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CN108680184B - A Zero-speed Detection Method Based on Generalized Likelihood Ratio Statistical Curve Geometric Transformation - Google Patents

A Zero-speed Detection Method Based on Generalized Likelihood Ratio Statistical Curve Geometric Transformation Download PDF

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CN108680184B
CN108680184B CN201810353728.0A CN201810353728A CN108680184B CN 108680184 B CN108680184 B CN 108680184B CN 201810353728 A CN201810353728 A CN 201810353728A CN 108680184 B CN108680184 B CN 108680184B
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CN108680184A (en
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刘锡祥
汪宋兵
黄荣
王启明
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Southeast University
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Abstract

本发明公开了一种基于广义似然比统计曲线几何变换的零速检测方法，包括以下步骤：S1：利用集成在惯性测量单元中的陀螺仪和加速度计输出的测量值构建广义似然比统计曲线；S2：通过信号的平移方法求取可能的零速区间；S3：对广义似然比统计曲线进行几何变换；S4：通过几何变换之后的广义似然比统计曲线之间的差值剔除伪零速区间；S5：通过几何变换之后的广义似然比统计曲线之间的对称关系剔除零速区间内的非零速边缘点。本方法模型简单，具有普适性，且计算复杂度低。

Figure 201810353728

The invention discloses a zero-speed detection method based on the geometric transformation of a generalized likelihood ratio statistical curve, comprising the following steps: S1: constructing a generalized likelihood ratio statistical Curve; S2: Obtain the possible zero-speed interval by the translation method of the signal; S3: Perform geometric transformation on the generalized likelihood ratio statistical curve; S4: Eliminate false values by the difference between the generalized likelihood ratio statistical curves after geometric transformation Zero-speed interval; S5: Eliminate non-zero-speed edge points in the zero-speed interval through the symmetrical relationship between the generalized likelihood ratio statistical curves after geometric transformation. The method has a simple model, is universal, and has low computational complexity.

Figure 201810353728

Description

Zero-speed detection method based on generalized likelihood ratio statistical curve geometric transformation
Technical Field
The invention relates to the field of navigation algorithms, in particular to a zero-speed detection method based on generalized likelihood ratio statistical curve geometric transformation.
Background
The pedestrian inertial navigation system based on the integral working mode has the characteristic of error accumulation, the error accumulation is particularly rapid for low-precision MEMS-SINS, and the inhibition of the increase of the system error is the basis of the work of the pedestrian inertial navigation system. Zero velocity update (zero velocity update ZUPT) is an effective method for improving the pedestrian navigation positioning accuracy under the MEMS-SINS independent working condition. Zero-speed detection is the premise of zero-speed correction, missing detection can reduce correction frequency, false detection can bring wrong correction, and both can reduce pedestrian navigation positioning accuracy. Currently, the commonly used zero-speed detection methods are mainly classified into two types, namely threshold judgment-based detection methods and machine learning-based detection methods.
However, due to pedestrian population difference, gait difference, low signal-to-noise ratio of the low-precision MEMS-IMU and the like, the zero-speed detection method based on threshold judgment has the problems of poor adaptability to different motion states, different individuals and the like. The artificial intelligence technology excessively depends on the universality of training data and the generalization capability of a training model, so that the artificial intelligence technology has the defects of complex algorithm and difficult training, and is limited in practical use.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a zero-speed detection method with low calculation complexity based on generalized likelihood ratio statistical curve geometric transformation.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the following technical scheme:
the invention discloses a zero-speed detection method based on generalized likelihood ratio statistical curve geometric transformation, which comprises the following steps:
s1: constructing a generalized likelihood ratio statistical curve by utilizing measured values output by a gyroscope and an accelerometer which are integrated in an inertial measurement unit;
s2: calculating a possible zero-speed interval by a signal translation method;
s3: carrying out geometric transformation on the generalized likelihood ratio statistical curve;
s4: eliminating a pseudo-zero speed interval through a difference value between generalized likelihood ratio statistical curves after geometric transformation;
s5: and eliminating non-zero-velocity edge points in a zero-velocity interval through the symmetrical relation between the generalized likelihood ratio statistical curves after geometric transformation.
Further, the step S1 is realized by the following process:
the measured value output by the inertia measuring unit is set as follows:
Figure BDA0001634081520000021
in the formula
Figure BDA0001634081520000022
3-dimensional accelerometer output for IMU at time kThe data is measured and the data is transmitted,
Figure BDA0001634081520000023
3-dimensional measurement data of the gyroscope output by the IMU at the moment k;
a generalized likelihood ratio statistical curve t (n) is obtained by equation (2):
Figure BDA0001634081520000024
in the formula of omeganA window of time series is measured for the IMU,
Figure BDA0001634081520000025
is the observed noise variance of the accelerometer,
Figure BDA0001634081520000026
is the observed noise variance of the gyroscope,
Figure BDA0001634081520000027
is at the window omeganAverage of the internal accelerometer data, N represents the window length, and g is the gravitational acceleration.
Further, the step S2 is realized by the following process:
s2.1: carrying out translation transformation on the generalized likelihood ratio statistical curve T (n) by the formula (3) to obtain the generalized likelihood ratio statistical curve T after the translation transformationmov_u(n)：
Tmov_u(n)＝T(n-m)+ΔY (3)
In the formula, m is the number of right-shift sampling points, and delta Y is an upward shift value;
s2.2: Δ Y is determined by the following method:
using a size of N1The window intercepts the current time TcTo the previous moment Tc-N1The interval is increased from 0 by a sliding threshold Th until Th is equal to DeltaY, and all the intervals of Th-T (N) > 0 are N2，N1And N2Satisfies the following formula:
N2/N1＝35％ (4)
s2.3: the interval satisfying the following formula is a possible zero velocity interval:
Tmov_u(n)＞T(n) (5)
further, in step S3, the generalized likelihood ratio statistical curve is geometrically transformed by equation (6):
Trev_u(n)＝-T(n)+ΔY (6)
in the formula Trev_u(n) is a generalized likelihood ratio statistical curve after geometric transformation, T (n) is a generalized likelihood ratio statistical curve, and Δ Y is an up-shift magnitude value.
Further, in step S4, the zero-velocity section and the non-zero-velocity section are determined by equation (7), so as to eliminate the pseudo-zero-velocity section:
Figure BDA0001634081520000031
in the formula, L is a generalized likelihood ratio statistical curve T after geometric transformationrev_uAbsolute value of difference between (n) and generalized likelihood ratio statistical curve T (n), L1For statistical curves T of generalized likelihood ratios after geometric transformationrev_u(n) and generalized likelihood ratio statistical curve T after translation transformationmov_u(n) the absolute value of the difference between; zero _ flag is a zero-speed zone bit, when the zero _ flag is 1, the zero-speed is represented, and when the zero _ flag is 0, the non-zero-speed is represented; L-L1Obtained according to formula (8);
L-L1＝-4T(n)+ΔY-Km| (8)
in the formula, delta Y is an upward shift value, m is the number of right shift sampling points, and K is a slope value of a curve.
Further, in step S5, the non-zero velocity edge points are removed by equation (9):
Figure BDA0001634081520000032
in the formula, zero _ flag is a zero-speed zone bit, when the zero _ flag is 1, the zero-speed is represented, and when the zero _ flag is 0, the non-zero speed is represented; l is after geometric transformationGeneralized likelihood ratio statistical curve Trev_u(n) the absolute value of the difference between the generalized likelihood ratio statistical curve T (n), as shown in formula (10); lambda is more than or equal to 0.9 and less than or equal to 0.95; Δ Y is the magnitude of the upshifting;
L＝|Trev_u(n)-T(n)|＝|(-T(n)+ΔY)-(T(n))|＝|-2T(n)+ΔY| (10)。
has the advantages that: the invention discloses a zero-speed detection method based on generalized likelihood ratio statistical curve geometric transformation, which has the following beneficial effects compared with the prior art:
1) compared with a threshold method, the zero-speed detection does not pass threshold judgment, and has better adaptation effect on different pedestrians and gaits;
2) compared with a machine learning algorithm, the zero-speed detection method is simple in model, universal and low in calculation complexity.
Drawings
FIG. 1 is a graph of measured data detected by an accelerometer according to an embodiment of the invention;
FIG. 2 is a graph of measured data detected by a gyroscope according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a generalized likelihood ratio statistical curve geometry transformation according to an embodiment of the present invention;
FIG. 4 is a diagram of the zero-speed test result of the test subject A according to the embodiment of the present invention;
FIG. 5 is a diagram illustrating the zero-speed test result of the second laboratory test;
FIG. 6 is a zero-speed test result chart of the experimenter C in the embodiment of the invention.
Detailed Description
The technical solution of the present invention will be further described with reference to the following embodiments.
The specific embodiment aims at a zero-speed correction algorithm in a foot-bound inertial pedestrian navigation system, performs zero-speed detection analysis, constructs a generalized likelihood ratio statistical curve by using measurement values of a gyroscope and an accelerometer, obtains a possible zero-speed interval by using translation of the generalized likelihood ratio statistical curve, performs geometric transformation (translation and turnover transformation) on the generalized likelihood ratio statistical curve again, eliminates the pseudo-zero-speed interval by using the absolute value magnitude relation of a difference value between the generalized likelihood ratio curves after the geometric transformation, and determines the zero-speed interval.
The specific embodiment discloses a zero-speed detection method based on generalized likelihood ratio statistical curve geometric transformation, which comprises the following steps:
s1: constructing a generalized likelihood ratio statistical curve by utilizing measured values output by a gyroscope and an accelerometer which are integrated in an inertial measurement unit; fig. 1 and fig. 2 are measured data detected by an accelerometer and a gyroscope, respectively, and a generalized likelihood ratio statistical curve t (n) is constructed by using the measured data of the accelerometer and the gyroscope, as shown in fig. 3;
s2: calculating a possible zero-speed interval by a signal translation method;
s3: carrying out geometric transformation on the generalized likelihood ratio statistical curve;
s4: eliminating a pseudo-zero speed interval through a difference value between generalized likelihood ratio statistical curves after geometric transformation;
s5: and eliminating non-zero-velocity edge points in a zero-velocity interval through the symmetrical relation between the generalized likelihood ratio statistical curves after geometric transformation.
Step S1 is realized by the following procedure:
the measured value output by the inertia measuring unit is set as follows:
Figure BDA0001634081520000041
in the formula
Figure BDA0001634081520000042
For the 3-dimensional measurement data of the accelerometer output by the IMU at time k,
Figure BDA0001634081520000043
3-dimensional measurement data of the gyroscope output by the IMU at the moment k;
a generalized likelihood ratio statistical curve t (n) is obtained by equation (2):
Figure BDA0001634081520000044
in the formula of omeganA window of time series is measured for the IMU,
Figure BDA0001634081520000045
is the observed noise variance of the accelerometer,
Figure BDA0001634081520000046
is the observed noise variance of the gyroscope,
Figure BDA0001634081520000051
is at the window omeganAverage of the internal accelerometer data, N represents the window length, and g is the gravitational acceleration.
Step S2 is realized by the following procedure:
s2.1: carrying out translation transformation on the generalized likelihood ratio statistical curve T (n) by the formula (3) to obtain the generalized likelihood ratio statistical curve T after the translation transformationmov_u(n)：
Tmov_u(n)＝T(n-m)+ΔY (3)
In the formula, m is the number of right-shift sampling points, and delta Y is an upward shift value;
s2.2: Δ Y is determined by the following method:
using a size of N1The window intercepts the current time TcTo the previous moment Tc-N1The interval is increased from 0 by a sliding threshold Th until Th is equal to DeltaY, and all the intervals of Th-T (N) > 0 are N2N1 and N2 satisfy the following formula:
N2/N1＝35％ (4)
s2.3: the interval satisfying the following formula is a possible zero velocity interval:
Tmov_u(n)＞T(n) (5)
in step S3, the generalized likelihood ratio statistical curve is geometrically transformed by equation (6):
Trev_u(n)＝-T(n)+ΔY (6)
in the formula Trev_u(n) is a generalized likelihood ratio statistical curve after geometric transformation, T (n) is a generalized likelihood ratio statistical curve, and Δ Y is an up-shift magnitude value.
In step S4, the zero-velocity section and the non-zero-velocity section are determined by equation (7), so as to eliminate the pseudo-zero-velocity section:
Figure BDA0001634081520000052
in the formula, L is a generalized likelihood ratio statistical curve T after geometric transformationrev_uAbsolute value of difference between (n) and generalized likelihood ratio statistical curve T (n), L1For statistical curves T of generalized likelihood ratios after geometric transformationrev_u(n) and generalized likelihood ratio statistical curve T after translation transformationmov_u(n) the absolute value of the difference between; zero _ flag is a zero-speed zone bit, when the zero _ flag is 1, the zero-speed is represented, and when the zero _ flag is 0, the non-zero-speed is represented; L-L1Obtained according to formula (8);
L-L1＝-4T(n)+ΔY-Km| (8)
in the formula, delta Y is an upward shift value, m is the number of right shift sampling points, and K is a slope value of a curve.
In step S5, the non-zero velocity edge points are removed by equation (9):
Figure BDA0001634081520000061
in the formula, zero _ flag is a zero-speed zone bit, when the zero _ flag is 1, the zero-speed is represented, and when the zero _ flag is 0, the non-zero speed is represented; l is a generalized likelihood ratio statistical curve T after geometric transformationrev_u(n) the absolute value of the difference between the generalized likelihood ratio statistical curve T (n), as shown in formula (10); lambda is more than or equal to 0.9 and less than or equal to 0.95; Δ Y is the magnitude of the upshifting;
L＝|Trev_u(n)-T(n)|＝|(-T(n)+ΔY)-(T(n))|＝|-2T(n)+ΔY| (10)。
the beneficial effects of the embodiment are verified by the following measured data: the experimental route is a straight line 100m, three experimenters, namely, the experimenters A (male), B (female) and C (male), each of the experimenters walks three groups of experiments at a slow speed, a normal pace and a fast speed, the actual speed participation experimental results are shown in the following table 1, the detection results of 10s to 35s are shown in figures 4, 5 and 6, the three experimenters A, B and C respectively correspond to the three experimenters A, B and C, the zero speed is represented when the logic value is 1, and the non-zero speed is represented when the logic value is 0.
Table 1: different experimenters different speed experimental data
Figure BDA0001634081520000062
Generalized likelihood ratio test correlation parameter sigmaaSet to 0.01, σωSet to 0.1 pi/180, window length N is set to 3; for the value of the translation value m, the value of m is 0.08s in the experiment; the value of omega is 5 s; λ in formula (11) is set to 0.9.

Claims (1)

1.一种基于广义似然比统计曲线几何变换的零速检测方法，其特征在于：包括以下步骤：1. a zero-speed detection method based on generalized likelihood ratio statistical curve geometric transformation, is characterized in that: comprise the following steps: S1：利用集成在惯性测量单元中的陀螺仪和加速度计输出的测量值构建广义似然比统计曲线，具体如下：S1: Construct a generalized likelihood ratio statistical curve using the measured values output by the gyroscope and accelerometer integrated in the inertial measurement unit, as follows: 设惯性测量单元输出的测量值为：Let the measured value output by the inertial measurement unit be:
Figure FDA0003179658420000011
Figure FDA0003179658420000011
 式中
Figure FDA0003179658420000012
为IMU在k时刻输出的加速度计的3维测量数据，
Figure FDA0003179658420000013
为IMU在k时刻输出的陀螺仪的3维测量数据；in the formula
Figure FDA0003179658420000012
is the 3D measurement data of the accelerometer output by the IMU at time k,
Figure FDA0003179658420000013
is the 3D measurement data of the gyroscope output by the IMU at time k; 通过式(2)获得广义似然比统计曲线T(n)：The generalized likelihood ratio statistical curve T(n) is obtained by formula (2):
Figure FDA0003179658420000014
Figure FDA0003179658420000014
 式中Ωn为IMU测量时间序列的窗口，
Figure FDA0003179658420000015
是加速度计的观测噪声方差，
Figure FDA0003179658420000016
是陀螺仪的观测噪声方差，
Figure FDA0003179658420000017
是在窗口Ωn内加速度计数据的平均值，N表示窗口长度，g是重力加速度；where Ω n is the window of the time series measured by the IMU,
Figure FDA0003179658420000015
is the observed noise variance of the accelerometer,
Figure FDA0003179658420000016
is the observed noise variance of the gyroscope,
Figure FDA0003179658420000017
is the average value of the accelerometer data in the window Ω n , N is the window length, g is the gravitational acceleration; S2：通过信号的平移方法求取可能的零速区间，具体如下：S2: The possible zero-speed interval is obtained by the translation method of the signal, as follows: S2.1：通过式(3)对广义似然比统计曲线T(n)进行平移变换，获得平移变换之后的广义似然比统计曲线Tmov_u(n)：S2.1: Perform translation transformation on the generalized likelihood ratio statistical curve T(n) by formula (3) to obtain the generalized likelihood ratio statistical curve T mov_u (n) after the translation transformation: Tmov_u(n)＝T(n-m)+ΔY (3)T mov_u (n)=T(nm)+ΔY (3) 式中m为右移采样点数，ΔY为上移大小值；In the formula, m is the number of sampling points shifted to the right, and ΔY is the size of the upward shift; S2.2：ΔY通过下面方法确定：S2.2: ΔY is determined by the following method: 利用一个大小为N1的窗口截取当前时刻Tc到前一时刻Tc-N1区间，利用一滑动阈值Th从0开始，不断增大，直至Th＝ΔY，此时Th-T(n)＞0的所有区间大小为N2，N1与N2满足下式：Use a window of size N 1 to intercept the interval from the current time T c to the previous time T c -N 1 , and use a sliding threshold Th to start from 0 and increase continuously until Th=ΔY, at this time Th-T(n) The size of all intervals > 0 is N 2 , and N 1 and N 2 satisfy the following formula: N2/N1＝35％ (4)N 2 /N 1 =35% (4) S2.3：满足下式的区间就是可能的零速区间：S2.3: The interval satisfying the following formula is the possible zero-speed interval: Tmov_u(n)＞T(n) (5)；T mov_u (n)>T(n) (5); S3：对广义似然比统计曲线进行几何变换，具体如下：S3: Perform geometric transformation on the generalized likelihood ratio statistical curve, as follows: 通过式(6)对广义似然比统计曲线进行几何变换：The generalized likelihood ratio statistical curve is geometrically transformed by formula (6): Trev_u(n)＝-T(n)+ΔY (6)T rev_u (n)=-T(n)+ΔY (6) 式中Trev_u(n)为几何变换之后的广义似然比统计曲线，T(n)为广义似然比统计曲线，ΔY为上移大小值；In the formula, T rev_u (n) is the generalized likelihood ratio statistical curve after geometric transformation, T(n) is the generalized likelihood ratio statistical curve, and ΔY is the upward shift value; S4：通过几何变换之后的广义似然比统计曲线之间的差值剔除伪零速区间，具体如下：S4: Eliminate the pseudo-zero speed interval by the difference between the generalized likelihood ratio statistical curves after geometric transformation, as follows: 通过式(7)判断零速区间与非零速区间，从而剔除伪零速区间：The zero-speed interval and the non-zero-speed interval are judged by formula (7), so as to eliminate the pseudo-zero-speed interval:
Figure FDA0003179658420000021
Figure FDA0003179658420000021
 式中L为几何变换之后的广义似然比统计曲线Trev_u(n)与广义似然比统计曲线T(n)之间差值的绝对值，L1为几何变换之后的广义似然比统计曲线Trev_u(n)与平移变换之后的广义似然比统计曲线Tmov_u(n)之间差值的绝对值；zero_flag为零速标志位，zero_flag为1时表示零速，zero_flag为0时表示非零速；L-L1根据式(8)得到；where L is the absolute value of the difference between the generalized likelihood ratio statistic curve T rev_u (n) after geometric transformation and the generalized likelihood ratio statistic curve T(n), and L 1 is the generalized likelihood ratio statistic after geometric transformation The absolute value of the difference between the curve T rev_u (n) and the generalized likelihood ratio statistical curve T mov_u (n) after translation transformation; Non-zero speed; LL 1 is obtained according to formula (8); L-L1＝-4T(n)+ΔY-|Km| (8)LL 1 =-4T(n)+ΔY-|Km| (8) 式中ΔY为上移大小值，m为右移采样点数，K为曲线的斜率值；where ΔY is the value of the upward shift, m is the number of sampling points shifted to the right, and K is the slope value of the curve; S5：通过几何变换之后的广义似然比统计曲线之间的对称关系剔除零速区间内的非零速边缘点，具体如下：S5: Eliminate the non-zero-speed edge points in the zero-speed interval through the symmetrical relationship between the generalized likelihood ratio statistical curves after geometric transformation, as follows: 通过式(9)将非零速边缘点剔除：The non-zero velocity edge points are eliminated by formula (9):
Figure FDA0003179658420000022
Figure FDA0003179658420000022
 式中zero_flag为零速标志位，zero_flag为1时表示零速，zero_flag为0时表示非零速；L为几何变换之后的广义似然比统计曲线Trev_u(n)与广义似然比统计曲线T(n)之间差值的绝对值，如式(10)所示；0.9≤λ≤0.95；ΔY为上移大小值；In the formula, zero_flag is the zero-speed flag bit, when zero_flag is 1, it means zero speed, and when zero_flag is 0, it means non-zero speed; L is the generalized likelihood ratio statistical curve T rev_u (n) and the generalized likelihood ratio statistical curve after geometric transformation The absolute value of the difference between T(n), as shown in formula (10); 0.9≤λ≤0.95; ΔY is the value of the upward shift; L＝|Trev_u(n)-T(n)|L=|T rev_u (n)-T(n)| ＝|(-T(n)+ΔY)-(T(n))|＝|(-T(n)+ΔY)-(T(n))| ＝|-2T(n)+ΔY| (10)。=|-2T(n)+ΔY| (10).
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