Design method of pseudolite vector tracking receiver based on positioning quality assessment fingerprint

Abstract

The invention proposes a method for designing a pseudo-satellite vector tracking receiver based on positioning quality assessment fingerprint, comprising the following steps: S1. using a vector tracking filter to filter a carrier and a C / A code simultaneously, and generating a code offset value and a carrier offset value; S2. performing quality assessment on intermediate signals such as intermediate frequency signals, C / A codes, and various characteristic parameters in the positioning process such as capture, correlation, filtering, etc., and obtaining a series of evaluation indicators such as power spectrum characteristics, S-curve offset values, etc.; S3. updating a fingerprint library with the obtained evaluation indicators; S4. using the data in the fingerprint library to adjust the parameters of modules such as a code generator, a code NCO, a code identifier, and a vector tracking filter, and obtaining a new quality assessment indicator, and updating the fingerprint library again, and continuously circulating; the invention can quantitatively analyze and record the positioning environment, and can adjust the receiver algorithm parameters according to the characteristics of the positioning environment, so as to achieve the effect of improving the positioning accuracy in a complex indoor multipath environment.

Description

Technical Field

[0001] The invention relates to a design method for a pseudo-satellite vector tracking receiver based on a positioning quality assessment fingerprint, and belongs to the technical field of navigation and positioning. Background Art

[0002] In terms of indoor positioning, the current research directions mainly include WIFI positioning, SLAM positioning, UWB positioning and pseudo-satellite positioning. Among them, pseudo-satellite technology has the advantages of high accuracy and seamless indoor and outdoor positioning, and is a relatively effective indoor positioning method. However, various indoor walls and debris will generate a large number of complex multipath signals, so multipath signals have become the main reason for interfering with indoor positioning accuracy. In recent years, in addition to many outdoor GNSS anti-multipath methods, many new anti-multipath methods have been applied to pseudo-satellite positioning systems.

[0003] At present, the technologies close to the present invention include narrow correlation method, strobe correlator algorithm, MEDLL algorithm and vector tracking receiver algorithm. Among them, the narrow correlator method will lead to a decrease in tracking robustness, while the strobe correlator method and MEDLL algorithm require very high hardware computing power. At the same time, the common disadvantage of these algorithms is that the parameters are all preset values, which cannot be adjusted intelligently according to the positioning environment, and the various characteristic parameters related to the environment in the positioning process are not utilized, which leads to poor positioning accuracy in complex indoor multipath environments. Summary of the invention

[0004] Technical problem: The present invention focuses on solving the problem of poor positioning accuracy of pseudo-satellite receivers under complex multipath conditions indoors. A pseudo-satellite vector tracking receiver design method based on positioning quality assessment fingerprint is provided. The goal to be achieved is to quantitatively analyze and record the positioning environment, and adjust the receiver algorithm parameters according to the characteristics of the positioning environment, thereby improving the positioning accuracy in complex multipath environments indoors.

[0005] Technical solution:

[0006] The method for designing a pseudolite vector tracking receiver based on positioning quality assessment fingerprint of the present invention comprises the following steps:

[0007] S1. Use a vector tracking filter to filter the carrier and C / A code at the same time to generate a code offset value and a carrier offset value;

[0008] S2. Perform quality assessment on various characteristic parameters in the positioning process including intermediate frequency signal, intermediate signal of C / A code and capture, correlation and filtering, and obtain corresponding evaluation indicators including power spectrum characteristics and S curve offset value;

[0009] S3. Update the fingerprint library using the evaluation index obtained in step S2;

[0010] S4. Using the data in the updated fingerprint library, adjust the parameters of the code generator, code NCO, code discriminator, and vector tracking filter to obtain new quality evaluation indicators, update the fingerprint library again, and continue the cycle.

[0011] Furthermore, the vector tracking filter described in step S1 first uses an ordinary filter for positioning, and uses the positioning result to initialize the vector tracking filter; the vector tracking filter is updated using the Kalman filtering method, and outputs the code correction and the carrier correction to the local code NCO module and the local carrier NCO module, and then outputs the positioning result.

[0012] Furthermore, the quality assessment described in step S2 is specifically performed as follows:

[0013] The first step is to collect data. A time interval T' is set. The quality assessment module continuously collects the original pseudo-satellite signal sampling values, positioning results, and various characterization values ​​in the capture, tracking, and positioning processes, such as capture results, correlator outputs, and phase detector outputs. When the collection time reaches T', the evaluation function is used to perform quality assessment on the collected data sequence.

[0014] The second step is quality assessment. Suppose that in the first step, a total of n data sequences α1, α2, …, α n , set up m evaluation methods f1,f2,…,f m , and obtain m quality evaluation indicators β1, β2, …, β m ,Right now:

[0015]

[0016] The third step is to clear the data and obtain m quality evaluation indicators β1, β2, …, β m After that, the quality assessment module sends the positioning result (x, y, z) and the quality assessment index to the fingerprint library, clears the collected data sequence, and starts data collection for the next time interval.

[0017] Furthermore, the specific method of updating the fingerprint database in step S3 is as follows:

[0018] The first step is to initialize the fingerprint library. Each data unit in the fingerprint library contains two parts. The first part is the coordinate value, which is based on (0,0,0) and the set coordinate interval Δ x ,Δ y ,Δ z Generate one by one; the second part is the fingerprint value, which is the quality assessment index β1,β2,…,β corresponding to the coordinate value m; During initialization, the positioning environment is measured to obtain a topographic map, and the topographic map is input into the simulation software. All coordinate points are simulated and positioned one by one in the simulation software to obtain a simulation positioning quality evaluation index as the initial value of the fingerprint library;

[0019] The second step is coordinate matching. After the fingerprint library receives the data sent by the quality assessment module, it first performs coordinate matching. Suppose the received positioning result is (x, y, z), and finds the nearest coordinate point (x′, y′, z′) in the corresponding fingerprint library so that the distance value Minimum, update the fingerprint value of this point;

[0020] Step 3: Fingerprint update. When the fingerprint is updated, the larger the distance value Δ is, the higher the received quality evaluation index β1, β2, …, β m The less accurate it is, the smaller the update should be, which means calculating the new fingerprint [β1′,β2′,…,β m ′] new The fingerprint update function g should be followed:

[0021] [β1′,β2′,…,β m ′] new =g([β1′,β2′,…,β m ′] old ,Δ,[β1,β2,…,β m ])

[0022] Furthermore, the parameter adjustment described in step S4 is specifically performed as follows:

[0023] The first step is coordinate matching. The parameter adjustment module receives the positioning result (x, y, z) and sends it to the fingerprint library for matching. The distance value is also used. Minimum method, get the fingerprint value β1,β2,…,β corresponding to the coordinate m ;

[0024] The second step is parameter adjustment. The fingerprint value is calculated by the parameter adjustment function to obtain a new parameter value. The key nodes such as the code spacing of the code generator or the phase detection function of the code identifier are modified. The original parameters are γ1, γ2, …, γ l , then the new parameters γ1′,γ2′,…,γ n ′ should follow the parameter update function h:

[0025]

[0026] The third step is to loop the operation, update the parameter value, continue positioning, obtain new quality assessment indicators, and update the fingerprint library again. This cycle continues, so that the positioning accuracy is gradually improved.

[0027] Beneficial effects: the benefits brought by the invention and the indicators achieved.

[0028] The present invention adopts a scheme of recording and updating a positioning quality assessment fingerprint library, and then using the fingerprint library to adjust the parameters of the receiver. The physical characteristics of the positioning environment can be expressed in the positioning quality assessment fingerprint, and recorded in the fingerprint library and updated in real time. Using the fingerprint library to adjust the receiver can make the algorithm parameters of the receiver adapt to the physical characteristics of the positioning environment at any time, so as to achieve the effect of improving the accuracy of the receiver. At the same time, the characteristics of vector tracking of this receiver make it have high robustness, and it can still have strong tracking ability when the pseudo satellite is not visible. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a design structure diagram of a pseudo-satellite vector tracking receiver based on positioning quality assessment fingerprint of the present invention. DETAILED DESCRIPTION

[0030] The implementation process of the present invention is described in detail below in conjunction with the embodiments and the accompanying drawings.

[0031] Example:

[0032] S1. Use a vector tracking filter to filter the carrier and C / A code simultaneously to generate a code offset value and a carrier offset value.

[0033] The first step is to use a common filter for positioning and use the positioning results to initialize the vector tracking filter. When the positioning is stable, collect two consecutive positioning result vectors. Among them, x1, y1, z1 represent the values ​​of the position coordinates in the x, y, and z directions in the first positioning result. Represents the value of the velocity vector in the x, y, and z directions, t b1 represents the clock error, t d1 Represents clock drift. Initial value of the state vector of the vector tracking filter

[0034] Represents the error value of each data in the positioning result. The initial value of the position vector of the vector tracking filter L(0) = [x2, y2, z2] T , initial value of velocity vector Initial value of clock error t b (0) = t b2 , clock drift initial value t d (0) = t d2 .

[0035] In the second step, the vector tracking filter is updated using the Kalman filter method, and the code correction and carrier correction are output to the local code NCO module and the local carrier NCO module, and then the positioning result is output. The details are as follows:

[0036] 1. Assume that the current time is k+1 and the previous time is k. The estimated value of the state vector at the previous time is known Error variance matrix P(k), position vector estimate Velocity vector estimate Clock error estimate Clock drift initial value

[0037] 2. Assume that the observation value update period is T, and use the motion equation to obtain the predicted value at time k+1 Follow this formula:

[0038]

[0039]

[0040]

[0041]

[0042] 3. Assume there are N pseudo-satellites in total, and calculate the unit sight vector for each pseudo-satellite. For the nth pseudo-satellite, let the position of the pseudo-satellite be L n , then the unit sight vector Follow the calculation formula below:

[0043]

[0044] 4. Generate the code NCO control value and carrier NCO control value of each pseudo-satellite. Assume the pseudo-satellite carrier frequency is f carr , the C / A code frequency is f code , the receiver intermediate frequency is f IF , then for the nth pseudo-satellite, the local C / A code phase at the previous moment is known Can generate local carrier frequency Local C / A code frequency and local C / A code phase Follow this formula:

[0045]

[0046]

[0047]

[0048] Where c is the speed of light.

[0049] 5. Input carrier NCO, code NCO, and code generator module to generate local carrier and local C / A code, which are mixed, correlated, and phase-detected with the intermediate frequency signal to obtain code discriminator output and carrier discriminator output. For the nth pseudo-satellite, the code discriminator module output is The output of the carrier discriminator module is

[0050] 6. Generate Observations where δρ n (k+1) is the pseudorange error of the nth pseudosatellite, is the pseudo-range rate error of the nth pseudo-satellite. Its calculation follows the following formula:

[0051]

[0052]

[0053] 7. Use the Kalman filter equation to estimate the state vector Update the error variance matrix P(k) according to the following formula:

[0054]

[0055] P(k+1|k)=FP(k)F T +Q

[0056] K(k+1)=P(k+1|k)H(k+1) T (H(k+1)P(k+1|k)H(k+1) T +R) -1

[0057]

[0058] P(k+1)=(IK(k+1)H(k+1))P(k+1|k)

[0059] Among them, F is the system state transfer matrix, Q is the system noise variance matrix, H is the observation matrix, and R is the observation noise variance matrix. The above parameters are defined as:

[0060] T 3×3 =T·I 3×3 ,

[0061]

[0062]

[0063]

[0064]

[0065]

[0066] in, is the variance of the receiver's dynamic noise on the xyz axis, as well as the clock error noise and clock drift noise, is the measurement noise variance of the pseudorange error and pseudorange rate error of the nth pseudosatellite. These values ​​are empirical values ​​in practical applications based on environmental conditions and receiver physical characteristics.

[0067] The final result The format is:

[0068]

[0069] 8. Use the updated state vector estimate and the predicted value at time k+1 Get the estimated value at time k+1 The update equation is as follows:

[0070]

[0071]

[0072]

[0073]

[0074] The above estimated value can be used for updating at the next moment.

[0075] S2. Perform quality assessment on intermediate signals such as intermediate frequency signals, C / A codes, and various characteristic parameters in the positioning process such as capture, correlation, and filtering, and obtain a series of evaluation indicators such as power spectrum characteristics and S-curve offset values.

[0076] The first step is to collect data. Set the time interval T' = 0.5s. The quality assessment module continuously collects the following data sequence. When the collection time reaches T', the evaluation function is used to perform quality assessment on the collected data sequence.

[0077] 1. Original pseudo-satellite signal α1.

[0078] 2. Positioning result α2 = [X, Y, Z]: X, Y, Z are the data sequences consisting of the numerical values ​​of the positioning results on the x, y, and z axes respectively.

[0079] 3. The code discriminator outputs α3.

[0080] 4. The local code NCO outputs α4.

[0081] 5. The pseudo-range value corresponding to each pseudo-satellite α5 = [ρ1, ρ2, …, ρ N ]:ρ n is the pseudorange data sequence corresponding to the nth pseudosatellite, and there are N pseudosatellites in total.

[0082] The second step is quality assessment. In the first step, 5 data sequences were collected and four methods were used to assess their quality. Four quality assessment indicators were obtained, as follows:

[0083] 1. Perform power spectrum analysis on the original pseudo-satellite signal α1 to obtain the power spectrum distortion rate β1.

[0084] 2. Calculate the variance of the coordinate values ​​in the α2 sequence, that is The positioning result dispersion β2 is obtained.

[0085] 3. Multipath error influence β3 = f3 (α3, α4):

[0086] (1) With the code discriminator output α3 as the y-axis and the local code NCO output α4 as the x-axis, plot the parameter function α3(α4).

[0087] (2) The parameter function should be an "S curve". Find the intersection of the curve and the x-axis that is closest to the origin. The x-axis coordinate value of this point is the multipath error influence β3.

[0088] 4. Single pseudo-satellite credibility β4 = f4 (α5):

[0089] Calculate the variance of the pseudorange sequences corresponding to all pseudolites. For the nth pseudolites, the corresponding pseudorange data sequence is ρ n , and the pseudorange variance is There are N pseudo-satellites in total, and the final credibility of a single pseudo-satellite is

[0090] The third step is to clear the data. After obtaining the quality assessment index, the quality assessment module will The quality assessment indicators β1, β2, β3, and β4 are sent to the fingerprint library, the collected data sequence is cleared, and data collection for the next time interval begins.

[0091] S3. Update the fingerprint library with the obtained evaluation indicators.

[0092] The first step is to initialize the fingerprint library. Each data unit in the fingerprint library contains two parts. The first part is the coordinate value, which is based on (0,0,0) and the set coordinate interval Δ x =Δ y =Δ z =0.1m one by one. The second part is the fingerprint value, which is the quality evaluation index β1, β2, β3, β4 corresponding to the coordinate value. During initialization, the topographic map is obtained by measuring the positioning environment, and the topographic map is input into the simulation software. All coordinate points are simulated and positioned one by one in the simulation software to obtain the simulation positioning quality evaluation index as the initial value of the fingerprint library.

[0093] The second step is coordinate matching. After the fingerprint library receives the data sent by the quality assessment module, it first performs coordinate matching. Suppose the coordinate value of the received data is (x, y, z), find the nearest coordinate point (x′, y′, z′) in the corresponding fingerprint library so that the distance value Minimum, update the fingerprint value of this point.

[0094] Step 3: Fingerprint update. New fingerprint [β1′,β2′,β3′,β4′] new The fingerprint update function should be followed:

[0095] When β1β2<β1′β2′:

[0096]

[0097] When β1β2>β1′β2′:

[0098] [β1′,β2′,β3′,β4′] new =[β1′,β2′,β3′,β4′] old

[0099] S4. Using the data in the fingerprint library, adjust the parameters of the code generator, code NCO, code identifier, vector tracking filter and other modules to obtain new quality evaluation indicators, update the fingerprint library again, and continue the cycle.

[0100] The first step is coordinate matching. The parameter adjustment module receives the positioning result (x, y, z) and sends it to the fingerprint library for matching, also using the distance value. The minimum method is used to obtain the fingerprint values ​​β1, β2, β3, and β4 corresponding to the coordinates.

[0101] The second step is parameter adjustment. The fingerprint value is calculated by the parameter adjustment function to obtain a new parameter value, and the key node parameters are modified as follows:

[0102] 1. For the code discriminator module, adjust the code discriminator function correction γ1: the code discriminator module input is the correlator output IE ,I P ,I L , the output is the code offset δ, following the function When the parameters are updated, the new parameter value γ1′=-k1β3, where k1 is the set correction ratio.

[0103] 2. For the local code generator module, adjust the correlator spacing γ2′=k2β1: k2 is the set correction ratio, which should make γ2′ no more than 0.1 code chip at the minimum and no more than 0.5 code chip at the maximum.

[0104] 3. For the vector tracking filter, adjust the single pseudo-satellite positioning weight: introduce a single pseudo-satellite positioning weight vector when multiple pseudo-satellites are positioned. It is used to introduce the credibility of each pseudo-satellite into the positioning solution. The pseudo-range obtained by the pseudo-satellite with higher credibility accounts for a higher proportion in the positioning solution. When updating, γ3′=β4.

[0105] The third step is loop operation. After updating the parameter value, continue positioning, obtain new quality evaluation indicators, and update the fingerprint library again. This cycle continues, so that the positioning accuracy is gradually improved.

[0106] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above specific embodiments. The above specific embodiments and the description in the specification are only for further illustrating the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of the present invention to be protected is defined by the claims and their equivalents.

Claims

1. A pseudolite vector tracking receiver design method based on positioning quality assessment fingerprint, characterized in that: The method comprises the following steps: S1. Use a vector tracking filter to filter the carrier and C / A code at the same time to generate a code offset value and a carrier offset value; S2. Perform quality assessment on various characteristic parameters in the positioning process including intermediate frequency signal, intermediate signal of C / A code and capture, correlation and filtering, and obtain corresponding evaluation indicators including power spectrum characteristics and S curve offset value; S3. Update the fingerprint library using the evaluation index obtained in step S2; S4. Using the data in the updated fingerprint library, adjust the parameters of the code generator, code NCO, code discriminator, and vector tracking filter to obtain new quality assessment indicators, update the fingerprint library again, and continue the cycle; The specific method of the quality assessment described in step S2 is as follows: The first step is to collect data. A time interval T′ is set. The quality assessment module continuously collects the original pseudo-satellite signal sampling values, positioning results, and various characterization values ​​in the capture, tracking, and positioning processes, such as capture results, correlator outputs, and phase detector outputs. When the collection time reaches T′, the evaluation function is used to perform quality assessment on the collected data sequence. The second step is quality assessment. Suppose that in the first step, a total of n data sequences α1, α2, …, α n , set up m evaluation methods f1,f2,…,f m , and obtain m quality evaluation indicators β1, β2, …, β m ,Right now: β1＝f1(α1,α2,…,α n ) β2＝f2(α1,α2,…,α n ) β m ＝f m (α1,α2,…,α n ) The third step is to clear the data and obtain m quality evaluation indicators β1, β2, …, β m After that, the quality assessment module sends the positioning result (x, y, z) and the quality assessment index to the fingerprint library, clears the collected data sequence, and starts data collection for the next time interval; The specific method of updating the fingerprint database in step S3 is as follows: The first step is to initialize the fingerprint library. Each data unit in the fingerprint library contains two parts. The first part is the coordinate value, which is based on (0,0,0) and the set coordinate interval Δ x ,Δ y ,Δ z Generate one by one; the second part is the fingerprint value, which is the quality assessment index β1,β2,…,β corresponding to the coordinate value m ; During initialization, the positioning environment is measured to obtain a topographic map, the topographic map is input into the simulation software, all coordinate points are simulated and positioned one by one in the simulation software, and the simulation positioning quality evaluation index is obtained as the initial value of the fingerprint library; The second step is coordinate matching. After the fingerprint library receives the data sent by the quality assessment module, it first performs coordinate matching. Suppose the received positioning result is (x, y, z), and finds the nearest coordinate point (x′, y′, z′) in the corresponding fingerprint library so that the distance value Minimum, update the fingerprint value of this point; Step 3: Fingerprint update. When the fingerprint is updated, the larger the distance value Δ is, the higher the received quality evaluation index β1, β2, …, β m The less accurate it is, the smaller the update should be, which means calculating the new fingerprint [β1′,β2′,…,β m ′] new The fingerprint update function g should be followed: [β1′,β2′,…,β m ′] new =g([β1′,β2′,…,β m ′] old ,Δ,[β1,β2,…,β m ]) The specific method of parameter adjustment in step S4 is as follows: The first step is coordinate matching. The parameter adjustment module receives the positioning result (x, y, z) and sends it to the fingerprint library for matching. The distance value is also used. Minimum method, get the fingerprint value β1,β2,…,β corresponding to the coordinate m ; The second step is parameter adjustment. The fingerprint value is calculated by the parameter adjustment function to obtain a new parameter value. The key nodes such as the code spacing of the code generator or the phase detection function of the code identifier are modified. The original parameters are γ1, γ2, …, γ l , then the new parameters γ1′,γ2′,…,γ n ′ should follow the parameter update function h: γ1′=h(γ1,Δ,β1,β2,…,β m ) γ2′=h(γ2,Δ,β1,β2,…,β m ) c l ′=h(γ l ,Δ,β1,β2,…,β m ) The third step is to loop the operation, update the parameter value, continue positioning, obtain new quality assessment indicators, and update the fingerprint library again. This cycle continues, so that the positioning accuracy is gradually improved.

2. The method for designing a pseudolite vector tracking receiver based on positioning quality assessment fingerprint according to claim 1, characterized in that: The vector tracking filter described in step S1 first uses an ordinary filter for positioning, and uses the positioning result to initialize the vector tracking filter; the vector tracking filter is updated using the Kalman filtering method, and outputs the code correction and the carrier correction to the local code NCO module and the local carrier NCO module, and then outputs the positioning result.

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