Method for estimating a position of an object
By exchanging signals between the object and multiple sensor modules, and utilizing basic arithmetic operations and continuous segmentation operations, the problem of insufficient accuracy in traditional positioning methods is solved, achieving high-precision and computationally efficient position estimation, which is suitable for low-power devices and keyless entry systems.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- APTIV TECHNOLOGIES AG
- Filing Date
- 2023-04-06
- Publication Date
- 2026-07-31
AI Technical Summary
Existing technologies lack accuracy in estimating object location, especially methods based on low-frequency positioning technology, which cannot provide high-precision positioning within 10cm. Furthermore, traditional methods are computationally complex and difficult to implement on low-power computing devices.
The distance is determined by exchanging signals between the object and multiple sensor modules, and the position is estimated by basic arithmetic operations. A new reference space is selected by using multiple successive segmentation operations and confidence factors until a predetermined criterion is met, thereby achieving accurate estimation of the object's position.
It provides higher accuracy location estimation, can be implemented on low-power computing devices, and has high computational efficiency, making it suitable for low-power devices. It also supports real-time positioning and keyless entry.
Smart Images
Figure CN116908780B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a method for estimating the position of an object relative to a plurality of sensor modules. Background Technology
[0002] Traditional methods for locating radio frequency transmitters (hereinafter referred to as objects) are based on low-frequency positioning techniques. These methods rely on repeatedly measuring the intensity of the electromagnetic field emitted by the object and comparing the measurements with previously collected data. This so-called "fingerprinting" method provides a rough estimate of the area where the object is located. However, the object's location estimated in this way lacks accuracy.
[0003] Modern methods rely on technologies such as Bluetooth-based positioning and ultra-wideband (UWB) positioning. Bluetooth-based positioning is based on phase ranging, time of flight, and / or angle of arrival measurements, while UWB positioning focuses on time-of-flight measurements. These technologies allow for the estimation of an object's location with significantly higher accuracy. For many applications, an accuracy of 10 cm or better is preferred.
[0004] Traditional methods for estimating object location based on measurement results employ solutions such as linear least squares or kernel-based solutions. Summary of the Invention
[0005] The novel method presented in this disclosure provides a method for estimating the position of an object relative to multiple sensor modules. The term "object" describes a device that transmits signals that can be measured by the multiple sensor modules.
[0006] Compared to traditional methods, the proposed method aims to achieve more accurate position estimation based on measurement data containing inaccuracies and can be computed more efficiently. The proposed method processes measurement data from multiple sensor modules using only basic arithmetic operations, without employing complex mathematical formulas. Therefore, it offers improved computational efficiency compared to conventional methods and can thus be implemented on low-power computing devices. Furthermore, the accuracy level of the estimated object's position can be tuned to specific requirements.
[0007] One aspect relates to a method for estimating the position of an object relative to a plurality of sensor modules mounted on a device. The method includes the steps of: for each of the plurality of sensor modules, determining a distance from the object to the corresponding sensor module by exchanging signals between the object and the corresponding sensor module; and determining an initial reference space indicating the position estimate of the object based on a plurality of determined distances and positions of the plurality of sensor modules on the device. The method further includes performing a plurality of successive segmentation operations of the initial reference space to improve the position estimate of the object until predetermined criteria are met. Each segmentation operation includes: dividing the current reference space into subspaces, each subspace indicating a possible position of the object; assigning a confidence factor on each subspace indicating a confidence level that the object is located in the corresponding subspace; and selecting a new reference space from the subspaces based on the confidence factor. Finally, the method includes determining the position of the object based on the new reference space according to the final segmentation operation.
[0008] On the other hand, the initial reference space and subspace are defined by boundaries parallel to one or more axes of the coordinate system, which is defined based on distance measurements from multiple sensor modules.
[0009] On the other hand, in each segmentation operation, the segmentation of the current reference space is performed parallel to one or more axes of the coordinate system.
[0010] On the other hand, one or more axes of the coordinate system to which the segmentation is performed change between successive segmentation operations.
[0011] On the other hand, each partitioning operation divides the current reference space into two subspaces of equal size.
[0012] On the other hand, the step of selecting a new reference space includes: for each subspace, determining the centroid of the corresponding subspace; for each subspace, calculating a confidence factor based on the distance from the position of each sensor module to the centroid of the corresponding subspace, wherein the distance is determined by each sensor module.
[0013] In another aspect, the plurality of distances includes multiple uncertainties based on the positioning accuracy of each of the plurality of sensor modules.
[0014] On the other hand, a predetermined criterion is satisfied when at least one of the first criterion and the second criterion is met, wherein the first criterion is met when the number of segmentation operations exceeds a predetermined number, and the second criterion is met when the size of the new reference space is less than a predetermined threshold size.
[0015] In another aspect, the plurality of distances are determined based on time-of-flight measurements, signal strength measurements, or phase-based ranging.
[0016] On the other hand, the object is provided by a key, a wearable device, or a portable computing device.
[0017] On the other hand, the device is a vehicle, a door, or a storage space.
[0018] On the other hand, the method also includes unlocking vehicles, doors, or storage spaces based on the determined object location.
[0019] On the other hand, the object and multiple sensor modules are based on Bluetooth or ultra-wideband technology.
[0020] In another aspect, a data processing apparatus includes a unit for performing the method described in any of the foregoing aspects.
[0021] In another aspect, a vehicle includes a data processing device according to the aforementioned aspect. Attached Figure Description
[0022] Figure 1 A flowchart is shown for a method to estimate the position of an object relative to multiple sensor modules.
[0023] Figure 2 An example of multiple consecutive partitioning operations based on a two-dimensional initial reference space is shown.
[0024] Figure 3 An example is shown showing the positional relationship between an object and three sensor modules that measure three distances.
[0025] Figure 4 An example of the bounding box of a first sensor module based on the determined distance to the object is shown.
[0026] Figure 5 An example of an initial reference space based on three defined distances is shown.
[0027] Figure 6A An example of selecting a new reference space from the partial space after the first partitioning operation is shown.
[0028] Figure 6B An example of selecting a new reference space from the partial space after the fourth partitioning operation is shown.
[0029] Figure 7A A side view of a vehicle including multiple sensor modules is shown.
[0030] Figure 7B A top view of the vehicle, which includes multiple sensor modules, is shown.
[0031] Figure 8 A schematic diagram of the hardware structure of the data processing device is shown. Detailed Implementation
[0032] The invention will now be described in conjunction with specific embodiments. These embodiments are provided to enable those skilled in the art to better understand the invention, but are not intended to limit the scope of the invention in any way, which is defined by the appended claims. In particular, the embodiments described independently throughout this specification may be combined to form other embodiments, provided that they are not mutually exclusive.
[0033] The proposed method aims to estimate the position of an object relative to multiple sensor modules. The term "object" describes a device that transmits a signal that can be measured by multiple sensor modules. This signal can be an acoustic signal, an optical signal such as a pulsed laser signal, or an invisible electromagnetic signal such as an infrared signal, a radio frequency signal, or a low-frequency signal.
[0034] The distance from an object to each of the multiple sensor modules is determined by exchanging signals between the object and the corresponding sensor modules. This signal exchange can be unidirectional or bidirectional. In the case of unidirectional signal exchange, the object can be configured to send a signal and each of the multiple sensor modules can be configured to receive a signal. In the case of bidirectional signal exchange, both the object and each of the multiple sensor modules can send and receive signals.
[0035] Determining the distance from an object to each of a plurality of sensor modules involves measuring the signals received by each of the plurality of sensor modules. This produces multiple distance measurements, each corresponding to a determined distance from the object to each corresponding sensor module.
[0036] Each defined distance may include uncertainty based on the measurement accuracy of the corresponding sensor module. Uncertainty can be a value of the standard deviation, variance, or dispersion of the measurement. Uncertainty can be based on limitations of the underlying technology used to receive and measure the signal. Uncertainty can be based on the standard deviation of a series of signal measurements at a fixed distance.
[0037] Multiple sensor modules can be dynamic, meaning that the positions of multiple sensor modules can change over time.
[0038] The positions of multiple sensor modules can change relative to each other. The sensor modules are mounted on a device comprising multiple sensor modules, such that the positions of the sensor modules can be static relative to each other, but the device itself can be dynamic, meaning its position can change over time. The device comprising multiple sensor modules can be a data processing apparatus for processing multiple distances according to the proposed method. Alternatively, the device can send multiple distances to an external data processing apparatus to process the measurement results.
[0039] The positions of multiple sensor modules, multiple distances, and the estimated position of an object can be given by two-dimensional or three-dimensional coordinates. To uniquely determine the object's position in two dimensions, distance measurements are required from at least three sensor modules. To uniquely determine the object's position in three dimensions, distance measurements are required from at least four sensor modules. For purposes of simplicity and illustrative purposes, the examples described in this disclosure are presented in two-dimensional coordinates. In this context, the term "space" refers to a region. In three dimensions, the term "space" refers to a volume.
[0040] Figure 1 A flowchart of a method 100 for estimating the position of an object relative to a plurality of sensor modules mounted on a device, according to one aspect of the present invention, is shown.
[0041] In step S10 of method 100, for each of the plurality of sensor modules, the distance from the object to the corresponding sensor module is determined by exchanging signals between the object and the corresponding sensor module.
[0042] In step S20 of method 100, an initial reference space for estimating the position of the indicated object is determined based on multiple determined distances and positions of multiple sensor modules on the device.
[0043] In step S30 of method 100, multiple successive segmentation operations are performed on the initial reference space for improving the location estimation of the object until a predetermined criterion is met. Each segmentation operation includes: dividing the current reference space into subspaces, each subspace indicating a possible location of the object; assigning a confidence factor on each subspace to indicate the confidence that the object is located in the corresponding subspace; and selecting a new reference space from the subspaces based on the confidence factor.
[0044] In the first segmentation operation of a series of consecutive segmentation operations, the initial reference space of step S20 is used as the current reference space. In subsequent segmentation operations, the new reference space based on the previous segmentation operation is used as the current reference space.
[0045] Figure 2 An example of multiple successive segmentation operations based on a 2D initial reference space R1, which has a rectangular shape and is divided into four successive segmentation operations as indicated by clockwise arrows, is shown. The initial reference space and subspaces can have any shape. Preferably, the initial reference space and subspaces can be defined by boundaries parallel to one or more axes of the coordinate system, which reduces the complexity of the segmentation operations. The coordinate system can be defined based on distance measurements from multiple sensor modules. The axes of the coordinate system can preferably be orthogonal.
[0046] Furthermore, one or more axes of the coordinate system to which the segmentation is performed can be changed between successive segmentation operations. In this example, the first segmentation operation is performed parallel to the y-axis, the second parallel to the x-axis, the third parallel to the y-axis, and the fourth parallel to the x-axis. In other words, the segmentation axes are changed alternately between successive segmentation operations. However, it is also possible to perform two or more successive segmentation operations parallel to the same axis.
[0047] In this example, a predetermined criterion is met when the number of segmentation operations exceeds a predetermined number of four. The number of segmentation operations can be less than four or more than four. By pre-determining the number of segmentation operations, method 100 can be executed within a fixed amount of time and using a fixed amount of processing power. Alternatively, the predetermined criterion can be a dynamically determined condition such that the predetermined criterion is met when the size of the selected new reference space is less than a predetermined threshold size. In this way, method 100 can be adjusted to estimate the position of object T at a specified level of accuracy.
[0048] In the first partitioning operation, the current (initial) reference space R1 is partitioned into two subspaces P. 1,1 and P 1,2 And based on each subspace P 1,2 and P 1,2 Confidence factor on, choose P 1,2 R2 will serve as the new reference space to be used in subsequent partitioning operations. Subspace P i,j The confidence factor can be based on the subspace P i,j The center of mass or center of gravity C i,j , where i is an integer enumerating multiple partitioning operations, and j is an integer enumerating multiple subspaces for a given partitioning operation. The centroid of the space describes the geometric center of the space and can be computed as the arithmetic mean position of all points in the space. The assignment of confidence factors will be described in detail later.
[0049] In the second partitioning operation, the current reference space R2 is partitioned into two subspaces P. 2,1 and P 2,2 And P 2,2 R3 is chosen as the new reference space. In the third partitioning operation, the current reference space R3 is partitioned into two subspaces P. 3,1 and P 3,2 And P 3,1 R4 is chosen as the new reference space. In the fourth partitioning operation, the current reference space R4 is partitioned into two subspaces P. 4,1 and P 4,2 And P 4,1 It was chosen as the new reference space R5.
[0050] In step S40 of method 100, the position of the object is determined based on the new reference space according to the final segmentation operation. The position of the object can be determined as the centroid of the new reference space based on the final segmentation operation. Figure 2 In the example, the position of the object is determined based on the new reference space R5, and can be based on the centroid C. 4,1 Determine the location of the object. The determination of the object's location will be described in detail later.
[0051] Figure 3 An example is shown of determining the positional relationship between an object T and three sensor modules S1, S2, and S3 at three distances d1, d2, and d3 in step S10 of method 100. In this example, the distances d1, d2, and d3 also include multiple uncertainties σ1, σ2, and σ3, as shown by the shaded error bands. Uncertainties σ1, σ2, and σ3 can be based on the measurement accuracy of sensor modules S1, S2, and S3, respectively. In this example, uncertainties σ1, σ2, and σ3 are symmetric, but they can be asymmetric. The positions of sensor modules S1, S2, and S3 are (x1, y1), (x2, y2), and (x3, y3), respectively. Circles around sensor modules S1, S2, and S3, each with radii corresponding to the determined distances d1, d2, and d3, can intersect at the position to be determined on object T.
[0052] Figure 4 It is shown according to the reference Figure 3 An example of the bounding box B1 of the first sensor module S1 given in the previous example. Bounding box B1 is based on the determined distance d1 to the object and the measurement uncertainty σ1. The coordinates of the lower / upper boundary of bounding box B1 are determined by subtracting the sum of distance d1 and uncertainty σ1 from each coordinate value of the position (x1, y1) of sensor module S1, and by adding the sum of distance d1 and uncertainty σ1 to each coordinate value of the position (x1, y1) of sensor module S1. Alternatively, the bounding box can be calculated without uncertainty σ1, i.e., by setting σ1 to zero. The bounding boxes B2 and B3 of sensor modules S2 and S3 can be calculated in the same manner. Note that the determination of the bounding box is only for the convenience of describing the determination of the initial reference space R1 and can be omitted in step S20 of method 100.
[0053] Figure 5 An example is shown where the initial reference space R1 is determined based on three defined distances d1, d2, and d3. The distances d1, d2, and d3 can include multiple uncertainties σ1, σ2, and σ3, as well as the reference space R1. Figure 3 and Figure 4 As discussed in the previous examples, the lower boundary x of the initial reference space R1 is determined by calculating the minimum and maximum coordinate values for all bounding boxes.min and y min and the upper boundary x max and y max The coordinates. In this example with three sensor modules S1, S2, and S3, the coordinate values of the initial reference space R1 can be calculated as:
[0054] x min =min{x1-(d1+σ1),x2-(d2+σ2),x3-(d3+σ3)}
[0055] =min i＝1,…n {x i -(d i +σ i )},
[0056] y min =min{y1-(d1+σ1),y2-(d2+σ2),y3-(d3+σ3)}
[0057] =min i＝1,…n {y i -(d i +σ i )},
[0058] x max =max{x1+(d1+σ1),x2+(d2+σ2),x3+(d3+σ3)}
[0059] =max i＝1,…n {x i +(d i +σ i )},and
[0060] y max =max{y1+(d1+σ1),y2+(d2+σ2),y3+(d3+σ3)}
[0061] =max i＝1,…n {y i +(d i +σ i )}.
[0062] In examples with more than n=3 sensor modules, the above formula can be adapted to include the bounding box coordinates of the additional sensor modules in the calculation of the minimum and maximum values. In examples with three-dimensional coordinates, the above formula can be extended to include an equivalent calculation for the z-coordinate, i.e., for the lower boundary z of the initial reference space R1. min =min i＝1,…n {z i -(d i +σ i)} and the upper boundary z max =max i＝1,…n {z i +(d i +σ i )}.
[0063] Figure 6A and Figure 6B Examples of selecting a new reference space from the subspace after the first and fourth partitioning operations are shown respectively, where the initial reference space R1 is selected based on the reference... Figures 3 to 5 The initial reference space R1 is given in the previous example.
[0064] In the first segmentation operation of step S30 of method 100, as follows Figure 6A As shown in the example, the current reference space R1 is divided into two subspaces P. 1,1 and P 1,2 Here, the current reference space R1 is divided to obtain two subspaces P of equal size (area). 1,1 and P 1,2 However, the current reference space R1 can be divided into more than two subspaces and / or subspaces of different sizes. For example, the current reference space R1 can be divided into any integer number of subspaces, and the subspaces can have equal or different sizes.
[0065] For the sake of brevity and simplicity, Figure 6A and Figure 6B Each of the multiple partitioning operations shown in the example divides the current reference space in half to obtain two subspaces of equal size. In a computer implementation of the partitioning operation, the partitioning can be performed by a shift operation that moves the binary number one position to the right, i.e., dividing the coordinates by a factor of two, where the least significant bit is removed. Such an operation is therefore inexpensive for a computer, which contributes to the overall efficiency of Method 100.
[0066] The next step after the first partitioning operation is to start from subspace P 1,1 and P 1,2 A new reference space R2 is selected. The new reference space R2 is selected based on confidence factors. Confidence factors are assigned to each subspace P. 1,1 and P 1,2 The confidence factor represents the confidence level that an object lies within its corresponding subspace. By definition, the confidence factor can be directly or inversely proportional to the probability that the object lies within its corresponding subspace. Therefore, to improve the location estimation of object T, a subspace with extreme confidence factors can be selected as a new reference space. For the definition implying a direct correlation, the subspace with the largest confidence factor is selected. For the definition implying an inverse correlation, the subspace with the smallest confidence factor is selected.
[0067] The process for selecting a new reference space can be the same for each of the multiple partitioning operations. Selecting a new reference space R i+1 This can include determining the corresponding subspace P for each subspace. i,j The center of mass or center of gravity C i,j Here, i is an integer enumerating multiple partitioning operations, and j is an integer enumerating multiple subspaces of a given partitioning operation.
[0068] Choose a new reference space R i+1 It may also include: for each subspace, calculating a confidence factor based on the distance from the location of each sensor module to the centroid of the corresponding subspace and the distance determined by each sensor module. In one example, each subspace P i,j The confidence factor is calculated from each sensor module S k Position to centroid C i,j The (Euclidean) distance is determined by each sensor module S k A defined distance d k The sum of absolute differences between δ i,j The absolute difference is divided by the value of each sensor module S. k A defined distance d k Here, k is an integer enumerating multiple sensor modules, and δ is the sum of the calculated values. i,j The index (in) Figure 6A and Figure 6B In the example, k = 1, 2, 3). Expressed as a formula, δ i,j The sum is calculated as
[0069]
[0070] in, From sensor module S k The position points to the centroid C i,j A vector, and its absolute value (length). From sensor module S k Position to centroid C i,j The (Euclidean) distance.
[0071] and δ i,j It can be used as a confidence factor, based on which a new reference space R is selected. i+1 Then, you can choose the calculated sum δ. i,j Minimum subspace P i,j As the new reference space R i+1 Confidence factors can be calculated in different ways. For example, confidence factors can be based on each sensor module S. k σ kThe uncertainty. In this case, and δ i,j It can be suitable for multiplying each addend by the uncertainty σ k A proportional factor is used to suppress the influence of inaccurate measurements on the confidence factor.
[0072] exist Figure 6A In the example shown, the subspace P is selected based on the aforementioned confidence factor. 1,2 This serves as the new reference space R2. Then, it can be referenced as described above. Figure 2 The process is repeated. Figure 6B In the example shown, after the fourth partitioning operation, the subspace P is selected. 4,1 R5 serves as the new reference space.
[0073] If the predetermined criteria are met, method 100 continues to step S40, where the position of object T is determined based on the new reference space R5 according to the last segmentation operation. Figure 6B In the example mentioned, the final segmentation operation is the fourth segmentation operation. The position of object T can be based on the centroid C of the new reference space R5. 4,1 The position of object T can be determined using the centroid C of the new reference space R5. 4,1 To identify.
[0074] Steps S20 to S40 can be repeated using different values of uncertainty and / or by changing the scheme for segmenting the current reference space, for example, by selecting different sets of one or more axes parallel to which the segmentation is performed. For example, in the repetition of steps S20 to S40, the first segmentation operation can segment the current reference space parallel to an axis orthogonal to the axis of the first segmentation operation in the initial execution of steps S20 to S40. The positions of objects determined in the initial execution of steps S20 to S40 can be compared with the corresponding positions of objects determined in the repetition, and a smaller δ can be selected. i,j The result.
[0075] For example, the initial execution of steps S20 to S40 can be performed using a first scheme for partitioning the current reference space. The first scheme can be defined by a set of axes, such as {x,y,x,y,...}, meaning that the first partitioning operation is performed parallel to the x-axis, the second partitioning operation is performed parallel to the y-axis, and so on. For the repetition of steps S20 to S40, a second scheme can be used, such as {y,x,y,x,...}, meaning that the first partitioning operation is performed parallel to the y-axis, the second partitioning operation is performed parallel to the x-axis, and so on. In other words, the first and second schemes describe alternating partitions, differing only in the choice of the first axis in each corresponding set of axes. δ can be used... i,jTo compare the initial execution information of steps S20 to S40 with the obtained positions of the repeatedly executed objects, where the lower δ i,j This indicates a high level of confidence in the estimated location of the object.
[0076] The target position estimate from method 100 can be repeatedly used to generate a time series of the target position. For example, method 100 can be repeated at fixed time intervals. The time series of the object's position can be used to construct the object's trajectory in order to track the object's movement.
[0077] The proposed method 100 and its aspects provide a computationally efficient method for locating objects with an adjustable level of accuracy. This allows for the estimation of the object's position in real time using low-power computing devices.
[0078] The multiple distances can be determined based on time-of-flight measurements, signal strength measurements, and / or phase-based ranging. For example, an object can transmit a timestamped signal, which is received by multiple sensor modules at multiple reception times. The difference between the timestamp and each reception time can be used to calculate the time of flight. The distance from the object to each sensor module can be calculated based on the calculated time of flight and the signal speed (e.g., the speed of light for electromagnetic signals). Alternatively, the signal strength, such as a Received Signal Strength Indicator (RSSI), can be compared to a reference value to calculate the distance from the object to each sensor module. Signal strength can also be used to determine the uncertainty in the time-of-flight measurement. Alternatively, in phase-based ranging techniques, the phase difference with a reference signal can be used to calculate the distance from the object to each sensor module. The object and multiple sensor modules can be based on Bluetooth or ultra-wideband technology.
[0079] The object can be provided by a key, wearable device, or portable computing device. For example, the object can be provided by a smartphone, smartwatch, fitness tracker bracelet, etc.
[0080] The device equipped with multiple sensor modules can be a vehicle, a door, or a storage space. Method 100 may further include unlocking the vehicle, door, or storage space.
[0081] Based on the determined location of the object, the user carrying the object can obtain "keyless entry" to the vehicle, door, or storage space, which means that the user does not need to manually unlock the vehicle, door, or storage space.
[0082] The data processing apparatus may include means for performing method 100 or any aspect thereof. For example, the data processing apparatus may include or be connected to multiple sensor modules to receive multiple defined distances from the object to the multiple sensor modules. (See reference...) Figure 8 Describe the hardware structure of the data processing equipment.
[0083] Figure 7A and Figure 7B Side and top views of vehicle 10 are shown respectively. In this example, vehicle 300 includes sensor modules S1 to S6 and a data processing unit 200. Sensor modules S1 to S6 are connected to data processing unit 200 via an interface module. Using data processing unit 200, vehicle 300 can execute method 100 to estimate the location of an object. The object can be provided by a key, wearable device, or portable computing device. Thus, data processing unit 200 of vehicle 300 can, for example, determine whether a user carrying the object is inside vehicle 300. This allows vehicle 300 to provide the user with "keyless entry" for certain operations of vehicle 300.
[0084] For example, if it is determined that a user is approaching vehicle 300, vehicle 300 may perform actions such as unlocking the doors. As another example, if it is determined that a user is inside vehicle 300, vehicle 300 may perform actions such as turning on the interior lights and / or starting the engine. As yet another example, if it is determined that a user has left vehicle 300, vehicle 300 may perform actions such as locking the doors and / or closing any open windows.
[0085] Figure 8 This is a schematic diagram of the hardware structure of a data processing device, which includes means for performing the steps of the method of any of the above embodiments.
[0086] The data processing apparatus 200 has an interface module 210 that provides means for sending and receiving information. The data processing apparatus 200 also has a processor 220 (e.g., a CPU) for controlling the data processing apparatus 200 and, for example, for performing the steps of the methods of any of the embodiments disclosed above. It also has a working memory 230.
[0087] (e.g., random access memory) and instruction memory 240, which stores computer programs having computer-readable instructions that, when executed by processor 220, cause processor 220 to perform any of the methods disclosed above.
[0088] Instruction memory 240 may include a ROM (e.g., in the form of electrically erasable programmable read-only memory (EEPROM) or flash memory) preloaded with computer-readable instructions. Alternatively, instruction memory 240 may include RAM or a similar type of memory, and computer-readable instructions may be input therefrom from a computer program product, such as a computer-readable storage medium like a CD-ROM.
[0089] In the foregoing description, several aspects have been described with reference to several embodiments. Therefore, the specification should be considered illustrative rather than restrictive. Similarly, the figures shown in the accompanying drawings, which highlight the functionality and advantages of the embodiments, are presented for illustrative purposes only. The architecture of the embodiments is flexible and configurable enough that it can be utilized in ways other than those shown in the accompanying drawings.
[0090] In one exemplary embodiment, the software implementations presented herein may be provided as computer programs or software, such as one or more programs having instructions or sequences of instructions, which are included or stored in an article of art, such as a machine-accessible or machine-readable medium, an instruction store, or a computer-readable storage device, each of which may be non-transitory. Programs or instructions on a non-transitory machine-accessible medium, machine-readable medium, instruction store, or computer-readable storage device can be used to program a computer system or other electronic equipment. Machine or computer-readable media, instruction stores, and storage devices may include, but are not limited to, floppy disks, optical disks, and magneto-optical disks, or other types of media / machine-readable media / instruction stores / storage devices suitable for storing or transmitting electronic instructions. The techniques described herein are not limited to any particular software configuration. They can be found in any computing or processing environment. As used herein, the terms “computer-readable,” “machine-accessible medium,” “machine-readable medium,” “instruction store,” and “computer-readable storage device” shall include any medium capable of storing, encoding, or transmitting instructions or sequences of instructions for execution by a machine, computer, or computer processor, and enabling the machine / computer / computer processor to perform any of the methods described herein. Furthermore, in this art, software is often referred to as taking an action or causing a result in one form or another (e.g., program, procedure, process, application, module, unit, logic, etc.). Such expressions are merely shorthand ways of describing how a processing system executes software to cause the processor to perform actions to produce a result.
[0091] Some implementations can also be achieved by preparing application-specific integrated circuits, field-programmable gate arrays, or by using appropriate networks to interconnect conventional component circuits.
[0092] Some implementations include a computer program product. A computer program product may be a storage medium or multiple storage media, one or more instruction stores, or one or more storage devices having instructions stored thereon or therein, which can be used to control or cause a computer or computer processor to execute any program of the exemplary implementations described herein. Storage media / instruction stores / storage devices may include, for example, but not limited to, optical discs, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory, flash memory cards, magnetic cards, optical cards, nanosystems, molecular memory integrated circuits, RAID, remote data storage / archiving / warehousing, and / or any other type of device suitable for storing instructions and / or data.
[0093] Some implementations stored on any of a computer-readable medium or multiple media, one or more instruction stores, or one or more storage devices include hardware for controlling the system and software for enabling the system or microprocessor to interact with a human user or other mechanisms using the results of the embodiments described herein. Such software may include, but is not limited to, device drivers, operating systems, and user applications. Finally, such computer-readable media or storage devices also include software for performing the exemplary aspects described above.
[0094] Included in the system's programming and / or software are software modules used to implement the processes described herein. In some example embodiments herein, the modules comprise software, although in other example embodiments herein, the modules comprise hardware or a combination of hardware and software.
[0095] While various embodiments of this disclosure have been described above, it should be understood that they are presented as examples and not as limitations. It will be apparent to those skilled in the art that various changes in form and detail can be made therein. Therefore, the exemplary embodiments described above are not restrictive.
Claims
1. A method for estimating the position of an object relative to a plurality of sensor modules mounted on a device, the method comprising the steps of: By exchanging signals between the object and the corresponding sensor module, the distance from the object to the corresponding sensor module is determined for each of the plurality of sensor modules; Based on multiple determined distances and positions of the plurality of sensor modules on the device, an initial reference space for indicating the position estimation of the object is determined; Perform multiple successive segmentation operations in the initial reference space to improve the position estimate of the object until a predetermined criterion is met, wherein each segmentation operation includes: The current reference space is divided into subspaces, each subspace indicating the possible location of the object; Assign confidence factors to corresponding subspaces within the subspace, the confidence factors indicating the confidence level of the object's location within that corresponding subspace; and Based on the confidence factor, a new reference space is selected from the subspace; and The position of the object is determined based on the new reference space according to the final segmentation operation.
2. The method according to claim 1, wherein, The initial reference space and the subspace are defined by boundaries parallel to one or more axes of the coordinate system, and The coordinate system is defined based on the distance measurement results of the multiple sensor modules.
3. The method according to claim 2, wherein, In each segmentation operation, the segmentation of the current reference space is performed parallel to one or more axes of the coordinate system.
4. The method according to claim 3, wherein, One or more axes of the coordinate system to which the segmentation is performed are changed between successive segmentation operations.
5. The method of claim 1, wherein, Each partitioning operation divides the current reference space into two subspaces of equal size.
6. The method of claim 1, wherein, The steps for selecting a new reference space include: For each subspace, determine the centroid of the corresponding subspace; For each subspace, the confidence factor is calculated based on the distance from the position of each sensor module to the centroid of the corresponding subspace and the distance determined by each sensor module.
7. The method of claim 1, wherein, The plurality of determined distances include multiple uncertainties based on the positioning accuracy of each of the plurality of sensor modules.
8. The method of claim 1, wherein, The predetermined standard is satisfied when at least one of the first and second standards is met, wherein... When the number of partitioning operations performed exceeds a predetermined number, the first criterion is met, and The second criterion is met when the size of the selected new reference space is less than the predetermined threshold size.
9. The method of claim 1, wherein, The determined distances are based on time-of-flight measurements, signal strength measurements, or phase-based ranging.
10. The method of claim 1, wherein, The object is provided by a key, wearable device, or portable computing device.
11. The method according to claim 1, wherein, The device is a vehicle, a door, or a storage space.
12. The method according to claim 1, further comprising the following steps: Based on the determined location of the object, the device is unlocked, which may be a vehicle, a door, or a storage space.
13. The method of claim 1, wherein, The object and the plurality of sensor modules are based on Bluetooth technology or ultra-wideband technology.
14. A data processing apparatus, the data processing apparatus comprising means for performing the method according to any one of claims 1 to 13.
15. A vehicle comprising the data processing apparatus of claim 14.