Sensor Module

Abstract

To provide a sensor module capable of reducing the risk of a decrease in calculation accuracy due to a change over time in the output stability of a sensor device. [Solution] A sensor module comprising: a plurality of sensor devices having detection axes along the same direction and detecting the same type of physical quantity; a memory unit that stores parameter information including index parameters that represent the output stability of each of the plurality of sensor devices; a plurality of integral processing units that perform integral processing based on the output signals of each of the plurality of sensor devices; and a calculation processing unit that performs calculation processing on the output signals of the plurality of integral processing units according to the parameter information and the integral time of the integral processing.

Description

[Technical Field]

[0001] The present invention relates to a sensor module. [Background technology]

[0002] Patent Document 1 describes an inertial navigation system that calculates optimal weighting according to the degree of normality of the function or the degree of performance of each inertial navigation system in a multiplexed inertial navigation system, and outputs optimal navigation information (attitude angle, azimuth angle, velocity, position) according to the output of each inertial navigation system and this weighting.The inertial navigation system described in Patent Document 1 can achieve safe and reliable navigation by providing highly accurate navigation information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2000-283788 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the inertial navigation system described in Patent Document 1, the output stability of the inertial sensor changes over time in accordance with the Allan variance, which may result in a significant decrease in the accuracy of the output navigation information. [Means for solving the problem]

[0005] One aspect of the sensor module according to the present invention is a plurality of sensor devices each having a detection axis aligned in the same direction and detecting the same type of physical quantity; a storage unit that stores parameter information including a parameter of an index that indicates the output stability of each of the plurality of sensor devices; a plurality of integration processing units that perform integration processing based on output signals from the plurality of sensor devices; a calculation processing unit that performs calculation processing on the output signals of the plurality of integration processing units in accordance with the parameter information and the integration time of the integration processing; Equipped with. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 2 is a diagram showing the functional configuration of a sensor module according to the present embodiment. [Figure 2] FIG. 10 is a diagram showing the simulation results of Allan variance. [Figure 3] FIG. 10 is a diagram showing a simulation result of the variance of the integral error. [Figure 4] An explanatory diagram of the X-axis, Y-axis, and Z-axis. [Figure 5] FIG. 2 is a diagram showing an example of the configuration of a sensor module according to the first embodiment. [Figure 6] FIG. 2 is a diagram showing an example of the configuration of a calculation processing unit in the first embodiment. [Figure 7] FIG. 10 is a diagram showing an example of the configuration of a sensor module according to a second embodiment. [Figure 8] FIG. 10 is a diagram showing an example of the configuration of a calculation processing unit in the third embodiment. [Figure 9] FIG. 1 is a diagram showing an example of a hybrid navigation system incorporating a sensor module according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0007] Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Note that the embodiments described below are not intended to unduly limit the scope of the present invention as defined in the claims. Furthermore, not all of the configurations described below are necessarily essential components of the present invention.

[0008] 1. First embodiment 1-1. Functional configuration of the sensor module FIG. 1 is a diagram illustrating the functional configuration of a sensor module according to this embodiment. As illustrated in FIG. 1, the sensor module 1 according to this embodiment includes n sensor devices 2-1 to 2-n, n signal processing units 3-1 to 3-n, n integration processing units 4-1 to 4-n, an arithmetic processing unit 5, a microcontroller unit 6, and a storage unit 7. n is an integer equal to or greater than 2. That is, the sensor module 1 includes multiple sensor devices 2-1 to 2-n, multiple signal processing units 3-1 to 3-n, and multiple integration processing units 4-1 to 4-n. Note that the sensor module 1 may be configured such that some of the components shown in FIG. 1 are omitted or modified, or other components are added. For example, if each of the sensor devices 2-1 to 2-n includes a signal processing unit, the sensor module 1 may not include the signal processing units 3-1 to 3-n.

[0009] The sensor devices 2-1 to 2-n have detection axes along the same direction and detect the same type of physical quantity. The physical quantity may be, for example, angular velocity or acceleration. Each of the sensor devices 2-1 to 2-n may output a digital signal having a value corresponding to the detected physical quantity, or may output an analog signal having a voltage corresponding to the detected physical quantity. Each of the sensor devices 2-1 to 2-n may be an inertial sensor, and the sensor module 1 may be an inertial sensor module.

[0010] The storage unit 7 stores sensor characteristic information 71 and parameter information 72. The sensor characteristic information 71 is information on bias, sensitivity, misalignment, temperature characteristics, etc. of each of the sensor devices 2-1 to 2-n. The parameter information 72 includes parameters that are indices representing the output stability of each of the sensor devices 2-1 to 2-n when stationary.

[0011] Allan variance is known as one of the indicators that expresses the stability of a sensor's output when stationary. The dominant characteristics of Allan variance change over time. For example, the characteristics of the Allan variance of an angular rate sensor are ARW (Angle Random Walk), BI (Bias Instability), RRW (Rate Random Walk), and RR (Rate Ramp). The Allan variance of an angular rate sensor, σ 2 is expressed by equation (1). In equation (1), σ arw 2 ,σ bi 2 ,σ rrw 2 ,σ rr 2 are the Allan variances of the ARW, BI, RRW, and RR components, respectively. τ is the averaging time, and N, B, K, and R are parameters that indicate the characteristics of ARW, BI, RRW, and RR, respectively. The parameter information 72 includes, for example, the values ​​of the parameters N, B, K, and R.

[0012]

number

[0013] The Allan variance of the acceleration sensor is the variance σ of the ARW in equation (1). arw 2 is the variance of VRW (Velocity Random Walk), σ vrw 2 This is expressed by equation (2) where

[0014]

number

[0015] The microcontroller 6 receives the sensor characteristic information 71 stored in the memory 7. and outputs it to each of the signal processing units 3-1 to 3-n. In addition, the micro control unit 6 reads out the parameter information 72 stored in the storage unit 7 and outputs it to the calculation processing unit 5.

[0016] Each of the signal processing units 3-1 to 3-n performs predetermined signal processing on the output signal of each of the sensor devices 2-1 to 2-n. That is, for each integer i between 1 and n, the signal processing unit 3-i performs predetermined signal processing on the output signal of the sensor device 2-i. The predetermined signal processing is, for example, filtering or correction. The filtering is, for example, low-pass filtering, high-pass filtering, or band-pass filtering, or may be a combination of two or more of these filtering processes. The correction is, for example, bias correction, sensitivity correction, alignment correction, temperature correction, etc. The signal processing unit 3-i performs correction processing on the output signal of the sensor device 2-i based on the sensor characteristic information 71. When the sensor device 2-i outputs an analog signal, the signal processing unit 3-i may perform predetermined processing including converting the analog signal to a digital signal.

[0017] Each of the integral processing units 4-1 to 4-n performs integral processing based on the output signal of each of the sensor devices 2-1 to 2-n. Specifically, each of the integral processing units 4-1 to 4-n performs integral processing on the output signal of each of the signal processing units 3-1 to 3-n. That is, the integral processing unit 4-i performs integral processing on the output signal of the signal processing unit 3-i. The integral processing of each of the integral processing units 4-1 to 4-n is reset by the microcontrol unit 6. Therefore, each of the integral processing units 4-1 to 4-n performs integral processing from the reset of the integral processing is released until the next reset. That is, the elapsed time from the reset of the integral processing is released corresponds to the integral time.

[0018] The calculation processing unit 5 performs calculation processing on the output signals of the integral processing units 4-1 to 4-n.

[0019] The sensor devices 2-1 to 2-n may each be an angular velocity sensor, and the output signals of the integral processing units 4-1 to 4-n may each be a signal corresponding to the attitude or orientation of the sensor module 1. The sensor devices 2-1 to 2-n may each be an acceleration sensor, and the output signals of the integral processing units 4-1 to 4-n may each be a signal corresponding to the velocity or position of the sensor module 1.

[0020] Here, when the sensor devices 2-1 to 2-n are angular velocity sensors, the variance σ of the angle obtained by integrating the angular velocity is calculated from the formula (1). ang 2 (t) is expressed by equation (3). In equation (3), t is the integral time. Also, a1, a2, a3, and a4 are coefficients of each term, and are fixed values ​​calculated in advance. Also, f arw (t) is a function of the integral error due to ARW, and f bi (t) is a function of the integration error due to BI, and f rrw (t) is a function of the integral error due to RRW, and f rr (t) is a function of the integral error due to RR. Therefore, the angular variance σ ang 2 (t) corresponds to the variance of the integral error of the angular velocity. For example, the variance σ ang 2 Based on the relationship between (t) and the integration time t and the integration errors due to ARW, BI, RRW, and RR, the parameters N, B, K, and R are calculated.

[0021]

number

[0022] Furthermore, when the sensor devices 2-1 to 2-n are acceleration sensors, the formula (2 ) and the variance of the velocity obtained by integrating the acceleration is σ vel 2(t) is expressed by equation (4). In equation (4), t is the integral time. Also, b1, b2, b3, and b4 are coefficients of each term and are fixed values ​​calculated in advance. Also, g vrw (t) is a function of the integral error due to VRW, and g bi (t) is a function of the integration error due to BI, and g rrw (t) is a function of the integral error due to RRW, and g rr (t) is a function of the integral error due to RR. Therefore, the velocity variance σ vel 2 (t) corresponds to the variance of the integral error of acceleration. Note that the parameters N, B, K, and R in equation (4) are different from the parameters N, B, K, and R in equation (3). For example, the variance σ obtained based on statistical analysis such as multiple regression analysis or theoretical analysis vel 2 Based on the relationship between (t) and the integration time t and the integration errors due to VRW, BI, RRW, and RR, the parameters N, B, K, and R are calculated.

[0023]

number

[0024] Furthermore, when the sensor devices 2-1 to 2-n are acceleration sensors, the variance σ of the position obtained by integrating the acceleration twice is calculated from the formula (2). pos 2 (t) is expressed by equation (5). In equation (5), t is the integral time. Also, c1, c2, c3, and c4 are coefficients of each term and are fixed values ​​calculated in advance. Also, h vrw (t) is a function of the integral error due to VRW, and h bi (t) is a function of the integration error due to BI, and h rrw (t) is a function of the integral error due to RRW, and h rr (t) is a function of the integration error due to RR. Therefore, the position variance σ pos 2 (t) corresponds to the variance of the integral error of the double integration of acceleration. Note that the parameters N, B, K, and R in equation (5) are the same as the parameters N, B, K, and R in equation (4).

[0025]

number

[0026] Because the values ​​of the parameters N, B, K, and R differ for each of the sensor devices 2-1 to 2-n, the Allan variance and the integral error variance also differ for each of the sensor devices 2-1 to 2-n. For example, for two different angular velocity sensors, Figure 2 shows the simulation results of the Allan variance, and Figure 3 shows the simulation results of the integral error variance. In Figure 2, the horizontal axis is the averaging time τ, and the vertical axis is the Allan variance. Also, in Figure 3, the horizontal axis is the integration time t, and the vertical axis is the integral error variance. In Figures 2 and 3, the solid line shows the simulation results of one angular velocity sensor, and the dashed line shows the simulation results of the other angular velocity sensor. As shown in Figure 2, the magnitude relationship between the two Allan variances changes before and after the averaging time τ≈80 seconds. As a result, as shown in Figure 3, the magnitude relationship between the two integral error variances changes before and after the integration time t≈5 seconds.

[0027] In this way, the variance of the integration error of the integral processing units 4-1 to 4-n changes depending on the values ​​of the parameters N, B, K, and R of each of the sensor devices 2-1 to 2-n and the integration time t of the integration processing of the integral processing units 4-1 to 4-n. Therefore, in this embodiment, the calculation processing unit 5 performs calculation processing on the output signals of the integral processing units 4-1 to 4-n depending on the parameter information 72 and the integration time t of the integration processing of the integral processing units 4-1 to 4-n. In other words, the calculation processing unit 5 improves the accuracy of the calculation processing results by changing and optimizing the calculation processing depending on the parameter information 72 and the integration time t. In this embodiment, the calculation processing unit 5 calculates the variance of the integration error in the integration processing performed by each of the integral processing units 4-1 to 4-n based on the parameter information 72 and the integration time t. The arithmetic processing unit 5 calculates the variance of the integral errors obtained by the integration processing units 4-1 to 4-n, and performs arithmetic processing according to the calculated variance of the n integral errors. The arithmetic processing unit 5 also calculates weighting coefficients for each of the output signals of the integration processing units 4-1 to 4-n based on the parameter information 72 and the integral time t, and performs arithmetic processing using the calculated weighting coefficients. For example, the arithmetic processing unit 5 calculates the weighting coefficients based on the variance of the n integral errors calculated based on the parameter information 72 and the integral time t.

[0028] 1-2.Specific configuration example of sensor module The sensor module 1A, which is a specific example of the sensor module 1, will be used as an example below to describe the detailed operation thereof.

[0029] The sensor module 1A is an inertial sensor module that detects acceleration in three mutually orthogonal axis directions and angular velocity around the three axes. As shown in Fig. 4, for example, the sensor module 1A is mounted on an automobile 10 so that the three axes are aligned along the X-axis, Y-axis, and Z-axis, respectively. The X-axis is an axis along the traveling direction of the automobile 10, the Y-axis is an axis extending to the right that is perpendicular to the traveling direction of the automobile 10, and the Z-axis is an axis along the downward direction that is perpendicular to the plane on which the automobile 10 is traveling.

[0030] The sensor module 1A calculates the roll angle φ, pitch angle θ, and yaw angle ψ of the automobile 10 based on the detected acceleration and angular velocity in the three axial directions. The roll angle φ is the angle of rotation about the X-axis of the automobile 10, the pitch angle θ is the angle of rotation about the Y-axis, and the yaw angle ψ is the angle of rotation about the Z-axis. The roll angle φ and pitch angle θ represent the attitude of the automobile 10, and the yaw angle ψ represents the relative orientation of the automobile 10.

[0031] Fig. 5 is a diagram showing a configuration example of a sensor module 1A. As shown in Fig. 5, the sensor module 1A includes inertial sensors, namely, an X-axis acceleration sensor 20X, a Y-axis acceleration sensor 20Y, a Z-axis acceleration sensor 20Z, an X-axis angular velocity sensor 21X, a Y-axis angular velocity sensor 21Y, a Z-axis angular velocity sensor 21Z, and a Z-axis angular velocity sensor 22Z. The sensor module 1A also includes filter processing units 30X, 30Y, 30Z, 31X, 31Y, 31Z, and 32Z, and correction processing units 35X, 35Y, 35Z, 36X, 36Y, 36Z, and 37Z. The sensor module 1A also includes attitude / orientation estimation units 40A and 40B, a calculation processing unit 50, a microcontroller unit 60, and a memory unit 70.

[0032] The X-axis acceleration sensor 20X detects acceleration using the X-axis as its detection axis and outputs a signal corresponding to the detected acceleration. The Y-axis acceleration sensor 20Y detects acceleration using the Y-axis as its detection axis and outputs a signal corresponding to the detected acceleration. The Z-axis acceleration sensor 20Z detects acceleration using the Z-axis as its detection axis and outputs a signal corresponding to the detected acceleration. For example, the X-axis acceleration sensor 20X, the Y-axis acceleration sensor 20Y, and the Z-axis acceleration sensor 20Z may each be a quartz acceleration sensor that has a sensor element made of quartz and detects acceleration with high precision, or may be a MEMS acceleration sensor such as a capacitance type that has a sensor element processed by MEMS technology on a silicon substrate. MEMS is an abbreviation for Micro Electro Mechanical Systems.

[0033] X-axis angular velocity sensor 21X detects angular velocity using the X-axis as the detection axis and outputs a signal corresponding to the detected angular velocity. Y-axis angular velocity sensor 21Y detects angular velocity using the Y-axis as the detection axis and outputs a signal corresponding to the detected angular velocity. Z-axis angular velocity sensor 21Z detects angular velocity using the Z-axis as the detection axis and outputs a signal corresponding to the detected angular velocity. Z-axis angular velocity sensor 22Z detects angular velocity using the Z-axis as the detection axis and outputs a signal corresponding to the detected angular velocity. For example, X-axis angular velocity sensor 21X, Y-axis angular velocity sensor 21Y, Z-axis angular velocity sensor 21Z, and Z-axis angular velocity sensor 22Z may each be a quartz gyro sensor that has a sensor element made of quartz and detects angular velocity with high accuracy, or a MEMS gyro sensor such as a capacitance type that has a sensor element formed by processing a silicon substrate using MEMS technology. may be.

[0034] For example, the X-axis acceleration sensor 20X, the Y-axis acceleration sensor 20Y, and the Z-axis acceleration sensor 20Z each output a digital signal whose value corresponds to the acceleration detected at a fixed sampling period Δt, and the X-axis angular velocity sensor 21X, the Y-axis angular velocity sensor 21Y, the Z-axis angular velocity sensor 21Z, and the Z-axis angular velocity sensor 22Z each output a digital signal whose value corresponds to the angular velocity detected at a fixed sampling period Δt.

[0035] The storage unit 70 stores the aforementioned sensor characteristic information 71 and parameter information 72. The sensor characteristic information 71 includes information on the bias, sensitivity, misalignment, temperature characteristics, etc. of each of the X-axis acceleration sensor 20X, the Y-axis acceleration sensor 20Y, the Z-axis acceleration sensor 20Z, the X-axis angular velocity sensor 21X, the Y-axis angular velocity sensor 21Y, the Z-axis angular velocity sensor 21Z, and the Z-axis angular velocity sensor 22Z. The parameter information 72 includes values ​​of Allan variance parameters N1, B1, K1, and R1, which are indices of output stability of the Z-axis angular velocity sensor 21Z, and values ​​of Allan variance parameters N2, B2, K2, and R2, which are indices of output stability of the Z-axis angular velocity sensor 22Z. The parameters N1, B1, K1, and R1 and the parameters N2, B2, K2, and R2 correspond to the parameters N, B, K, and R in the aforementioned equation (1), respectively.

[0036] The micro control unit 60 reads out sensor characteristic information 71 stored in the storage unit 70 and outputs it to each of the correction processing units 35X, 35Y, 35Z, 36X, 36Y, 36Z, and 37Z. The micro control unit 60 also reads out parameter information 72 stored in the storage unit 70 and outputs it to the calculation processing unit 50. The micro control unit 60 also outputs a signal to the attitude and orientation estimation units 40A and 40B to reset an integration process, which will be described later. Furthermore, the micro control unit 60 counts the elapsed time since the reset of the integration process by the attitude and orientation estimation units 40A and 40B was released, and outputs the counted time to the calculation processing unit 50 as an integration time t.

[0037] The filter processing unit 30X performs filtering on the output signal of the X-axis acceleration sensor 20X to reduce signal components in unnecessary bands. The filter processing unit 30Y performs filtering on the output signal of the Y-axis acceleration sensor 20Y to reduce signal components in unnecessary bands. The filter processing unit 30Z performs filtering on the output signal of the Z-axis acceleration sensor 20Z to reduce signal components in unnecessary bands.

[0038] The filter processing unit 31X performs filtering on the output signal of the X-axis angular velocity sensor 21X to reduce signal components in unnecessary bands. The filter processing unit 31Y performs filtering on the output signal of the Y-axis angular velocity sensor 21Y to reduce signal components in unnecessary bands. The filter processing unit 31Z performs filtering on the output signal of the Z-axis angular velocity sensor 21Z to reduce signal components in unnecessary bands.

[0039] Correction processing units 35X, 35Y, 35Z, 36X, 36Y, 36Z, and 37Z perform correction processing for bias, sensitivity, misalignment, temperature characteristics, and the like on the output signals of filter processing units 30X, 30Y, 30Z, 31X, 31Y, 31Z, and 32Z based on sensor characteristic information 71 output from microcontrol unit 60. Correction processing unit 35X outputs a signal having a corrected X-axis acceleration value, correction processing unit 35Y outputs a signal having a corrected Y-axis acceleration value, and correction processing unit 35Z outputs a signal having a corrected Z-axis acceleration value. Correction processing unit 36X outputs a signal having a corrected X-axis angular velocity value, correction processing unit 36Y outputs a signal having a corrected Y-axis angular velocity value, and correction processing units 36Z and 37Z each output a signal having a corrected Z-axis angular velocity value.

[0040] The attitude / orientation estimation unit 40A estimates the relative attitude and orientation of the sensor module 1A based on the output signals of the correction processing units 35X, 35Y, 35Z, 36X, 36Y, and 36Z. The attitude / orientation estimation unit 40B estimates the relative attitude and orientation of the sensor module 1A based on the output signals of the correction processing units 35X, 35Y, 35Z, 36X, 36Y, and 37Z. Because the sensor module 1A is fixed to the automobile 10, the relative attitude and orientation of the sensor module 1A correspond to the relative attitude and orientation of the automobile 10.

[0041] Specifically, the attitude / orientation estimation units 40A and 40B each calculate the angular velocity of the roll angle φ, pitch angle θ, and yaw angle ψ using equation (6). xis the X-axis angular velocity, and ω y is the Y-axis angular velocity, and ω z is the Z-axis angular velocity.

[0042]

number

[0043] Equation (6) is a differential equation with respect to time, and by multiplying both sides by the sampling period Δt, the relative values ​​of the roll angle φ, pitch angle θ, and yaw angle ψ are obtained. Then, the attitude and orientation estimation units 40A and 40B calculate the roll angle φ, pitch angle θ, and yaw angle ψ by integrating the relative values ​​of the roll angle φ, pitch angle θ, and yaw angle ψ for each sampling period Δt. This integration of the relative values ​​of the roll angle φ, pitch angle θ, and yaw angle ψ corresponds to the integration process. The attitude and orientation estimation units 40A and 40B continue the integration process from the time the reset of the integration process is released by the microcontrol unit 60 until the next reset. The time during which this integration process continues corresponds to the integration time t.

[0044] Furthermore, since the X-axis acceleration sensor 20X, the Y-axis acceleration sensor 20Y, and the Z-axis acceleration sensor 20Z can detect gravitational acceleration, the attitude and orientation estimation units 40A and 40B can calculate the absolute values ​​of the roll angle φ and the pitch angle θ using equation (7) based on the values ​​of the three-axis acceleration when the sensor module 1A is stationary. x is the X-axis acceleration, and a y is the Y-axis acceleration, and a z is the Z-axis acceleration.

[0045]

number

[0046] Generally, angular velocity sensors and acceleration sensors have advantages and disadvantages when it comes to attitude estimation. Therefore, in order to compensate for the weaknesses of each sensor and estimate the attitude with high accuracy, the attitude and orientation estimation units 40A and 40B use a Kalman filter or a complementary filter to perform a calculation to integrate the roll angle φ and pitch angle θ obtained based on equation (6) with the roll angle φ and pitch angle θ obtained based on equation (7). Then, the attitude and orientation estimation unit 40A outputs the estimated roll angle φ1, pitch angle θ1, and yaw angle ψ1, and the attitude and orientation estimation unit 40B outputs the estimated roll angle φ2, pitch angle θ2, and yaw angle ψ2.

[0047] The arithmetic processing unit 50 performs arithmetic processing on the roll angle φ1, pitch angle θ1, and yaw angle ψ1, which are output signals from the attitude / orientation estimation unit 40A, and the roll angle φ2, pitch angle θ2, and yaw angle ψ2, which are output signals from the attitude / orientation estimation unit 40B. In this embodiment, the arithmetic processing unit 50 performs arithmetic processing on the roll angle φ1, pitch angle θ1, and yaw angle ψ1, which are output signals from the attitude / orientation estimation unit 40B. Based on the parameters N1, B1, K1, R1, N2, B2, K2, and R2 included in the parameter information 72 and the integral time t, the variance σ1 of the integral error in the integral processing performed by each of the attitude and orientation estimation units 40A and 40B is calculated. 2 (t),σ2 2 Calculate (t) and calculate the variance σ1 2 (t),σ2 2 Specifically, the calculation processing unit 50 performs calculation processing according to the calculated variance σ1 2 (t),σ2 2 Based on (t), a weighting factor w1 for the output signal of the attitude / orientation estimation unit 40A and a weighting factor w2 for the output signal of the attitude / orientation estimation unit 40B are calculated, and calculation processing is performed using the calculated weighting factors w1 and w2.

[0048] 6 is a diagram showing an example of the configuration of the arithmetic processing unit 50. As shown in FIG. 6, the arithmetic processing unit 50 includes a variance calculation unit 51, a weighting coefficient calculation unit 52, and a weighted average calculation unit 53.

[0049] The variance calculation unit 51 calculates the variance σ1 of the integral error in the integral processing performed by the attitude / orientation estimation unit 40A using the above-mentioned equation (3) based on the parameters N1, B1, K1, R1 and the integral time t. 2 Furthermore, the variance calculation unit 51 calculates the variance σ2 of the integral error in the integral processing performed by the attitude and orientation estimation unit 40B using the above-mentioned formula (3) based on the parameters N2, B2, K2, R2 and the integral time t. 2 In the formula (3), N is N1 or N2, B is B1 or B2, K is K1 or K2, R is R1 or R2, and σ ang 2 (t) is σ1 2 (t) or σ2 2 (t).

[0050] The weighting coefficient calculation unit 52 calculates the variance σ1 calculated by the variance calculation unit 51. 2 Based on (t), the weighting coefficient w1 for the roll angle φ1, pitch angle θ1, and yaw angle ψ1 is calculated by the equation (8). 2 Based on (t), a weighting coefficient w2 for the roll angle φ2, pitch angle θ2, and yaw angle ψ2 is calculated using equation (8).

[0051]

number

[0052] The weighted average calculation unit 53 calculates the roll angle φ according to equation (9) using the roll angles φ1 and φ2 and the weighting coefficients w1 and w2 calculated by the weighting coefficient calculation unit 52. The weighted average calculation unit 53 also calculates the pitch angle θ according to equation (9) using the pitch angles θ1 and θ2 and the weighting coefficients w1 and w2. The weighted average calculation unit 53 also calculates the yaw angle ψ according to equation (9) using the yaw angles ψ1 and ψ2 and the weighting coefficients w1 and w2. In equation (9), x i is φ i ,θ i ,ψ i and y is one of φ, θ, or ψ.

[0053]

number

[0054] 5, the detection axis of Z-axis angular velocity sensor 21Z and Z-axis angular velocity sensor 22Z is ​​the Z-axis, and the physical quantity they detect is angular velocity. That is, Z-axis angular velocity sensor 21Z corresponds to sensor device 2-1 in FIG. 1, Z-axis angular velocity sensor 22Z corresponds to sensor device 2-2 in FIG. 1, and integer n in FIG. 1 is 2. Also, filter processing unit 30Z and correction processing unit 35Z correspond to signal processing unit 3-1 in FIG. 1, and filter processing unit 32Z and correction processing unit 37Z correspond to signal processing unit 3-2 in FIG. 1. Also, attitude / orientation estimation unit 40A corresponds to integration processing unit 4-1 in FIG. 1, and attitude / orientation estimation unit 40B corresponds to integration processing unit 4-2 in FIG. 1. Also, calculation processing unit 50 corresponds to calculation processing unit 5 in FIG. 1. 1. The micro control unit 60 corresponds to the micro control unit 6 in FIG. 1. The storage unit 70 corresponds to the storage unit 7 in FIG.

[0055] 1-3.Effects As described above, the sensor module 1 of the first embodiment can perform appropriate arithmetic processing on multiple signals obtained by integration processing based on the output signals of multiple sensor devices 2-1 to 2-n that have detection axes aligned in the same direction and detect the same type of physical quantity, in accordance with the Allan variance parameters N, B, K, and R, which are indicators of output stability, and the integration time t. In particular, the sensor module 1 of the first embodiment calculates the variance of the integration error of each of the multiple integration processing units 4-1 to 4-n for each output signal of the multiple integration processing units 4-1 to 4-n based on the Allan variance parameters N, B, K, and R and the integration time t of each of the multiple sensor devices 2-1 to 2-n, and appropriately weights the signals based on the calculated multiple variances, thereby enabling highly accurate arithmetic processing. Therefore, the sensor module 1 of the first embodiment can reduce the risk of a decrease in arithmetic accuracy due to changes over time in the output stability of the multiple sensor devices 2-1 to 2-n.

[0056] In particular, sensor module 1A, which is a specific example of the sensor module 1 of the first embodiment, has two Z-axis angular velocity sensors 21Z and 22Z, both of which have the Z axis as the detection axis, and performs appropriate arithmetic processing on the roll angle φ1, pitch angle θ1, and yaw angle ψ1 obtained by integration processing based on the output signal of Z-axis angular velocity sensor 21Z, and the roll angle φ2, pitch angle θ2, and yaw angle ψ2 obtained by integration processing based on the output signal of Z-axis angular velocity sensor 22Z, in accordance with the Allan variance parameters N1, B1, K1, and R1 of Z-axis angular velocity sensor 21Z, the Allan variance parameters N2, B2, K2, and R2 of Z-axis angular velocity sensor 22Z, and the integration time t, to output highly accurate roll angle φ, pitch angle θ, and yaw angle ψ.

[0057] 2. Second embodiment In the following, in the second embodiment, the same components as those in the first embodiment are given the same reference numerals, and explanations that overlap with those in the first embodiment are omitted or simplified, and differences from the first embodiment are mainly described.

[0058] The functional configuration and operation of the sensor module 1 of the second embodiment are the same as those in Fig. 1, and therefore will not be illustrated or described again. Below, the sensor module 1B, which is a specific example of the sensor module 1 of the second embodiment, will be used as an example to describe its detailed operation.

[0059] Like the sensor module 1A shown in FIG. 5, the sensor module 1B is an inertial sensor module that detects acceleration in three mutually perpendicular axis directions and angular velocity around the three axes, and is mounted on the automobile 10 so that the three axes are aligned along the X-axis, Y-axis, and Z-axis, respectively, as shown in FIG.

[0060] The sensor module 1B calculates the speed and position of the automobile 10 in, for example, the NED coordinate system, which is a coordinate system fixed to the Earth, based on the detected accelerations and angular velocities in the three axial directions. NED is an abbreviation for North-East-Down.

[0061] 7 is a diagram showing a configuration example of a sensor module 1B. As shown in FIG. 7, the sensor module 1B includes inertial sensors, namely, an X-axis acceleration sensor 20X, an X-axis acceleration sensor 22X, a Y-axis acceleration sensor 20Y, a Z-axis acceleration sensor 20Z, an X-axis angular velocity sensor 21X, a Y-axis angular velocity sensor 21Y, and a Z-axis angular velocity sensor 21Z. The sensor module 1B also includes filter processing units 30X, 32X, 30Y, 30Z, 31X, 31Y, and 31Z, and correction processing units 35X, 37X, 35Y, 35Z, 36X, and 36Y. , 36Z. Sensor module 1B also includes attitude / orientation estimation units 40A, 40B, coordinate conversion units 41A, 41B, gravitational acceleration separation units 42A, 42B, velocity / position estimation units 43A, 43B, an arithmetic processing unit 50, a microcontrol unit 60, and a memory unit 70. That is, sensor module 1B is configured such that, compared to sensor module 1A shown in FIG. 5, it includes an X-axis acceleration sensor 22X instead of Z-axis angular velocity sensor 22Z, and further includes coordinate conversion units 41A, 41B, gravitational acceleration separation units 42A, 42B, and velocity / position estimation units 43A, 43B.

[0062] The processing of the X-axis acceleration sensor 20X, Y-axis acceleration sensor 20Y, Z-axis acceleration sensor 20Z, X-axis angular velocity sensor 21X, Y-axis angular velocity sensor 21Y, and Z-axis angular velocity sensor 21Z is the same as in the first embodiment, and therefore description thereof will be omitted.

[0063] The X-axis acceleration sensor 22X detects acceleration using the X-axis as the detection axis and outputs a signal corresponding to the detected acceleration. For example, the X-axis acceleration sensor 22X may be a quartz acceleration sensor having a sensor element made of quartz and capable of detecting acceleration with high precision, or may be a capacitive or other MEMS acceleration sensor having a sensor element formed by processing a silicon substrate using MEMS technology. For example, the X-axis acceleration sensor 22X outputs a digital signal having a value corresponding to the acceleration detected at a fixed sampling period Δt.

[0064] The storage unit 70 stores sensor characteristic information 71 and parameter information 72. The sensor characteristic information 71 includes information on the bias, sensitivity, misalignment, temperature characteristics, etc. of each of the X-axis acceleration sensor 20X, the X-axis acceleration sensor 22X, the Y-axis acceleration sensor 20Y, the Z-axis acceleration sensor 20Z, the X-axis angular velocity sensor 21X, the Y-axis angular velocity sensor 21Y, and the Z-axis angular velocity sensor 21Z. The parameter information 72 includes values ​​of Allan variance parameters N1, B1, K1, and R1, which are indices representing the output stability of the X-axis acceleration sensor 20X, and values ​​of Allan variance parameters N2, B2, K2, and R2, which are indices representing the output stability of the X-axis acceleration sensor 22X. The parameters N1, B1, K1, and R1 and the parameters N2, B2, K2, and R2 correspond to the parameters N, B, K, and R in the above-mentioned equation (1), respectively.

[0065] The micro control unit 60 reads out sensor characteristic information 71 stored in the storage unit 70 and outputs it to each of the correction processing units 35X, 37X, 35Y, 35Z, 36X, 36Y, and 36Z. The micro control unit 60 also reads out parameter information 72 stored in the storage unit 70 and outputs it to the calculation processing unit 50. The micro control unit 60 also outputs a signal to reset an integration process, which will be described later, to the attitude and orientation estimation units 40A and 40B and the velocity and position estimation units 43A and 43B. Furthermore, the micro control unit 60 counts the elapsed time since the reset of the integration process by the attitude and orientation estimation units 40A and 40B and the velocity and position estimation units 43A and 43B was released, and outputs the counted time as an integration time t to the calculation processing unit 50.

[0066] The processing of the filter processing units 30X, 30Y, 30Z, 31X, 31Y, and 31Z is the same as in the first embodiment, and therefore a description thereof will be omitted. The filter processing unit 32X performs filtering on the output signal of the X-axis acceleration sensor 22X to reduce signal components in unnecessary bands.

[0067] The processing of the correction processing units 35X, 35Y, 35Z, 36X, 36Y, and 36Z is the same as in the first embodiment, and therefore a description thereof will be omitted.

[0068] The correction processing unit 37X corrects the bias, sensitivity, and micro-detection of the output signal of the filter processing unit 32X based on the sensor characteristic information 71 output from the micro control unit 60. Correction processing is then performed on the X axis acceleration sensor 37X, which performs correction processing for misalignment, temperature characteristics, etc. Then, correction processing section 37X outputs a signal having the corrected value of the X axis acceleration.

[0069] The attitude / orientation estimation unit 40A estimates the relative attitude and orientation of the sensor module 1B based on the output signals of the correction processing units 35X, 35Y, 35Z, 36X, 36Y, and 36Z. The processing of the attitude / orientation estimation unit 40A is the same as that of the first embodiment, and therefore a description thereof will be omitted. The attitude / orientation estimation unit 40B estimates the relative attitude and orientation of the sensor module 1B based on the output signals of the correction processing units 37X, 35Y, 35Z, 36X, 36Y, and 36Z. The attitude / orientation estimation unit 40B differs from the first embodiment in that it receives the output signals of the correction processing units 37X and 36Z instead of the output signals of the correction processing units 35X and 37Z. However, since the processing is the same as that of the first embodiment, a description thereof will be omitted. Since the sensor module 1B is fixed to the automobile 10, the relative attitude and orientation of the sensor module 1B correspond to the relative attitude and orientation of the automobile 10.

[0070] The coordinate conversion unit 41A converts the triaxial acceleration values ​​of the XYZ coordinate system, which are the output signals of the correction processing units 35X, 35Y, and 35Z, into triaxial accelerations of the NED coordinate system, based on the roll angle φ1, pitch angle θ1, and yaw angle ψ1, which are output signals of the attitude and orientation estimation unit 40A. Furthermore, the coordinate conversion unit 41B converts the triaxial acceleration values ​​of the XYZ coordinate system, which are the output signals of the correction processing units 37X, 35Y, and 35Z, into triaxial accelerations of the NED coordinate system, based on the roll angle φ2, pitch angle θ2, and yaw angle ψ2, which are output signals of the attitude and orientation estimation unit 40B.

[0071] Specifically, the coordinate conversion units 41A and 41B each convert the three-dimensional acceleration vector a representing the three-axial acceleration in the XYZ coordinate system into the three-dimensional acceleration vector A representing the three-axial acceleration in the NED coordinate system using equation (10).

[0072]

number

[0073] In equation (10), C g / lis a rotation matrix called a direction cosine matrix that represents the coordinate transformation of a vector in three-dimensional space, and is expressed by equation (11). In equation (11), the roll angle φ, pitch angle θ, and yaw angle ψ are roll angle φ1, pitch angle θ1, and yaw angle ψ1, or roll angle φ2, pitch angle θ2, and yaw angle ψ2. As shown in equation (11), coordinate transformation of acceleration is possible using the roll angle φ, pitch angle θ, and yaw angle ψ.

[0074]

number

[0075] The three-axis acceleration, which is the output signal of the coordinate conversion units 41A and 41B, includes both gravitational acceleration and motion acceleration. The gravitational acceleration separation units 42A and 42B separate the gravitational acceleration included in the output signal of the coordinate conversion units 41A and 41B, respectively. Specifically, the gravitational acceleration separation units 42A and 42B separate the three-dimensional gravitational acceleration vector G from the three-dimensional acceleration vector A, which is the output signal of the coordinate conversion units 41A and 41B and represents the three-axis acceleration, by using equations (12) and (13), respectively, to obtain the three-dimensional acceleration vector A m In equation (13), g is the gravitational acceleration.

[0076]

number

[0077]

number

[0078] The velocity and position estimation units 43A and 43B estimate the velocity and position of the sensor module 1B based on the output signals of the gravitational acceleration separation units 42A and 42B, respectively. Specifically, the velocity and position estimation units 43A and 43B use equation (14) to calculate a three-dimensional acceleration vector A representing the three-axis acceleration, which is the output signal of the gravitational acceleration separation units 42A and 42B. mIn addition, the velocity and position estimation units 43A and 43B each further integrate the three-dimensional velocity vector V calculated by equation (14) using equation (15) to calculate a three-dimensional position vector P in the NED coordinate system. In equation (14), A m,k is the three-dimensional acceleration vector A at time k m and V k-1 is the three-dimensional velocity vector V at time k-1. Also, in equations (14) and (15), V k is the three-dimensional velocity vector V at time k, and Δt is the sampling period. k-1 is the three-dimensional position vector P at time k-1, and P k is the three-dimensional position vector P at time k.

[0079]

number

[0080]

number

[0081] The arithmetic processing unit 50 performs arithmetic processing on a three-dimensional velocity vector V1, which is the output signal of the velocity / position estimation unit 43A, and a three-dimensional velocity vector V2, which is the output signal of the velocity / position estimation unit 43B, based on parameter information 72. The arithmetic processing unit 50 also performs arithmetic processing on a three-dimensional position vector P1, which is the output signal of the velocity / position estimation unit 43A, and a three-dimensional position vector P2, which is the output signal of the velocity / position estimation unit 43B, based on parameter information 72. The parameter information 72 is information including values ​​of Allan variance parameters N1, B1, K1, and R1, which are indices representing the output stability of the X-axis acceleration sensor 20X, and values ​​of Allan variance parameters N2, B2, K2, and R2, which are indices representing the output stability of the X-axis acceleration sensor 22X.

[0082] In this embodiment, the calculation processing unit 50 calculates the velocity variance σ, which is the variance of the integral error in the acceleration integration process performed by the velocity / position estimation unit 43A, using the above-mentioned equation (4) based on the parameters N1, B1, K1, R1 and the integral time t. vel1 2 Furthermore, the calculation processing unit 50 calculates the velocity variance σ (t), which is the variance of the integral error in the acceleration integration process performed by the velocity / position estimation unit 43B, using the above-mentioned equation (4) based on the parameters N2, B2, K2, R2 and the integral time t. vel2 2 Then, the calculation processing unit 50 calculates the calculated variance σ vel1 2 (t),σ vel2 2 Specifically, the calculation processing unit 50 performs calculation processing according to the calculated variance σ vel1 2 (t),σ vel2 2 Based on (t), the weighting coefficient w1 for the output signal of the speed / position estimation unit 43A and the speed The calculation processing unit 50 then calculates a weighting coefficient w2 for the output signal of the angle / position estimation unit 43B. Then, the calculation processing unit 50 calculates the three-dimensional velocity vector V by using the three-dimensional velocity vectors V1 and V2 and the weighting coefficients w1 and w2 according to the above-mentioned equation (9). In equation (9), x i is V i and y is V.

[0083] Furthermore, the calculation processing unit 50 calculates the position variance σ, which is the variance of the integral error in the double integration of acceleration performed by the velocity / position estimation unit 43A, using the above-mentioned equation (5) based on the parameters N1, B1, K1, R1 and the integral time t. pos1 2 Furthermore, the calculation processing unit 50 calculates the position variance σ (t), which is the variance of the integral error in the double integration of acceleration performed by the velocity / position estimation unit 43B, using the above-mentioned equation (5) based on the parameters N2, B2, K2, R2 and the integral time t. pos2 2 Then, the calculation processing unit 50 calculates the calculated variance σ pos1 2(t),σ pos2 2 Specifically, the calculation processing unit 50 performs calculation processing according to the calculated variance σ pos1 2 (t),σ pos2 2 Based on (t), the calculation processing unit 50 calculates a weighting factor w1 for the output signal of the velocity / position estimation unit 43A and a weighting factor w2 for the output signal of the velocity / position estimation unit 43B using the above-mentioned equation (8). Then, the calculation processing unit 50 calculates a three-dimensional position vector P using the three-dimensional position vectors P1, P2 and the weighting factors w1, w2 using the above-mentioned equation (9). In equation (9), x i HA P i and y is P.

[0084] 7, the detection axis of the X-axis acceleration sensor 20X and the X-axis acceleration sensor 22X are both the X-axis, and the physical quantity they detect is acceleration. That is, the X-axis acceleration sensor 20X corresponds to the sensor device 2-1 in FIG. 1, the X-axis acceleration sensor 22X corresponds to the sensor device 2-2 in FIG. 1, and the integer n in FIG. 1 is 2. The filter processing unit 30X and the correction processing unit 35X correspond to the signal processing unit 3-1 in FIG. 1, and the filter processing unit 32X and the correction processing unit 37X correspond to the signal processing unit 3-2 in FIG. 1. The velocity / position estimation unit 43A corresponds to the integral processing unit 4-1 in FIG. 1, and the velocity / position estimation unit 43B corresponds to the integral processing unit 4-2 in FIG. 1. The calculation processing unit 50 corresponds to the calculation processing unit 5 in FIG. 1. The microcontrol unit 60 corresponds to the microcontrol unit 6 in FIG. 1. The memory unit 70 corresponds to the memory unit 7 in FIG. 1.

[0085] The sensor module 1 of the second embodiment described above can achieve the same effects as the sensor module 1 of the first embodiment. In particular, sensor module 1B, which is a specific example of sensor module 1 of the second embodiment, includes two X-axis acceleration sensors 20X and 22X, both of which have the X-axis as their detection axis, and performs appropriate arithmetic processing on a three-dimensional velocity vector V1 and a three-dimensional position vector P1 obtained by integration processing based on the output signal of X-axis acceleration sensor 20X, and a three-dimensional velocity vector V2 and a three-dimensional position vector P2 obtained by integration processing based on the output signal of X-axis acceleration sensor 22X, in accordance with Allan variance parameters N1, B1, K1, and R1 of X-axis acceleration sensor 20X and Allan variance parameters N2, B2, K2, and R2 of X-axis acceleration sensor 22X, and integration time t, thereby outputting a highly accurate three-dimensional velocity vector V and a three-dimensional position vector P.

[0086] 3. Third embodiment Hereinafter, for the third embodiment, components similar to those in the first or second embodiment will be given the same symbols, and explanations that overlap with those in the first or second embodiment will be omitted or simplified, and the following will mainly describe the differences from the first and second embodiments.

[0087] The functional configuration and operation of the sensor module 1 of the third embodiment are the same as those in FIG. 1, and therefore illustration and description thereof will be omitted. However, in the sensor module 1 of the third embodiment, the processing of the arithmetic processing unit 5 is different from those of the first and second embodiments. The arithmetic processing unit 5 in the third embodiment calculates integral processing units 4-1 to 4- The arithmetic processing unit 5 selects at least one signal from the output signals of the integral processing units 4-1 to 4-n and performs arithmetic processing based on the selected signal. For example, the arithmetic processing unit 5 may select one signal from the output signals of the integral processing units 4-1 to 4-n and perform arithmetic processing by outputting the selected signal as is. Alternatively, the arithmetic processing unit 5 may select multiple signals from the output signals of the integral processing units 4-1 to 4-n and perform arithmetic processing on the selected multiple signals.

[0088] Specifically, the calculation processing unit 5 calculates the variance σ1 of the integral error in the integral processing of each of the integral processing units 4-1 to 4-n based on the parameter information 72 and the integral time t. 2 (t)~σ n 2 (t) and calculate the variance σ1 2 (t)~σ n 2 (t) of σ j 2 When (t) is smallest, the output signal of the integral processing unit 4-j may be selected. However, since the calculation variation of the Allan variance increases as the averaging time τ increases, the variation of the calculated parameters also increases in the part where the averaging time τ is large, and the accuracy of each parameter of the sensor devices 2-1 to 2-n may differ. Therefore, the calculation processing unit 5 calculates the coefficients k1 to k2 according to the parameter information 72, the integral time t, and the accuracy of each parameter of the sensor devices 2-1 to 2-n. n For each integer i between 1 and n, the lower the accuracy of the parameters N, B, K, and R of the sensor device 2-i, the lower the coefficient k i For example, the calculation processing unit 5 may increase the variance σ for each integer i between 1 and n. i 2 (t) and coefficient k i With i σ i 2 (t) is calculated and k1σ1 2 (t)~k n σ n 2 k out of (t) j σ j 2 When (t) is smallest, the output signal of the integral processing unit 4-j may be selected. n may be a fixed value and may be included in the parameter information 72. Alternatively, the coefficient k i The value of the coefficient k may be changed according to the integral time t in the integral processing of the integral processing unit 4-i. i The value of may also be increased.

[0089] A specific example of the sensor module 1 of the third embodiment is similar to the sensor module 1B shown in FIG. 5, but the configuration of the arithmetic processing unit 50 is different from that shown in FIG.

[0090] Fig. 8 is a diagram showing an example of the configuration of the arithmetic processing unit 50 in the third embodiment. As shown in Fig. 8, the arithmetic processing unit 50 includes a variance calculation unit 51, a determination unit 54, and a switching unit 55. The processing of the variance calculation unit 51 is similar to the processing of the variance calculation unit 51 in Fig. 6, and therefore a description thereof will be omitted.

[0091] The determination unit 54 determines the variance σ1 calculated by the variance calculation unit 51. 2 (t),σ2 2 Based on (t) and the coefficients k1 and k2, the decision unit 54 outputs variables r1 and r2. Specifically, the decision unit 54 calculates the variance σ1 calculated by the variance calculation unit 51. 2 (t) and the product k1σ1 2 (t). The determination unit 54 also calculates the variance σ2 calculated by the variance calculation unit 51. 2 (t) and the product k2σ2 2 Then, the determination unit 54 calculates k1σ1 2 (t) ≥ k2σ2 2 In the case of (t), output is r1=0, r2=1, and k1σ1 2 (t) <k2σ2 2 In the case of (t), output is r1=1, r2=0.

[0092] The switching unit 55 calculates the roll angle φ according to equation (16) using the roll angles φ1 and φ2 and variables r1 and r2, which are output signals from the determination unit 54. The switching unit 55 also calculates the pitch angle θ according to equation (16) using the pitch angles θ1 and θ2 and variables r1 and r2. The switching unit 55 also calculates the yaw angle ψ according to equation (16) using the yaw angles ψ1 and ψ2 and variables r1 and r2. In equation (16), x i is φ i ,θ i ,ψ i and y is one of φ, θ, or ψ.

[0093]

number

[0094] A specific example of the sensor module 1 of the third embodiment may be the same as the sensor module 1B shown in Fig. 7. In this case, in the formula (16), x i is V i or P i and y is V or P.

[0095] According to the sensor module 1 of the third embodiment described above, for each of the output signals of the plurality of integration processing units 4-1 to 4-n, the variance of the integration error of each of the plurality of integration processing units 4-1 to 4-n is calculated based on the Allan variance parameters N, B, K, and R of each of the plurality of sensor devices 2-1 to 2-n and the integration time t, and an appropriate signal is selected from the output signals of the plurality of integration processing units 4-1 to 4-n based on the Allan variance parameters N, B, K, and R of each of the plurality of sensor devices 2-1 to 2-n and the integration time t based on the calculated variances, thereby enabling highly accurate arithmetic processing. n Therefore, by selecting an appropriate signal from the output signals of the plurality of integral processing units 4-1 to 4-n while taking into consideration the accuracy of the Allan variance parameters N, B, K, and R of each of the plurality of sensor devices 2-1 to 2-n, it is possible to perform calculation processing with higher accuracy. Therefore, the sensor module 1 of the third embodiment can reduce the risk of a decrease in calculation accuracy due to changes over time in the output stability of the plurality of sensor devices 2-1 to 2-n.

[0096] In addition, according to the sensor module 1 of the third embodiment, the same effects as those of the sensor module 1 of the first or second embodiment can be obtained.

[0097] 4. Application Examples The sensor module 1 of each of the above embodiments can be used, for example, in a hybrid navigation system that uses GNSS and an INS (Inertial Navigation System). Fig. 9 shows an example of a hybrid navigation system incorporating the sensor module 1A shown in Fig. 5. The hybrid navigation system shown in Fig. 9 includes the sensor module 1, a GNSS receiver 100, a speed / position estimation unit 110, a wheel speed sensor 120, a coordinate conversion unit 130, a speed / position estimation unit 140, and a hybrid navigation calculation unit 150, and is mounted on an automobile 10.

[0098] The sensor module 1A outputs the roll angle φ, pitch angle θ, and yaw angle ψ calculated by the arithmetic processing unit 50 to the outside.

[0099] The GNSS receiver 100 receives satellite signals transmitted from multiple satellites constituting a Global Navigation Satellite System (GNSS) via an antenna (not shown), performs positioning based on the received satellite signals, and outputs location information. Examples of GNSS include the Global Positioning System (GPS), the Quasi Zenith Satellite System (QZSS), the European Geostationary Navigation Overlay Service (EGNOS), the Global Navigation Satellite System (GLONASS), GALILEO, and BeiDou.

[0100] The speed and position estimation unit 110 estimates the speed and position of the automobile 10 based on the position information output from the GNSS receiver 100, and outputs a three-dimensional speed vector V1 and a three-dimensional position vector P1 in the NED coordinate system.

[0101] The wheel speed sensor 120 detects the rotation speed of the wheels of the automobile 10 and outputs a wheel speed signal.

[0102] The coordinate conversion unit 130 converts the wheel speed signals output from the wheel speed sensors 120 into triaxial speed signals in the NED coordinate system based on the roll angle φ, pitch angle θ, and yaw angle ψ output from the sensor module 1.

[0103] The speed / position estimation unit 140 estimates the speed and position of the automobile 10 based on the three-axis speed signal output from the coordinate conversion unit 130, and outputs a three-dimensional speed vector V2 and a three-dimensional position vector P2 in the NED coordinate system.

[0104] The hybrid navigation calculation unit 150 performs hybrid navigation calculation processing using the three-dimensional velocity vector V1 and three-dimensional position vector P1, which are output signals from the velocity / position estimation unit 110, and the three-dimensional velocity vector V2 and three-dimensional position vector P2, which are output signals from the velocity / position estimation unit 140, to calculate the three-dimensional velocity vector V and three-dimensional position vector P, which indicate the velocity and position of the automobile 10.

[0105] The integrated navigation in the system shown in Figure 9 is loose coupling, which integrates the estimated speed and position results based on GNSS and the estimated speed and position results based on INS. Other integrated navigation methods include tight coupling, which integrates raw data based on GNSS with the estimated results of INS, and deep coupling, which feeds back the estimated results of INS to GNSS tracking.

[0106] 5. Variations The present invention is not limited to the present embodiment, and various modifications are possible within the scope of the present invention.

[0107] For example, the sensor module 1A shown in Fig. 5 may be combined with the sensor module 1B shown in Fig. 7. Specifically, in the sensor module 1B, the coordinate conversion units 41A and 41B may perform coordinate conversion using the roll angle φ, pitch angle θ, and yaw angle ψ, which are output signals from the arithmetic processing unit 50 of the sensor module 1A. This configuration is particularly effective for navigation of a moving body such as an automobile 10, whose main movement is along the X-axis and around the Z-axis.

[0108] Furthermore, for example, in the third embodiment, the arithmetic processing unit 5 may select two or more signals from the output signals of the integral processing units 4-1 to 4-n, multiply the selected two or more signals by the weighting coefficient calculated by equation (8), and perform the arithmetic processing of equation (9). That is, the arithmetic processing unit 50 shown in Fig. 6 and the arithmetic processing unit 50 shown in Fig. 8 may be combined.

[0109] Furthermore, in the above-described embodiments, an example has been given in which the sensor module 1 is mounted on an automobile 10, but the sensor module 1 may be mounted on a moving body other than an automobile. Examples of moving bodies other than automobiles include agricultural machinery such as tractors, construction machinery such as excavators, automated guided vehicles, robotic lawn mowers, robotic vacuum cleaners, aircraft such as jet planes and helicopters, ships, rockets, artificial satellites, railroad vehicles, aerial drones, and underwater drones.

[0110] The above-described embodiment and modifications are merely examples, and the present invention is not limited to these. For example, the embodiments and modifications can be combined as appropriate.

[0111] The present invention includes configurations that are substantially the same as the configurations described in the embodiments, for example, configurations with the same functions, methods, and results, or configurations with the same purpose and effects. The present invention also includes configurations that replace non-essential parts of the configurations described in the embodiments. The present invention also includes configurations that achieve the same effects or purposes as the configurations described in the embodiments. The present invention also includes configurations that add publicly known technology to the configurations described in the embodiments.

[0112] The following can be derived from the above-described embodiment and modifications.

[0113] One aspect of the sensor module is a plurality of sensor devices each having a detection axis aligned in the same direction and detecting the same type of physical quantity; a storage unit that stores parameter information including a parameter of an index that indicates the output stability of each of the plurality of sensor devices; a plurality of integration processing units that perform integration processing based on output signals from the plurality of sensor devices; a calculation processing unit that performs calculation processing on the output signals of the plurality of integration processing units in accordance with the parameter information and the integration time of the integration processing; Equipped with.

[0114] This sensor module can perform appropriate arithmetic processing on multiple signals obtained by integrating output signals from multiple sensor devices that have detection axes aligned in the same direction and detect the same type of physical quantity, in accordance with the parameter of the index representing output stability and the integration time. Therefore, this sensor module can reduce the risk of a decrease in arithmetic accuracy due to changes over time in the output stability of the multiple sensor devices.

[0115] In one embodiment of the sensor module, The calculation processing unit may calculate a weighting coefficient for each of the output signals of the plurality of integration processing units based on the parameter information and the integration time, and perform the calculation processing using the calculated plurality of weighting coefficients.

[0116] This sensor module enables highly accurate calculations to be performed by appropriately weighting each output signal from the multiple integration processing units based on the parameters of an index representing the output stability of each of the multiple sensor devices and the integration time.

[0117] In one embodiment of the sensor module, The calculation processing unit may select at least one signal from the output signals of the plurality of integration processing units based on the parameter information and the integration time, and perform the calculation processing based on the selected signal.

[0118] This sensor module enables highly accurate calculations to be performed by selecting an appropriate signal from the output signals of multiple integration processing units based on the parameters of an index representing the output stability of each of the multiple sensor devices and the integration time.

[0119] In one embodiment of the sensor module, The calculation processing unit may select at least one signal from the output signals of the multiple integration processing units based on the parameter information, the integration time, and a coefficient corresponding to the accuracy of the parameters of each of the multiple sensor devices included in the accuracy of the parameter information.

[0120] This sensor module enables more accurate calculations to be performed by selecting an appropriate signal from the output signals of multiple integration processing units, taking into consideration the accuracy of the parameters of the index representing the output stability of each of the multiple sensor devices.

[0121] In one embodiment of the sensor module, The calculation processing unit calculates a variance of an integral error in the integral process performed by each of the plurality of integral processing units based on the parameter information and the integral time, and The calculation process may be performed according to the variance of the integration error.

[0122] According to this sensor module, highly accurate arithmetic processing can be performed in accordance with the variance of the integration errors of the plurality of integration processing sections.

[0123] In one embodiment of the sensor module, each of the plurality of sensor devices is an angular velocity sensor; Each of the output signals of the plurality of integral processing units may be a signal corresponding to an attitude or an orientation.

[0124] This sensor module can perform appropriate arithmetic processing on multiple signals corresponding to attitude or orientation obtained by integrating the output signals of multiple angular velocity sensors having detection axes aligned in the same direction, in accordance with an index parameter representing output stability and integration time. Therefore, this sensor module can reduce the risk of a decrease in the accuracy of calculations related to attitude or orientation due to changes over time in the output stability of the multiple angular velocity sensors.

[0125] In one embodiment of the sensor module, Each of the multiple sensor devices is an acceleration sensor, Each of the output signals of the plurality of integral processing units may be a signal corresponding to a velocity or a position.

[0126] This sensor module can perform appropriate calculations on multiple signals corresponding to velocity or position obtained by integrating the output signals of multiple acceleration sensors with detection axes aligned in the same direction, according to the parameter representing the output stability and the integration time. Therefore, this sensor module can reduce the risk of a decrease in the accuracy of calculations related to velocity or position due to changes over time in the output stability of the multiple acceleration sensors. [Explanation of symbols]

[0127] 1, 1A, 1B... sensor module, 2-1 to 2-n... sensor device, 3-1 to 3-n... signal processing unit, 4-1 to 4-n... integration processing unit, 5... calculation processing unit, 6... micro control unit, 7... memory unit, 10... automobile, 20X... X-axis acceleration sensor, 20Y... Y-axis acceleration sensor, 20Z... axis acceleration sensor, 21X... X-axis angular velocity sensor, 21Y... Y-axis angular velocity sensor, 21Z... Z-axis angular velocity sensor, 22X... X-axis acceleration sensor, 22Z... Z-axis angular velocity sensor, 30X, 30Y, 30Z, 31X, 31Y, 31Z, 32X, 32Z... filter processing unit, 35X, 35Y, 35Z, 36X, 36Y, 36Z, 37X, 37Z... correction processing unit, 40A, 40B... attitude and direction estimation unit, 41A, 41B... coordinate conversion unit, 42A, 42B... gravity acceleration separation unit, 43A, 43B... speed and position estimation unit, 50... calculation processing unit, 51... variance calculation unit, 52... weighting coefficient calculation unit, 53... weighted average calculation unit, 54... determination unit, 55... switching unit, 60... micro control unit, 70... memory unit, 71... sensor characteristic information, 72... parameter information, 100... GNSS receiver, 110... speed and position estimation unit, 120... wheel speed sensor, 130... coordinate conversion unit, 140... speed and position estimation unit, 150... combined navigation calculation unit

Claims

1. a plurality of sensor devices each having a detection axis aligned in the same direction and detecting the same type of physical quantity; a storage unit that stores parameter information including a parameter of an index that indicates the output stability of each of the plurality of sensor devices; a plurality of integration processing units that perform integration processing based on output signals from the plurality of sensor devices; a calculation processing unit that performs calculation processing on the output signals of the plurality of integration processing units in accordance with the parameter information and the integration time of the integration processing; A sensor module comprising:

2. In claim 1, The calculation processing unit calculates a weighting coefficient for each of the output signals of the plurality of integration processing units based on the parameter information and the integration time, and performs the calculation processing using the calculated plurality of weighting coefficients.

3. In claim 1, The calculation processing unit selects at least one signal from the output signals of the plurality of integration processing units based on the parameter information and the integration time, and performs the calculation processing based on the selected signal.

4. In claim 3, The calculation processing unit selects at least one signal from the output signals of the multiple integration processing units based on the parameter information, the integration time, and a coefficient corresponding to the accuracy of the parameters of each of the multiple sensor devices included in the accuracy of the parameter information.

5. In claim 1, The calculation processing unit calculates the variance of the integral error in the integral processing performed by each of the plurality of integral processing units based on the parameter information and the integral time, and performs the calculation processing according to the calculated variance of the plurality of integral errors.

6. In claim 1, each of the plurality of sensor devices is an angular velocity sensor; A sensor module, wherein each of the output signals of the plurality of integral processing units is a signal corresponding to an attitude or an orientation.

7. In claim 1, Each of the multiple sensor devices is an acceleration sensor, A sensor module, wherein each of the output signals of the plurality of integral processing units is a signal corresponding to a velocity or a position.

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