Position estimation device, position estimation system, position estimation method, control circuit, and storage medium

Abstract

A position estimation device (1) for estimating the position of a mobile station on the basis of the results of distance measurements between each of a plurality of fixed stations and a mobile station (100) includes: a distance calculation unit (13) for calculating an estimated distance between each of the plurality of fixed stations and the mobile station on the basis of the estimated position of the mobile station and the positions of the plurality of fixed stations; an error calculation unit (14) for calculating, for each of the fixed stations, the error between the distance measurement result and the estimated distance; a least squares processing unit (16) that is an estimated position adjustment amount calculation unit for calculating an adjustment amount of the estimated position on the basis of a distance error that is the error calculated by the error calculation unit and a partial differential result of the estimated distance; and a position information update unit (17) for updating position information indicating the estimated position on the basis of the adjustment amount. The position estimation device (1) estimates the position of the mobile station by repeatedly executing an estimated position update process a set number of times, the estimated position update process including a process for calculating the estimated distance for each of the fixed stations, a process for calculating the error for each of the fixed stations, a process for calculating the adjustment amount, and a process for updating the position information.

Description

Position estimation device, position estimation system, position estimation method, control circuit, and storage medium

[0001] The present disclosure relates to a position estimation device, a position estimation system, a position estimation method, a control circuit, and a storage medium that estimate the position of an object using an ultra-wide band (UWB) signal.

[0002] There is a technology that uses the transmission and reception timing of UWB communications to measure the distance from the propagation time between a fixed station fixed on the ground and a mobile station, and estimates the position of the mobile station using the obtained distance measurement result and the position of the fixed station. The UWB signal transmitted and received between the mobile station and the fixed station for distance measurement is an ultrashort pulse signal in the time domain. Therefore, using UWB signals makes it possible to grasp the transmission and reception timing of communications with high resolution, measure the distance with high accuracy, and ultimately estimate the position of the mobile station with high accuracy.

[0003] A common location estimation algorithm used for location estimation is the least squares method, which solves a three-dimensional nonlinear simultaneous equation relating the positions of multiple fixed stations and the distance measurement results between the fixed stations and the mobile station to estimate the three-dimensional position (x, y, z) of a mobile station. The mobile station's position is estimated by successive approximation calculations. However, when the fixed station and the mobile station receive reflected waves from walls or other obstacles, the pulse waveform of the UWB signal is distorted, causing the distance measurement result to be longer than the actual distance between the fixed station and the mobile station. As a result, a low-accuracy distance measurement result is used for location estimation. This poses a problem in that the mobile station's position cannot be estimated appropriately.

[0004] As a technique for solving such problems, for example, Patent Document 1 discloses a method for identifying whether radio wave propagation between a fixed station and a mobile station is line of sight (LOS) or non-line of site (NLOS) based on statistical data of amplitude and delay of a UWB signal, and improving the accuracy of location estimation based on the identification result. The method disclosed in Patent Document 1 uses the peaks of the received waveform of the UWB signal, the average excess delay spread of multipath components, and the root mean square delay spread as statistical data of amplitude and delay when the UWB signal is received.

[0005] Patent No. 4567093

[0006] However, the technology described in Patent Document 1 has a problem in that, in areas where the radio wave environment is unknown, it is necessary to collect statistical data related to multipath channels in advance, and if the statistical data is not obtained in advance, it is not possible to estimate the location with high accuracy. For this reason, it is desirable to realize a technology that can estimate the location with high accuracy without requiring advance preparation such as data collection.

[0007] The present disclosure has been made in consideration of the above, and aims to provide a position estimation device that can estimate position with high accuracy even in an environment where the accuracy of ranging based on the timing of signal transmission and reception is reduced due to the influence of reflected waves, etc.

[0008] In order to solve the above-mentioned problems and achieve the object, the present disclosure provides a position estimation device that estimates the position of a mobile station based on ranging results between each of a plurality of fixed stations and the mobile station, which are derived based on results of wireless communication between each of the plurality of fixed stations and the mobile station, and includes: a distance calculation unit that calculates, for each fixed station, an estimated distance, which is the distance between each of the plurality of fixed stations and the mobile station, based on the estimated position of the mobile station and the positions of the plurality of fixed stations; an error calculation unit that calculates, for each fixed station, an error between the ranging result and the estimated distance; an estimated position adjustment amount calculation unit that calculates an adjustment amount for the estimated position based on a distance error, which is the error calculated by the error calculation unit, and a partial differentiation result of the estimated distance; and a position information update unit that updates position information indicating the estimated position based on the adjustment amount, and is characterized in that the position estimation device estimates the position of the mobile station by repeatedly executing an estimated position update process, including a process by the distance calculation unit to calculate the estimated distance for each fixed station, a process by the error calculation unit to calculate the error for each fixed station, a process by the estimated position adjustment amount calculation unit to calculate the adjustment amount, and a process by the position information update unit to update the position information.

[0009] The position estimation device according to the present disclosure has the advantage of being able to estimate a position with high accuracy even in an environment in which the accuracy of distance measurement based on the timing of signal transmission and reception is reduced due to the influence of reflected waves and the like.

[0010] FIG. 1 is a diagram showing an example of the configuration of a position estimation system according to a first embodiment. FIG. 2 is a diagram showing an example of waveform distortion occurring in a multipath environment. FIG. 3 is a diagram showing a first example of a ranging result and a position estimation result. FIG. 4 is a diagram showing a second example of a ranging result and a position estimation result. FIG. 4 is a diagram showing an example of the configuration of a position estimation device according to a first embodiment. FIG. 2 shows a second example of the difference. FIG. 3 shows a third example of the ranging error between a fixed station and a mobile station. FIG. 4 shows an example of the weighting coefficients used in the least squares method processing in the position estimation device according to the third embodiment. FIG. 5 shows an example of the configuration of the position estimation device according to the fourth embodiment.

[0011] Hereinafter, a position estimation device, a position estimation system, a position estimation method, a control circuit, and a storage medium according to embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0012] First Embodiment. FIG. 1 is a diagram illustrating an example of the configuration of a position estimation system 200 according to a first embodiment. The position estimation system 200 includes a mobile station 100 that functions as a position estimation device, and a plurality of fixed stations 210 to 213 that are fixed on the ground. While FIG. 1 illustrates four fixed stations included in the position estimation system 200, the position estimation system 200 may include five or more fixed stations. In the following description, the fixed stations 210 to 213 may be referred to as fixed stations #0 to #3, respectively. When it is not necessary to distinguish between the fixed stations 210 to 213, the reference numerals may be omitted and the fixed stations may simply be referred to as "fixed stations."

[0013] The position estimation system 200 measures the distance between the mobile station 100 and each of the base stations 210 to 213 based on the transmission and reception timing of radio signals transmitted and received by radio communication between the mobile station 100 and each of the base stations 210 to 213, and estimates the position of the mobile station 100 based on the obtained multiple ranging results. Here, the radio signals used by the position estimation system 200 are, for example, UWB signals. As described above, when UWB signals are used, it is possible to grasp the transmission and reception timing of communication with high resolution, and it is possible to estimate the position of the mobile station 100. Note that in this embodiment and in embodiments 2 to 4 described below, examples of performing ranging using UWB signals are described, but each embodiment is also applicable to systems that perform ranging using radio signals other than UWB signals.

[0014] The positions of the mobile station 100 and the base stations 210-213 are expressed as three-dimensional positions, and the coordinate axes defining this three-dimensional space are the x-axis, y-axis, and z-axis. The positions of the base stations 210-213 are known, and the position of the base station 210 (base station #0) is (x0, y0, z0), the position of the base station 211 (base station #1) is (x1, y1, z1), the position of the base station 212 (base station #2) is (x2, y2, z2), and the position of the base station 213 (base station #3) is (x3, y3, z3).

[0015] Each fixed station transmits a beacon signal including its own location information, identification information, etc., and the mobile station 100 can recognize information about the fixed stations with which it can communicate based on the received beacon signal. The mobile station 100 calculates the propagation time required for wireless communication from the timing of transmission and reception of UWB signals between the mobile station 100 and the recognized fixed station, and obtains the distance measurement result to the fixed station.

[0016] When transmitting and receiving UWB signals between the mobile station 100 and each fixed station, if there is an object that reflects radio waves, such as a wall, reflected waves 232 are received in addition to direct waves 231, as shown in Figure 1. In this case, distortion occurs in the waveform of the received UWB signal.

[0017] 2 is a diagram showing an example of waveform distortion occurring in a multipath environment. As shown in FIG. 2, in a multipath environment, a received waveform 240 detected on the receiving side is distorted as a result of combining a waveform 241 of a direct wave 231 and a waveform 242 of a reflected wave 232. As shown in FIG. 2, the distorted received waveform 240 has a signal waveform that is later in time than the waveform 241 of the direct wave 231. Therefore, the distance measurement result based on this received waveform 240 tends to indicate an increased distance. In other words, the error increases. If the accuracy of the ranging result decreases in this way, the accuracy of estimating the position of the mobile station 100 based on the ranging result also decreases.

[0018] Here, examples of cases where the accuracy of the ranging result is degraded and cases where the accuracy of the ranging result is not degraded will be described with reference to Figures 3 and 4. Figure 3 is a diagram showing a first example of ranging results and position estimation results. Specifically, Figure 3 shows an example of the relationship between the ranging result and the position estimation result of the mobile station 100 when the accuracy of the ranging result is not degraded and there is no ranging error. Figure 4 is a diagram showing a second example of ranging results and position estimation results. Specifically, Figure 4 shows an example of the relationship between the ranging result and the position estimation result of the mobile station 100 when the accuracy of the ranging result is degraded and there is a ranging error.

[0019] 3, the distance measurement result between the mobile station 100 and the base station 210 is r0, the distance measurement result between the mobile station 100 and the base station 211 is r1, the distance measurement result between the mobile station 100 and the base station 212 is r2, and the distance measurement result between the mobile station 100 and the base station 213 is r3. The black circle indicates the position estimation result 110, which coincides with the actual position of the mobile station 100.

[0020] In the example of "(b) Case where there is a ranging error" shown in Figure 4, the ranging result between the mobile station 100 and the base station 211 contains an error due to the influence of reflected waves, etc., and this ranging result is designated as r1'. The ranging results between the mobile station 100 and the other base stations (base stations 210, 212, 213) do not contain any error, and these ranging results are designated as r0, r2, and r3, as in Figure 3. The ranging result r1' is assumed to be larger than the ranging result r1 in the example shown in Figure 3 (r1' > r1). The white circle indicates the actual position 120 of the mobile station 100, and the black circle indicates the position estimation result 110'. As shown in Figure 4, when there is a ranging error, an error occurs in the position estimation result.

[0021] In the first embodiment, a position estimation device that can suppress a decrease in the accuracy of position estimation even when the accuracy of the distance measurement result is decreased due to the influence of reflected waves will be described.

[0022] First, the principle of the position estimation method performed by the position estimation device 1 according to the first embodiment will be described based on the examples shown in FIGS.

[0023] In the example of Figure 3, mobile station 100 can communicate with base stations 210, 211, 212, and 213 under ideal radio wave propagation conditions, and the intersection of four circles with radii equal to the distance measurement results r0, r1, r2, and r3 is the location estimation result. For example, the least squares method is used as an algorithm for this location estimation. On the other hand, as in the example of Figure 4, where radio wave propagation conditions are not ideal due to the influence of reflected waves, errors occur in the distance measurement results. Because the distance measurement results contain errors, the distance measurement results between mobile station 100 and base stations 210, 211, 212, and 213 are r0, r1', r2, and r3, respectively, with r1' > r1. If the location of mobile station 100 is estimated using the least squares method using distance measurement results with errors such as r1', the intersection of four circles with radii equal to the distance measurement results cannot be obtained. In this case, the location that minimizes the squared error is estimated, and this is location estimation result 110'. That is, a position estimation error occurs with respect to the actual position 120 of the mobile station 100, which is indicated by a white circle. Therefore, the position estimation device 1 according to this embodiment repeatedly performs calculations to estimate the position of the mobile station 100 so as to reduce the influence of ranging results with large ranging errors, which are affected by reflected waves, etc., among the ranging results between the fixed stations and the mobile station 100. Specifically, the position estimation result is obtained by repeatedly performing calculations to estimate the position using the least squares method, which weights the ranging results between each fixed station and the mobile station 100 in inverse proportion to the square of the ranging error.

[0024] The location estimation device 1 according to the first embodiment will be described in detail below.

[0025] Fig. 5 is a diagram showing a configuration example of a position estimation device 1 according to the first embodiment. As shown in Fig. 5, in this embodiment, the position estimation device 1 is provided in a mobile station 100. Note that the position estimation device 1 may also be provided as a separate device outside the mobile station 100.

[0026] The position estimation device 1 includes an information acquisition unit 11, a position information setting unit 12, a distance calculation unit 13, an error calculation unit 14, a partial differential processing unit 15, a least squares method processing unit 16, a position information update unit 17, an update termination determination unit 18, and an estimation processing termination determination unit 19.

[0027] FIG. 6 is a flowchart showing an example of an operation of the position estimation device 1 according to the first embodiment to estimate the position of the mobile station 100.

[0028] 5 and 6, detailed operation of the position estimation device 1 will be described for each component. Here, as an example, the description will be given assuming that the number of fixed stations included in the position estimation system 200 is N=4, and the position information of the mobile station 100 is coordinates (x, y, z), as shown in Fig. 1. The fixed stations included in the position estimation system 200 are capable of transmitting and receiving UWB signals to and from the mobile station 100, and are seen as fixed stations capable of distance measurement from the mobile station 100 side.

[0029] In the position estimation device 1, the information acquisition unit 11 acquires the position information of each fixed station and the distance measurement results between each fixed station and the mobile station 100 (step S11). The information acquisition unit 11 acquires the position information and the distance measurement results periodically, for example, at a predetermined cycle.

[0030] The location information of each fixed station is acquired by receiving a beacon signal transmitted from each fixed station and analyzing the received beacon signal. The beacon signal is analyzed, for example, by a transmission / reception processing unit (not shown) of the mobile station 100. Note that the information acquisition unit 11 may also analyze the beacon signal.

[0031] The ranging results between each fixed station and the mobile station 100 are derived based on the results of wireless communication between each fixed station and the mobile station 100. Specifically, the mobile station 100 transmits and receives UWB signals to and from each fixed station, calculates the propagation time required for wireless communication from the transmission and reception timing of each UWB signal, and obtains ranging results indicating the distance to each fixed station from the calculated propagation time. The processing for obtaining the ranging results is performed, for example, by a transmission and reception processing unit of the mobile station 100. Note that the information acquisition unit 11 may perform each process from calculating the propagation time to obtaining the ranging results. The mobile station 100 may calculate the propagation time, and the information acquisition unit 11 may perform the process of calculating the ranging results from the propagation time.

[0032] The information acquisition unit 11 may acquire the location information of each fixed station and the distance measurement results between each fixed station and the mobile station 100 simultaneously or at different times. For example, the information acquisition unit 11 may acquire the distance measurement results between each fixed station and the mobile station 100 every time the mobile station 100 receives a report signal transmitted from each fixed station.

[0033] The location information setting unit 12 sets the location information of the mobile station 100 (step S12). Specifically, the location information setting unit 12 sets the location information (x (k) , y (k) , z (k) ) where k is the number of convergences for iterative approximation, k=1, 2, ..., N Loop The location information of the mobile station 100 when k=1 (x (1) , y (1) , z (1) ) does not necessarily need to be accurate. In step S12, which is executed first after step S11 is executed, that is, in the initial stage before the position of the mobile station 100 has been calculated, the coordinates of the periphery of a plurality of fixed stations may be set as the initial value of the position information of the mobile station 100. In step S12, which is executed first after step S11 is executed (first step S12), the position information setting unit 12 sets the position information and sets the number of repetitions j to j=1.

[0034] The distance calculation unit 13 calculates the distance between each of the fixed stations and the mobile station 100 (step S13). In step S13, the distance calculation unit 13 calculates the distance between each of the fixed stations and the mobile station 100 using the position information of each of the fixed stations acquired by the information acquisition unit 11 in step S11, and the position information set by the position information setting unit 12 in step S12 or the position information updated by the position information update unit 17 (described later). Specifically, in step S13 (first step S13), which is executed first after step S12 is executed, the distance calculation unit 13 calculates the distance between each of the fixed stations and the mobile station 100 using the position information of each of the fixed stations and the position information set by the position information setting unit 12. In subsequent steps S13 (second and subsequent steps S13), the distance calculation unit 13 calculates the distance between each of the fixed stations and the mobile station 100 using the position information of each of the fixed stations and the position information updated by the position information update unit 17. In step S13, which is the first step executed after step S12, the distance calculation unit 13 sets the number of convergences k to k=1, and then calculates the distance between each fixed station and the mobile station 100.

[0035] The distance calculation unit 13 calculates the position information of each fixed station by (x i , y i , z i ), and the location information of the mobile station 100 is (x (k) , y (k) , z (k) ), the distance calculation unit 13 calculates the distance r between the i-th fixed station and the mobile station 100. i(k) is calculated according to the following formula (1). In the following description, the distance r calculated by the distance calculation unit 13 is i(k) The estimated distance r i(k) It is sometimes referred to as.

[0036]

[0037] In equation (1), i=0, 1, ..., N-1, where N is the number of fixed stations for which distances have been measured, and in the configuration shown in FIG.

[0038] In addition, when the location information of the mobile station 100 after being updated by the location information update unit 17 described later is input via the update completion determination unit 18, the distance calculation unit 13 re-calculates the estimated distance using the input location information.

[0039] The error calculation unit 14 calculates the distance error (step S14). Specifically, the error calculation unit 14 calculates the distance r between the i-th fixed station and the mobile station 100 included in the distance measurement result acquired by the information acquisition unit 11 in step S11. i and the estimated distance r calculated by the distance calculation unit 13. i(k) The distance error Δr i(k) is calculated according to the following formula (2).

[0040]

[0041] The partial differential processor 15 calculates the distance r i The partial derivative of is calculated (step S15).

[0042] Here, the distance error Δr calculated by the error calculation unit 14 i(k) and the estimated distance r i(k) There is a relationship shown in the following equation (3) between the partial differential calculation of

[0043]

[0044] In formula (3), Δx (k) , Δy (k) and Δz (k) indicate the errors between the x-coordinate, y-coordinate, and z-coordinate of the estimated position of the mobile station 100 and the actual coordinates (actual x-coordinate, y-coordinate, and z-coordinate), respectively.

[0045] The distance error Δr from each of the multiple fixed stations #i (i=0, 1, . . . , N−1) obtained from equation (3) i(k) and the estimated distance r i(k) The amount of minute change in the position of the mobile station 100 shown in the following equation (4) can be calculated from N simultaneous equations that show the relationship with the partial differential calculation results of the above equations. The amount of minute change in the position of the mobile station 100 shown in the following equation (4) corresponds to the amount of adjustment for each coordinate of the estimated position of the mobile station 100. In the following explanation, the "adjustment amount for each coordinate of the estimated position" will be referred to as the "adjustment amount of the estimated position."

[0046]

[0047] The partial differential processor 15 calculates the estimated distance r i(k) Calculate the partial derivative of .

[0048]

[0049] Regarding the above equation (3), when distance measurement is possible between N fixed stations, the estimated distance r i(k) The matrix representation of the partial differential calculation is given by the following equation (6).

[0050]

[0051] In addition, in the above equation (3), the distance error Δr i(k) is expressed as a vector for the number of fixed stations, the following equation (7) is obtained.

[0052]

[0053] The partial differential processor 15 calculates the estimated distance r i(k) After calculating the partial derivative of i(k) and the distance error Δr for the number of fixed stations shown in Equation (7). i(k) and the vector representation of the vectors are output to the least squares method processing unit 16.

[0054] The least squares method processing unit 16 calculates the amount of adjustment for the estimated position using the least squares method (step S16).

[0055] In step S16, the least squares method processing unit 16, which is an estimated position adjustment amount calculation unit, calculates the adjustment amount of the estimated position of the mobile station 100 by the unweighted least squares method when j=0. (k) is expressed as a vector by the following equation (8), the least squares method processing unit 16 calculates the distance error Δr (k) Using the vector representation of (k) In this case, the adjustment amount Δp of the estimated position calculated by the least squares method processing unit 16 is calculated. (k) The vector representation of is given by the following equation (9).

[0056]

[0057]

[0058] Furthermore, when 1<j, the least squares processing unit 16 calculates the adjustment amount Δp of the estimated position of the mobile station 100 by the weighted least squares method. (k) In this case, the adjustment amount Δp of the estimated position calculated by the least squares method processing unit 16 is calculated. (k) The vector representation of is given by the following equation (10).

[0059]

[0060] In equation (10), W is a diagonal matrix for weighting, and is given by the following equation (11).

[0061]

[0062] The weighting coefficients, which are elements of the diagonal matrix in equation (11), are W i = 1 / σ i 2 (i=0, 1, . . . , N−1) and use the estimated error (variance σ) of the distance measurement. Specifically, the weighting coefficient W i is the distance error Δr shown in equation (2) estimated when the position between the i-th fixed station and the mobile station 100 is converged k times. i(k) is used, and is expressed by the following equation (12), which is inversely proportional to the square of the distance measurement error.

[0063]

[0064] However, the weighting coefficient W i is the distance error Δr i(k) As approaches zero, approaches infinity, and the contribution of a particular fixed station can become very high, which reduces the accuracy of the location estimation, and makes the least-squares method computationally unstable.

[0065] FIG. 7 shows the distance error Δr i(k) and the weighting factor W i As shown in FIG. i(k) As approaches zero, the weighting factor W iTherefore, in the position estimation device 1 according to the present embodiment, clipping is applied as shown in FIG. i(k) When approaches zero, the weighting factor W i 8 is a diagram for explaining the clipping process used in the position estimation device 1 according to the first embodiment. i The distance error Δr when clipping is performed with the upper limit set to 1 i(k) and the weighting factor W i The weighting coefficient W i The upper limit setting value does not necessarily have to be 1. Any value may be set depending on the position estimation accuracy and the stability of the numerical calculation.

[0066] The calculation result by the least squares method processor 16 , that is, the vector of the adjustment amount of the estimated position of the mobile station 100 shown in equation (9) or equation (10), is input to the position information updater 17 .

[0067] The location information update unit 17 updates the location information of the mobile station 100 (step S17). Specifically, the location information update unit 17 uses the vector of the adjustment amount of the estimated location of the mobile station 100 input from the least squares method processing unit 16, and updates the location information (x (k) , y (k) , z (k) ) to update.

[0068]

[0069] The location information update unit 17 outputs the updated location information of the mobile station 100 to the update termination determination unit 18 .

[0070] When the location information of the mobile station 100 is input from the location information update unit 17, the update termination decision unit 18 determines whether or not to terminate the estimated location update process, which is a process for updating the location information of the mobile station 100, i.e., whether or not to terminate the estimated location update process when the number of convergences k reaches N. Loop It is determined whether or not this is the case (step S18).

[0071] The number of convergences k is k=N Loop(step S18: Yes), the update completion determination unit 18 determines that the update of the location information has been completed, and updates the location information (x (k) , y (k) , z (k) ), (k=N Loop +1) to the estimation process end determination unit 19. In response to this, the estimation process end determination unit 19 executes step S19, which will be described later.

[0072] The number of convergences k is k<N Loop (step S18: No), the update termination determination unit 18 determines to continue updating the location information, and terminates the update of the location information (x (k) , y (k) , z (k) ), (k=1,2,...,N Loop ), and outputs it to the distance calculation unit 13 as updated position information. At this time, the update termination determination unit 18 increments the convergence count k (step S21). When the updated position information of the mobile station 100 is input from the update termination determination unit 18, the distance calculation unit 13 recalculates the distance between each fixed station and the mobile station 100 using the input position information (step S13). Thereafter, the convergence count k is incremented until k=N Loop The position estimation device 1 repeats the processes of steps S13 to S18 and S21 described above until

[0073] When the location information of the mobile station 100 is input from the update end decision unit 18, the estimation process end decision unit 19 decides whether or not to end the location estimation process of the mobile station 100, that is, whether or not the number of repetitions j reaches N. rep It is determined whether or not this is the case (step S19).

[0074] The number of repetitions j is j=N rep (step S19: Yes), the estimation process end determination unit 19 determines that the location estimation process is ended, and updates the location information (x (k) , y (k) , z (k) ), (k=N Loop +1) is output as the position estimation result (step S20).

[0075] The number of repetitions j is j<N rep If (step S19: No), the estimation process termination determination unit 19 determines to continue the location estimation process and outputs the location information of the mobile station 100 input from the update termination determination unit 18 to the location information setting unit 12. At this time, the estimation process termination determination unit 19 increments the number of repetitions j (step S22). When the location information of the mobile station 100 is input from the estimation process termination determination unit 19, the location information setting unit 12 sets the input location information (x (k) , y (k) , z (k) ), (k=N Loop +1), and the location information of the j+1th mobile station 100 is set according to the following equation (14).

[0076]

[0077] After that, the number of repetitions j is j=N rep The position estimation device 1 repeats the processes of steps S12 to S19, S21 and S22 described above until the ....

[0078] As described above, the position estimation device 1 according to the first embodiment repeatedly performs an update process using the least squares method, which assigns weights inversely proportional to the square of the distance error to the estimated position of the mobile station 100, so as to reduce the influence of distance measurement results with large distance measurement errors due to the influence of reflected waves and the like, among the distance measurement results between multiple fixed stations and the mobile station 100. This prevents a decrease in the accuracy of the position estimation. Furthermore, by repeatedly using the same distance measurement results, the accuracy of the position estimation of the mobile station 100 can be improved.

[0079] Second Embodiment Fig. 9 is a diagram showing a configuration example of a location estimation device 1a according to a second embodiment. Note that the location estimation device 1a is assumed to be provided in a mobile station 100, similar to the location estimation device 1 according to the first embodiment, but the mobile station 100 is not shown in Fig. 9.

[0080] The position estimation device 1a has a configuration in which an averaging processing unit 20 is added to the position estimation device 1 according to the first embodiment. The components of the position estimation device 1a other than the averaging processing unit 20 are the same as the components of the position estimation device 1 according to the first embodiment that are assigned the same reference numerals. Therefore, a description of the components other than the averaging processing unit 20 will be omitted.

[0081] Fig. 10 is a flowchart showing an example of an operation of the position estimation device 1a according to the second embodiment to estimate the position of the mobile station 100. The flowchart shown in Fig. 10 is obtained by adding step S30 to the flowchart of Fig. 6 showing the operation of the position estimation device 1 according to the first embodiment to perform position estimation. The processing of each step other than step S30 of the position estimation device 1a according to the second embodiment is the same as the processing of the position estimation device 1 according to the first embodiment, which is assigned the same step number. Therefore, a description of the processing of each step other than step S30 will be omitted. The processing of step S30 is executed by the averaging processing unit 20.

[0082] 10 is "Yes", the averaging processing unit 20 of the position estimation device 1a receives the estimated position of the mobile station 100 output by the estimation processing end determination unit 19. When the averaging processing unit 20 receives the estimated position of the mobile station 100 from the estimation processing end determination unit 19, the averaging processing unit 20 performs averaging processing on the received estimated position (step S30). That is, the averaging processing unit 20 performs averaging processing on the estimated position of the mobile station 100 so as to suppress degradation in the position estimation accuracy of the mobile station 100 caused by ranging errors between multiple fixed stations and the mobile station 100.

[0083] The estimated position obtained by averaging processing unit 20 performing the averaging process is output to the outside of position estimation device 1a as the position estimation result of mobile station 100, and is also output to position information setting unit 12. When the position estimation result is input from averaging processing unit 20, position information setting unit 12 holds it, and sets the held position estimation result as the position information of mobile station 100 in the first step S12 of the next position estimation operation.

[0084] FIG. 11 shows a first example of a ranging error between a fixed station and a mobile station 100, FIG. 12 shows a second example of a ranging error between a fixed station and a mobile station 100, and FIG. 13 shows a third example of a ranging error between a fixed station and a mobile station 100. These FIGS. 11 to 13 show examples of ranging errors between each of four fixed stations #0 to #3 and the mobile station 100. Specifically, FIG. 11 shows a case where there is no ranging error between each of the fixed stations #0 to #3 and the mobile station 100. FIG. 12 shows a case where slight variations in ranging error occur between each of the fixed stations #0 to #3 and the mobile station 100. FIG. 13 shows a case where large ranging errors occur between each of the fixed stations #0 to #3 and the mobile station 100 due to the influence of reflected waves and the like.

[0085] In the first example shown in Fig. 11, ranging errors 30, 31, 32, and 33 between each of base stations #0 to #3 and mobile station 100 are all zero. In the second example shown in Fig. 12, there is slight variation in ranging errors 40, 41, 42, and 43 between each of base stations #0 to #3 and mobile station 100. In the third example shown in Fig. 13, there is variation in ranging errors 50, 51, 52, and 53 between each of base stations #0 to #3 and mobile station 100, and in particular, the ranging result between base station #3 and mobile station 100 includes a large ranging error 53 due to the influence of reflected waves.

[0086] As in the second example shown in FIG. 12 and the third example shown in FIG. 13, when there is variation in distance measurement errors between fixed stations, a small error may occur in the calculation result by the least squares method processing unit 16, and as a result, an error may also occur in the position estimation result of the mobile station 100. Furthermore, the position estimation result output by the estimation process completion determination unit 19 is used as the initial value (x (1) , y (1) , z (1)), so if there is a large variation in ranging errors between fixed stations, it may be difficult to improve the accuracy of the location estimation, for example, by increasing the number of convergence times k for location estimation or by increasing the number of repetition times j to suppress the contribution of ranging results affected by reflected waves to the location estimation. To solve such problems, the location estimation device 1a according to the second embodiment includes an averaging processor 20. The averaging processor 20 averages the estimated location of the mobile station 100 output by the estimation process termination determination unit 19, which is obtained each time the information acquisition unit 11 acquires new ranging results between the fixed station and the mobile station 100, using an FIR (Finite Impulse Response) filter, an IIR (Infinite Impulse Response) filter, or the like. This makes it possible to improve the accuracy of the location estimation.

[0087] As described above, the position estimation device 1a according to the second embodiment includes an averaging processing unit 20 that averages position estimation results obtained by executing the same processing as the position estimation device 1 according to the first embodiment. This makes it possible to suppress the influence of the variation even when there is variation in ranging errors between fixed stations that are the targets of ranging, thereby improving the accuracy of the position estimation of the mobile station 100. It also reduces the number of convergences and iterations of the position estimation. It also makes it possible to accurately suppress the contribution of ranging results affected by reflected waves to the position estimation, thereby improving the accuracy of the position estimation of the mobile station 100.

[0088] Third Embodiment Next, a third embodiment will be described. The position estimation device according to the third embodiment has the same configuration as the position estimation device 1 according to the first embodiment (see FIG. 5). However, the weighting coefficient W used in the weighted least squares method processing by the least squares method processing unit 16 is i Therefore, in this embodiment, the least squares method processor 16 and the weighting coefficient W used by the least squares method processor 16 are i The following description will focus on only the above, and the description of other components common to the first embodiment will be omitted.

[0089] The weighting coefficient W used in the third embodiment i14 will be used to explain the weighting coefficients used in the least squares method processing in the position estimation device according to the third embodiment. FIG. 14 shows the correspondence between the ranging error and the weighting coefficients. In FIG. 14, the horizontal axis indicates the absolute value of the ranging error, and the vertical axis indicates the weighting coefficients. σ D is the distance error Δr i σ is the lower limit of the absolute value of D = 0.03. U is the distance error Δr i σ is the upper limit of the absolute value of U = 0.15.

[0090] As shown in FIG. 14, when the distance measurement error between the fixed station and the mobile station 100 is sufficiently small, the position estimation device 1 according to the third embodiment uses a weighting coefficient W that does not interfere with the numerical calculation. i is used to estimate the position of the mobile station 100.

[0091] The weighting coefficient W used by the least squares method processing unit 16 of the position estimation device 1 according to the third embodiment i is given by the following equation (15): In the following equation (15), i represents the number of the fixed station, where i=0, 1, . . . , N-1.

[0092]

[0093] The weighting coefficient W in the above equation (12) used in the position estimation device 1 according to the first embodiment i Then, the distance error Δr i(k) As approaches zero, the weighting factor W i In contrast, the weighting coefficient W in the equation (15) used in the position estimation device 1 according to the third embodiment is i So, instead of clipping, the distance error Δr i(k) The squared value of ((Δr i(k) ) 2 ) lower limit σ D 2 Also, (Δr i(k) ) 2As is increased, the weighting coefficient W i becomes small rapidly and approaches zero. In particular, when the position information of the mobile station 100 initially set by the position information setting unit 12 during position estimation is significantly different from the actual position of the mobile station 100, the distance error Δr i(k) tends to be large, and the weighting coefficient W i This is equivalent to a decrease in the number of effective fixed stations that can be used for location estimation, which may result in a decrease in the accuracy of location estimation. i So that the lower limit of (Δr i(k) ) 2 The upper limit of σ U 2 Set.

[0094] As described above, in the position estimation device 1 according to the third embodiment, the least squares method processing unit 16 calculates the weighting coefficient W i By setting upper and lower limits for the weighting coefficient W i It is possible to prevent r from becoming a large value in the numerical calculation, and even if the accuracy of the location information of the mobile station 100 set when estimating the location of the mobile station 100 is low, it is possible to suppress a decrease in the number of effective fixed stations that can be used for location estimation, and it is possible to prevent a decrease in the accuracy of the location estimation.

[0095] 15 is a diagram showing a configuration example of a location estimation device 1b according to a fourth embodiment. Note that the location estimation device 1b is assumed to be provided in a mobile station 100, similar to the location estimation device 1 according to the first embodiment, but the mobile station 100 is not shown in FIG.

[0096] The position estimation device 1b has a configuration in which the least squares method processing unit 16 of the position estimation device 1 according to the first embodiment is replaced with a least squares method processing unit 16b. That is, the position estimation device 1b differs from the position estimation device 1 according to the first embodiment in the least squares method processing used when estimating the position of the mobile station 100. The components of the position estimation device 1b other than the least squares method processing unit 16b are the same as the components of the position estimation device 1 according to the first embodiment that are assigned the same reference numerals. Therefore, a description of the components other than the least squares method processing unit 16b will be omitted.

[0097] Fig. 16 is a flowchart showing an example of an operation of the location estimation device 1b according to the fourth embodiment to estimate the location of the mobile station 100. In the flowchart shown in Fig. 16, step S16 of the flowchart of Fig. 6 showing the operation of the location estimation device 1 according to the first embodiment to estimate the location has been replaced with step S16b. The processing of each step other than step S16b of the location estimation device 1b according to the fourth embodiment is the same as the processing of the location estimation device 1 according to the first embodiment that has the same step number. Therefore, a description of the processing of each step other than step S16b will be omitted.

[0098] In step S16b, the least squares method processing unit 16b uses the least squares method to calculate an adjustment amount for the estimated position of the mobile station 100, similar to the least squares method processing unit 16 of the position estimation device 1 according to the first embodiment. However, the calculation methods of the least squares method processing unit 16b and the least squares method processing unit 16 are partially different.

[0099] The least squares method processing unit 16 of the position estimation device 1 according to the first embodiment executes unweighted least squares method processing when the number of iterations j is j = 1, and executes least squares method processing with weighting inversely proportional to the square of the distance error when the number of iterations j is 1 < j. In contrast, the least squares method processing unit 16b of the position estimation device 1b according to the fourth embodiment executes weighted least squares method processing even when the number of iterations j is j = 1. As a result, compared to the least squares method processing unit 16, the least squares method processing unit 16b further reduces the influence of the ranging results between the fixed station and the mobile station 100, which have large ranging errors due to reflected waves, etc., thereby improving the accuracy of position estimation.

[0100] When the number of repetitions j is j=1, the least squares method processing unit 16b calculates a weighting coefficient W i weighted least squares processing is performed using

[0101]

[0102] Furthermore, when the number of iterations j is 1<j, the least squares method processing unit 16b calculates a weighting coefficient W i weighted least squares processing is performed using

[0103]

[0104] In equations (16) and (17), i represents the number of the fixed station, where i=0, 1, ..., N-1. σ, which represents the allowable variance of the ranging error, Da 2 is (Δr i(k) ) 2 The lower limit of σ Ua 2 is (Δr i(k) ) 2 The upper limit of σ Db 2 is (Δr i(k) ) 2 The lower limit of σ Ub 2 is (Δr i(k) ) 2 is the upper limit of σ Da 2 >σ Db 2 , σ Ua 2 <σ Ub 2 Let's say.

[0105] The weighting coefficient W shown in Equation (16) and Equation (17) i 17 and 18, a specific example of the weighting coefficients W used in the least squares method process in the position estimation device 1b according to the fourth embodiment will be described. Specifically, the weighting coefficient W used when j=1 is iFIG. 18 shows a second example of the weighting coefficients used in the least squares method process in the position estimation device 1b according to the fourth embodiment. Specifically, the weighting coefficient W used when j>1 is i An example is shown below.

[0106] 17 and 18, the horizontal axis indicates the absolute value of the distance measurement error, and the vertical axis indicates the weighting coefficient. Da , σ Db is the distance error Δr i 17 and 18, as an example, σ Da = 0.05, σ Db = 0.03. Ua , σ Ub is the distance error Δr i 17 and 18, as an example, σ Ua = 0.07, σ Ub = 0.15.

[0107] 17 and 18, FIG. 17, which shows the case where the number of repetitions j is j=1, has a larger weighting coefficient W i is the upper limit value of W i Therefore, the range where the weighting coefficient W i It is possible to prevent a decrease in the value of (Δr i(k) ) 2 >σ Ua 2 Under the condition, the weighting coefficient W i can be set to a large value.

[0108] As mentioned above, the weighting factor W i By setting the above, when the number of repetitions j is j=1, even in a situation where there is a large variation in the ranging error between the fixed station and the mobile station 100, it is possible to use the least squares method that weights the ranging result between the fixed station and the mobile station 100 according to the ranging error while suppressing the influence of the large ranging error due to reflected waves.

[0109] As described above, in the position estimation device 1b according to the fourth embodiment, even when the number of repetitions j described in the first embodiment is j=1, the weighting coefficient W i This prevents a decrease in the value of the weighting coefficient due to variations in ranging error between the fixed station and the mobile station 100, and also suppresses the influence of large ranging error due to reflected waves, thereby improving the accuracy of position estimation.

[0110] Next, the hardware configuration of the base stations 210 to 213 and the mobile station 100 in which the position estimation devices 1, 1a, and 1b described in the first to fourth embodiments are provided will be described. The base stations 210 to 213 according to the first to fourth embodiments and the mobile station 100 in which the position estimation devices 1, 1a, and 1b are provided include a processing circuit. The processing circuit executes each process for performing the position estimation described in each of the first to fourth embodiments, thereby realizing each function of the base stations 210 to 213 and the position estimation devices 1, 1a, and 1b according to the first to fourth embodiments.

[0111] FIG. 19 is a diagram illustrating an example in which the processing circuit included in the base stations 210 to 213 or the mobile station 100 according to the first to fourth embodiments is configured with a processor and a memory. When the processing circuit is configured with a processor 601 and a memory 602, each function of the processing circuit included in the base stations 210 to 213 or the mobile station 100 is realized by software, firmware, or a combination of software and firmware. The software or firmware is written as a program and stored in the memory 602. The processing circuit realizes each function by having the processor 601 read and execute the program stored in the memory 602. That is, the processing circuit includes the memory 602 for storing a program that results in the processing of the base stations 210 to 213 or the mobile station 100. Furthermore, these programs can also be said to cause a computer to execute the processing procedure or method of the base stations 210 to 213 or the mobile station 100. The program stored in the memory 602 may be provided in a state where it is stored in a storage medium or via a communication path.

[0112] Here, the processor 601 may be a CPU (Central Processing Unit), a processing device, an arithmetic unit, a microprocessor, a microcomputer, or a DSP (Digital Signal Processor), etc. The memory 602 may be, for example, a non-volatile or volatile semiconductor memory such as a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable ROM), or an EEPROM (Electrically EPROM), a magnetic disk, a flexible disk, an optical disk, a compact disk, a minidisk, or a DVD (Digital Versatile Disc).

[0113] Furthermore, the processing circuits provided in the base stations 210 to 213 or the mobile station 100 according to the first to fourth embodiments may be configured using dedicated hardware.

[0114] 20 is a diagram showing an example in which the processing circuit provided in the base stations 210 to 213 or the mobile station 100 according to the first to fourth embodiments is configured with dedicated hardware. When the processing circuit is configured with dedicated hardware, the processing circuit 603 shown in FIG. 20 corresponds to, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination thereof. Each function of the base stations 210 to 213 or the mobile station 100 may be realized by the processing circuit 603 separately for each function, or all functions may be realized collectively by the processing circuit 603.

[0115] Note that the functions of the base stations 210 to 213 or the mobile station 100 according to the first to fourth embodiments may be partially implemented by dedicated hardware and partially implemented by software or firmware. In this way, the processing circuit can implement each of the above-described functions by dedicated hardware, software, firmware, or a combination of these.

[0116] The configurations shown in the above embodiments are merely examples, and may be combined with other known technologies, or different embodiments may be combined with each other. It is also possible to omit or modify parts of the configurations as long as they do not deviate from the gist of the invention.

[0117] 1, 1a, 1b Position estimation device, 11 Information acquisition unit, 12 Position information setting unit, 13 Distance calculation unit, 14 Error calculation unit, 15 Partial differential processing unit, 16, 16b Least squares method processing unit, 17 Position information update unit, 18 Update termination determination unit, 19 Estimation process termination determination unit, 20 Averaging processing unit, 100 Mobile station, 110, 110' Position estimation result, 120 Actual position of mobile station, 200 Position estimation system, 210, 211, 212, 213 Fixed station, 231 Direct wave, 232 Reflected wave, 240 Received waveform, 241 Direct wave waveform, 242 Reflected wave waveform.

Claims

1. A position estimation device that estimates a position of a mobile station based on distance measurement results between each of a plurality of fixed stations and the mobile station derived based on results of wireless communication between each of the plurality of fixed stations and the mobile station, comprising: a distance calculation unit that calculates an estimated distance, which is the distance between each of the plurality of fixed stations and the mobile station, for each of the fixed stations based on the estimated position of the mobile station and the positions of the plurality of fixed stations; an error calculation unit that calculates an error between the distance measurement result and the estimated distance for each of the fixed stations; an estimated position adjustment amount calculation unit that calculates an adjustment amount for the estimated position based on the distance error, which is the error calculated by the error calculation unit, and a partial differentiation result of the estimated distance; and a position information update unit that updates position information indicating the estimated position based on the adjustment amount, a process in which the distance calculation unit calculates the estimated distance for each fixed station, a process in which the error calculation unit calculates the error for each fixed station, a process in which the estimated position adjustment amount calculation unit calculates the adjustment amount, and a process in which the position information update unit updates the position information, the process being repeated a predetermined number of times to estimate the position of the mobile station.

2. The position estimation device according to claim 1, characterized in that the estimated position adjustment amount calculation unit calculates the adjustment amount using a least squares method that performs weighting inversely proportional to the squared value of the distance error.

3. The position estimation device according to claim 2, characterized in that the estimated position adjustment amount calculation unit calculates the adjustment amount using a least squares method that does not perform the weighting during the first estimated position update process, and calculates the adjustment amount using a least squares method that performs weighting that is inversely proportional to the square of the distance error during the second or subsequent estimated position update process.

4. The position estimation device according to claim 2 or 3, characterized in that the weighting coefficient used in the weighting is set to a value in the range between a predetermined minimum value and a predetermined maximum value based on the squared value of the distance error.

5. A position estimation device as described in any one of claims 1 to 4, characterized in that when the estimated position update process is repeatedly executed the specified number of times, the position indicated by the obtained updated position information is set as a new estimated position of the mobile station, and the estimated position update process is repeatedly executed the specified number of times, thereby executing a position estimation process a specified number of times.

6. A position estimation device as described in any one of claims 2 to 4, characterized in that when the estimated position update process has been repeatedly executed the specified number of times, the position indicated by the updated position information obtained is set as a new estimated position of the mobile station, and the estimated position update process is repeatedly executed the specified number of times, a weighting coefficient used in the weighting is set to a value in the range of a specified minimum value to a specified maximum value based on the squared value of the distance error, the weighting coefficient used in the weighting in the first position estimation process is a minimum value when the squared value of the distance error is a first value or less, and the weighting coefficient used in the weighting in the second or subsequent position estimation processes is a minimum value when the squared value of the distance error is a second value or less that is smaller than the first value.

7. A location estimation device as described in claim 5 or 6, characterized in that it is provided with an averaging processing unit that performs an averaging process on the location information obtained by executing the location estimation process the specified number of times, and the location information after the averaging process is performed is used as the location estimation result of the mobile station.

8. A position estimation system comprising: said mobile station comprising a position estimation device according to any one of claims 1 to 7; and said fixed station.

9. A position estimation method in which a position estimation device estimates the position of a mobile station based on distance measurement results between each of a plurality of fixed stations and the mobile station, which are derived based on the results of wireless communication between each of the plurality of fixed stations and the mobile station, comprising: a distance calculation step for calculating, for each fixed station, an estimated distance between each of the plurality of fixed stations and the mobile station based on the estimated position of the mobile station and the positions of the plurality of fixed stations; an error calculation step for calculating, for each fixed station, an error between the distance measurement result and the estimated distance; an estimated position adjustment amount calculation step for calculating an adjustment amount for the estimated position based on the distance error, which is the error calculated in the error calculation step, and the partial differentiation result of the estimated distance; and a position information update step for updating position information indicating the estimated position based on the adjustment amount, the position estimation method comprising the steps of:

10. A control circuit for controlling a position estimation device that estimates the position of a mobile station based on distance measurement results between each of a plurality of fixed stations and the mobile station derived based on the results of wireless communication between each of the plurality of fixed stations and the mobile station, characterized in that the control circuit causes the position estimation device to execute a process of estimating the position of the mobile station by repeatedly executing the following steps a predetermined number of times: a distance calculation step for calculating an estimated distance between each of a plurality of fixed stations and the mobile station for each of the fixed stations based on the estimated position of the mobile station and the positions of the plurality of fixed stations; an error calculation step for calculating an error between the distance measurement result and the estimated distance for each of the fixed stations; an estimated position adjustment amount calculation step for calculating an adjustment amount for the estimated position based on the distance error, which is the error calculated in the error calculation step, and the partial differentiation result of the estimated distance; and a position information update step for updating position information indicating the estimated position based on the adjustment amount.

11. A storage medium storing a program for controlling a position estimation device that estimates a position of a mobile station based on distance measurement results between each of a plurality of fixed stations and the mobile station derived based on results of wireless communication between each of the plurality of fixed stations and the mobile station, wherein the program causes the position estimation device to execute a process of estimating the position of the mobile station by repeatedly executing the following steps a predetermined number of times: a distance calculation step for calculating, for each fixed station, an estimated distance between each of a plurality of fixed stations and the mobile station based on the estimated position of the mobile station and the positions of the plurality of fixed stations; an error calculation step for calculating, for each fixed station, an error between the distance measurement result and the estimated distance; an estimated position adjustment amount calculation step for calculating an adjustment amount for the estimated position based on the distance error, which is the error calculated in the error calculation step, and a partial differentiation result of the estimated distance; and a position information update step for updating position information indicating the estimated position based on the adjustment amount.

Images