CN114285500B - UWB indoor positioning channel quality assessment method - Google Patents
UWB indoor positioning channel quality assessment method Download PDFInfo
- Publication number
- CN114285500B CN114285500B CN202111526782.9A CN202111526782A CN114285500B CN 114285500 B CN114285500 B CN 114285500B CN 202111526782 A CN202111526782 A CN 202111526782A CN 114285500 B CN114285500 B CN 114285500B
- Authority
- CN
- China
- Prior art keywords
- signal
- channel quality
- path
- cir
- positioning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- 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
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Radar Systems Or Details Thereof (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention belongs to the technical field of indoor positioning, and particularly provides a UWB indoor positioning channel quality evaluation method which is used for accurately evaluating the measurement error between a positioning tag and a positioning base station under the current UWB channel condition. The method comprises the steps of calculating a channel ideal factor A, a power dispersion factor B, a first path strength factor C and a first path blocking factor D between an unknown node and an anchor node based on Channel Impulse Response (CIR) data, and finally weighting to obtain a channel quality evaluation value, wherein the higher the channel quality evaluation value is, the higher the possibility that the current ranging error is smaller is; the invention uses CIR data to calculate channel quality evaluation value, effectively uses environment prior information according to wireless channel characteristics, reliably evaluates the range error under the current UWB channel condition, and can give continuous channel quality evaluation result rather than simple binary classification, even if the nodes are both LOS or NLOS condition, the difference of the range error can be effectively evaluated.
Description
Technical Field
The invention belongs to the technical field of indoor positioning, and particularly relates to a UWB indoor positioning channel quality evaluation method.
Background
The indoor positioning system is mainly divided into three types, namely, based on visual information, based on wireless signals and other methods; while wireless-based positioning systems can be divided into four categories: the system comprises an infrared positioning system, a radio frequency system, a global positioning system and an ultrasonic system, wherein the radio frequency system commonly uses a WLAN technology, a Radio Frequency Identification (RFID) technology, a bluetooth technology, a Zigbee technology, a Wireless Sensor Network (WSNs) technology, an Ultra-Wideband (UWB) technology and the like.
The positioning based on wireless generally comprises the steps of ranging and positioning calculation, firstly, the distance between an unknown node and an anchor node with a known position is obtained by adopting a ranging algorithm, then, the position of the unknown node is calculated by adopting different positioning models according to ranging values, and the positioning accuracy is directly influenced by the ranging accuracy, wherein the unknown node is generally a positioning label, and the anchor node with the known position is generally a positioning base station. Common ranging algorithms include a Received Signal Strength (RSS) based algorithm and a Time of arrival (TOA) based algorithm, and the TOA is mainly used for calculating a distance between two nodes by measuring a Time difference between a received signal and a positioning base station, namely Time of flight (TOF) of the signal, and multiplying a propagation speed c of electromagnetic waves, wherein a propagation path of a direct wave is called a direct path, other propagation paths are called multipath, and the TOF can be calculated by detecting the arrival Time of a first path signal because the propagation distance of the direct path is shortest and fastest. Compared with other radio frequency systems, the UWB technology is particularly outstanding in the aspect of time resolution, has better performance in the aspects of multipath resistance and penetrability, and is suitable for high-precision indoor positioning service; however, since the indoor environment is complex and the wireless channel is complex and variable, the UWB signal is often affected by strong multipath effect and Non Line of Sight (NLOS), which results in a decrease in the ranging accuracy, and the position accuracy calculated from the ranging value with a large error also decreases.
Therefore, one premise for improving the indoor UWB positioning accuracy is to estimate the distance measurement error under the current UWB channel condition, and select different positioning accuracy promotion strategies according to different channel conditions. Currently, the influence of non-Line-of-Sight on the ranging accuracy is often considered in the industry, and a simple regression based on k-NN is adopted to perform machine learning methods such as nonparametric learning and recursive decision tree learning on a feature vector to classify Line of Sight (LOS) and non-Line-of-Sight (LOS), or the magnitude of a ranging error is directly evaluated by using the statistical characteristics of a ranging value. However, the method using machine learning requires a large amount of data to train the model, and the environmental adaptability is not strong, and the LOS and NLOS classifications can only give binary evaluation results, and when both LOS and NLOS conditions exist between nodes, the difference between the range error magnitudes cannot be effectively evaluated, and when a UWB signal is affected by strong multipath and NLOS, the reliability of the range value is poor, and the evaluation of the range error magnitude by the statistical characteristics of the range value is also not accurate.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a UWB indoor positioning channel quality assessment method, which is used for accurately assessing the distance measurement error between a positioning tag and a positioning base station under the current UWB channel condition and improving the environmental adaptability and reliability of the UWB indoor positioning channel quality assessment method. The Channel Impulse Response (CIR) reflects the characteristics of the Channel, contains all necessary information for analyzing the wireless Channel, and can be calculated by a frequency domain method, a time domain method and a spread spectrum sliding correlation method; based on the above, the invention acquires CIR data and calculates the channel quality between an unknown node (positioning tag) and an anchor node (positioning base station) for estimating the reliability of ranging under the current UWB channel condition, wherein the higher the channel quality estimation value is, the higher the probability that the current ranging error is smaller is, the lower the channel quality estimation value is, and the lower the probability that the current ranging error is smaller is.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a UWB indoor positioning channel quality assessment method is characterized by comprising the following steps:
s0, selecting CIR data without multipath or with multipath signal power not more than 10% of the power of the first-path signal as a channel quality evaluation template, carrying out signal extraction and first-path detection on the channel quality evaluation template in the same way as the steps S2-S4, and counting the pulse width w of the first-path signal of the processed channel quality evaluation template as a standard pulse width;
s1, obtaining a distance measurement value and CIR data to be evaluated in real time;
s2, calculating a self-adaptive signal detection threshold T1, and extracting signals of the CIR data to be evaluated based on the self-adaptive signal detection threshold;
s3, judging the result of signal extraction, and if a signal is detected, executing a step S4; otherwise, outputting a channel quality assessment value Q =0, and jumping to the step S11;
s4, extracting peak values of the CIR signals to be evaluated obtained in the S2, setting a first path detection threshold value T2, and taking the first peak exceeding the first path detection threshold value T2 as a first path signal;
s5, aligning the first path signal of the CIR to be evaluated with the first path signal of the channel quality evaluation template to obtain a preprocessed channel quality evaluation template S and a CIR signal M to be evaluated, wherein the total point number of S and M is N; recording an index value n corresponding to the occurrence time of the first path signal in M 0 And an index value n corresponding to the occurrence time of the maximum peak 1 ；
S6, calculating a channel ideal factor A:
wherein M is i I point, mu, representing the preprocessed CIR signal M to be evaluated M And σ M Respectively, the mean value and the standard deviation of M; s i Indication deviceIth point, mu, of the processed channel quality assessment template S S And σ S Respectively, mean value and standard deviation of S;
s7, calculating a power dispersion factor B:
wherein A is FP The amplitude, A, of the first-path signal of the preprocessed CIR signal M to be evaluated n The amplitude of the nth peak of M;
s8, calculating a first path strength factor C:
s9, calculating a first path blocking factor D;
reducing the self-adaptive signal detection threshold T1 to obtain a new self-adaptive signal detection threshold T3, extracting the CIR data to be evaluated based on the self-adaptive signal detection threshold T3, extracting the peak value of the extracted CIR signal to be evaluated, and counting to obtain an index value n 0 The number of signal peaks P' appearing before, calculating a first path blocking factor D:
s10, calculating a channel quality factor Q:
Q＝a·A+b·B+c·C+d·D
wherein, a, B, C, D are the weights of the channel ideal factor a, the power dispersion factor B, the first path strength factor C and the first path blocking factor D, respectively, and a + B + C + D =1, and a is the maximum.
S11, repeating the steps S1-S10 until the calculation of the channel quality evaluation values Q of the channels between all the positioning labels to be evaluated and the positioning base stations is completed.
Further, in step S2, the adaptive signal detection threshold T1 is calculated by applying a constant false alarm detection algorithm to the CIR data to be evaluatedThe false alarm probability of the constant false alarm detection is not more than 10 -3 (ii) a In step S9, the specific process of lowering the adaptive signal detection threshold T1 is to increase the false alarm probability of the constant false alarm detection by one order of magnitude.
Further, in step S4, the first-path detection threshold T2 is set to 10% to 30% of the maximum peak value of the CIR signal.
Further, in the step 10, the weights a, B, C, and D of the channel ideal factor a, the power dispersion factor B, the first path strength factor C, and the weight of the first path blocking factor D are sequentially decreased.
Further, the step S1 includes the steps of:
s101, acquiring the flight time between the positioning tag and the positioning base station by adopting a unilateral double-pass ranging method or a bilateral double-pass ranging method;
s102, calculating CIR data by the ranging ending frame and the local known data frame through spread spectrum sliding correlation, and taking the CIR data as CIR data to be evaluated;
further, the ranging end frame is: when a single-side double-pass method is adopted and ranging is initiated by a positioning tag or a double-side double-pass method is adopted and ranging is initiated by a positioning base station, a ranging ending frame is used for calculating the flight time for the positioning tag and then sending a UWB data frame to the positioning base station; when a single-side double-pass method is adopted and ranging is initiated by the positioning base station or a double-side double-pass method is adopted and ranging is initiated by the positioning tag, the ranging ending frame is used for calculating the flight time for the positioning base station and then is sent to the positioning tag, and the positioning tag sends back the UWB data frame of the positioning base station after receiving the UWB data frame.
The invention has the beneficial effects that:
the invention provides a UWB indoor positioning Channel quality assessment method, based on Channel Impulse Response (CIR) data, calculating a Channel quality assessment value between an unknown node (positioning label) and an anchor node (positioning base station), wherein the higher the Channel quality assessment value is, the higher the probability that a current ranging error is smaller is, the lower the Channel quality assessment value is, the lower the probability that the current ranging error is smaller is; has the following advantages:
1. the method utilizes CIR data to calculate the channel quality evaluation value, effectively utilizes environment prior information according to the characteristics of a wireless channel, and reliably evaluates the range error under the current UWB channel condition;
2. the method does not need a large amount of data to carry out model training, is convenient to deploy and has strong adaptability to the environment;
3. the invention can provide continuous channel quality evaluation results instead of simple binary classification, namely, when the higher the channel quality evaluation value is, the higher the probability that the current ranging error is smaller is, the lower the channel quality evaluation value is, the lower the probability that the current ranging error is smaller is, even if the nodes are both LOS or NLOS conditions, the distance between the nodes can be effectively evaluated.
Drawings
FIG. 1 is a system block diagram of a UWB indoor positioning system in an embodiment of the invention; wherein, 1 is UWB positioner, 2 is the location label, 3 is the location basic station, 4 is the location engine.
Fig. 2 is a flowchart of a UWB indoor positioning channel quality evaluation method in an embodiment of the present invention.
Fig. 3 is a schematic diagram of UWB signal propagation between a positioning base station and a positioning tag under NLOS conditions and corresponding CIR.
Fig. 4 is a schematic diagram of UWB signal propagation and corresponding CIR between a positioning base station and a positioning tag under another NLOS condition.
Fig. 5 is a schematic diagram of UWB signal propagation between a positioning base station and a positioning tag under LOS conditions and corresponding CIR.
Fig. 6 is a schematic diagram of incorrectly detecting the head path signal.
Detailed Description
For a better understanding of the invention, its embodiments will be described in greater detail below with reference to the accompanying drawings and examples, in which various other embodiments of the invention may be illustrated and described, and any changes which may be made by those skilled in the art within the scope of the claims are considered to be within the scope of the invention.
The present embodiment provides a UWB indoor positioning channel quality assessment method, wherein an employed UWB indoor positioning system is shown in fig. 1, and the UWB positioning system includes a UWB positioning device 1 and a positioning engine 4; the UWB positioning device 1 comprises a positioning tag 2 and a positioning base station 3, the UWB positioning device 1 obtains a distance value for positioning calculation and obtains CIR data for channel quality evaluation, and a positioning engine 4 is used for CIR data analysis and channel quality evaluation value calculation.
The UWB indoor positioning channel quality assessment method is shown in fig. 2, and specifically includes the following steps:
s0, selecting CIR data without multipath or with multipath signal power not more than 10% of the power of the first-path signal as a channel quality evaluation template, carrying out signal extraction and first-path detection on the channel quality evaluation template in the same way as the steps S2-S4, counting the pulse width w of the first-path signal of the processed channel quality evaluation template as a standard pulse width, and preferably, using the CIR data obtained by measurement in a microwave darkroom as the channel quality evaluation template;
s1, obtaining a distance measurement value in real time for positioning calculation, and obtaining CIR data in a real environment, wherein the CIR data is called as CIR to be evaluated;
specifically, S1 includes the steps of:
s101, acquiring the flight time between a positioning tag and a positioning base station by adopting a single-side double-pass ranging method or a double-side double-pass ranging method;
when a single-side double-pass method is adopted, the device A initiates ranging, the device B delays for a fixed time to send back a data frame after receiving the data frame, the device A receives the data frame and calculates the flight time, and therefore the device receiving the last ranging response frame is the device initiating ranging; when a bilateral two-way method is adopted, the device A initiates ranging, the device B receives and then sends back a data frame to the device A, the device A receives and then sends back the data frame to the device B, and the device B receives the data frame and calculates the flight time, so that the device receiving the last ranging response frame is not the device initiating ranging; the device a is any one of the UWB positioning device apparatuses 1, and the device B is another device other than the device a in the UWB positioning device apparatus 1;
s102, calculating CIR data by using the ranging ending frame and a local known data frame through spread spectrum sliding correlation to serve as the CIR data to be evaluated;
when a single-side double-pass method is adopted and ranging is initiated by the positioning tag 2, or a double-side double-pass method is adopted and ranging is initiated by the positioning base station 3, the equipment for receiving the last ranging response frame is the positioning tag 2, the positioning tag 2 calculates the flight time and then sends the positioning base station 3, and the UWB data frame is a ranging ending frame; when a single-side double-pass method is adopted and ranging is initiated by the positioning base station 3, or a double-side double-pass method is adopted and ranging is initiated by the positioning tag 2, the equipment for receiving the last ranging response frame is the positioning base station 3, the positioning base station 3 calculates the flight time and then sends the positioning tag 2, the positioning tag 2 sends back the positioning base station 3 after receiving the ranging response frame, and the UWB data frame is a ranging ending frame;
s103, obtaining a distance measurement value based on flight time conversion, and uploading the distance measurement value and the CIR data to be evaluated to a positioning engine 4;
s2, a positioning engine 4 calculates a self-adaptive signal detection threshold T1, performs signal extraction on CIR data to be evaluated, extracts signals with amplitudes higher than the threshold, and filters noise with amplitudes lower than the threshold;
specifically, S2 includes the steps of:
s201, calculating to obtain a self-adaptive signal detection threshold T1 by adopting a constant false alarm detection algorithm to the CIR to be evaluated, wherein the false alarm probability of constant false alarm detection is not more than 10 -3 ；
S202, performing signal detection on the CIR data to be evaluated according to the self-adaptive signal detection threshold value T1, extracting a signal with amplitude higher than the self-adaptive signal detection threshold value T1, and filtering out noise with amplitude lower than the self-adaptive threshold value;
s3, judging the signal extraction result, if the signal is not detected, namely the amplitude of the CIR data to be evaluated is lower than the self-adaptive signal detection threshold value T1, commanding the channel quality evaluation value Q =0 and skipping to the step S11; otherwise, executing step S4;
s4, performing peak value extraction and first path detection on the CIR signal obtained by signal extraction;
specifically, S4 includes the steps of:
s401, peak extraction: when the amplitude of the nth point is simultaneously greater than the amplitudes of the (n-1) th point and the (n + 1) th point, the nth point is determined as a peak;
s402, first path detection: setting a first-path detection threshold T2, taking a first peak exceeding the preset first-path detection threshold T2 as a first-path signal, and setting the first-path detection threshold T2 to be 10% -30% of the maximum peak of the CIR signal;
s5, aligning the first path signal of the CIR to be evaluated with the first path signal of the channel quality evaluation template to obtain a preprocessed channel quality evaluation template S and a CIR signal M to be evaluated, wherein the total point number of S and M is N; the alignment means that the first path signal of the CIR signal to be evaluated is overlapped with the first path signal of the channel quality evaluation template at the occurrence time by intercepting the signals with fixed lengths before and after the first path signal or circularly shifting; recording an index value n corresponding to the occurrence time of the first path signal in M 0 And an index value n corresponding to the occurrence time of the maximum peak 1 ；
S6, calculating a channel ideal factor A;
the channel ideal factor A is a correlation coefficient between a channel quality evaluation template S preprocessed through the steps S2-S5 and a CIR signal M to be evaluated, and the larger the correlation coefficient is, the larger the channel quality evaluation value is; the correlation coefficient represents the similarity degree between the two signals, and the more similar the channel quality evaluation template and the CIR to be evaluated, the closer the current channel is to an ideal channel, the higher the possibility that the ranging error is smaller is; on the contrary, the difference between the current channel and the ideal channel is larger, and the probability of smaller ranging error is lower; based on this, the invention provides a calculation formula of the channel ideality factor A as follows:
wherein M is i I point, mu, representing the preprocessed CIR signal M to be evaluated M And σ M Respectively, the mean value and standard deviation of M; s i Point i, μ, representing the preprocessed channel quality assessment template S S And σ S Respectively, mean value and standard deviation of S;
s7, calculating a power dispersion factor B;
the power dispersion factor B is the ratio of the first path power to the total power of the preprocessed signals, and the larger the ratio is, the larger the channel quality evaluation value is; the larger the proportion of the first path power in the total power of the preprocessed signals is, the smaller the power of the multi-path signals is, and the higher the possibility that the distance measurement error is smaller is; conversely, the higher the power of the multipath signal is, the lower the probability of the smaller ranging error is; the environment shown in fig. 3 is simpler than the environment shown in fig. 4, the number of multipaths is smaller, the probability of the occurrence of the ranging error is higher, and the ratio of the first path power to the total signal power is higher; based on this, the invention provides a calculation formula of the power dispersion factor B as follows:
wherein A is FP The amplitude, A, of the first-path signal of the preprocessed CIR signal M to be evaluated n The amplitude of the nth peak of M;
s8, calculating a first-path strength factor C;
the first path intensity factor C is related to the time difference between the first path signal and the maximum peak of the CIR, and the larger the time difference is, the smaller the channel quality evaluation value is; if the UWB signal directly reaches the receiver without being reflected after being transmitted from the transmitter, the first path signal is the maximum CIR peak, as shown in fig. 5; the larger the time difference between the first path signal and the maximum peak of the CIR is, the higher the possibility that the UWB signal directly reaches the receiver from the UWB positioning equipment sender is, so that the higher the possibility that the ranging error is smaller is; on the contrary, the higher the possibility that the UWB signal reaches the receiver from the UWB positioning device sender through attenuation and even cannot reach the receiver, the lower the possibility that the ranging error is small, as shown in fig. 3; based on this, the invention provides a calculation formula of the first-path strength factor C as follows:
wherein n is 0 And n 1 The index value obtained in step S5;
s9, calculating a first path blocking factor D;
the first path blocking factor D is the probability that a correct first path signal is not detected, and the larger the probability is, the smaller the channel quality evaluation value is; if the obstruction between the sender and the receiver of the UWB positioning equipment enables the real first-path signal to be attenuated to be lower than the signal detection threshold, the detected first-path signal is actually a multi-path signal, and the calculated flight time inevitably brings a ranging error; as shown in fig. 6, when the true head path signal is lower than the signal detection threshold, the multi-path signal 1 is considered as the head path signal; based on this, the invention provides a method for calculating the first path blocking factor D, which comprises the following steps:
s901, increasing the false alarm probability of the constant false alarm detection in the step S201 by one order of magnitude to obtain a new adaptive signal detection threshold T3, namely reducing the adaptive signal detection threshold T1;
s902, performing signal extraction again on the CIR data to be evaluated by using a self-adaptive signal detection threshold T3;
s903, extracting peak value of the CIR signal to be evaluated obtained in the step S902, and counting to obtain a corresponding index value n appearing at the appearance moment of the original first path signal 0 The number of previous signal peaks P'; after lowering the signal detection threshold, appears at n 0 The larger the number P' of the previous signal peaks is, the higher the probability that the correct first-path signal is not detected is, and the smaller the possibility that the distance measurement error is;
s904, calculating the index value n corresponding to the appearance moment of the original first path signal by taking the standard pulse width w in the step S0 as a reference 0 The maximum number of peaks P that may occur before, P is calculated as:
the calculation formula of the first path blocking factor D is as follows:
s10, calculating a channel quality factor Q;
the channel quality factor Q is a weighted sum of a channel ideal factor a, a power dispersion factor B, a first path strength factor C and a first path blocking factor D, wherein the channel ideal factor a is a main index, and a calculation formula thereof is as follows:
Q＝a·A+b·B+c·C+d·D
wherein, a, B, C, D are weights of a channel ideal factor a, a power dispersion factor B, a first path strength factor C and a first path blocking factor D, respectively, a + B + C + D =1, and the weight a of the main indicator channel ideal factor a is the largest, the specific weight is selected by an empirical method, in this embodiment, the weight selection strategy is a =0.4, B =0.3, C =0.2, D =0.1;
s11, repeating the steps S1-S10 until the calculation of the channel quality evaluation values Q of the channels between all the positioning labels to be evaluated and the positioning base stations is completed.
Based on the above UWB indoor positioning channel quality evaluation method, the present embodiment can provide continuous channel quality evaluation results rather than simple classification, that is, when the channel quality evaluation value is higher, the probability of the current ranging error is higher, the channel quality evaluation value is lower, and the probability of the current ranging error is lower, so that even if the nodes are both in LOS or NLOS conditions, the distance between the nodes can be effectively evaluated.
While the invention has been described with reference to specific embodiments, any feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise; all of the disclosed features, or all of the method or process steps, may be combined in any combination, except mutually exclusive features and/or steps.
Claims (5)
1. A UWB indoor positioning channel quality assessment method is characterized by comprising the following steps:
s0, selecting CIR (channel impulse response) data without multipath or with multipath signal power not more than 10% of the first path signal power as a channel quality evaluation template, performing signal extraction and first path detection on the channel quality evaluation template in the same way as the steps S2-S4, and taking the pulse width w of the first path signal of the processed channel quality evaluation template as a standard pulse width;
s1, obtaining a distance measurement value and CIR data to be evaluated; the method specifically comprises the following steps:
s101, acquiring the flight time between a positioning tag and a positioning base station by adopting a single-side double-pass ranging method or a double-side double-pass ranging method;
s102, calculating CIR data by the ranging ending frame and the local known data frame through spread spectrum sliding correlation, and taking the CIR data as CIR data to be evaluated;
s103, obtaining a distance measurement value based on flight time conversion;
s2, calculating a self-adaptive signal detection threshold T1, and extracting signals of the CIR data to be evaluated based on the self-adaptive signal detection threshold; the method specifically comprises the following steps:
s201, calculating to obtain a self-adaptive signal detection threshold T1 by using a constant false alarm detection algorithm on the CIR data to be evaluated, wherein the false alarm probability of constant false alarm detection is not more than 10 -3 ；
S202, carrying out signal detection on the CIR data to be evaluated according to the self-adaptive signal detection threshold T1, extracting signals with the amplitude higher than the self-adaptive signal detection threshold T1, and filtering out noise with the amplitude lower than the self-adaptive signal detection threshold T1;
s3, judging the result of signal extraction, and if a signal is detected, executing a step S4; otherwise, outputting a channel quality assessment value Q =0, and jumping to the step S11;
s4, extracting peak values of the CIR signals to be evaluated obtained in the S2, setting a first path detection threshold T2, and taking the first peak exceeding the first path detection threshold T2 as a first path signal;
s5, aligning a first path signal in the CIR signal to be evaluated with a first path signal of a channel quality evaluation template to obtain a preprocessed channel quality evaluation template S and the CIR signal M to be evaluated, wherein the total point number of S and M is N; recording an index value n corresponding to the occurrence time of the first path signal in M 0 And an index value n corresponding to the occurrence time of the maximum peak 1 ；
S6, calculating a channel ideal factor A:
wherein M is i I point, mu, representing the preprocessed CIR signal M to be evaluated M And σ M Respectively, the mean value and standard deviation of M; s. the i Point i, μ, representing the preprocessed channel quality assessment template S S And σ S Respectively the mean value and the standard deviation of S;
s7, calculating a power dispersion factor B:
wherein A is FP The amplitude, A, of the first-path signal of the preprocessed CIR signal M to be evaluated n The amplitude of the nth peak of M;
s8, calculating a first-path strength factor C:
s9, calculating a first path blocking factor D;
reducing the self-adaptive signal detection threshold T1 to obtain a new self-adaptive signal detection threshold T3, extracting the signal of the CIR data to be evaluated based on the self-adaptive signal detection threshold T3, extracting the peak value of the extracted CIR signal to be evaluated, and counting to obtain an index value n 0 The number of signal peaks P' appearing before, calculating a first path blocking factor D:
s10, calculating a channel quality assessment value Q:
Q＝a·A+b·B+c·C+d·D
wherein, a, B, C, D are the weights of channel ideal factor a, power dispersion factor B, first path strength factor C and first path blocking factor D, respectively, a + B + C + D =1, and a is the maximum;
s11, repeating the steps S1-S10 until the calculation of the channel quality evaluation values Q of the channels between all the positioning labels to be evaluated and the positioning base stations is completed.
2. The UWB indoor positioning channel quality estimation method of claim 1, wherein the specific procedure of lowering the adaptive signal detection threshold T1 in the step S9 is to increase a false alarm probability of constant false alarm detection by an order of magnitude.
3. The UWB indoor positioning channel quality estimation method of claim 1, wherein the first path detection threshold T2 is set to 10% to 30% of the maximum peak value of the CIR signal in the step S4.
4. The UWB indoor positioning channel quality estimation method of claim 1, wherein in the step S10, weights a, B, C, D of the channel ideal factor a, the power dispersion factor B, the first path intensity factor C, and the first path blocking factor D are sequentially decreased.
5. The UWB indoor positioning channel quality estimation method of claim 1, wherein the ranging end frame is: when a unilateral two-way ranging method is adopted and ranging is initiated by a positioning tag or a bilateral two-way ranging method is adopted and ranging is initiated by a positioning base station, a ranging ending frame is used for calculating the flight time for the positioning tag and then sending a UWB data frame of the positioning base station; when the unilateral double-range distance measurement method is adopted and the positioning base station initiates distance measurement or the bilateral double-range distance measurement method is adopted and the positioning label initiates distance measurement, the distance measurement ending frame calculates the flight time for the positioning base station and then sends the flight time to the positioning label, and the positioning label receives the flight time and then sends back the UWB data frame of the positioning base station.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111526782.9A CN114285500B (en) | 2021-12-14 | 2021-12-14 | UWB indoor positioning channel quality assessment method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111526782.9A CN114285500B (en) | 2021-12-14 | 2021-12-14 | UWB indoor positioning channel quality assessment method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114285500A CN114285500A (en) | 2022-04-05 |
| CN114285500B true CN114285500B (en) | 2023-03-24 |
Family
ID=80872073
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111526782.9A Active CN114285500B (en) | 2021-12-14 | 2021-12-14 | UWB indoor positioning channel quality assessment method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114285500B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115348540B (en) * | 2022-08-16 | 2023-05-16 | 青岛柯锐思德电子科技有限公司 | Tracking method for continuous positioning in NLOS environment |
| CN115343672B (en) * | 2022-08-16 | 2023-08-15 | 青岛柯锐思德电子科技有限公司 | Head path signal enhancement and detection method based on arrival phase difference assistance |
| CN116224225B (en) * | 2023-05-10 | 2023-07-25 | 北京白水科技有限公司 | Method, device and equipment for determining range confidence degree applied to radio range finding |
| CN116582814B (en) * | 2023-05-12 | 2023-10-10 | 青岛柯锐思德电子科技有限公司 | UWB ranging error calculation method |
| CN116582815B (en) * | 2023-05-22 | 2023-10-24 | 青岛柯锐思德电子科技有限公司 | LOS and NLOS scene judging method based on ranging channel evaluation |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018133264A1 (en) * | 2017-01-18 | 2018-07-26 | 深圳大学 | Indoor automatic human body positioning detection method and system |
| CN112748397A (en) * | 2020-12-22 | 2021-05-04 | 重庆邮电大学 | UWB positioning method based on self-adaptive BP neural network under non-line-of-sight condition |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9002286B2 (en) * | 2009-03-30 | 2015-04-07 | Massachusetts Institute Of Technology | Method and system for identification and mitigation of errors in non-line-of-sight distance estimation |
| CN109490826B (en) * | 2018-10-26 | 2022-12-09 | 西安电子科技大学 | Ranging and position positioning method based on radio wave field intensity RSSI |
| CN110351655B (en) * | 2019-07-10 | 2021-06-18 | 博睿泰克科技（宁波）有限公司 | Indoor positioning method and system based on signal multipath propagation measurement |
| CN112533149B (en) * | 2020-11-27 | 2022-06-07 | 桂林理工大学 | Moving target positioning algorithm based on UWB mobile node |
| CN113469110B (en) * | 2021-07-15 | 2024-05-07 | 北京邮电大学 | UWB non-line-of-sight signal identification method based on deep learning |
-
2021
- 2021-12-14 CN CN202111526782.9A patent/CN114285500B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018133264A1 (en) * | 2017-01-18 | 2018-07-26 | 深圳大学 | Indoor automatic human body positioning detection method and system |
| CN112748397A (en) * | 2020-12-22 | 2021-05-04 | 重庆邮电大学 | UWB positioning method based on self-adaptive BP neural network under non-line-of-sight condition |
Non-Patent Citations (2)
| Title |
|---|
| 一种顾及TOA数据优选的抗差卡尔曼滤波室内定位方法;卢彦懿等;《地理信息世界》;20200825(第04期);全文 * |
| 刘泽龙等.UWB密集多径下基于检测概率差值最大的TOA估计方法.《信号处理》.2012,(第08期),全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114285500A (en) | 2022-04-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114285500B (en) | UWB indoor positioning channel quality assessment method | |
| CN103323812B (en) | For the system and method for locating | |
| US10374902B2 (en) | Method for clustering wireless channel MPCs based on a KPD doctrine | |
| US20080032708A1 (en) | Method for estimating jointly time-of-arrival of signals and terminal location | |
| JP4957770B2 (en) | POSITION ESTIMATION SYSTEM, POSITION ESTIMATION DEVICE, POSITION ESTIMATION METHOD, AND PROGRAM | |
| CN108038419B (en) | Wi-Fi-based indoor personnel passive detection method | |
| CN109490826B (en) | Ranging and position positioning method based on radio wave field intensity RSSI | |
| CN107607937B (en) | Radar target ranging method based on time reversal | |
| KR20070054664A (en) | Method for estimating time of arrival of signal received in wireless communication system | |
| JP6032469B2 (en) | Source estimation method and source estimation apparatus using the same | |
| Liu et al. | A research on CSI-based human motion detection in complex scenarios | |
| KR101042751B1 (en) | Apparatus and method for measure distance in wireless environment | |
| CN103532647A (en) | Sight distance propagation path judging method and system on basis of time domain features of WiFi (Wireless Fidelity) physical layer | |
| JP2013205398A (en) | Sending source estimation method and sending source estimation apparatus utilizing the same | |
| US7916814B2 (en) | Method and apparatus for real-time pulse parameter estimator | |
| CN106879068A (en) | The arrival time method of estimation of signal under a kind of strong multi-path environment | |
| JP2023549054A (en) | Hybrid method for time-of-arrival based ranging | |
| CN105554882A (en) | 60GHz non-line of sight (NLOS) identification and wireless fingerprint positioning method based on energy detection | |
| CN104459685A (en) | Multi-target high-speed positioning method for statistic MIMO radar | |
| CN114205906B (en) | Indoor positioning method based on UWB channel quality assessment | |
| KR102416604B1 (en) | Mehtod and device for precise positioning for wireless communication system | |
| CN111263321A (en) | Method for improving indoor ultra-wideband positioning accuracy of TOA (time of arrival) | |
| CN113608193A (en) | Radar multi-target distance and speed estimation method based on UNet | |
| Silva et al. | Towards non-line-of-sight ranging error mitigation in industrial wireless sensor networks | |
| CN105738866B (en) | A kind of identification of 60GHz non line of sight and wireless fingerprint positioning method based on energy measuring |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |