CN106595653A - Wearable autonomous navigation system for pedestrian and navigation method thereof - Google Patents

Abstract

The invention discloses a wearable autonomous navigation system for a pedestrian and a navigation method thereof. The wearable autonomous navigation system comprises five sensor modules and one comprehensive processing module, wherein the five sensor modules are arranged on the head, arms and feet of the pedestrian respectively; each sensor module comprises a first microcontroller, and an inertial measurement unit, a magnetic sensor, a gas pressure sensor and a first Bluetooth unit which are connected with the first microcontroller respectively; the inertial measurement unit comprises a triaxial accelerometer and a triaxial gyroscope; comprehensive processing module comprises a second microcontroller, and a wireless communication unit, a visual sensor, a satellite receiver, a second Bluetooth unit and a display unit which are connected with the second microcontroller respectively. The five sensor modules acquire a variety of navigation information and transmit the navigation information to the comprehensive processing module, and the variety of navigation information are fused to solve out a navigation result. Based on distributed wearable multi-point information acquisition, data fusion and data filtering are performed according to an inertial navigation method and other navigation methods, so that the pedestrian navigation performance is improved.

Description

A wearable pedestrian autonomous navigation system and navigation method thereof

technical field

The invention belongs to the technical field of pedestrian navigation, and in particular relates to a wearable pedestrian autonomous navigation system and a navigation method thereof.

Background technique

Pedestrian navigation and positioning is an important emerging branch in the field of navigation. It can determine and monitor the position of pedestrians and the movement state of human body in real time, so as to effectively improve the rapid response ability and task execution efficiency of military operations, emergency search and rescue personnel. At the same time, this technology also It can be applied to the civilian field to ensure the safety of pedestrians, and has broad military and civilian application prospects. Pedestrian navigation technology has become one of the research hotspots in the field of navigation.

At present, the commonly used pedestrian navigation methods mainly rely on satellite navigation system (GNSS), wireless communication and other positioning technologies, supplemented by micro-inertial sensors to improve the continuity and reliability of pedestrian navigation systems, but this type of pedestrian navigation methods mainly face The following key questions:

Although the satellite navigation system (GNSS) has a relatively stable real-time positioning capability, its positioning performance cannot be guaranteed in an environment with poor signal conditions. Although the combination scheme with the inertial system can improve the anti-jamming capability of the navigation system to a certain extent, However, under the conditions of severe satellite signal pollution or even shielding, system performance cannot be guaranteed.

Although inertial navigation has strong autonomy, the performance of micro-inertial devices still has a certain gap compared with traditional inertial devices. Inertial devices and magnetic sensors are also affected by uncertain factors such as ambient temperature, making it impossible for micro-inertial systems to complete for a long time. precise navigation.

Wireless communication and positioning technology can provide navigation and positioning functions in known areas, but in unknown environments such as combat, emergency rescue and disaster relief, it is often impossible to install effective wireless sensor nodes, or wireless communication signals are blocked or interfered by obstacles. High precision positioning.

In order to improve the environmental adaptability of the pedestrian navigation system, there are currently two main methods: 1) Use the gait stationary phase in human walking to correct the zero-speed of the micro-inertial system to reduce the accumulation speed of positioning errors over time. 2) Use the dead reckoning (DR) method to estimate the pedestrian's position and speed through the estimation of the step frequency step and heading. Both of the above two methods can only be applied to the steady-state motion of the human body (such as: walking at a constant speed, etc.), and cannot be applied to non-stationary motion (multiple gait forms mixed with conversion, etc.).

Contents of the invention

In order to solve the technical problems raised by the above-mentioned background technology, the present invention aims to provide a wearable pedestrian autonomous navigation system and its navigation method, which can make up for the shortcomings of a single pedestrian navigation method. Based on distributed wearable multi-point information collection, the inertial navigation method and other navigation methods for data fusion filtering to improve pedestrian navigation performance.

In order to realize above-mentioned technical purpose, technical scheme of the present invention is:

A wearable pedestrian autonomous navigation system, including 5 sensor modules and 1 comprehensive processing module, the sensor module and comprehensive processing module are set on the pedestrian, wherein the 5 sensor modules are respectively set on the pedestrian's head, arms and On the biped, each sensor module includes a first microcontroller and an inertial measurement unit, a magnetic sensor, an air pressure sensor, and a first bluetooth unit respectively connected to it, and the inertial measurement unit includes a three-axis accelerometer and a three-axis gyroscope , the integrated processing module includes a second micro-controller and a wireless communication unit, a visual sensor, a satellite receiver, a second bluetooth unit and a display unit respectively connected thereto, the integrated processing module obtains GNSS signals through the satellite receiver, and comprehensively processes The module performs data interaction with the remote monitoring center through the wireless communication unit, and the data transmission between each sensor module and the comprehensive processing module is realized through the first Bluetooth unit and the second Bluetooth unit; the five sensor modules collect various navigation information and transmit them to The integrated processing module integrates various received navigation information, calculates the navigation result, displays the navigation result on the display unit, and uploads it to the remote monitoring center.

Further, the comprehensive processing module further includes a temperature sensor connected to the second microcontroller, and performs temperature compensation on other sensors according to the temperature signal.

Further, the sensor module further includes a vision sensor connected to the first microcontroller.

Further, the remote monitoring center pre-stores map information of the navigation area.

A navigation method based on a wearable pedestrian autonomous navigation system, comprising the following steps:

(1) Utilize the inertial measurement unit in the sensor module to initialize the horizontal attitude angle of the system, and the inertial measurement unit combines the heading angle of the magnetic sensor initialization system;

(2) Obtain GNSS signals through satellite receivers, if the acquired GNSS signals are valid, then perform initial position positioning according to GNSS signals;

(3) If the GNSS signal obtained is invalid, wireless communication positioning is carried out by the wireless communication unit, and the wireless communication positioning includes an offline training stage and an online positioning stage; the offline training stage collects the wireless positioning reference information of each sampling point in the navigation area, Construct the fingerprint database, upload the fingerprint database to the remote monitoring center for storage; in the online positioning stage, query the fingerprint database through the actually collected wireless information, find the closest reference information, and then locate the initial position;

(4) If the wireless communication unit cannot obtain effective or sufficient wireless information, then start the visual sensor in the comprehensive processing module to collect images, and upload the collected information data to the remote monitoring center through the comprehensive processing module, and the remote monitoring center will The information database of the navigation area is established, and the information data collected by the visual sensor is matched with the information data stored in the information database in advance, so as to locate the initial position;

(5) During the walking process of pedestrians, the inertial information collected by the sensor module is corrected through the method of pure strapdown combined with zero speed correction:

Set the width of the sliding window to N, and define the following variables:

Among them, ω _x , ω _y , and ω _z are the ranges of the output of the three-axis gyroscope within the range of the sliding window, max represents the maximum value, min represents the minimum value, and A is the output vector of the three-axis accelerometer within the range of the sliding window The maximum magnitude of , i=0,1,2,...,N;

When the value of at least one variable among ω _x , ω _y , ω _z , and A is within the preset threshold range, it is considered that the inertial measurement unit in the current sensor module is in a zero-speed state, and zero-speed correction is performed;

(6) According to the information collected by the five sensor modules, judge the relative position and posture of the limbs and the head of the human body, so as to judge the behavior pattern of pedestrians;

(7) Combined with the result of step (6), the PDR algorithm is used for navigation solution, including four steps: step frequency detection, step size estimation, course determination and position calculation, the specific steps are as follows:

① Step frequency detection: monitor the vertical direction changes of the accelerometer in real time to get the number of steps, and filter out the interference caused by the static and slight shaking of the human body by setting the threshold, and obtain the accurate number of steps;

② Step length estimation: Rely on the sensor modules installed on both feet to collect inertial data, calculate the zero-speed moment during walking in real time, and obtain step length information based on the length and interval of the zero-speed moment;

③ Course determination: collect gyroscope signals and magnetic sensor signals according to the sensor module, and combine the map information of the navigation area obtained from the remote monitoring center to obtain the information of the forward course;

④Position calculation: According to the results obtained in steps ② and ③, the position change relative to the previous moment is solved, so as to obtain the specific position at this moment;

(8) The inertial navigation information is fused with GNSS information, wireless communication positioning information, and visual positioning information by using a multi-information fusion filtering algorithm to obtain the final navigation result.

Further, in step (3), when the navigation area is indoors, select WiFi signals for wireless communication positioning; when the navigation area is outdoors, select mobile phone base station signals for wireless communication positioning.

Further, in step (8), the multi-information fusion filtering algorithm is a Kalman filtering algorithm.

Furthermore, during the entire navigation process, it is judged in real time whether the GNSS signal is valid, and the position at the first GNSS effective moment is used as the reference point for the combination of inertial navigation information and GNSS information, and the navigation results obtained by fusion of various navigation information are all The relative position to this datum point.

The beneficial effect brought by adopting the above-mentioned technical scheme:

The present invention is based on distributed and wearable multi-point information collection, and on the basis of accurately judging the body shape of pedestrians, the inertial navigation algorithm and other navigation algorithms (visual sensors, satellite receivers, magnetic sensors, etc.) are fused and filtered. There is obvious drift after long-time work, and various auxiliary navigation methods are used to correct errors, thereby improving navigation accuracy and realizing multi-level positioning and navigation services in different ranges.

Description of drawings

Fig. 1 is a block diagram of the navigation system of the present invention;

Fig. 2 is a wearing schematic diagram of the navigation system of the present invention;

Fig. 3 is a flow chart of the navigation method of the present invention.

detailed description

The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

As shown in Figure 1 and Figure 2, a wearable pedestrian autonomous navigation system includes 5 sensor modules and 1 integrated processing module, the sensor module and integrated processing module are set on the pedestrian, and the 5 sensor modules are respectively set On the pedestrian's head, arms and feet, each sensor module includes a first microcontroller and an inertial measurement unit, a magnetic sensor, an air pressure sensor and a first bluetooth unit respectively connected thereto, and the inertial measurement unit includes A three-axis accelerometer and a three-axis gyroscope, the integrated processing module includes a second microcontroller and a wireless communication unit, a visual sensor, a satellite receiver, a second bluetooth unit and a display unit respectively connected thereto.

The comprehensive processing module acquires GNSS signals through the satellite receiver, and the comprehensive processing module performs data interaction with the remote monitoring center through the wireless communication unit, and the data transmission between each sensor module and the comprehensive processing module is realized through the first Bluetooth unit and the second Bluetooth unit. Five sensor modules collect various navigation information and transmit them to the comprehensive processing module. The comprehensive processing module fuses the various navigation information received, calculates the navigation result, displays the navigation result on the display unit, and uploads it to the remote monitoring center .

In this embodiment, the remote monitoring center pre-stores map information of the navigation area.

In this embodiment, the comprehensive processing module further includes a temperature sensor connected to the second microcontroller, and performs temperature compensation on other sensors according to the temperature signal, thereby improving the accuracy of the sensors.

In this embodiment, the MPU-6050 chip is used for the inertial measurement unit, the HMC-5883 chip is used for the magnetic sensor, the BMP-180 chip is used for the air pressure sensor, and the STM32 chip is used for the first and second microcontrollers.

The present invention also proposes a navigation method based on the above-mentioned wearable pedestrian autonomous navigation system, as shown in Figure 3, comprising the following steps:

(1) Utilize the inertial measurement unit in the sensor module to initialize the horizontal attitude angle of the system, and the inertial measurement unit combines the heading angle of the magnetic sensor initialization system;

(2) Obtain GNSS signals through satellite receivers, if the acquired GNSS signals are valid, then perform initial position positioning according to GNSS signals;

(3) If the GNSS signal obtained is invalid, wireless communication positioning is carried out by the wireless communication unit, and the wireless communication positioning includes an offline training stage and an online positioning stage; the offline training stage collects the wireless positioning reference information of each sampling point in the navigation area, Construct the fingerprint database, upload the fingerprint database to the remote monitoring center for storage; in the online positioning stage, query the fingerprint database through the actually collected wireless information, find the closest reference information, and then locate the initial position; when the navigation area is When indoors, select the WiFi signal for wireless communication positioning; when the navigation area is outdoors, select the mobile phone base station signal for wireless communication positioning;

(4) If the wireless communication unit cannot obtain effective or sufficient wireless information, then start the visual sensor in the comprehensive processing module to collect images, and upload the collected information data to the remote monitoring center through the comprehensive processing module, and the remote monitoring center will The information database of the navigation area is established, and the information data collected by the visual sensor is matched with the information data stored in the information database in advance, so as to locate the initial position;

(5) During the walking process of pedestrians, the information collected by the two sensor modules on the feet is corrected through the method of pure strapdown combined with zero speed correction:

Set the width of the sliding window to N, and define the following variables:

Among them, ω _x , ω _y , and ω _z are the ranges of the output of the three-axis gyroscope within the range of the sliding window, max represents the maximum value, min represents the minimum value, and A is the output vector of the three-axis accelerometer within the range of the sliding window The maximum magnitude of , i=0,1,2,...,N;

When the value of at least one variable among ω _x , ω _y , ω _z , and A is within the preset threshold range, it is considered that the inertial measurement unit currently installed on the foot is in a zero-speed state, and zero-speed correction is performed;

(6) According to the information collected by the five sensor modules, judge the relative position and posture of the limbs and the head of the human body, thereby judging the behavior pattern of pedestrians (running, walking, jumping, lying down, etc.); analyze the air pressure data collected by each air pressure sensor, The height of each air pressure sensor can be obtained, that is, the height relationship between the pedestrian's head, arm, and foot, so as to judge some behavioral modes of the pedestrian, such as lying flat, standing, etc., thereby assisting the inertial navigation to judge the behavior;

(7) Combined with the result of step (6), the PDR algorithm is used for navigation solution, including four steps: step frequency detection, step size estimation, course determination and position calculation, the specific steps are as follows:

① Step frequency detection: monitor the vertical direction changes of the accelerometer in real time to get the number of steps, and filter out the interference caused by the static and slight shaking of the human body by setting the threshold, and obtain the accurate number of steps;

② Step length estimation: Rely on the sensor modules installed on both feet to collect inertial data, calculate the zero-speed moment during walking in real time, and obtain step length information based on the length and interval of the zero-speed moment;

③ Course determination: collect gyroscope signals and magnetic sensor signals according to the sensor module, and combine the map information of the navigation area obtained from the remote monitoring center to obtain the information of the forward course;

④Position calculation: According to the results obtained in steps ② and ③, the position change relative to the previous moment is solved, so as to obtain the specific position at this moment;

(8) Using multi-information fusion filtering algorithm (such as Kalman filtering algorithm) to fuse inertial navigation information with GNSS information, wireless communication positioning information, and visual positioning information to obtain the final navigation result.

During the entire navigation process, it is judged in real time whether the GNSS signal is valid, and the position at the first GNSS effective moment is used as the reference point for the combination of inertial navigation information and GNSS information. The relative position of the point.

The system navigation and positioning results and the information collected and summarized by the comprehensive processing module are uploaded to the remote monitoring center through the wireless communication module for single/multiple person position and behavior identification, and at the same time establish a variety of physical field models based on location, and analyze The multi-person group service strategy realizes various location-based navigation value-added services.

The embodiment is only to illustrate the technical idea of the present invention, and cannot limit the protection scope of the present invention with this. Any modification made on the basis of the technical solution according to the technical idea proposed in the present invention all falls within the protection scope of the present invention. .

Claims (8)

1. a kind of Wearable pedestrian autonomous navigation system, it is characterised in that：Including 5 sensor assemblies and 1 integrated treatment mould Block, the sensor assembly and integrated treatment module are arranged on pedestrian, wherein 5 sensor assemblies are separately positioned on pedestrian Head, on both arms and biped, each sensor assembly includes that the first microcontroller and the inertia that is attached thereto respectively are surveyed Amount unit, Magnetic Sensor, baroceptor and the first bluetooth unit, the Inertial Measurement Unit includes three axis accelerometer and three Axle gyroscope, the integrated treatment module includes the second microcontroller and the wireless communication unit, the vision that are attached thereto respectively Sensor, DVB, the second bluetooth unit and display unit, integrated treatment module obtains GNSS letters by DVB Number, integrated treatment module carries out data interaction, each sensor assembly and synthesis by wireless communication unit and remote monitoring center Data transfer is realized by the first bluetooth unit and the second bluetooth unit between processing module；5 sensor assemblies collection Various navigation informations simultaneously send integrated treatment module to, and integrated treatment module is merged the various navigation informations for receiving, Navigation results are calculated, navigation results are shown on the display unit, and be uploaded to remote monitoring center.

2. a kind of Wearable pedestrian autonomous navigation system according to claim 1, it is characterised in that：The integrated treatment module Also include the temperature sensor being connected with the second microcontroller, temperature-compensating is carried out to other sensors according to temperature signal.

3. a kind of Wearable pedestrian autonomous navigation system according to claim 1, it is characterised in that：The sensor assembly is also Including the vision sensor being connected with the first microcontroller.

4. a kind of Wearable pedestrian autonomous navigation system according to claim 1, it is characterised in that：The remote monitoring center The cartographic information of navigation area is prestored.

5. the air navigation aid of Wearable pedestrian's autonomous navigation system is based on, it is characterised in that comprised the following steps：

(1) using the horizontal attitude angle of the Inertial Measurement Unit initialization system in sensor assembly, Inertial Measurement Unit knot Close the course heading of Magnetic Sensor initialization system；

(2) GNSS signal is obtained by DVB, if the GNSS signal for obtaining is effectively, is carried out initially according to GNSS signal The positioning of position；

(3) if the GNSS signal for obtaining is invalid, radio communication positioning is carried out by wireless communication unit, the radio communication is determined Position includes off-line training step and tuning on-line stage；Off-line training step, gathers wireless the determining of each sampled point of navigation area Position reference information, constructs fingerprint database, and fingerprint database upload remote monitoring center is preserved；The tuning on-line stage, Go to be inquired about by the wireless messages of actual acquisition to fingerprint database, search out immediate reference information, so as to carry out The positioning of initial position；

(4) if wireless communication unit cannot obtain effective or sufficient wireless messages, the vision in integrated treatment module is started Sensor, carries out image acquisition, the information data for collecting is uploaded to into remote monitoring center by integrated treatment module, remotely Surveillance center has pre-build the information database of navigation area, the information data that vision sensor is collected and information data The information data that storehouse pre-saves is matched, so as to enter the positioning of line home position；

(5) pedestrian in the process of walking, is believed by pure strapdown with reference to the inertia that the method for zero-velocity curve is gathered to sensor assembly Breath is modified：

The width for arranging sliding window is N, is defined as follows variable：

${\mathrm{\ω}}_x=|{\mathrm{\ω}}_x^{\max }-{\mathrm{\ω}}_x^{\min }|$

${\mathrm{\ω}}_y=|{\mathrm{\ω}}_y^{\max }-{\mathrm{\ω}}_y^{\min }|$

${\mathrm{\ω}}_z=|{\mathrm{\ω}}_z^{\max }-{\mathrm{\ω}}_z^{\min }|$

$A=\underset{i=0}{\overset{N}{max}}\left(\sqrt{{a_{xi}}^2+{a_{yi}}^2+{a_{zi}}^2}\right)$

Wherein, ω_x、ω_y、ω_zThe extreme difference of three-axis gyroscope output respectively in the range of sliding window, max represents maximum, Min represents minimum, and A is the maximum amplitude of three axis accelerometer output vector in the range of sliding window, i=0,1,2 ..., N；

Work as ω_x、ω_y、ω_z, in A at least 1 variable value in default threshold range, then it is assumed that current sensor mould Inertial Measurement Unit in block is in zero-speed state, carries out zero-velocity curve；

(6) according to the information of 5 sensor assembly collections, the relative position and attitude of human limb and head are judged, so as to sentence The behavioral pattern of line-break people；

(7) with reference to the result of step (6), navigation calculation, including four steps are carried out using PDR algorithms：Cadence detection, step-length are estimated Meter, course determine and position calculation, comprise the following steps that：

1. cadence detection：The change of accelerometer vertical direction is monitored in real time and draws step number, by arranging threshold value, filter out human body quiet The interference for only bringing with slight jitter, obtains accurate step number；

2. step-size estimation：Inertial data is gathered by the sensor assembly for being located at biped, during the zero-speed that real-time resolving goes out in walking Carve, according to the length and interval at zero-speed moment step information is tried to achieve；

3. course determines：Gyroscope signal and magnetic sensor signal are gathered according to sensor assembly, with reference to from remote monitoring center The navigation area cartographic information of acquisition, obtains the information in advance course；

4. position calculation：According to the result that 2. and 3. step obtains, the location variation for going up a moment relatively is solved, so as to obtain The particular location at this moment；

(8) it is using Multi-information acquisition filtering algorithm that inertial navigation information is fixed with GNSS information, radio communication location information, vision Position information is merged, and obtains final navigation results.

6. air navigation aid according to claim 5, it is characterised in that：In step (3), when navigation area is indoor, choosing Selecting WiFi signal carries out radio communication positioning；When navigation area is outdoor, selects cellular base station signal to carry out radio communication and determine Position.

7. air navigation aid according to claim 5, it is characterised in that：In step (8), the Multi-information acquisition filtering algorithm For Kalman filtering algorithm.

8. air navigation aid according to claim 5, it is characterised in that：In whole navigation procedure, real-time judge GNSS signal It is whether effective, and the datum mark that the position of first time GNSS significant instant is combined as inertial navigation information with GNSS information, The navigation results that afterwards various navigation information fusions are obtained are the relative positions with the datum mark.