Indoor positioning method and system based on IMU, RSSI and spatial constraint

Abstract

The invention discloses an IMU (Inertial Measurement Unit), RSSI (Received Signal Strength Indicator) and spatial constraint-based indoor positioning method, which comprises the following steps that: terminal equipment detects a Bluetooth Hello packet in real time; when the terminal equipment detects that the Bluetooth ID in the Bluetooth Hello packet belongs to the anchor point area, determining whether the terminal equipment enters the anchor point area or not according to the strength of a signal received by the terminal equipment; the anchor point area is composed of a plurality of Bluetooth nodes arranged indoors; each Bluetooth node corresponds to one Bluetooth ID (Identity); when the terminal equipment enters the anchor point area, the terminal equipment is switched from inertial navigation positioning to Bluetooth fingerprint positioning, and the position of the current terminal equipment is obtained through Bluetooth fingerprint positioning combined with spatial constraint and RSSI constraint; when the terminal equipment leaves the anchor point area, the terminal equipment is switched from Bluetooth fingerprint positioning to inertial navigation positioning based on the current position of the terminal equipment, and the technical problems that the existing indoor navigation method is difficult to meet the requirements of low cost and user equipment support, and the positioning precision is low are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless positioning, and relates to an indoor positioning method and system based on IMU, RSSI and spatial constraints. Background Art

[0002] In the environment of an indoor basement, the whole building is surrounded by reinforced concrete, so that the cellular signal cannot penetrate the structure to reach the inside of the parking lot, ultimately resulting in the user losing communication "connection" inside the underground parking lot. Similarly, the positioning signals of the Global Navigation Satellite System (GNSS) that users rely on daily cannot penetrate these structures either, which often causes users to be unable to use positioning services inside the underground parking lot. The internal structure of the underground parking lot is complex, and users often face the problem of being unable to accurately locate their vehicles or parking spaces. This not only increases the time to find the vehicle but also reduces the overall utilization efficiency of the parking lot. If the vehicle and parking space of the user can be effectively located, it will greatly save the user's time and improve the operation efficiency of the underground parking lot. Therefore, it is particularly important to find a method to solve the positioning problem in the underground parking lot.

[0003] Indoor positioning technology accurately determines the position of an object or person in an indoor environment by using a variety of sensors and signal sources. Indoor positioning technology can be divided into two categories: The first category is indoor positioning technology based on external signal sources, which relies on externally installed signal sources to achieve positioning. Common external signal sources include Wi-Fi, Bluetooth, ultra-wideband, cellular mobile networks, and pseudolites. These signal sources transmit signals, which are received and processed by the indoor positioning system to determine the position of the terminal. The second category is indoor positioning technology based on natural signal sources, which completely relies on the sensors built into the terminal device and can achieve positioning without relying on external signal sources. Typical natural signal source positioning technologies include inertial navigation and geomagnetic navigation. These methods do not rely on external facilities and have high autonomy and flexibility.

[0004] The terminals used for personal user positioning are usually mobile phones, tablets, etc. These terminals generally use the GNSS positioning and navigation system, which is usually in a failure state inside the underground parking lot. Even if the satellite positioning signal of GNSS is received, these signals often undergo multipath reflection from the walls and the ground, and the positioning accuracy cannot be guaranteed. However, devices such as Bluetooth, Wi-Fi, and inertial components are usually installed on terminals such as mobile phones and tablets, and these devices can be used in indoor navigation. Therefore, the unaffected Bluetooth, Wi-Fi, and inertial components can be used to implement positioning services.

[0005] There are various ways of positioning based on Bluetooth RSSI (Received Signal Strength Indicator), mainly including signal ranging method, fingerprint positioning method, etc. The environment in an underground parking lot is complex, and the accuracy of the signal ranging method will be affected due to the occlusion of obstacles and the like. If the position fingerprint method based on signal strength is adopted, the influence of obstacles on the positioning accuracy can be reduced. However, a large amount of manpower and time are consumed in the process of collecting fingerprints to establish a fingerprint database. Therefore, using Bluetooth fingerprint positioning alone is not suitable for a huge basement environment.

[0006] Mobile phones and tablets are generally equipped with inertial components such as gyroscopes and accelerometers, and the inertial navigation method can be realized by using the equipped IMU (Inertial Measurement Unit). Inertial navigation positioning does not depend on external information interaction and is not affected by the lack of GNSS positioning signals in an underground parking lot. However, most smartphones use low-cost IMUs, and the quality of the output observation values is poor, and the cumulative error will increase rapidly. Therefore, using inertial navigation alone cannot provide reliable navigation positioning for a long time.

[0007] To solve the above problems, integrated navigation has emerged. Integrated navigation refers to a navigation method that combines multiple different types of navigation and positioning technologies. This method can overcome the limitations of a single navigation method and provide more accurate, reliable and continuous positioning services. There are two main categories of mainstream integration schemes: the first category relies on the integration of positioning technologies of external signal sources and natural signal sources. The second is the integration of two different positioning technologies based on external signal sources.

[0008] However, current indoor integrated navigation methods often require a variety of sensors and devices, increasing the hardware cost and maintenance cost. And it requires the support of the user's terminal device, which is not conducive to popularization and use. The literature "Research on Indoor Positioning Technology Based on the Fusion of Ultra-Wideband Position Fingerprint and Inertial Navigation" proposes a navigation positioning method for indoor scenarios. This method combines UWB (Ultra-Wide-Band) position fingerprint positioning and IMU positioning, effectively improving the accuracy and reliability of indoor positioning. However, the UWB position fingerprint positioning method adopted by this method has high requirements for time synchronization between UWB base stations. And the signal coverage range of the base station is small, the energy consumption is high, and it needs to be densely deployed in the basement environment, with a large cost.

[0009] Traditional typical Bluetooth fingerprint positioning and navigation algorithms include the Nearest Neighborhood (NN) algorithm, the K-Nearest Neighborhood (KNN) algorithm, and the Weighted K-Nearest Neighborhood (WKNN) algorithm. The basic idea of the NN algorithm is to compare the signal features collected in real time with the signal features stored in the fingerprint database one by one, and select the most similar fingerprint point as the positioning result. To avoid the large positioning error caused by only taking one neighbor in the NN algorithm, the KNN algorithm introduces multiple neighbors for improvement, and calculates the mean of the matching results to determine the coordinates of the point to be located. The WKNN algorithm introduces the concept of contribution degree on the basis of the KNN algorithm. Different from directly averaging the matching results in the KNN algorithm, the WKNN algorithm uses the similarity as the contribution degree. The higher the similarity, the greater the weight, and the greater the impact on the positioning result. The Enhanced Weight K-Nearest Neighborhood (EWKNN) algorithm is mentioned in the literature "Research and Performance Comparison of RSSI Indoor Positioning Online Matching Algorithms". This algorithm is an improvement of the weighted K-nearest neighbor algorithm, which also includes the reference points with a large Euclidean distance from the point to be located at the RSSI fingerprint level in the calculation of weights. And based on the WKNN algorithm by adding a threshold R, it realizes the dynamic selection of the value of K, that is, the number of reference points. However, the above methods only calculate the contribution degree at the signal strength level and have limitations. Summary of the Invention

[0010] An object of the present invention is to solve the technical problems that the existing indoor navigation methods are difficult to meet the requirements of low cost and user equipment support, and at the same time have low positioning accuracy, and provide an indoor positioning method and system based on IMU, RSSI and spatial constraints.

[0011] To achieve the above object, the present invention adopts the following technical solutions: The first aspect of the present invention provides an indoor positioning method based on IMU, RSSI and spatial constraints, including the following steps: The terminal device detects Bluetooth Hello packets in real time; When the Bluetooth ID in the Bluetooth Hello packet detected by the terminal device belongs to the anchor point area, determine whether the terminal device enters the anchor point area according to the signal strength received by the terminal device; the anchor point area is composed of multiple Bluetooth nodes set indoors; each Bluetooth node corresponds to a Bluetooth ID; When the terminal device enters the anchor point area, the terminal device switches from inertial navigation positioning to Bluetooth fingerprint positioning, and obtains the current position of the terminal device through Bluetooth fingerprint positioning combined with RSSI constraint and spatial constraint; When the terminal device leaves the anchor area, based on the current location of the terminal device, the terminal device switches from Bluetooth fingerprint positioning to inertial navigation positioning.

[0012] Further, determining whether the terminal device enters the anchor area according to the signal strength received by the terminal device is specifically as follows: Estimate the distance from the Bluetooth in the anchor area to the terminal device according to the signal strength received by the terminal:

[0013] When then the terminal device enters the anchor area; Among them, represents the distance from the Bluetooth node in the anchor area to the terminal device, is a preset distance threshold; is the transmission power of the Bluetooth node in the anchor area, is the receiving power of the terminal device, is the path loss of the transmitting antenna of the Bluetooth node in the anchor area, is the path loss of the receiving antenna of the terminal device; is the path loss in terms of distance, ; n is the path loss parameter.

[0014] Further, obtaining the current location of the terminal device through Bluetooth fingerprint positioning combining spatial constraint and RSSI constraint is specifically as follows: The terminal device receives the RSSI value of the Bluetooth node in real time, and calculates the Euclidean distance between the terminal device and the reference point according to the received RSSI value; Select the reference point corresponding to the k values with the smallest Euclidean distance as the nearest neighbor reference point of the terminal device; Estimate the current location of the terminal device based on the neighboring reference point.

[0015] Further, the Euclidean distance between the terminal device and the reference point is described as:

[0016] Among them, is the signal strength of the th Bluetooth node received; is the RSSI value of the th corresponding Bluetooth node in the fingerprint database at the rd reference point; N is the number of available Bluetooth nodes; represents the RSSI Euclidean distance from the terminal device to the th reference point.

[0017] Further, estimating the position of the current terminal device based on the neighboring reference points specifically includes: Calculating the spatial distance from each neighboring reference point to the position of the terminal device at the previous moment based on the neighboring reference points and the position of the terminal device at the previous moment; Determining the spatial weight based on the spatial distance; Determining the RSSI weight based on the Euclidean distance between the terminal device and the reference point; Estimating the position of the current terminal device based on the neighboring reference points, the spatial weight, and the RSSI weight.

[0018] Further, the spatial distance is described as:

[0019] wherein, is the position of the th neighboring reference point; the position of the terminal device at the previous moment is ; is the th spatial distance from the neighboring reference point to the position of the terminal device at the previous moment.

[0020] Further, the spatial weight is:

[0021] wherein, is the attenuation coefficient; is the spatial distance; is the neighboring circle radius; is the th spatial weight from the neighboring reference point to the position of the terminal device at the previous moment.

[0022] Further, the RSSI weight is:

[0023] wherein, is the weight parameter affecting the RSSI distance; is the RSSI Euclidean distance indicating the terminal device to the th reference point; indicates the RSSI weight of the terminal device to the th reference point.

[0024] Further, the position of the current terminal device is:

[0025]

[0026] wherein, ; ; is the spatial weight from the th nearest neighbor reference point to the position of the terminal device at the previous moment; represents the RSSI weight from the terminal device to the th reference point.

[0027] The second aspect of the present invention provides an indoor positioning system based on IMU, RSSI, and spatial constraints, including: A real-time detection module, where the terminal device detects Bluetooth Hello packets in real time; An anchor point area determination module. When the Bluetooth ID in the Bluetooth Hello packet detected by the terminal device belongs to the anchor point area, it determines whether the terminal device enters the anchor point area according to the signal strength received by the terminal device; the anchor point area is composed of multiple Bluetooth nodes set indoors; each Bluetooth node corresponds to a Bluetooth ID; A Bluetooth positioning module. When the terminal device enters the anchor point area, the terminal device switches from inertial navigation positioning to Bluetooth fingerprint positioning, and obtains the current position of the terminal device through Bluetooth fingerprint positioning combining spatial constraints and RSSI constraints; An inertial navigation module. When the terminal device leaves the anchor point area, based on the current position of the terminal device, the terminal device switches from Bluetooth fingerprint positioning to inertial navigation positioning.

[0028] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses an indoor positioning method based on IMU, RSSI, and spatial constraints. By detecting Bluetooth Hello packets in real time and estimating the distance between the terminal device and the Bluetooth node according to the RSSI value, when the terminal device enters the anchor point area, it switches to Bluetooth fingerprint positioning, calculates the Euclidean distance between the terminal device and the reference point using the RSSI values of multiple Bluetooth nodes, selects the nearest k reference points for position estimation, and improves the positioning accuracy. When the terminal device leaves the anchor point area, it switches to inertial navigation positioning and uses IMU data for positioning. Since there is an error accumulation problem in inertial navigation, and it is corrected by Bluetooth fingerprint positioning within the anchor point area, the error accumulation can be effectively reduced, and the positioning accuracy and stability can be improved. Through spatial constraint conditions, considering the position information of the terminal device at the previous moment, calculating the spatial distance and spatial weight, and combining the RSSI weight, the positioning result is further optimized, and the reliability and accuracy of the positioning result are improved. The method of the present invention can adapt to complex indoor environments, such as multi-story buildings, places with many obstacles, etc., and can perform positioning more accurately through the Bluetooth fingerprint database and spatial constraint conditions. Description of the Drawings

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the embodiments. It should be understood that the following drawings only show certain embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0030] Figure 1 is the flowchart of the indoor positioning method based on IMU, RSSI and spatial constraints of the present invention; Figure 2 is the schematic diagram of the original trajectory and the anchor point area; Figure 3 is the trajectory diagram of pure inertial navigation positioning; Figure 4 is the trajectory diagram of inertial navigation positioning corrected by the anchor point area; Figure 5 is the schematic diagram of the Bluetooth node; Figure 6 is the schematic diagram of the reference point; Figure 7 is the schematic diagram of the real trajectory; Figure 8 is the schematic diagram of trajectory prediction using the method of the present invention; Figure 9 is the schematic diagram of the comparison of the average positioning errors of different positioning algorithms; Figure 10 is the schematic diagram of the comparison of the root mean square errors of different positioning algorithms; Figure 11 is the schematic diagram of the CDF comparison of different positioning algorithms. Detailed implementation manners

[0031] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and marked in the accompanying drawings here can be arranged and designed in various different configurations.

[0032] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0033] It should be noted that similar reference numerals and letters denote similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0034] The present invention will be further described in detail below with reference to the accompanying drawings: See Figure 1 , the present invention provides an indoor positioning method based on IMU, RSSI and spatial constraints, including the following steps: Step 1: Anchor area judgment: When the terminal device enters the indoor basement, the positioning program of the present invention is run, and the program will default to start the inertial navigation positioning, Bluetooth Hello packet reception, and distance measurement functions based on the signal strength of the Hello packet. Specifically: Set the anchor area at the trajectory turning point, as Figure 2 shown. The anchor area is composed of multiple Bluetooth nodes, and these Bluetooth nodes periodically broadcast Hello packets. The terminal device will judge whether the received Hello packet comes from a Bluetooth node within the anchor area according to the Bluetooth node ID in the received Hello packet. If the Hello packet received by the terminal device belongs to a Bluetooth node in the anchor area, it will judge whether it has reached the anchor area according to the received signal strength. If it is not a node in the anchor area, the terminal device will not process the Hello packet.

[0035] Judge whether the terminal device has reached the anchor area according to the signal strength received by the terminal device. Specifically: For all signal propagation models:

[0036] In Equation (1), is the transmitter power (the transmission power of the Bluetooth node), is the receiver power (the reception power of the terminal device), is the path loss of the transmitting antenna (the path loss of the transmitting antenna of the Bluetooth node), is the path loss of the receiving antenna (the path loss of the transmitting antenna of the terminal device), is the path loss; is the shadow fading loss. The units of the above variables are all dB. And since the positions and characteristics of obstacles in the indoor basement environment are relatively stable, the variation range of shadow fading is relatively narrow. Compared with other influencing factors, the proportion of shadow fading is very small, so it can be ignored .

[0037] This embodiment adopts the One-slope model, which can be expressed as:

[0038] In formula (2), is the path loss in terms of distance, , where n is the distance between the receiver (terminal device) and the transmitter (Bluetooth node), and

[0039] is the path loss parameter, which can be obtained empirically and generally can be taken as 2. According to formula (2), the received power of the terminal device can be obtained:

[0040] The terminal device continuously updates the estimated distance. When , the terminal device considers itself to enter the anchor point area and enables Bluetooth positioning; represents the distance from the Bluetooth node in the anchor point area to the terminal device, and is a pre-set distance threshold used to determine whether the terminal device enters the anchor point area. In this article, is set to 0.5 m.

[0041] Step 2, Bluetooth fingerprint positioning: When the terminal device arrives at the anchor point area, it will enable Bluetooth fingerprint positioning. In the present invention, a fingerprint positioning method based on RSSI constraint and spatial constraint is used in the anchor point area to improve the effect of position calibration and the overall positioning accuracy.

[0042] It is mainly divided into two stages: an offline collection stage and an online positioning stage.

[0043] In the offline collection stage, in order to construct a position fingerprint database, the anchor point area is divided at a certain grid interval and reference points are selected, and the RSSI values of all Bluetooth nodes in the anchor point area are collected at the reference points. The coordinates of the reference points and the corresponding RSSI values are stored in the database to form a fingerprint database, and the relationship between the fingerprint points (reference points) and the corresponding RSSI values is established in the fingerprint database.

[0044] The online positioning stage is the stage where the Bluetooth fingerprint positioning system is actually used for real-time location determination. The online positioning stage utilizes the fingerprint database constructed in the offline collection stage to determine the location of the terminal device through the Bluetooth signals received in real-time by the terminal device. The RSSI values received in real-time are compared with the RSSI values of each reference point in the fingerprint database, and the Euclidean distance is calculated. This Euclidean distance is used to measure the similarity between the characteristics of the currently measured Bluetooth signal strength distribution and the characteristics of the Bluetooth signal strength distribution recorded at the reference points in the fingerprint database, obtaining N RSSI Euclidean distances. The formula is as follows:

[0045] Wherein, is the signal strength of the th Bluetooth node received; is the RSSI value of the th corresponding th Bluetooth node in the fingerprint database at the N th reference point; is the number of available Bluetooth nodes; represents the RSSI Euclidean distance from the terminal device to the

[0046] Select the reference points corresponding to the values with the smallest median as the nearest neighbor reference points of the terminal device. For the set of nearest neighbor spatial positions , where each position , and the position of the terminal device at the previous moment , define the spatial distance as:

[0047] To reduce the influence of noise on the positioning result, a spatial weight is used to introduce geometric constraints, limit the range of candidate points, and impose penalties on points outside the reasonable range. Define the spatial weight as:

[0048] Where is the radius of the neighboring circle, and the neighboring circle is a circle drawn with the previous predicted position of the terminal device as the center and r as the radius. The spatial weights of the points inside the circle are not penalized, while those outside the circle are penalized. The selection of the radius of this neighboring circle is related to the density of the collection points in the fingerprint offline collection stage and the actual spatial distance between two adjacent points to be measured. For the spatial distance less than or equal to For points where the weight is not penalized, the original weight is retained; otherwise, as the distance increases, the weight rapidly decreases to ensure that the contribution of distant points is small. is the attenuation coefficient used to control the attenuation rate of the weight. The larger the value, the faster the weight decays and the stronger the geometric constraint.

[0049] Define the RSSI weight as:

[0050] where is the weight parameter affecting the RSSI distance. In this embodiment, is set to 3 to strengthen the influence of points closer to the terminal device among the neighboring points and weaken the influence of points farther from the terminal device among the neighboring points.

[0051] Combining the spatial weight and the RSSI weight, define the combined weight as:

[0052] Normalize the combined weight to obtain the normalized weight as:

[0053] The final estimated position can be expressed as:

[0054] This method comprehensively considers the similarity of the RSSI fingerprint between the terminal device and the reference point and the spatial geometric relationship of the position of the terminal device at the previous moment. At the RSSI fingerprint level, neighboring nodes are considered, and the parameter is introduced to adjust the weight of the RSSI distance. At the spatial constraint level, the position of the previous predicted point is used as prior knowledge, the neighboring circle radius limit is introduced, and the weights of candidate points beyond the range are adjusted through an exponential decay function. Finally, by comprehensively considering RSSI and geometric relationships, effective fusion of multi-source information is achieved, the influence of noise in RSSI data is effectively reduced, and accurate position prediction information is obtained.

[0055] Step three, inertial navigation: When occurs, the terminal device leaves the anchor point area and re-enables inertial navigation to clear the errors accumulated over time and achieve calibration of inertial navigation. After calibration, using the position determined by fingerprint positioning when leaving the anchor point area as the starting point, the calibrated inertial navigation continues to provide positioning services. Inertial navigation performs attitude update, speed update, and position update respectively, as follows: (1) Attitude update algorithm Common attitude update algorithms include the Euler angle method, direction cosine, and quaternion method. Among them, the quaternion method is more common. The quaternion is as follows:

[0056] In the formula, , , , is a real number , , are not only mutually orthogonal unit vectors but also imaginary unit vectors , and the relationship between them is:

[0057] In the formula, represents quaternion multiplication.

[0058] Given the initial value of the quaternion, it is updated by the quaternion differential equation.

[0059]

[0060] In the formula, is the measured value of the gyroscope. The quaternion differential equation for attitude update is as follows:

[0061] Through the attitude update differential equation, the attitude angle can be obtained by solving the quaternion, as shown in the following formula:

[0062] Among them, is the pitch angle, is the roll angle, is the yaw angle.

[0063] (2)Velocity update algorithm The angular velocity of the Earth's rotation is , and using to represent the latitude, its projection in the navigation coordinate system (n-system) can be expressed as:

[0064] The rotational angular velocity The projection in the navigation coordinate system n is:

[0065] In the formula, is the radius of the meridian circle, is the radius of the prime vertical circle, is the velocity in the north direction, is the velocity in the eastward direction, is the elevation.

[0066] Given the initial velocity of the carrier, it can be updated by the velocity differential equation:

[0067] where, is the specific force, is the Coriolis acceleration, is the centripetal acceleration relative to the ground caused by the movement of the carrier.

[0068] (3)Position update algorithm The position update is obtained by integrating the velocity, is the velocity in the upward direction and can be given by the following position update differential equation:

[0069] In the Bluetooth fingerprint positioning of the present invention, in addition to the Euclidean distance, other methods can also be used to match the real-time measured RSSI value with the records in the fingerprint database. Such as Manhattan distance, Chebyshev distance, and cosine similarity can all replace the Euclidean distance algorithm.

[0070] In the attitude update algorithm of inertial navigation, in addition to the quaternion method, the Euler angle algorithm, direction cosine method, and rotation vector method can also be used.

[0071] To further prove the superiority of the present invention, in this embodiment, a simulation experiment is carried out on the above indoor positioning method based on IMU, RSSI, and spatial constraints. The predetermined walking trajectory is as shown by the blue dotted line in Figure 2 At the same time, an anchor point area is set at the turning point, with a size of 3.68 , and is represented as the black rectangular area in Figure 2 .

[0072] Figure 3 is the result of pure inertial navigation using only inertial components. It can be observed that there is an obvious deviation between the navigation trajectory and the original trajectory. The maximum error reaches 8.8166m, the average error reaches 3.1853m, and the root mean square error reaches 3.7310m. The navigation effect is poor.

[0073] Figure 4 is the navigation result after combining Bluetooth fingerprint positioning with the anchor point area. It can be seen that the navigation effect has been significantly improved. The maximum error is 1.0632m, the average error is 0.2053m, and the root mean square error is 0.2754m.

[0074] Then, a simulation experiment is carried out separately on the indoor positioning method based on IMU, RSSI, and spatial constraints. The positions of the Bluetooth nodes are as shown in Figure 5as shown by the green dots. The reference point positions are as Figure 6 shown. Figure 7 is the predetermined walking trajectory. Figure 8 is a schematic diagram comparing the predicted trajectory and the real trajectory obtained by using the improved fingerprint positioning algorithm of the present invention. It can be seen that the positioning trajectory is close to the real trajectory and the positioning effect is good. Figure 9 , 10 Figures 10 and 11 are the comparison of the average positioning error (ALE), root mean square error (RMSE), and cumulative distribution function (CDF) indexes between the method of the present invention and the traditional NN, KNN, and WKNN algorithms. It can be seen that the navigation effect of the method of the present invention has been significantly improved compared with the traditional algorithms. The average error is 0.509462m, and the root mean square error is 0.713766m. When the error values are 0.5m and 1.0m, the method of the present invention reaches the CDF values of 52% and 94% respectively, which is better than the other three algorithms.

[0075] An embodiment of the present invention discloses an indoor positioning system based on IMU, RSSI, and spatial constraints, including: a real-time detection module, and the terminal device detects Bluetooth Hello packets in real time; an anchor point area determination module. When the Bluetooth ID in the Bluetooth Hello packet detected by the terminal device belongs to the anchor point area, it determines whether the terminal device enters the anchor point area according to the signal strength received by the terminal device; the anchor point area is composed of multiple Bluetooth nodes set indoors; each Bluetooth node corresponds to a Bluetooth ID; a Bluetooth positioning module. When the terminal device enters the anchor point area, the terminal device switches from inertial navigation positioning to Bluetooth fingerprint positioning, and obtains the current position of the terminal device through Bluetooth fingerprint positioning combining spatial constraints and RSSI constraints; an inertial navigation module. When the terminal device leaves the anchor point area, based on the current position of the terminal device, the terminal device switches from Bluetooth fingerprint positioning to inertial navigation positioning.

[0076] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An indoor positioning method based on IMU, RSSI and space constraints, characterized in that: The following steps are involved: The terminal device detects the Bluetooth Hello packet in real time; When the terminal device detects that the Bluetooth ID in the Bluetooth Hello packet belongs to the anchor area, it determines whether the terminal device enters the anchor area according to the signal strength received by the terminal device; the anchor area is composed of multiple Bluetooth nodes set up indoors; each Bluetooth node corresponds to a Bluetooth ID; When the terminal device enters the anchor point area, the terminal device switches from inertial navigation positioning to Bluetooth fingerprint positioning, and obtains the current location of the terminal device through Bluetooth fingerprint positioning combined with spatial constraints and RSSI constraints; When the terminal device leaves the anchor area, based on the current location of the terminal device, the terminal device switches from Bluetooth fingerprint positioning to inertial navigation positioning.

2. The indoor positioning method based on IMU, RSSI and space constraints according to claim 1, characterized in that: The determining, according to the signal strength received by the terminal device, whether the terminal device enters the anchor point area is specifically: According to the signal strength received by the terminal, estimate the distance between the Bluetooth in the anchor point area and the terminal device: when , the terminal device enters the anchor point area; in, Indicates the distance from the Bluetooth node in the anchor area to the terminal device. is a preset distance threshold; is the transmission power of the Bluetooth node in the anchor area, is the receiving power of the terminal device, is the path loss of the transmitting antenna of the Bluetooth node in the anchor area, is the path loss of the receiving antenna of the terminal device; yes Path loss over distance, ; n is the path loss parameter.

3. The indoor positioning method based on IMU, RSSI and space constraints according to claim 1, characterized in that: The Bluetooth fingerprint positioning by combining spatial constraints with RSSI constraints to obtain the location of the current terminal device is specifically as follows: The terminal device receives the RSSI value of the Bluetooth node in real time, and calculates the Euclidean distance between the terminal device and the reference point based on the received RSSI value; Select the one with the smallest Euclidean distance k The reference point corresponding to the value is used as the neighboring reference point of the terminal device; Estimate the current location of the terminal device based on nearby reference points.

4. The indoor positioning method based on IMU, RSSI and space constraints according to claim 3, characterized in that: The Euclidean distance between the terminal device and the reference point is described as: in, To receive the The signal strength of each Bluetooth node; For the The corresponding RSSI value of Bluetooth nodes; N is the number of available Bluetooth nodes; Indicates that the terminal device reaches RSSI Euclidean distance of reference points.

5. The indoor positioning method based on IMU, RSSI and space constraints according to claim 3, characterized in that: The estimating the position of the current terminal device based on the adjacent reference point is specifically as follows: Based on the neighboring reference points and the position of the terminal device at the last moment, the spatial distance from each neighboring reference point to the position of the terminal device at the last moment is calculated; Based on the spatial distance, determine the spatial weight; Determine the RSSI weight based on the Euclidean distance between the terminal device and the reference point; Estimate the location of the current terminal device based on neighboring reference points, spatial weights, and RSSI weights.

6. The indoor positioning method based on IMU, RSSI and space constraints according to claim 5, characterized in that: The spatial distance is described as: in, For the The location of the nearest reference point; the location of the terminal device at the last moment ; For the The spatial distance from a neighboring reference point to the location of the terminal device at the previous moment.

7. The indoor positioning method based on IMU, RSSI and space constraints according to claim 5, characterized in that: The spatial weight is: in, is the attenuation coefficient; is the spatial distance; is the radius of the adjacent circle; For the The spatial weight of the nearest reference point to the terminal device's position at the last moment.

8. The indoor positioning method based on IMU, RSSI and space constraints according to claim 5, characterized in that: The RSSI weight is: in, is the weight parameter that affects the RSSI distance; To indicate that the terminal device is RSSI Euclidean distance of reference points; Indicates that the terminal device reaches RSSI weight of each reference point.

9. The indoor positioning method based on IMU, RSSI and space constraints according to claim 5, characterized in that: The current location of the terminal device is: in, ; ; For the The spatial weight of the nearest reference point to the terminal device's position at the last moment; Indicates that the terminal device reaches RSSI weight of each reference point.

10. An indoor positioning system based on IMU, RSSI and space constraints, based on the indoor positioning method based on IMU, RSSI and space constraints according to claim 1, characterized in that: include: Real-time detection module, the terminal device detects Bluetooth Hello packets in real time; Anchor area judgment module, when the terminal device detects that the Bluetooth ID in the Bluetooth Hello packet belongs to the anchor area, determines whether the terminal device enters the anchor area according to the signal strength received by the terminal device; the anchor area is composed of multiple Bluetooth nodes set up indoors; each Bluetooth node corresponds to a Bluetooth ID; Bluetooth positioning module: When the terminal device enters the anchor point area, the terminal device switches from inertial navigation positioning to Bluetooth fingerprint positioning, and obtains the current location of the terminal device through Bluetooth fingerprint positioning that combines spatial constraints with RSSI constraints; Inertial navigation module: when the terminal device leaves the anchor area, based on the current location of the terminal device, the terminal device switches from Bluetooth fingerprint positioning to inertial navigation positioning.

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