CN108680184B - Zero-speed detection method based on generalized likelihood ratio statistical curve geometric transformation - Google Patents
Zero-speed detection method based on generalized likelihood ratio statistical curve geometric transformation Download PDFInfo
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Abstract
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. The method has the advantages of simple model, universality and low computational complexity.
Description
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:
in the formula
3-dimensional accelerometer output for IMU at time kThe data is measured and the data is transmitted,
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):
in the formula of omeganA window of time series is measured for the IMU,
is the observed noise variance of the accelerometer,
is the observed noise variance of the gyroscope,
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:
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):
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:
in the formula
For the 3-dimensional measurement data of the accelerometer output by the IMU at time k,
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):
in the formula of omeganA window of time series is measured for the IMU,
is the observed noise variance of the accelerometer,
is the observed noise variance of the gyroscope,
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:
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):
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
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. A zero-speed detection method based on generalized likelihood ratio statistical curve geometric transformation is characterized in that: the method comprises the following steps:
s1: the generalized likelihood ratio statistical curve is constructed by utilizing the measured values output by the gyroscope and the accelerometer which are integrated in the inertial measurement unit, and the generalized likelihood ratio statistical curve is specifically as follows:
the measured value output by the inertia measuring unit is set as follows:
in the formula
For the 3-dimensional measurement data of the accelerometer output by the IMU at time k,
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):
in the formula of omeganA window of time series is measured for the IMU,
is the observed noise variance of the accelerometer,
is the observed noise variance of the gyroscope,
is at the window omeganAverage of the internal accelerometer data, N represents the window length, g is the acceleration of gravity;
s2: the possible zero-velocity interval is obtained by a signal translation method, which specifically comprises the following steps:
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)；
s3: performing geometric transformation on the generalized likelihood ratio statistical curve, specifically as follows:
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 delta Y is an up-shift value;
s4: eliminating a pseudo-zero speed interval through a difference value between generalized likelihood ratio statistical curves after geometric transformation, which is specifically as follows:
judging a zero-speed interval and a non-zero-speed interval by the formula (7) so as to eliminate a pseudo zero-speed interval:
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;
s5: and eliminating non-zero-velocity edge points in a zero-velocity interval through a symmetrical relation between generalized likelihood ratio statistical curves after geometric transformation, which is specifically as follows:
removing non-zero-speed edge points by the following formula (9):
wherein zero _ flag is the zero speed flag, zerWhen the o _ 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)。
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| CN111707294B (en) * | 2020-08-20 | 2020-11-06 | 中国人民解放军国防科技大学 | Pedestrian navigation zero-speed interval detection method and device based on optimal interval estimation |
| CN113092819B (en) * | 2021-04-14 | 2022-11-18 | 东方红卫星移动通信有限公司 | Dynamic zero-speed calibration method and system for foot accelerometer |
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