the actual distance difference estimated value updated for a plurality of times is smaller than a preset threshold, the result representing the Kalman filtering is stable, namely, the stable and accurate distance difference estimated value can be obtained.
The pre-constructed distance difference measurement model is as follows:
dij(k)＝cijxij(k)+vij(k)
Wherein x ij(k)＝aijxij(k-1)+wij (k-1)
In the above formula, d ij (k) represents the difference between the distance from the internet of things device corresponding to the kth period to the positioning base station i and the distance from the internet of things device corresponding to the kth period to the positioning base station j; c ij represents a measurement coefficient; v ij (k) represents the measurement error, which is zero in mean and zero in varianceIs white gaussian noise; a ij denotes a state transition coefficient; w ij (k-1) represents the excitation signal corresponding to the (k-1) th period, and the average value is zero and the variance is/>Is white gaussian noise; x ij (k) represents a true value of a distance difference between a distance from the internet of things device corresponding to the kth period to the positioning base station i and a distance from the internet of things device to the positioning base station j.
Each time the Kalman filtering process is executed, the estimated value of the true value of the distance difference is updated according to the following formula
In the method, in the process of the invention,An estimated value representing the true value of the distance difference corresponding to the kth period, b ij (k) represents the measurement weighting coefficient, expressed as the formula:
representing the variance of the measurement error,/> The one-step covariance estimation value updated when the k-th period is subjected to Kalman filtering is expressed as the formula:
p ij (k-1) represents the updated covariance coefficient when the k-1 th cycle performs the kalman filter, Representing the variance of the excitation signal w ij.
In this embodiment, the kalman filter processing is performed on the distance difference obtained by calculating after the kth acquisition of the wireless positioning signal, where the steps of the kalman filter processing are as follows:
Based on covariance coefficient p ij (k-1) updated at the time of last period Kalman filtering process, covariance one-step estimation value is updated
One-step estimation based on updated covarianceUpdating the measurement weighting coefficient b ij (k);
One-step estimation based on updated covariance And the measurement weighting coefficient b ij (k) is used for updating the covariance coefficient p ij (k) for the next Kalman filtering process;
Based on the updated measurement weighting coefficient b ij (k) and the estimated value of the distance difference true value obtained by the previous period Kalman filtering process And the distance difference d ij (k) calculated in the current period, and updating the estimated value of the true value of the distance difference
With continued reference to fig. 3, taking as an example the distance difference d 21 to the base station BS 1 and the base station BS 2 measured by the internet of things node N 1 the kth time, the distance difference d 21 is generated by the first-order autoregressive signal x 21 excited by the white noise w 21, and x 21 represents the true value of the distance difference between N 1 to the base station BS 2 and the base station BS 1. The measurement model of the distance difference d 21 can be expressed as:
d21(k)＝c21x21(k)+v21(k)
using a kalman filter, filtering the measured distance difference d 21 to obtain an estimate of the true distance difference x 21, the process comprising:
first, the covariance one-step estimate is updated:
Secondly, updating the measurement weighting coefficient:
Third, updating covariance coefficient:
Finally, the estimate of x ij (k) is updated:
Through the above process, after periodically measuring the distance difference d 21 each time, the node performs Kalman filtering once to obtain an updated estimated value of the accurate distance difference true value After several iterations, the kalman filtering algorithm converges, and a stable and accurate estimated value of the true distance difference can be obtained, and can be used as the true distance difference x 21 from the internet of things device N 1 to the base station BS 2 and the base station BS 1.
By using the same method, the distance difference d 31 is subjected to Kalman filtering, so that the stable and accurate real distance difference x 31 from the Internet of things equipment N 1 to the base station BS 3 and the base station BS 1 can be obtained, and the like.
4. Determining an actual location of an internet of things device
Referring to fig. 1, in this embodiment, calculating an actual position of an internet of things device according to distance difference data after kalman filtering processing includes:
Judging whether the number of the positioning base stations of the wireless positioning signal transmitting end is equal to 3 or more than 3;
If the number of the positioning base stations is equal to 3, calculating the position of the Internet of things equipment according to the obtained 2 distance difference data;
if the number of the positioning base stations is more than 3, grouping the positioning base stations, and determining 2 distance difference data corresponding to each positioning base station combination;
For each positioning base station combination, respectively resolving the position of the Internet of things equipment according to the corresponding distance difference, or after determining the positioning base station combination with the optimal layout, settling the position of the Internet of things equipment only aiming at the distance difference obtained by the optimal positioning base station combination;
determining the positioning base station combination with the optimal layout according to a preset optimal layout selection strategy according to the position relation between each positioning base station in each positioning base station combination and the Internet of things equipment;
And taking the position of the Internet of things equipment obtained by combining and calculating according to the optimal positioning base station as the actual position of the Internet of things equipment.
And will be described in detail below.
4.1 To implement OTDOA positioning, at least three positioning base stations, i.e. two distance difference data, are required, and at the same time, the relative layout positions of the positioning base stations also affect the positioning accuracy of the nodes. Because wireless positioning signals possibly received by each internet of things device are more than 3, in order to further improve positioning accuracy, in the embodiment, for the case that the internet of things device can receive more than 3 (excluding 3) wireless positioning signals, the positioning base station combination with the optimal layout is selected, and the real distance difference calculated by the combination is determined to determine the positioning of the internet of things device.
Referring to fig. 1, firstly, whether wireless positioning signals received by the internet of things equipment come from more than 3 positioning base stations is judged, if the wireless positioning signals come from only 3 positioning base stations, the positions of the internet of things equipment are obtained directly according to real distance differences after kalman filtering processing; if there are 4 or more wireless positioning signals, the positioning base stations need to be grouped to determine the positioning base station combination with the optimal layout.
4.2 Selection of the positioning base station combinations for optimal layout
4.2.1 Grouping the received positioning base stations from which the wireless positioning signals originate, the node of the internet of things lists all measurable positioning base stations as a group every third. As shown in fig. 4, the node 1 of the internet of things device may receive positioning signals of four base stations, that is, BS 1、BS2、BS3 and BS 4, and combine any three base stations, and theoretically, there are 4 combination manners. Two combinations of which, base station combination 1, are shown in the example of fig. 4, including BS 1、BS2 and BS 3; base station combination 2, comprising BS 1、BS3 and BS 4.
4.2.2 The node 1 of the internet of things uses three positioning base stations { BS i,BSj,BSk }, corresponding to two distance differences, to calculate the position of the node, and the calculated position of the node is denoted as p m. In this embodiment, the base station combination 1 uses the corresponding distance differences d 21 and d 31 to calculate the corresponding positions, denoted as p 1; the base station combination 2 uses the corresponding distance differences d 31 and d 41 to solve for the corresponding position, denoted p 2. In fig. 4, p 1 and p 2 are located very close together, not separately labeled, and do not affect the description of the subsequent steps.
4.2.3 Calculating the included angles between the M combinations and the positioning base station respectively by using the calculated node positions, wherein the three included angles are the same, and finding the largest included angle z m in the combinations;
In this embodiment, for base station combination 1 (BS 1、BS2 and BS 3), node 1 calculates three angles between the node and the base station, namely angle 1, by using the positions of the three base stations and the calculated position p 1: BS 1 -node 1-BS 2; angle 2: BS 1 -node 1-BS 3; angle 3: BS 2 -node 1-BS 3. The largest angle is then selected from among them, which is denoted as "largest angle 1" in fig. 4.
For base station combination 2 (BS 1、BS3 and BS 4), node 1 calculates three angles between the node and the base station, namely angle 1, using the positions of the three base stations and the calculated position p 2, respectively: BS 1, node 1, BS 3; angle 2: BS 1, node 1, BS 4; angle 3: BS 3, node 1, BS 4. Then, the largest angle is selected from among them. In fig. 4, it is denoted as "maximum angle 2".
Repeating the steps to obtain the maximum angle corresponding to each combination.
4.2.4 Find the smallest included angle among all M included angles, its correspondent positioning base station combination { BS i,BSj,BSk }, namely the positioning node combination with optimal layout.
In the two combinations shown in fig. 4, the minimum angle is "maximum angle 1", and if the angles of the other combinations are all larger than "maximum angle 1", the base station combination corresponding to the maximum angle 1 is the optimal base station combination, that is, base station combination 1.
Therefore, three base stations (BS 1, BS2 and BS 3) of the combination 1 are selected as three base stations capable of obtaining optimal node positioning, and according to the distance difference true value after the combination filtering processing, the corresponding node positioning position of the internet of things equipment is obtained through settlement, wherein p 1 is obtained.
So far, the node 1 completes the high-precision positioning process.
Example 2
Based on the same inventive concept as embodiment 1, this embodiment introduces a wireless positioning device of an electric power internet of things device, including:
The positioning signal acquisition module is configured to periodically acquire wireless positioning signals sent by at least three positioning base stations;
The channel estimation module is configured to perform channel estimation respectively for each positioning base station according to the wireless positioning signals acquired in each period;
The distance difference calculation module is configured to calculate the distance difference between the Internet of things equipment and different positioning base stations according to the channel estimation result;
the Kalman filtering module is configured to perform distance difference data Kalman filtering processing based on the distance differences obtained in a plurality of periods;
and the position determining module is configured to calculate the actual position of the Internet of things equipment according to the distance difference data after the Kalman filtering processing.
Specific functional implementation of the above functional modules refer to the relevant matters in embodiment 1, as described below for the channel estimation module and the kalman filter module.
The channel estimation module calculates the distance difference between the internet of things equipment and different positioning base stations according to the channel estimation result, and the method comprises the following steps:
according to the channel result, calculating the propagation delay of each positioning base station in the combination of any three positioning base stations;
Calculating the time delay difference between the other two positioning base stations and the positioning base station serving as the reference by taking the propagation time delay of any one of the positioning base stations in the combination as the reference;
And calculating a corresponding distance difference according to the time delay difference to obtain the distance difference between the Internet of things equipment and the reference positioning base station and the distance difference between the Internet of things equipment and the other two positioning base stations respectively.
The Kalman filtering module performs a distance difference data Kalman filtering process based on the distance differences obtained in a plurality of periods, and the Kalman filtering process comprises the following steps:
After the wireless positioning signals are acquired in each period, after the distance difference is calculated based on the wireless positioning signals in the period, kalman filtering processing is carried out once based on a pre-constructed distance difference measurement model, and an updated real distance difference estimated value is obtained;
After each Kalman filtering process, judging whether the Kalman filtering algorithm is converged, if so, taking the currently obtained true distance difference estimated value as the true distance difference, and if not, continuing the distance difference calculation and the Kalman filtering process after the wireless positioning signal is acquired in the next period.
Example 3
The embodiment introduces an electric power internet of things device, which comprises the wireless positioning device described in the embodiment 2, and the determination of the actual position of the electric power internet of things device is realized through the wireless positioning device.
In addition, the wireless positioning method described in embodiment 1 can also be executed by the self processor to determine the actual position of the power internet of things device.
Example 4
The present embodiment describes a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the wireless positioning method of the electric power internet of things device as described in embodiment 1.
In summary, the embodiment of the invention combines Kalman filtering and a specific positioning base station layout optimizing technology, and can realize the accurate calculation of the distance difference between the Internet of things equipment and the positioning base station with the optimal layout, thereby obtaining the accurate equipment position.
It will be appreciated by those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are all within the protection of the present invention.
Claims (12)
1. The wireless positioning method of the electric power internet of things equipment is characterized by comprising the following steps of:
periodically acquiring wireless positioning signals sent by at least three positioning base stations;
according to the wireless positioning signals acquired in each period, channel estimation is respectively carried out on the corresponding positioning base stations;
According to the channel estimation result, calculating the distance difference between the Internet of things equipment and different positioning base stations;
performing distance difference data Kalman filtering processing based on the distance differences obtained in a plurality of periods;
Calculating the actual position of the Internet of things equipment according to the distance difference data after Kalman filtering processing;
The calculating the actual position of the internet of things device according to the distance difference data after the Kalman filtering processing comprises the following steps: judging whether the number of the positioning base stations of the wireless positioning signal transmitting end is equal to 3 or more than 3; if the number of the positioning base stations is equal to 3, calculating the position of the Internet of things equipment according to the obtained 2 distance difference data; if the number of the positioning base stations is more than 3, grouping the positioning base stations, and determining 2 distance difference data corresponding to each positioning base station combination; aiming at each positioning base station combination, respectively calculating the position of the equipment of the Internet of things according to the corresponding distance difference; determining the positioning base station combination with the optimal layout according to a preset optimal layout selection strategy according to the position relation between each positioning base station in each positioning base station combination and the Internet of things equipment; taking the position of the Internet of things equipment obtained by combining and calculating according to the optimal positioning base station as the actual position of the Internet of things equipment; wherein,
The determining the positioning base station combination with the optimal layout according to the position relation between each positioning base station in each positioning base station combination and the internet of things equipment and the preset optimal layout selection strategy comprises the following steps: for any positioning base station combination, calculating the included angle of a connecting line between the position point of the Internet of things equipment and the position point of each base station in the combination according to the calculated position of the Internet of things equipment, and determining the maximum included angle of the connecting line to obtain the maximum included angle corresponding to each positioning base station combination; and comparing the maximum included angles corresponding to all the positioning base station combinations, selecting the positioning base station combination with the minimum maximum included angle, and taking the positioning base station combination as the positioning base station combination with the optimal layout.
2. The method according to claim 1, wherein the calculating the distance difference between the internet of things device and the different positioning base stations according to the channel estimation result comprises:
according to the channel result, calculating the propagation delay of each positioning base station in the combination of any three positioning base stations;
Calculating the time delay difference between the other two positioning base stations and the positioning base station serving as the reference by taking the propagation time delay of any one of the positioning base stations in the combination as the reference;
And calculating a corresponding distance difference according to the time delay difference to obtain the distance difference between the Internet of things equipment and the reference positioning base station and the distance difference between the Internet of things equipment and the other two positioning base stations respectively.
3. The method of claim 2, wherein the distance difference calculation formula is:
；
Wherein, Representing arrival of an Internet of things device at a positioning base station/>Distance to positioning base station/>Is the difference between the distances of (2); /(I)Representing the propagation velocity of radio waves in the air; /(I) ，/>Representing positioning base station/>Time delay/>And locating base station/>Time delay/>And a time delay difference between them.
4. A method according to claim 3, wherein said subjecting the distance difference data to a kalman filter process based on the distance differences obtained over a plurality of cycles comprises:
after the wireless positioning signals are acquired in each period, calculating to obtain distance difference data based on the wireless positioning signals in the period, and performing Kalman filtering processing on the obtained distance difference data once based on a pre-constructed distance difference measurement model to obtain an updated real distance difference estimated value;
After each Kalman filtering process, judging whether the Kalman filtering algorithm is converged, if so, taking the currently obtained true distance difference estimated value as the true distance difference, and if not, continuing the distance difference calculation and the Kalman filtering process after the wireless positioning signal is acquired in the next period.
5. The method of claim 4, wherein the pre-constructed distance difference measurement model is:
；
Wherein, ；
In the above-mentioned method, the step of,Represents the/>Internet of things equipment corresponding to each period arrives at a positioning base station/>Distance to positioning base station/>Is the difference between the distances of (2); /(I)Representing the measurement coefficient; /(I)Indicating the measurement error, which is zero mean and varianceIs white gaussian noise; /(I)Representing state transition coefficients; /(I)Represents the/>Excitation signals corresponding to each period are zero mean and variance/>Is white gaussian noise; /(I)Represents the/>Internet of things equipment corresponding to each period arrives at a positioning base station/>Distance to positioning base station/>A distance difference true value between the distances of (a);
the primary Kalman filtering is performed by updating the estimated value of the true value of the distance difference according to the following formula ：
；
In the method, in the process of the invention,Representation of the corresponding/>Estimated value of the true value of the distance difference of each period,/>The measurement weighting coefficient is expressed as the formula:
；
representing the variance of the measurement error,/> Represents the/>The covariance one-step estimation value updated when the kalman filtering is performed in each period is expressed as the formula:
；
Represents the/> Updated covariance coefficient when Kalman filtering is performed in each cycle,/>Representing the excitation signal/>Is a variance of (c).
6. The method of claim 5, wherein the step of performing the kalman filter process for each cycle comprises:
updating a covariance one-step estimation value based on the covariance coefficient updated in the previous period of Kalman filtering processing;
Updating the measurement weighting coefficient based on the updated covariance one-step estimation value;
Updating the covariance coefficient based on the updated covariance one-step estimation value and the measurement weighting coefficient;
And updating the estimated value of the distance difference true value based on the updated measurement weighting coefficient, the estimated value of the distance difference true value obtained by the Kalman filtering process of the previous period and the distance difference obtained by the calculation of the current period.
7. The method of claim 1, wherein the positioning base station is a positioning base station with a positioning function or an internet of things device with a positioning function, and the positioning function is capable of positioning self-position information and transmitting a wireless positioning signal;
the wireless positioning signal is an OTDOA wireless positioning signal.
8. A wireless positioning device of electric power internet of things equipment is characterized by comprising:
The positioning signal acquisition module is configured to periodically acquire wireless positioning signals sent by at least three positioning base stations;
The channel estimation module is configured to perform channel estimation respectively for each positioning base station according to the wireless positioning signals acquired in each period;
The distance difference calculation module is configured to calculate the distance difference between the Internet of things equipment and different positioning base stations according to the channel estimation result;
the Kalman filtering module is configured to perform distance difference data Kalman filtering processing based on the distance differences obtained in a plurality of periods;
the position determining module is configured to calculate the actual position of the Internet of things equipment according to the distance difference data after Kalman filtering processing;
The position determining module calculates the actual position of the Internet of things equipment according to the distance difference data after Kalman filtering processing, and the position determining module comprises the following steps: judging whether the number of the positioning base stations of the wireless positioning signal transmitting end is equal to 3 or more than 3; if the number of the positioning base stations is equal to 3, calculating the position of the Internet of things equipment according to the obtained 2 distance difference data; if the number of the positioning base stations is more than 3, grouping the positioning base stations, and determining 2 distance difference data corresponding to each positioning base station combination; aiming at each positioning base station combination, respectively calculating the position of the equipment of the Internet of things according to the corresponding distance difference; determining the positioning base station combination with the optimal layout according to a preset optimal layout selection strategy according to the position relation between each positioning base station in each positioning base station combination and the Internet of things equipment; taking the position of the Internet of things equipment obtained by combining and calculating according to the optimal positioning base station as the actual position of the Internet of things equipment; wherein,
The determining the positioning base station combination with the optimal layout according to the position relation between each positioning base station in each positioning base station combination and the internet of things equipment and the preset optimal layout selection strategy comprises the following steps: for any positioning base station combination, calculating the included angle of a connecting line between the position point of the Internet of things equipment and the position point of each base station in the combination according to the calculated position of the Internet of things equipment, and determining the maximum included angle of the connecting line to obtain the maximum included angle corresponding to each positioning base station combination; and comparing the maximum included angles corresponding to all the positioning base station combinations, selecting the positioning base station combination with the minimum maximum included angle, and taking the positioning base station combination as the positioning base station combination with the optimal layout.
9. The wireless positioning device of the electric power internet of things device according to claim 8, wherein the channel estimation module calculates a distance difference between the internet of things device and different positioning base stations according to a channel estimation result, and the wireless positioning device comprises:
according to the channel result, calculating the propagation delay of each positioning base station in the combination of any three positioning base stations;
Calculating the time delay difference between the other two positioning base stations and the positioning base station serving as the reference by taking the propagation time delay of any one of the positioning base stations in the combination as the reference;
And calculating a corresponding distance difference according to the time delay difference to obtain the distance difference between the Internet of things equipment and the reference positioning base station and the distance difference between the Internet of things equipment and the other two positioning base stations respectively.
10. The wireless positioning device of the power internet of things device according to claim 8, wherein the kalman filter module performs a distance difference data kalman filter process based on the distance differences obtained in a plurality of periods, and the wireless positioning device comprises:
after the wireless positioning signals are acquired in each period, calculating to obtain distance difference data based on the wireless positioning signals in the period, and performing Kalman filtering processing on the obtained distance difference data once based on a pre-constructed distance difference measurement model to obtain an updated real distance difference estimated value;
After each Kalman filtering process, judging whether the Kalman filtering algorithm is converged, if so, taking the currently obtained true distance difference estimated value as the true distance difference, and if not, continuing the distance difference calculation and the Kalman filtering process after the wireless positioning signal is acquired in the next period.
11. An electric power internet of things device, characterized by comprising the wireless positioning device according to any one of claims 8-10, the wireless positioning device being configured to determine an actual position of the electric power internet of things device.
12. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements a wireless location method of an electric internet of things device according to any of claims 1-7.
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