Multilayer statistical wireless terminal position determination

Abstract

Embodiments of the present application relate to multilayer statistical wireless terminal position determination. Embodiments are disclosed for determining a position of a wireless terminal. The wireless terminal measures signal strengths of a plurality of wireless transmitters. Based on this information, a plurality of position probability surfaces are generated. Each position probability surface indicates a plurality of probabilities that the wireless terminal is in each geographical region of a corresponding plurality of geographical regions. These probability surfaces are then averaged to determine a composite position probability surface. A motion probability surface is also determined, which stores a plurality of probabilities indicative of changes in motion of the wireless terminal. The composite position probability surface is then updated based on the motion probability surface. A position estimate of the wireless terminal is then determined based on the updated composite position probability surface.

Description

Technical Field

[0001] This application relates to wireless communication, and more specifically to methods and / or apparatus for determining the location of objects associated with a wireless network. Background Technology

[0002] Location estimation of wireless terminals is essential in many fields, such as navigation and positioning of moving objects and location identification of clients experiencing performance degradation.

[0003] The location of a wireless terminal can be estimated using two different methods. One method is based on the signal strength received by the wireless terminal from multiple radio transmitters located in known locations. According to this method, the wireless terminal measures the signal strength of each transmitter and reports the signal strength to a positioning engine. The positioning engine infers the attenuation of the received signal along the path from the transmitter to the receiver. The attenuation of the transmitted signal is used to determine the distance from each transmitter to the receiver. The positioning engine then uses the known locations of the transmitters to determine the location of the wireless terminal.

[0004] According to the second method, the wireless terminal transmits a signal, which is received by multiple receivers located at known locations. Similarly, the receivers transmit corresponding signal strength information to a positioning engine, which estimates the distance from the wireless terminal to the fixed receivers. The positioning engine then uses the known locations of the receivers to determine the location of the wireless terminal.

[0005] Measuring signal strength is prone to error due to noise and multipath propagation, and therefore can be inaccurate. For example, when estimating the location of a device based on three transmitters, the location information based on any two transmitters may conflict with (disagree with) the location inferred based on information from the third transmitter.

[0006] A method is needed that can compensate for distorted signal strength measurements and provide consistent location estimation for wireless terminals. Attached Figure Description

[0007] Figure 1 It is a schematic diagram of a system implemented in one or more disclosed embodiments;

[0008] Figure 2 The aggregation of multiple location probability surfaces is shown;

[0009] Figure 3 It is a diagram of the data flow implemented in one or more disclosed embodiments;

[0010] Figure 4 It is an example probability curve of a single dimension of signal strength error;

[0011] Figure 5It is a graphical representation of the probability of an example two-dimensional location probability surface;

[0012] Figure 6 It is a graphical representation of the synthetic position probability surface;

[0013] Figure 7 An example shift in the position probability curve caused by the projected motion of a wireless terminal is shown;

[0014] Figure 8 The simplified velocity probability in a single direction is shown;

[0015] Figure 9 A two-dimensional motion (Vx, &Vy) probability surface is shown;

[0016] Figure 10 Example message portions implemented in one or more of the disclosed embodiments are shown;

[0017] Figure 11 Example data structures implemented in one or more of the disclosed embodiments are shown;

[0018] Figure 12 This is a flowchart of the process used to estimate the location of a wireless terminal;

[0019] Figure 13 This is a flowchart of the process used to determine the motion probability surface;

[0020] Figure 14 This is a flowchart of the process of applying a motion probability surface to a synthetic position probability surface;

[0021] Figure 15 This is a flowchart of the process used to determine the motion probability surface;

[0022] Figure 16 This is a flowchart of the process for determining the probability surface of mixed locations; and

[0023] Figure 17 A block diagram of an example machine on which any one or more of the techniques (e.g., methods) discussed herein can be executed is shown. Detailed Implementation

[0024] This disclosure generally relates to estimating the location of wireless terminals associated with a wireless network. This is achieved using methods based on triangulation (e.g., distance from Dist). i Unlike traditional systems that determine the location of a wireless terminal by averaging the area defined by the intersection points of points, at least some of the disclosed embodiments rely on a location probability surface (LPS) to determine the estimated location.

[0025] LPS represents the probability that a wireless terminal is located within a plurality of corresponding geographic regions. These geographic regions can be represented as a grid using a data structure such as a two-dimensional array. In some embodiments, the grid may represent a total of 50×50 meters of two-dimensional geographic regions, and each cell in the grid or region is, for example, 0.5×0.5 meters. Other grid and cell / region sizes are within the scope of the disclosed embodiments. In some embodiments, each of the plurality of geographic regions is a two-dimensional geographic region. In other embodiments, the plurality of geographic regions are all three-dimensional geographic volumes. Thus, in some embodiments, the location probability surface represents the probability that WT is located in each of a plurality of different geographic volumes, for example, 50×50×50 meters, where each volume or region of the plurality of regions represents a 0.5×0.5×0.5 meter region in this example. Other region sizes are expected and are within the scope of the disclosed embodiments.

[0026] To generate the LPS, the disclosed embodiments receive a signal strength measurement from the wireless terminal. In the following discussion, this signal strength measurement is referred to as SS. Meas Signal strength measurement is the measurement of signals generated and transmitted by a wireless transmitter and received by a wireless terminal. In some embodiments, Received Signal Strength Indication (RSSI) represents the signal strength measurement.

[0027] Based on signal strength measurements, the disclosed embodiments determine multiple probabilities in each of the corresponding multiple geographic regions where the wireless terminal is located.

[0028] The general equation describing the behavior of radio signals is as follows:

[0029] SS i =PLE*log(Dist) i )+Int+Dir i Equation 1

[0030] in:

[0031] SS i The signal strength experienced (and measured) by the wireless terminal from the i-th wireless transmitter.

[0032] PLE: Path Loss Index; (e.g., -20 dB for line-of-sight loss; <-20 dB for environments with attenuation, such as transmitting signals through semi-transparent objects).

[0033] Dist i : The distance between the wireless terminal and the i-th transmitter

[0034] int: The intercept function of the transmitter's power.

[0035] Dir iDirectional adjustment, which reflects the antenna gain along the path between the i-th transmitter and the wireless terminal.

[0036] As part of determining the probabilities in the location probability surface, the disclosed embodiments determine the expected signal strength of each of a plurality of regions. The expected signal strength of a region is based on one or more of the transmit power of a wireless transmitter and the distance between the wireless transmitter and the corresponding region. Thus, as described in Equation 1 above, these embodiments determine each corresponding expected signal strength measurement based on the corresponding distance between the corresponding region and the wireless transmitter. In some embodiments, the distance between the corresponding region and the wireless transmitter is determined based on the known locations of both the corresponding region and the wireless transmitter. For example, some embodiments receive configuration information defining the location of a known wireless transmitter. Additionally, these embodiments receive configuration input defining the locations of a plurality of regions. In some embodiments, the locations of the plurality of regions are inferred based on the locations of known wireless transmitters (e.g., a geographical area between known wireless transmitters is divided into multiple regions).

[0037] Signal strength measurements of wireless terminals are affected by noise. In some cases, the measured signal strength contains approximately six (6) dB of noise (SD = 6 dB). Therefore, SS Meas With SS Exp One difference between them can be attributed to noise. There are one or more other factors, including multipath, receiver gain, and other parameters associated with the channel model. Gaussian noise can cause SS... Exp With SS Meas The differences between them.

[0038] Then, according to the following Equation 2, some disclosed embodiments determine the difference between the measured signal strength and the desired signal strength:

[0039] SS Error =SS Exp -SS Meas Equation 2

[0040] in:

[0041] SS Error The difference between the expected signal strength and the measured signal strength.

[0042] SS Exp In some embodiments, the expected signal strength value is calculated via Equation 1, and

[0043] SS Meas : Measure signal strength value (measured by the wireless terminal).

[0044] In some embodiments, since the noise level can be empirically measured as approximately six (6) dB, the SSError The probability curve is assumed to be Gaussian, where Sigma = 6. In other embodiments, other curves may be used. For SS... Error For each dimension and for each cell, a probability (i, x, y) is determined, where i is the index of the i-th dimension corresponding to the i-th measured signal strength received from the i-th radio transmitter, and x & y are the consistent coordinates of a particular cell in the grid.

[0045] Therefore, in order to generate a location probability surface, the expected signal strength of the signal in each of the multiple regions is determined. In at least some embodiments, this determination is based at least on the location of the wireless transmitter that generates the signal. A corresponding difference between the expected signal strength and the measured signal strength of the region is also determined. The probability that the wireless terminal is located in each region is then determined based on the corresponding difference.

[0046] Some disclosed embodiments define multiple location probability surfaces, with one surface for each wireless transmitter, received and measured by the wireless terminal. To support this, the wireless terminal performs multiple signal strength measurements (one for each wireless transmitter). In some embodiments, these signal strength measurements are transmitted by the wireless terminal to a network management system for further processing. The signal strength measurements are used to determine the signal strength error for each cell. The signal strength error is then used to determine the probability that the mobile device is located in each particular cell. Throughout this disclosure, these probability surfaces are referred to as Pi(SS). Exp -SS Meas ), where index i represents the i-th dimension on which the probability surface is derived.

[0047] Some wireless terminal implementations determine signal strength measurements periodically, iteratively, continuously, or according to commands from the user. These measurements are then transmitted to the network management system. For each dimension of the measured signal strength and for each cell in the grid, the system is based on SS... Error The value of a specific dimension is used to calculate the probability that a wireless terminal is located in a specific unit.

[0048] To determine the probability that a wireless terminal is located within a specific region or cell of a grid, some embodiments multiply corresponding probability values ​​from multiple location probability surfaces. In this context, the corresponding value is a value representing the probability value within an equivalent region or grid cell. One or more disclosed embodiments implement this method via Equation 3 below:

[0049] P x，y =P1,x,y*P2,x,y*....*P n-1，x，y *P n，x，y Equation 3

[0050] in:

[0051] P x,y The probability that the wireless terminal is located in a cell associated with the x and y coordinates.

[0052] P1,x,y: Based on SS Error The first dimension is the probability that the wireless terminal is located in cells x and y.

[0053] P2,x,y: Based on SS Error The second dimension is the probability that the wireless terminal is located in cells x and y.

[0054] Pn-1,x,y: Based on SS Error The probability that the wireless terminal is located in cell x and cell y in the (n-1)th dimension.

[0055] Pn,x,y: Based on SS Error In the nth dimension, the probability that the wireless terminal is located in cells x and y is...

[0056] *: Multiplication operator

[0057] Since the probabilities of each location probability surface are independent, the peak or maximum probability within each location probability surface can be different. Therefore, unit-by-unit multiplication of n surfaces produces a new probability surface, which can have multiple peaks and valleys. This new probability surface is referred to herein as a synthetic location probability surface. Some embodiments determine the estimated location of a wireless terminal based on the synthetic location probability surface. For example, in some embodiments, the region corresponding to the highest probability in the synthetic location probability surface is used as the estimated location of the wireless terminal. In other embodiments, the weighted value of each region or unit used for the synthetic location probability surface is used to estimate the location of the wireless terminal. For example, in some aspects, a weighted value along a first or second dimension can be determined for each unit or region (e.g., regions with higher association probabilities can be weighted more heavily than regions with lower association probabilities).

[0058] The above explanation treats each region or cell of the location probability surface and / or the synthetic location probability surface as a singular point, evaluating the expected signal strength at the center of the region at that singular point. Other embodiments may perform this calculation in a continuous domain. In this case, for each dimension of the i-th received signal, the probability that the wireless terminal is in a specific region (e.g., cell x, y) is calculated using the following formula:

[0059] P x，y，i =∫ x ∫ y P x，y , (SS) Exp -SS Meas Equation 4 (dxdy)

[0060] In some embodiments, multiple location probability surfaces and / or synthetic location probability surfaces are calculated periodically (e.g., once per second) based on updated signal strength measurements performed by the wireless terminal, and reported to a network management server.

[0061] Some disclosed embodiments generate a predicted location probability surface for a future time (e.g., T+1) based on information available at a previous time (e.g., T). The predicted location probability surface at time T+1 is then used to generate a mixed location probability surface at time T+1. The mixed location probability surface at time T+1 can also be generated based on a synthetic location probability surface at time T+1.

[0062] The predicted position probability surface is generated based on motion estimation of the wireless terminal. The motion estimation is based in part on accelerometer and / or gyroscope information received from the wireless terminal itself. In at least some embodiments, the motion estimation is also based on previous hybrid position probability surfaces of the wireless terminal. Thus, for example, in some embodiments, the motion estimation at time T+2 is based on one or more hybrid position probability surfaces at times T=0 and / or T=1.

[0063] Each motion estimate of the wireless terminal is associated with a probability that the corresponding motion estimate is accurate for the wireless terminal. In some embodiments, the combination of motion estimates and their associated probabilities can be organized into a motion probability surface. Each cell of the surface represents a set of motion parameters of the wireless terminal and the probability that these parameters accurately represent the motion of the wireless terminal.

[0064] In some embodiments, motion estimates are generated based on motion information received from a wireless terminal. The information received from the wireless terminal may include acceleration in each of the x, y, and z dimensions. In some embodiments, the accuracy associated with each acceleration measurement is also obtained from the wireless terminal. In other embodiments, the accuracy information is configured or hard-coded at a network management system that performs these calculations. The acceleration measurements provided by the wireless terminal are applied to a distribution (e.g., Gaussian) to generate multiple different motion estimates (and their associated probabilities), any one of which may reflect the actual motion of the wireless terminal at the applicable time. For example, in at least some embodiments, the motion estimates and their associated probabilities take the form [Vx, Vy, Vz, Prob], where Vx is the velocity in the X direction, Vy is the velocity in the Y direction, Vz is the velocity in the Z direction, and Prob is the probability that the wireless terminal exhibits the motion described by [Vx, Vy, Vz]. In some embodiments, only two-dimensional motion is estimated. In these embodiments, the motion estimates and their associated probabilities take the form [Vx, Vy, Prob] in at least some embodiments.

[0065] Equation 5 below provides an example equation illustrating the generation of a motion probability surface. Equation 5 is used in at least some of the disclosed embodiments:

[0066]

[0067] in:

[0068] P m (t): The probability surface of motion at time t.

[0069] P blended (t): Mixed position probability surface at time t

[0070] WT acc Speed ​​or acceleration information from a wireless terminal (e.g., Vx, Vy, Vz, or A) x A y A z ),

[0071] Aggregated mixed-location probability surface operators. In some embodiments, the aggregated mixed-location probability surface operator averages the probabilities in corresponding regions or cells of two mixed-location probability surfaces.

[0072] Used to transmit motion information (WT) from wireless terminals acc ) Applied to the mixing location probability surface of the aggregation (via The generated operators. Below Figure 13 Described One embodiment.

[0073] Note that in some embodiments, multiple prior mixed position probability surfaces are used when generating the motion probability surface. For example, an additional surface P can be utilized. blended (t-2), P blended (t-3) and / or P blended -x(t-4) to generate P m (t+1).

[0074] Some estimates apply multiple motion estimates (e.g., motion probability surfaces) to one or more previous mixed location probability surfaces to generate a predicted location probability surface. Equation 6 below gives an example of generating a predicted location probability surface:

[0075]

[0076] in:

[0077] P predicted (t+1): The predicted location probability surface of the wireless terminal at time t+1.

[0078] P predicted (t): Mixed position probability surface at time t

[0079] Motion operators. Below... Figure 14 Described One embodiment, and

[0080] P m (t): The probability surface of motion at time t

[0081] The following discussion Figure 3 The operation of Equation 6 is illustrated below. In some embodiments, the mixed position probability surface at time t is based on the predicted position probability surface at time t and the synthetic position probability surface at time t. This is shown via Equation 7 below:

[0082]

[0083] in:

[0084] P blended (t): Mixed position probability surface at time t

[0085] P predicted (t): The predicted location probability surface at time t.

[0086] P composite (t): The composite position probability surface at time t, and

[0087] The mixing operator is used to combine the predicted location probability surface and the synthesized location probability surface. In one example embodiment... The average of the corresponding probabilities in the predicted location probability surface and the synthetic location probability surface is calculated.

[0088] In some embodiments, the hybrid location probability surface at time t is generated by a weighted sum of the predicted location probability surface and the synthetic location probability surface at time t. This is illustrated by Equation 8 below:

[0089] P blended (t)=α*P composite (t)+β*P predicted (t) Equation 8

[0090] in:

[0091] P blended (t): the probability of mixed location at time.

[0092] P composite (t): The composite position probability surface at time t.

[0093] P predicted (t): The predicted location probability surface at time t.

[0094] α and β: Parameters, for example, α + β = 1. In some embodiments, the value of each of α and β is 0.5.

[0095] Some embodiments use a hybrid location probability surface at time t+1 and a motion probability surface at time t+1 to estimate the predicted location probability surface of the device at time t+2. Some disclosed embodiments iteratively compute new synthetic location probability surfaces, motion estimates, predicted location probability surfaces, and hybrid location probability surfaces, and iteratively estimate the location of the mobile terminal based on these iteratively determined data structures.

[0096] In some embodiments, the location of the device is determined to be in the area with the highest probability. One embodiment of this method is represented by the following Equation 9:

[0097] {x, y}(t) = Max(P) Blended Equation 9 (t)

[0098] in:

[0099] {x, y}(t): The estimated position of the device at time t.

[0100] P blended (t): the probability surface of mixed locations at time t, and

[0101] Max(P blended (t)): The Max function returns P. blended The identifier of the highest probability in (t) (e.g., x, y coordinates).

[0102] Some embodiments rank regions based on their associated probabilities. Then, the highest-ranking set of these regions (e.g., k regions) is selected. The position is then based on the k highest-ranking regions (rather than on regions ranked lower than the kth highest-ranking region). One embodiment of this method is mathematically represented by the following equation 10:

[0103]

[0104] in:

[0105] x,y(location): Estimated location based on the mixed location probability surface.

[0106] k: A limit on the maximum number of probabilities to be considered when determining the estimated location.

[0107] (x i y i): The x and y coordinates of the i-th ranked peak, and

[0108] P(x i ,y i ): The wireless terminal is located at location (x i y i The probability of ).

[0109] Figure 1 This is a schematic diagram of a system 100 implemented in one or more disclosed embodiments. System 100 includes four access points 102a-d. Each of the four access points 102a-d generates a corresponding signal 104a-d. Signals 104a, 104b, 104c, and 104d are received by a wireless terminal 106. The wireless terminal 106 measures the strength of each signal 104a-d and generates a message 108 indicating the signal strength. For example, Figure 1 Message 108 is shown, indicating the Received Signal Strength Indication (RSSI) of the signal from access point 102a and a second RSSI value of the signal from access point 102b. RSSI is merely one example of signal strength measurement, and other embodiments use different measurements of signal strength.

[0110] Wireless terminal 106 transmits message 108 to network management system 110. Network management system 110 uses the signal strength measurement included in message 108 to estimate the location or geographic location of wireless terminal 106. In some embodiments, network management system 110 divides geographic area 112 into multiple regions. Figure 1 Regions 114a, 114b, and 114c are shown, while other regions within geographic region 112 are unlabeled to maintain graphic clarity. In some disclosed embodiments, the network management system 110 calculates the probability that the wireless terminal 106 is located in each of the plurality of regions (including regions 114a-c). These probabilities are based on signals 104a-d received by the wireless terminal 106. Specifically, the probabilities are based on the strength of signals 104a-d measured by the wireless terminal. In some embodiments, access points 102a-d are in known locations. Therefore, the distance between each access point 102a-d and each region (e.g., 114a-c) can be determined by the network management system. Based on these distances, the network management system 110 determines the expected signal strength of the signal received from each access point 102a-d in each region (e.g., 114a-c). By comparing the expected received signal strength with the signal strength measured by the wireless terminal, the network management system can determine the probability that the wireless terminal is located in that region.

[0111] In some embodiments, these probabilities are refined via additional probabilities of the location of the wireless terminal based on motion information of the wireless terminal. For example, in some embodiments, the wireless terminal provides motion information 109 to the network management system in message 108. In other embodiments, different messages are used to provide motion information 109 from the wireless terminal 106 to the network management system 110. In some embodiments, the wireless terminal 106 derives motion information from an accelerometer integrated into the wireless terminal 106.

[0112] In some embodiments, the movement of the wireless terminal 106 is inferred by the network management system 110 from changes in the order of the wireless terminal's position, as explained further below.

[0113] Figure 2 The aggregation of multiple location probability surfaces is shown. (As above regarding...) Figure 1 The network management system 110 determines multiple probabilities of the wireless terminal 106 within corresponding multiple regions (e.g., regions 114a-c). Figure 2 Multiple location probability surfaces 202a-d are shown. Each location probability surface 202a-d is generated based on signals from different wireless transmitters. For example, in one embodiment, each location probability surface 202a-d is generated separately based on signals from access points 102a-d. Each location probability surface 202a-d includes multiple probabilities. These multiple probabilities... Figure 2 The location probability surfaces are shown in a grid-like structure as labeled grid 204, representing the location probability surfaces 202a. The probabilities included in each location probability surface 202a-d are not shown graphically; therefore, the location probability surfaces are presented as flat for simplicity.

[0114] Each cell in grid 204 represents a different probability included in the location probability surface. Each probability corresponds to a region, such as those mentioned above. Figure 1 The discussed regions are 114a-c. In other words, each probability represents the likelihood that the wireless terminal 106 is located within the region corresponding to the cell containing that probability. In at least some of the disclosed embodiments, a plurality of location probability surfaces (e.g., 202a-d) are generated. Each location probability surface is generated based on one or more individual signal strength measurements by the wireless terminal 106 of signals from a single wireless transmitter (e.g., any one of AP 102a, AP 102b, AP 102c, or AP 102d). Therefore, Figure 2 Each of the location probability surfaces 202a, 202b and 202c can be generated based on signal strength measurements of different signals generated by different wireless transmitters.

[0115] Figure 2A plurality of location probability surfaces 202a-d are shown being aggregated to generate a synthetic location probability surface 214. Each cell (e.g., cell 215) of the synthetic location probability surface 214 has a corresponding cell in each location probability surface (e.g., cell 211d of location probability surface 202d, cell 211a of location probability surface 202c, and cell 211a of location probability surface 202a). For clarity, corresponding cells for location probability surface 202b are not labeled. These cells correspond because each represents an equivalent region or location. In at least some embodiments, aggregating corresponding probabilities in the plurality of location probability surfaces includes multiplying the probabilities. Thus, for example, aggregating two or more location probability surfaces includes: for each region in a plurality of regions represented by the two location probability surfaces, aggregating a first probability and a second probability corresponding to the corresponding region represented by the two or more location probability surfaces. Then, in at least some embodiments, the location estimation of the wireless terminal is based on the aggregated first and second probabilities. In some embodiments, the location estimation is not solely based on the first and second probabilities; for example, these embodiments aggregate more than two location probability surfaces.

[0116] Figure 3 This is a diagram of the data flow in a processing pipeline implemented in one or more of the disclosed embodiments. The processing pipeline is shown operating on several types of data at each of the five time bases T0-T5 shown on time axis 302. The elapsed time between each time base T0-T5 is equal. The amount of elapsed time between each time base may vary depending on the embodiment. Some embodiments generate a new set of datasets (e.g., synthetic position probability surfaces, motion probability surfaces, predicted position probability surfaces, and mixed position probability surfaces) every one second, five seconds, ten seconds, 30 seconds, one minute, or any elapsed time period. Various embodiments acquire new accelerometer measurements from a wireless terminal at different or equal intervals (discussed further below).

[0117] Although in some embodiments, each type of data shown is generated at each time base T0-T4, some data is omitted from the figure for clarity. Figure 3 Data stream 300 is shown, which includes synthetic position probability surfaces 304a-e, accelerometer information 305b-e, motion probability surfaces 306b-e, predicted position probability surfaces 307d-e, and mixed position probability surfaces 308a-e.

[0118] In some embodiments, any synthetic location probability surface 304a-e is similar to the above regarding Figure 2 The synthetic position probability surface 214 is discussed. Figure 3Motion probability surfaces 306b-e are also shown. (For clarity, another motion probability surface labeled 306a is omitted in the figure.) Each accelerometer information 305b-e is generated from motion information provided by a wireless terminal, the location of which is determined by... Figure 3 The data stream shown is estimated at 300. For each time period T0-T4, Figure 3 The diagram shows that the wireless terminal has provided corresponding motion information, such as acceleration information, represented by each accelerometer information 305b-e.

[0119] Each motion probability surface 306b-e is generated based on motion information received from the mobile device (represented as accelerometer information 305b-e). For example, in at least some embodiments, the accelerometer information 305b-e indicates velocity in each of the X, Y, and Z directions. According to other embodiments, the accelerometer information 305b-e represents acceleration in the X, Y, and Z directions. In at least some embodiments, the motion probability surface 306b-e is also generated based on one or more mixed position probability surfaces from previous time periods. For example, Figure 3 The motion probability surface 306c is shown to be generated based at least on the mixed position probability surfaces 308a and 308b (see, for example, Equation 5 above). The motion probability surface 306d is generated based on one or more of the mixed position probability surfaces 308a, 308b and 308c. Figure 3 The lines indicating the dependence of motion probability surface 306d on any mixed position probability surfaces 308a, 308b or 308c are omitted to maintain the clarity of the graph.

[0120] Then, in at least some embodiments, each of the motion probability surfaces 306b-d is used to generate a corresponding predicted location probability surface, such as Figure 3 The predicted position probability surface 307d is shown (generated via the motion probability surface 306c, as illustrated) (see also Equation 6 above). Then, the predicted position probability surface 307d and the synthesized position probability surface 304d (based on signal strength measurements corresponding to time T3) are used to generate the mixed position probability surface 308d (see also Equation 7 discussed above). Although the arrows indicate specific data flows used to generate the mixed position probability surface 308d in at least some embodiments, the reader should recognize that similar data flows will be used to generate each mixed position probability surface 308a-e. However, for clarity, the arrows showing all these data flows have been omitted.

[0121] therefore, Figure 3Some embodiments are described as generating a first synthetic position probability surface 304a, a second synthetic position probability surface 304b, a third synthetic position probability surface 304c, and then a fourth synthetic position probability surface 304d incrementally or iteratively. Each of these synthetic position probability surfaces is associated with... Figure 3 The time references are represented as specific time bases from T0 to T4. Multiple motion probability estimates for the wireless terminal are also generated for each time base. (See figure...) Figure 3 In this context, the information is represented as motion probability surfaces 306b-e. In some embodiments, motion estimation is represented as a motion probability surface. In other embodiments, motion estimation is represented via a structure other than the motion probability surface. The mixed location probability surface and the motion probability surface are then used to generate a predicted location probability surface for subsequent time periods. When a subsequent time period arrives, the predicted location probability surface for the subsequent time period and the composite location probability surface for that time period are then used to generate the mixed location probability surface for that time period.

[0122] Notice, Figure 3 The above discussion describes the operation when data stream 300 has reached full initialization or a steady-state operating mode. Those skilled in the art will understand that when any one or more of the disclosed embodiments are first initialized, a data pipeline with multiple time periods, such as T0-T4, may not necessarily be available. Therefore, if data is unavailable, some of the operations discussed above are not performed. For example, the first mixed position probability surface generated by the disclosed embodiments is not based on a previous mixed position probability surface because that data is unavailable. Similarly, when the first mixed position probability surface is generated, the predicted position probability surface is unavailable because predicted position probability surfaces are typically created for future time bases. Therefore, at least in some embodiments, when the first mixed position probability surface is generated, it may simply be a copy of the corresponding synthetic position probability surface (e.g., a mixed position probability surface 308a generated based on synthetic position probability surface 304a, without using the predicted position probability surface).

[0123] Figure 4 Example P(SS) Error The example probability curve for a single dimension is shown in graph 400. A Gaussian probability curve is shown, but other embodiments may provide alternative probability distributions.

[0124] Figure 5 This is a graphical representation of the probability of a two-dimensional location probability surface, 500. Probability 502 is presented in a donut shape. The donut shape shows a ring 504 indicating a relatively high probability location for the subject device, with the surrounding area exhibiting a lower probability.

[0125] Figure 6This is a graphical representation 600 of the example location probability surface. As described above, the location probability surface 602 is generated based on the difference between the expected received signal strength values ​​and the measured received signal strength values ​​for multiple regions.

[0126] Figure 7 One-dimensional position probability curves for the device at times t and t+1 are shown. First curve 700a and second curve 700b show example offsets in the position probability curves resulting from the wireless terminal moving at a deterministic speed of d meters per second. Since some embodiments estimate the speed as a deterministic number (d meters per second), the shape of the position probability curve derived at time t+1 is the same as that at time t. Because the device moves at a speed of d meters per second, the position at time t+1 is given by L(t+1) = L(t) + 1*d, and the probability P(L(t+1)) that the device is in any given position is the same probability curve P(L(t)) shifted to the right by d. This is also illustrated above with reference to Equation 6.

[0127] Figure 8 This is a graph 800 showing the simplified velocity probability in a single direction. The graph shows an average velocity of s meters per second with the highest probability. However, the device can travel at a higher or lower velocity than s meters per second with a lower probability. Using the motion probability curve based on the mixed position probability surface at time t to estimate the predicted position probability surface of the device at time t+1 yields a predicted position probability surface at time t+1 that appears different from the position probability surface at time t or the mixed position probability surface at time t.

[0128] Figure 9 This is a graph 900 illustrating an example two-dimensional motion probability surface 902. In some embodiments, each cell of the motion probability surface indicates velocity and direction, for example, via Vx and Vy values. In some other embodiments, motion probabilities are calculated for three-dimensional space, for example, for Vx, Vy, and Vz.

[0129] Returning to the example of a two-dimensional motion probability surface, each cell of the motion probability surface also indicates the probability that the wireless terminal exhibits a motion consistent with the motion estimate indicated by that cell. Figure 9 A simplified two-dimensional representation of the motion probability surface is shown because it is difficult to clearly illustrate a three-dimensional surface in a written document. The height of the motion probability surface 902 indicates the motion parameter value (e.g., V) that the wireless terminal will exhibit corresponding to that unit. x V y The probability of ).

[0130] Figure 10An example message portion implemented in one or more disclosed embodiments is shown. In some embodiments, the message portion is transmitted by a wireless terminal (e.g., 106) to a network management system (e.g., 110). Message portion 1000 includes a wireless terminal identifier field 1005, a speed / direction field 1020, an accuracy field 1025, and a signal strength measurement digital segment 1030. A variable number of field pairs, such as field pair 1034a and field pair 1034b, follow the signal strength measurement digital segment 1030. Each field pair includes a signal strength field (e.g., 10351, ..., 1035...). n ) and the transmitter ID field (e.g., transmitter ID field 10371, ..., transmitter ID field 1037) n ).

[0131] The wireless terminal identifier field 1005 (e.g., via the station address of the wireless terminal) uniquely identifies the wireless terminal (e.g., 106). The speed / direction field 1020 indicates the speed and direction of the wireless terminal identified via the wireless terminal identifier field 1005 (e.g., V). x V y V z Accuracy field 1025 indicates the variability or accuracy of the velocity / direction information included in field 1020. In some embodiments, the wireless terminal is configured with parameters defining accuracy field 1025. In some embodiments, the wireless terminal obtains the accuracy information stored in accuracy field 1025 via hard-coded values ​​(e.g., values ​​hard-coded and obtained from a built-in accelerometer). In some embodiments, accuracy field 1025 indicates values ​​for the probability distribution used to generate motion values, as described herein with respect to motion probability surfaces and, for example, below. Figure 15 Discussed.

[0132] Alternatively, the speed / direction field provides an indication of the mobile device's acceleration along the X, Y, and Z axes, obtained from its internal accelerometers, such as gyroscopes.

[0133] Figure 11 Example data structures implemented in one or more of the disclosed embodiments are shown. Although the data structures are discussed as relational database tables, those skilled in the art will understand that the disclosed embodiments may use a variety of different data structure types, including traditional in-memory data structures (such as linked lists, arrays, graphs, trees), or unstructured data structures, hierarchical data stores, object-oriented data stores, serialized data stores, or any other data structure architecture.

[0134] Figure 11A wireless transmitter table 1102, a location probability surface table 1112, a motion probability surface table 1122, a signal table 1142, and a surface mapping table 1152 are shown. The wireless transmitter table 1102 indicates the location of known wireless transmitters. The wireless transmitter table 1102 includes a transmitter identifier field 1104, a transmit power field 1106, and a transmitter location field 1108. The transmitter identifier field 1104 uniquely identifies the wireless transmitter. The transmit power field 1106 indicates the power level used by the transmitter (identified via field 1104). At least some disclosed embodiments use the transmit power information included in the transmit power field 1106 to determine the expected signal strength of the wireless transmitter in different areas at different distances from the wireless transmitter. The transmitter location field 1108 indicates the geographical location of the wireless transmitter (e.g., latitude, longitude, and altitude, e.g., above ground level, value). At least some of the disclosed embodiments use transmitter location information included in transmitter location field 1108 to calculate the distance between the wireless transmitter and different areas (e.g., 114a-c) within a geographic area (e.g., 112).

[0135] The location probability surface table 1112 includes a surface identifier field 1114, a surface type field 1115, a grid / cell identifier field 1116, a location / region coordinate field 1118, and a probability field 1119. The surface identifier field 1114 uniquely identifies the surface. The identified surface can be a location probability surface (based on signal strength information from a single wireless transmitter), a synthetic location probability surface (based on multiple location probability surfaces), a predicted location probability surface, or a mixed location probability surface. The surface type field 1115 indicates the type of surface. For example, in various embodiments, the surface type field 1115 indicates whether the surface (identified via field 1114) is a location probability surface, a predicted location probability surface, a mixed location probability surface, or a synthetic location probability surface. The grid / cell identifier field 1116 uniquely identifies the cells / grids or regions included in the surface. For example, in some embodiments, field 1116 identifies... Figure 1One of the regions 114a-c is shown. The location / region coordinate field 1118 defines the boundary of the region corresponding to the grid or cell identified in field 1116. For example, in some embodiments, field 1118 defines the coordinates of the center of a geographic region (e.g., any of 114a-c). The probability field 1119 stores the probability value of a wireless terminal being located within this region (represented by the grid / cell identified via the grid / cell identifier field 1116). Note that each row of the example location probability surface table 1112 represents a single cell in the probability surface. Therefore, a surface typically comprising multiple cells is represented by the location probability surface table 1112 via multiple rows, each row having an equal surface identifier field 1114 value but a different grid / cell ID field 1116 value. Other methods of representing probability surfaces are contemplated in the disclosed embodiments, and these other methods are not limited to those described in the original text. Figure 11 The provided example represents a limitation.

[0136] Motion probability surface table 1122 includes a motion probability surface identifier field 1124, a motion estimation identifier field 1126, a motion estimation field 1128, and a probability field 1129. The motion probability surface identifier field 1124 uniquely identifies the motion probability surface. For example, in some embodiments, the motion probability surface of a particular wireless terminal is defined at each time reference (such as...). Figure 3 Generated at the time reference T0-T4 shown. In some embodiments, the motion probability surface ID 1124 is equivalent to a unique wireless terminal ID, such as... Figure 10 WT ID 1005. Some embodiments generate motion probability surfaces for multiple different wireless terminals. Each of these different motion probability surfaces is distinguished and identified via a motion probability surface identifier field 1124, which identifies a specific wireless terminal associated with the aforementioned motion probability surface. A motion estimation identifier field 1126 uniquely identifies a specific unit or motion estimate / probability included in the motion probability surface. A motion estimation field 1128 indicates a possible motion estimate of the wireless terminal at a specific time. In some embodiments, the motion estimation field 1128 is transmitted via V x V y and V zThe value is used to represent motion. Other embodiments use other parameters to represent motion. The probability field 1129 stores the probability that the wireless terminal exhibits motion at a specific time corresponding to the motion specified by the motion estimation field 1128. Note that each row of the motion probability surface table 1122 represents a single cell in the motion probability surface. Therefore, a motion probability surface that typically includes multiple cells is represented by the motion probability surface table 1122 via multiple rows, each row having the same surface identifier field 1124 value but a different motion value ID field 1126. Other methods of representing motion probability surfaces are contemplated in the disclosed embodiments, and these other methods are not limited by the values ​​specified in the table. Figure 11 The provided example represents a limitation.

[0137] Signal table 1142 includes a measurement identifier field 1144, a transmitter identifier field 1145, a wireless terminal identifier field 1146, a measurement field 1147, and a measurement time field 1148. The measurement identifier field 1144 uniquely identifies a specific signal measurement. The transmitter identifier field 1145 identifies the wireless transmitter that generated the signal. The wireless terminal identifier field 1146 identifies the wireless terminal that measured the signal. The measurement field 1147 stores the signal value measured by the wireless terminal (identified via 1146) (generated by the transmitter identified via the transmitter identifier field 1145). The measurement time field 1148 identifies the time when the network management system (e.g., 110) performs or receives the measurement.

[0138] Surface mapping table 1152 includes a surface identifier field 1154 and a wireless transmitter identifier field 1156. The surface identifier field 1154 uniquely identifies a probabilistic surface. In some embodiments, the surface identifier field 1154 is cross-referenced with the surface identifier field 1114. The wireless transmitter identifier field 1156 identifies a wireless transmitter. In some embodiments, the wireless transmitter identifier field 1156 identifies a wireless transmitter that measures signals from its signal to generate the surface identified by the surface identifier field 1154.

[0139] Figure 12 This is a flowchart of a process for estimating the location of a wireless terminal. In some embodiments, the following describes... Figure 12 One or more functions discussed in process 1200 are executed by hardware processing circuitry. For example, in some embodiments, they are stored in electronic memory (e.g., as described below). Figure 17 The instructions in 1704 and / or 1706 (discussed below) (e.g., regarding Figure 17 Discussion 1724) will involve hardware processing circuitry (e.g., the following regarding...). Figure 17 The configuration (1702) discussed below is configured to perform one or more of the functions discussed below.

[0140] After initiating operation 1205, process 1200 moves to operation 1210, in which a signal strength value is received. In some embodiments, the signal strength value is represented via a Received Signal Strength Indication (RSSI). Signal strength represents the strength of a signal originating from a wireless transmitter received by the wireless terminal. Signal strength is measured by the wireless terminal (e.g., 106). For example, as described above regarding... Figure 1 As discussed, wireless terminal 106 receives signals from one or more wireless transmitters, such as one or more access points 102a-d. In some embodiments, signal strength values ​​are received by network management system 110 from the wireless terminal (e.g., 106). In some embodiments, the signal strength values ​​are received in a message. In some embodiments, the message is received indirectly from the wireless terminal via access points (e.g., 102a-d).

[0141] In operation 1215, the signal strength error of multiple areas within a geographic region is determined. For example, as mentioned above... Figure 1 The geographical area discussed is divided into multiple zones (e.g., 114a-c). The expected signal strength for each zone is determined based on the distance between the wireless transmitter and the center point or other representative point of the corresponding zone. In some embodiments, the expected signal strength is also based on the transmit power of the wireless transmitter. For example, in some embodiments, the network management system 110 receives transmit power information from one or more access points 102a-d. In some embodiments, the network management system (e.g., 110) controls the transmit power of the AP. The transmit power information is then used to determine the expected signal strength for each zone based on the distance between the wireless transmitter and the zone. Operation 1215 then correlates the received signal strength value of operation 1210 with the expected signal strength to determine the signal strength error for that zone. For example, in some embodiments, the difference between the received signal strength and the expected signal strength for that zone is determined. In some embodiments, this difference is the signal strength error. In some embodiments, the absolute value of this difference is the signal strength error. In some embodiments, the error for each zone is determined according to Equation 2 discussed above. Therefore, when determining the probability that a wireless terminal is located in each region corresponding to a cell or grid position on a location probability surface, each probability on the location probability surface is based on the corresponding difference between the signal strength measurement on which the surface is based and the expected signal strength of the wireless terminal when it is located in the region corresponding to the corresponding probability.

[0142] In operation 1220, a probability surface is generated based on the error. The probability surface defines the correspondence between each region (e.g., any of 114a-c) and the probability that a wireless terminal is located in that region. Therefore, the probability surface defines the probability of each region on the surface. In some embodiments, this probability is inversely proportional to the error of the region. Thus, a region with low error is more likely to represent the location of the wireless terminal than a region with high error. Some embodiments use Gaussian estimation to generate the probability of a region based on its error.

[0143] Decision operation 1225 determines whether additional signal measurements are available. For example, additional signal measurements may be performed on signals generated by other wireless transmitters (e.g., different wireless transmitters for each iteration of operations 1210, 1215, and 1220). If additional signal measurements are available, process 1200 returns to operation 1210 and generates an additional location probability surface. Otherwise, process 1200 moves from decision operation 1225 to operation 1230, which aggregates the location probability surfaces generated in the iterations of operations 1210, 1215, and 1220. For example, some embodiments aggregate corresponding probabilities in a first location probability surface and a second location probability surface. In some embodiments, aggregating location probability surfaces includes multiplying the probabilities in corresponding cells or grids of each surface (e.g., probabilities associated with the same region, grid, or cell of different probability surfaces).

[0144] In operation 1235, the synthetic position probability surface is determined based on aggregation. In other words, the synthetic position probability surface consists of the aggregated probabilities from operation 1230. Therefore, the corresponding cells of the position probability surface are aggregated, and the resulting values ​​(probabilities) are stored in the corresponding cells of the synthetic position probability surface.

[0145] In operation 1240, the predicted location probability surface is obtained. The following is about... Figure 14 This further explains the generation of the probability surface for predicted locations. As mentioned above... Figure 3 The discussion focuses on generating a predicted location probability surface based on a motion probability surface (or motion estimation of WT) and one or more hybrid location probability surfaces from a previous time period or time base. Equation 6 provides an example embodiment of how to generate a predicted location probability surface.

[0146] As described above, a motion probability surface defines possible motion directions and amplitudes, and the probability that a wireless terminal has each defined direction and amplitude at a given time. Therefore, each cell or grid of the motion probability surface indicates a velocity direction and amplitude (e.g., Vx, Vy, Vz), and the probability that the wireless terminal moves with that velocity amplitude and direction within a relevant time period. Motion probability surfaces have been discussed in this disclosure. While some embodiments use motion probability surfaces to represent possible motion values, other data structures are used in other embodiments. For example, in some embodiments, multidimensional arrays are used, where the probability and motion value are defined by each “row” of a multidimensional (e.g., column) array.

[0147] In operation 1245, a mixed position probability surface is determined. The mixed position probability surface is based on the synthetic position probability surface determined in operation 1235. As described above, for example, regarding... Figure 3 The hybrid location probability surface discussed is also based on the predicted location probability surface. The following section discusses... Figure 14 This paper discusses one embodiment of generating a predicted location probability surface based on a motion probability surface. Other methods are also within the scope of the disclosed embodiments.

[0148] In operation 1250, the location of the wireless terminal is determined based on a mixed location probability surface. For example, in some embodiments, as described above, the region with the highest indicated probability associated with it in the mixed location probability surface is used as the estimated location. If multiple regions have the same highest probability, the region aggregated from the two regions can be used as the estimated location. In some embodiments, the regions represented by the mixed location probability surface are ranked according to their associated probabilities. A predetermined number of regions or a predetermined percentage of regions with the highest probability are identified. The identified regions are then used to determine the location estimate, while other regions are excluded from the location estimate. In other embodiments, a weighted average of consecutive regions is used to estimate the location of the wireless terminal. After estimating the location of the wireless terminal, process 1200 moves to end box 1255.

[0149] Figure 13This is a flowchart of the process for determining motion estimates for a wireless terminal. Motion estimates define multiple possible sets of different motion parameters for the wireless terminal. For example, in some embodiments, each set of motion parameters indicates the orientation and size of the wireless terminal (e.g., via Vx, Vy, and Vz values). Associated with each motion estimate is the probability that the wireless terminal will exhibit motion based on the motion estimate. In some embodiments, multiple motion estimates and probabilities of the wireless terminal at a given time are represented as a motion probability surface. A motion probability surface typically defines the possible motions of the wireless terminal at a given time. For example, if a first mixed position probability surface and a second mixed position probability surface are determined for a first time period and a second time period, respectively, the motion probability surface can determine the possible motions of the wireless terminal during the time spanning the first and second time periods to help explain how the wireless terminal moves between the position estimate provided by the first mixed probability surface and the second position estimate provided by the second mixed position probability surface.

[0150] In some embodiments, the following about Figure 13 One or more functions discussed in process 1300 are executed by hardware processing circuitry. For example, in some embodiments, they are stored in electronic memory (e.g., as described below). Figure 17 The instructions in 1704 and / or 1706 (discussed below) (e.g., regarding Figure 17 Discussion 1724) will involve hardware processing circuitry (e.g., the following regarding...). Figure 17 The configuration (1702) discussed below is configured to perform one or more of the functions discussed below.

[0151] After initiating operation 1305, in operation 1310, one or more mixed location probability surfaces of the wireless terminal are determined. For example, as mentioned above regarding... Figure 3 The discussion focuses on providing hybrid probability surfaces 308a and 308b as input when generating motion estimates and probabilities on motion probability surface 306c or in another form. (The above refers to...) Figure 12 Operation 1245 describes an example of determining the probability surface of mixed locations.

[0152] In operation 1325, motion information is received from the wireless terminal. In various embodiments, the motion information indicates one or more of the wireless terminal's velocity, acceleration information, and orientation. In at least some embodiments, the motion information indicates the wireless terminal's acceleration in three dimensions. In some embodiments, the motion information also indicates the variability or accuracy of the orientation, velocity, and / or acceleration information.

[0153] In operation 1330, motion estimates are generated based on the received motion information and one or more mixed position probability surfaces. In some embodiments, the motion estimates determine velocities in two or three dimensions based on position estimates derived from each mixed position probability surface (e.g., the highest probability region in each surface, or other methods described above). These velocity estimates are then enhanced by integrating acceleration information over the time period covered by the velocity estimates. In some embodiments, acceleration information received from the wireless terminal (e.g., via operation 1325) is integrated over half of the applicable time period and added to the velocity estimates derived from the mixed probability surfaces. Given the accelerometer in the wireless terminal is prone to drift, enhancing the velocity estimates provided by the mixed position probability surfaces by integrating acceleration over a single time period prevents the accumulation of errors that might otherwise occur if acceleration is integrated over multiple time periods.

[0154] As described above, in some embodiments, the motion estimates generated in operation 1330, and their associated probabilities, are represented as a motion probability surface. The following section discusses... Figure 15 Let's discuss one embodiment of operation 1330 in conjunction with process 1500. In operation 1335, the probability of each motion estimate is determined. In some embodiments, operation 1335 makes the probabilities of the motion estimates conform to a predetermined distribution, such as a Gaussian distribution. After operation 1335 is completed, process 1300 moves to end operation 1340.

[0155] Figure 14 This is a flowchart of the process used to generate a surface predicting location probabilities. (As mentioned above...) Figure 3 The discussion focuses on generating a predicted location probability surface at a first time reference (or time period) and predicting the probability surface of wireless terminals at future time references (or time periods). For example, Figure 14 The diagram shows a mixed position probability surface for time T1. It also includes a motion probability surface referencing time T1 and a predicted position probability surface referencing time T2. These various time bases are associated with these diverse surfaces and the above... Figure 3 Consistent with the explanation, Figure 3 A hybrid position probability surface 308c from time reference T2 and a motion estimate 306c from time reference T2 (represented as a motion probability surface in at least some embodiments) are shown for generating the predicted position probability surface 307d of time reference T3.

[0156] In some embodiments, the following about Figure 14 One or more functions discussed in process 1400 are executed by hardware processing circuitry. For example, in some embodiments, they are stored in electronic memory (e.g., as described below). Figure 17The instructions in 1704 and / or 1706 (discussed below) (e.g., regarding Figure 17 Discussion 1724) will involve hardware processing circuitry (e.g., the following regarding...). Figure 17 The configuration (1702) discussed below is configured to perform one or more of the functions discussed below.

[0157] After starting operation 1405, process 1400 moves to operation 1410, where operation 1410 selects a region or location cell on the mixed location probability surface. A mixed location probability surface is generated at time T=1.

[0158] In operation 1415, a cell in the motion probability surface is selected. The motion probability surface is used for time T=1. In operation 1420, the probability defined by the selected motion cell is obtained. A motion estimate of the wireless terminal defined by the cell is also obtained. In various embodiments, the motion estimate defines the velocity (e.g., V) in one or more of the x, y, and z directions. x V y V z ).

[0159] In operation 1425, a new location is determined based on the selected location unit and motion information. For example, operation 1425 determines the new location or resulting region by determining which region the wireless terminal would be located in if it exhibited motion defined by motion estimation within a specific time interval. In some embodiments, this time interval is the elapsed time between two sequentially determined mixed location probability surfaces (e.g., the elapsed time between t=1 and t=2).

[0160] In operation 1430, the probability that the wireless terminal moves to a new location unit is determined. This probability is based on two probabilities. The first probability is the probability associated with the selected location unit (e.g., from operation 1410). The second probability is associated with the selected unit on the motion probability surface (e.g., via operation 1415). These two probabilities are aggregated (e.g., multiplied in some embodiments) to obtain the probability that the wireless terminal in the selected location exhibits motion and moves to the new location. In operation 1435, the resulting probability is associated with the unit corresponding to the new location.

[0161] Decision operation 1440 determines whether the wireless terminal has an additional motion estimate (e.g., a cell in the motion probability surface). If it does, the process returns to operation 1415 and selects a different motion estimate (e.g., a cell in the motion probability surface). Otherwise, process 1400 moves from decision operation 1440 to decision operation 1445.

[0162] Decision operation 1445 determines whether there are additional regions or cells to process in the mixed location probability surface. If not, process 1400 moves to operation 1450. Otherwise, processing returns to operation 1410, and a different region or cell is selected.

[0163] In operation 1450, the corresponding probabilities determined by operations 1430 / 1435 are aggregated. In other words, probabilities associated with the same region or cell in the new predicted location probability surface are aggregated (e.g., added). In other words, if two or more motion estimates included in the motion probability surface have equivalent result regions, the probabilities associated with these motion estimates are aggregated and assigned to the corresponding regions in the predicted location probability surface. Therefore, the predicted location probability surface indicates the probability that the wireless terminal will be located in a specific region based on the aggregated probability of that region. The new predicted location probability surface corresponds to the position of WT at time T2 (e.g., a time period after the mixed location probability surface of operation 1410 and a time period after the motion estimate or motion probability surface of operation 1415). After operation 1450, process 1400 moves to end operation 1455.

[0164] Figure 15 This is a flowchart of a process for determining multiple different motion estimates of a wireless terminal. In some embodiments, these motion estimates are represented as a motion probability surface. In other embodiments, the motion estimates of the wireless terminal are represented as a set of data values ​​that define the motion estimates and the probability that the WT will move at a specific speed and direction, for example, using an array or other data structure configured to store multiple data values. In some embodiments, the following describes... Figure 15 One or more functions discussed in process 1500 are executed by hardware processing circuitry. For example, in some embodiments, they are stored in electronic memory (e.g., as described below). Figure 17 The instructions in 1704 and / or 1706 (discussed below) (e.g., regarding Figure 17 Discussion 1724) will involve hardware processing circuitry (e.g., the following regarding...). Figure 17 The configuration (1702) discussed below is configured to perform one or more of the functions discussed below.

[0165] After starting operation 1505, processing 1500 proceeds to operation 1510. In operation 1510, probability distribution parameters for the motion of the wireless terminal are determined. In some aspects, the probability distribution parameters are determined based on variability information in the motion of the wireless terminal. For example, in some aspects, the probability distribution parameters are based on the accuracy field 1025. In some aspects, the probability distribution parameters define the type of probability distribution. For example, in some embodiments, the probability distribution parameters define whether the probability distribution is a Gaussian distribution, a Cauchy distribution, a Behrens-Fischer distribution, a Laplace distribution, or any other type of probability distribution.

[0166] In operation 1515, boundary parameters of the motion are determined. For example, in some embodiments, an average motion value or a mean motion value is determined. In some embodiments, constraints on the motion are determined. For example, some embodiments of operation 1515 determine motion values ​​at the lower and higher percentiles of a limiting distribution (e.g., motion estimation field 1128). In some embodiments, the motion estimation is constrained based on the lower and higher percentiles. In some embodiments, the constraint is based on a multiple of the standard deviation of the motion values. For example, some embodiments constrain the generated motion estimate to no more than five standard deviations from the average motion estimate.

[0167] In operation 1520, a motion estimate is generated (e.g., stored in motion estimate field 1128). In some embodiments, the motion estimate is generated based on determined motion boundaries and distribution parameters. In some embodiments, the motion estimate is generated based on one or more mixed location probability surfaces, as described above. Figure 3 The aforementioned (e.g., hybrid position probability surfaces 308a and 308b) are used to generate motion estimate 306c (represented as a motion probability surface in some embodiments)). This point is also referenced above regarding operation 1310. In some embodiments, the motion estimate is generated based on acceleration information provided by the wireless terminal (e.g., as described above regarding operation 1325).

[0168] In operation 1525, motion values ​​are correlated with motion probabilities based on distribution parameters. For example, in a normal or Gaussian distribution, values ​​closer to the mean are more numerous than those farther from the mean. Therefore, operation 1525 correlates motion values ​​with their probabilities of occurrence to construct a probability distribution of motion values ​​based on motion parameters (variance, boundary, median, or mean) and distribution parameters. Thus, the result of operation 1525 is a motion estimate and its associated motion probabilities. Therefore, the motion estimate included in the motion probability surface has associated motion probabilities.

[0169] Decision operation 1530 determines whether more motion values ​​are needed to complete the allocation. If more values ​​are needed, process 1500 returns to operation 1520. Otherwise, process 1500 moves from decision operation 1530 to end operation 1535.

[0170] Figure 16 This is a flowchart of a process for determining possible hybrid surfaces for a wireless terminal based on predicted location probability surfaces and synthetic location probability surfaces. For example, the following describes... Figure 16 The process 1600 discussed represents an example embodiment of how to generate a hybrid position probability surface 308d based on the predicted position probability surface 307d and the synthetic position probability surface 304d.

[0171] In some embodiments, the following about Figure 16 One or more functions discussed in process 1600 are executed by hardware processing circuitry. For example, in some embodiments, they are stored in electronic memory (e.g., as described below). Figure 17 The instructions in 1704 and / or 1706 (discussed below) (e.g., regarding Figure 17 Discussion 1724) will involve hardware processing circuitry (e.g., the following regarding...). Figure 17 The configuration (1702) discussed below is configured to perform one or more of the functions discussed below.

[0172] After starting operation 1605, process 1600 proceeds to operation 1610. In operation 1610, a synthetic location probability surface is acquired. For example, in some embodiments, operation 1610 includes the above-mentioned... Figure 12 The discussion covers operations 1210-1235.

[0173] Operation 1615 obtains the predicted location probability surface. In some embodiments, operation 1615 is based on the above regarding... Figure 14 The process 1400 discussed is used to obtain the predicted location probability surface. For example, in some embodiments, the predicted location probability surface at time T is based on one or more hybrid location probability surfaces at times T-1, T-2, etc., and a motion surface generated at time T-1 or a motion estimate of the mobile terminal at time T-1.

[0174] Operation 1620 generates a hybrid location probability surface for time T based on the predicted location probability surface and the synthetic location probability surface. In some embodiments, operation 1620 averages corresponding cells or regions of each of the synthetic location probability surface and the predicted location probability surface to generate corresponding cells or regions of the hybrid location probability surface. In this case, corresponding cells are cells representing equivalent geographical areas. In some embodiments, a weighted average is used to generate the hybrid location probability surface, wherein the probabilities indicated by the synthetic location probability surface are assigned a first weight and the probabilities indicated by the predicted location probability surface are assigned a different second weight. After operation 1620 is completed, process 1600 moves to end operation 1635.

[0175] Figure 17 A block diagram of an example machine 1700 on which any one or more of the techniques (e.g., methods) discussed herein may be performed. The machine 1700 (e.g., a computer system) may include a hardware processor 1702 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), main memory 1704, and static memory 1706, some or all of which may communicate with each other via interconnect links 1708 (e.g., a bus).

[0176] Specific examples of main memory 1704 include random access memory (RAM) and semiconductor memory devices, which in some embodiments may include storage locations in semiconductors such as registers. Specific examples of static memory 1706 include non-volatile memory, such as semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) and flash memory devices; disks, such as internal hard disks and removable disks; magneto-optical disks; RAM; and CD-ROM and DVD-ROM disks.

[0177] Machine 1700 may also include a display device 1710, an input device 1712 (e.g., a keyboard), and a user interface (UI) navigation device 1714 (e.g., a mouse). In one example, the display device 1710, input device 1712, and UI navigation device 1714 may be a touchscreen display. Machine 1700 may also include a mass storage device (e.g., a drive unit) 1716, a beacon signal generation device 1718, a network interface device 1720, and one or more sensors 1721, such as a Global Positioning System (GPS) sensor, a compass, an accelerometer, or other sensors. Machine 1700 may include an output controller 1728, such as serial (e.g., Universal Serial Bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connections, to communicate with or control one or more peripheral devices (e.g., a printer, a card reader, etc.). In some embodiments, hardware processor 1702 and / or instruction set 1724 may include processing circuitry and / or transceiver circuitry.

[0178] Mass storage device 1716 may include machine-readable medium 1722 on which one or more sets of data structures or instructions 1724 (e.g., software) embodied or utilized by any one or more technologies or functions described herein are stored. In at least some embodiments, machine-readable medium 1722 is a non-transitory computer-readable storage medium. Instructions 1724 may also reside wholly or at least partially in main memory 1704, static memory 1706, or hardware processor 1702 during execution by machine 1700. In one example, one or any combination of hardware processor 1702, main memory 1704, static memory 1706, or mass storage device 1716 may constitute a machine-readable medium.

[0179] Specific examples of machine-readable media may include: non-volatile memory, such as semiconductor memory devices (e.g., EPROM or EEPROM) and flash memory devices; disks, such as internal hard disks and removable disks; magneto-optical disks; RAM; and CD-ROM and DVD-ROM disks.

[0180] Although machine-readable medium 1722 is shown as a single medium, the term "machine-readable medium" can include a single medium or multiple media (e.g., a centralized or distributed database and / or associated caches and servers) configured to store instructions 1724.

[0181] The means of machine 1700 may be one or more of the following: a hardware processor 1702 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), and one or more hardware memories (including one or more of main memory 1704 and static memory 1706). In some embodiments, the means of machine 1700 may also include one or more sensors 1721, a network interface device 1720, one or more antennas 1760, a display device 1710, an input device 1712, a UI navigation device 1714, a mass storage device 1716, an instruction set 1724, a beacon signal generation device 1718, and an output controller 1728. The means may be configured to perform one or more methods and / or operations disclosed herein. The means may be intended as a component of machine 1700 to perform one or more methods and / or operations disclosed herein, and / or as part of one or more methods and / or operations disclosed herein. In some embodiments, the means may include pins or other means for receiving power. In some embodiments, the means may include power regulation hardware.

[0182] The term "machine-readable medium" can include any medium capable of storing, encoding, or carrying instructions executable by machine 1700 and causing machine 1700 to perform any one or more of the techniques disclosed herein, or capable of storing, encoding, or carrying data structures used or associated with such instructions. Examples of non-limiting machine-readable media can include solid-state memory as well as optical and magnetic media. Specific examples of machine-readable media can include: non-volatile memory, such as semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable hard disks; magneto-optical disks; random access memory (RAM); and CD-ROM and DVD-ROM disks. In some examples, machine-readable media can include non-transitory machine-readable media. In some examples, machine-readable media can include machine-readable media that do not transiently propagate signals.

[0183] Instruction 1724 can also be transmitted or received via a communication network 1726 using a transmission medium through network interface device 1720 using any of a variety of transport protocols (e.g., Frame Relay, Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), etc.). Example communication networks may include local area networks (LANs), wide area networks (WANs), packet data networks (e.g., the Internet), mobile phone networks (e.g., cellular networks), conventional telephone (POTS) networks, and wireless data networks (e.g., those referred to as…). The Institute of Electrical and Electronics Engineers (IEEE) 802.11 series of standards, known as The standards include the IEEE 802.16 series, the IEEE 802.15.4 series, the Long Term Evolution (LTE) series, the Universal Mobile Telecommunications System (UMTS) series, and peer-to-peer (P2P) networks.

[0184] In one example, network interface device 1720 may include one or more physical jacks (e.g., Ethernet, coaxial, or telephone jacks) or one or more antennas for connection to communication network 1726. In one example, network interface device 1720 may include one or more antennas 1760 for wireless communication using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) technologies. In some examples, network interface device 1720 may use multi-user MIMO technology for wireless communication. The term "transmission medium" should be considered to include any intangible medium capable of storing, encoding, or carrying instructions for execution by machine 1700, and includes digital or analog communication signals or other intangible media to facilitate communication of such software.

[0185] As described herein, examples may include logically multiple components, modules, or mechanisms on which or can operate. A module is a tangible entity (e.g., hardware) capable of performing a specified operation and may be configured or arranged in a certain way. In one example, circuitry may be arranged as a module in a specified manner (e.g., internally or with respect to external entities such as other circuitry). In one example, all or part of one or more computer systems (e.g., standalone client or server computer systems) or one or more hardware processors may be configured by firmware or software (e.g., instructions, application portions, or applications) to perform a specified operation. In one example, the software may reside on a machine-readable medium. In one example, the software, when executed by the underlying hardware of the module, causes the hardware to perform the specified operation.

[0186] The techniques of various embodiments can be implemented using software, hardware, and / or a combination of software and hardware. Various embodiments relate to apparatuses such as management entities (e.g., network monitoring nodes), routers, gateways, switches, access points, DHCP servers, DNS servers, AAA servers, and user equipment devices (e.g., wireless nodes such as mobile wireless terminals, base stations, communication networks, and communication systems). Various embodiments also relate to methods for controlling and / or operating one or more communication devices, such as network management nodes, access points, wireless terminals (WTs), user equipment (UEs), base stations, control nodes, DHCP nodes, DNS servers, AAA nodes, mobility management entities (MMEs), networks, and / or communication systems. Various embodiments also relate to non-transitory machines (e.g., computers) and readable media (e.g., ROM, RAM, CDs, hard disks, etc.) including machine-readable instructions for controlling one or more steps of the machine-implemented method.

[0187] It should be understood that the specific order or hierarchy of steps in the disclosed process is provided as an example method. It should be understood that the specific order or hierarchy of steps in the process can be rearranged based on design preferences while remaining within the scope of this disclosure. The appended method claims present the elements of each step in an exemplary order and do not imply limitation to the specific order or hierarchy presented.

[0188] In various embodiments, the devices and nodes described herein are implemented using one or more modules to perform steps corresponding to one or more methods (e.g., signal generation, transmission, processing, analysis, and / or reception steps). Therefore, in some embodiments, various features are implemented using modules. Such modules can be implemented using software, hardware, or a combination of software and hardware. In some embodiments, each module is implemented as an individual circuit having a device or system including separate circuitry for implementing the functionality corresponding to each described module. Many of the above-described methods or method steps can be implemented using machine-executable instructions (such as software) included in a machine-readable medium (such as a memory device, e.g., RAM, floppy disk, etc.) to control a machine (e.g., a general-purpose computer with or without additional hardware) to implement all or part of the above-described methods, for example, in one or more nodes. Therefore, various embodiments particularly relate to a machine-readable medium, e.g., a non-transitory computer-readable medium, including machine-executable instructions for causing a machine (e.g., a processor and associated hardware) to perform one or more steps of the above-described methods(s). Some embodiments relate to a device including a processor configured to implement one, more, or all of the operations of the disclosed embodiments.

[0189] In some embodiments, one or more processors (e.g., CPUs) of one or more devices (e.g., communication devices, such as routers, switches, network-connected servers, network management nodes, wireless terminals (UEs), and / or access nodes) are configured to perform steps of methods described as being performed by the device. The processor configuration can be implemented by using one or more modules (e.g., software modules) to control the processor configuration and / or by including hardware (e.g., hardware modules) within the processor to perform the enumerated steps and / or control the processor configuration. Therefore, some, but not all, embodiments relate to communication devices, such as user equipment, having a processor that includes modules corresponding to each step of the various described methods performed by a device including the processor. In some, but not all, embodiments, the communication device includes modules corresponding to each step of the various described methods performed by a device including the processor. These modules can be implemented purely in hardware, such as as circuitry, or can be implemented using software and / or hardware or a combination of software and hardware.

[0190] Some embodiments relate to a computer program product comprising a computer-readable medium including code for inducing one or more computers to perform various functions, steps, actions, and / or operations (e.g., one or more of the steps described above). Depending on the embodiment, the computer program product may and sometimes does include different code for each step to be performed. Thus, the computer program product may and sometimes does include code for each individual step of a method, such as a method of operating a communication device (e.g., a network management node, access point, base station, wireless terminal, or node). This code may be in the form of machine (e.g., computer) executable instructions stored on a computer-readable medium such as RAM (random access memory), ROM (read-only memory), or other types of storage devices. In addition to relating to a computer program product, some embodiments also relate to a processor configured to implement one or more of the various functions, steps, actions, and / or operations of one or more of the methods described above. Thus, some embodiments relate to a processor (e.g., a CPU) configured to implement some or all of the steps of the methods described herein. This processor may be used in, for example, a communication device or other device described in this application.

[0191] Although described in the context of communication systems including wired, optical, cellular, Wi-Fi, Bluetooth, and BLE, at least some of the methods and apparatus of various embodiments are applicable to a variety of communication systems, including IP-based and non-IP-based, OFDM and non-OFDM, and / or non-cellular systems.

[0192] In view of the foregoing description, many other variations of the methods and apparatus of the various embodiments described above will be apparent to those skilled in the art. Such variations will be considered to be within the scope of this description. The methods and apparatus can be used, and in various embodiments, with IP- and non-IP-based, wired and wireless technologies such as CDMA, Orthogonal Frequency Division Multiplexing (OFDM), Wi-Fi, Bluetooth, BLE, optical, and / or various other types of communication technologies, which can be used to provide communication links between network-connected or associated devices or other devices (including receiver / transmitter circuitry and logic and / or routines) to implement these methods.

[0193] Example 1 is a system comprising: hardware processing circuitry; one or more hardware memories storing instructions that, when executed, configure the hardware processing circuitry to perform an operation comprising: receiving from a wireless terminal a signal strength measurement of a signal received by the wireless terminal, the signal being generated by a first wireless transmitter; for each of a plurality of regions, determining an expected signal strength of the signal from the wireless transmitter in the corresponding region; for each of the plurality of regions, determining a corresponding first difference between the expected signal strength in the corresponding region and the signal strength measurement; for each of the plurality of regions, determining a corresponding first probability that the wireless terminal is located in the corresponding region based on one of the first differences corresponding to the corresponding region; and estimating a first position of the wireless terminal at a first time based on the determined first probability.

[0194] In Example 2, the subject matter described in Example 1 may optionally include: each of the plurality of regions represents a two-dimensional geographic region or a three-dimensional geographic volume.

[0195] In Example 3, the subject matter of any one or more of Examples 1 to 2 may optionally include: the operation further comprising: receiving from the wireless terminal a second signal strength measurement of a second signal received by the wireless terminal and generated by the second wireless transmitter; for each of the plurality of regions, determining a corresponding second expected signal strength of the signal from the second wireless transmitter in the corresponding region; for each of the plurality of regions, determining a corresponding second difference between the second expected signal strength in the corresponding region and the second signal strength measurement; for each of the plurality of regions, determining a corresponding second probability that the wireless terminal is located in the corresponding region based on one of the second differences corresponding to the corresponding region, wherein the estimation of the first location of the wireless terminal is further based on the determined second probability.

[0196] In Example 4, any one or more of the topics described in Examples 2 to 3 may optionally include: the operation further includes: for each of the plurality of regions, aggregating the first probability and the second probability corresponding to the respective region, wherein the estimate of the first position is based on the aggregated first probability and the second probability.

[0197] In Example 5, the subject matter of any one or more of Examples 3 to 4 may optionally include: the operation further includes: receiving acceleration information from the wireless terminal; generating multiple motion estimates of the wireless terminal based on the acceleration information; generating associated motion probabilities for the multiple motion estimates; generating a predicted location probability surface, the generation of the predicted location probability surface including: for each of the multiple regions, generating a third probability that the wireless terminal is located in the corresponding region based on the multiple motion estimates and the associated motion probabilities, wherein the predicted location probability surface is generated to indicate each of the third probabilities.

[0198] In Example 6, the subject matter described in Example 5 may optionally include: wherein the generation of the third probability includes: for each motion estimate in the motion estimates, if the wireless terminal exhibits motion according to the corresponding motion estimate, determining the resulting region in which the wireless terminal is located; aggregating probabilities associated with motion estimates having equivalent resulting regions, wherein the third probability of a region is an aggregated probability of the region.

[0199] In Example 7, any one or more of the topics described in Examples 5 to 6 may optionally include: wherein the motion probability is generated according to a predetermined distribution.

[0200] In Example 8, any one or more of the topics described in Examples 4 to 7 may optionally include: the operation further includes generating a first mixed probability surface based on the predicted location probability surface and the aggregated first and second probabilities, wherein the estimate of the first location is based on the first mixed probability surface.

[0201] In Example 9, the subject matter described in Example 8 may optionally include: the operation further includes estimating the second location of the wireless terminal at a second time earlier than the first time, the estimation being based on a second mixed location probability surface, wherein the predicted location probability surface is based on the second mixed probability surface.

[0202] In Example 10, the subject matter described in Example 9 may optionally include: wherein the plurality of motion estimates are determined in the second time, and the predicted location probability surface indicates the probability that the wireless terminal is located in each of the plurality of regions in the first time.

[0203] In Example 11, any one or more of the topics described in Examples 8 to 10 may optionally include: the operation further includes determining a region with the highest probability in the first mixed probability surface, wherein the estimation of the first location estimates the location of the wireless terminal as a region associated with the highest probability indicated by the first mixed probability surface.

[0204] In Example 12, any one or more of the topics described in Examples 8 to 11 may optionally include: the operation further includes identifying a predetermined number of regions with the highest probability represented by the first mixed probability surface, wherein the estimate of the first location is based on a weighted average that is based on the identified regions and excludes other regions.

[0205] In Example 13, the subject matter described in Example 12 may optionally include: the operation further includes weighting each identified region based on the associated probability of each identified region, wherein the estimate of the first location is based on the weight of each identified region.

[0206] In Example 14, any one or more of the topics described in Examples 8 to 13 may optionally include: wherein the generation of the first mixed location probability surface includes averaging the corresponding probabilities in the predicted location probability surface and the aggregated first and second probabilities.

[0207] In Example 15, the subject matter described in Example 14 may optionally include: wherein the average of the corresponding probabilities is a weighted average.

[0208] In Example 16, any one or more of the topics described in Examples 9 to 15 may optionally include: the generation of the plurality of motion estimates is further based on a third mixed location probability surface; and the operation further includes estimating a third location of the wireless terminal at a time earlier than the second time, the estimation of the third location being based on the third mixed location probability surface.

[0209] Example 17 is a method for estimating the location of a wireless terminal, comprising: receiving an acceleration measurement representing the motion of the wireless terminal at a first time; estimating the location of the wireless terminal at the first time based on a first hybrid location probability surface; generating a predicted location probability surface of the wireless terminal based on the acceleration measurement and the first hybrid location probability surface, the predicted location probability surface indicating multiple probabilities that the wireless terminal is located in corresponding multiple geographic regions at a second time after the first time; aggregating multiple location probability surfaces, each location probability surface based on signal strength of different wireless transmitters at the wireless terminal, each location probability surface indicating multiple probabilities that the wireless terminal is located in the corresponding multiple geographic regions at the second time, the aggregation generating a composite location probability surface; generating a second hybrid location probability surface based on the predicted location probability surface and the composite location probability surface; and estimating a second location of the wireless terminal at the second time based on the second hybrid location probability surface.

[0210] In Example 18, the subject matter described in Example 17 may optionally include generating each of the plurality of location probability surfaces, including: generating a expected signal strength of a corresponding wireless transmitter in each of the plurality of regions; for each region, determining a difference between the expected signal strength in the corresponding region and the signal strength of the corresponding wireless transmitter at the wireless terminal; and for each region, generating a probability that the wireless terminal is located in the corresponding region based on the difference.

[0211] In Example 19, the subject matter of any one or more of Examples 17 to 18 may optionally include: wherein the estimation of the second position of the wireless terminal at the second time is based on a second mixed position probability surface, the method further comprising: estimating the third position of the wireless terminal at a time earlier than the first time based on a third mixed probability surface; generating a motion estimate and associated probability of the wireless terminal based on the third mixed probability surface and the acceleration measurement, wherein the generation of the predicted position probability surface is also based on the generated motion estimate and associated probability.

[0212] Example 20 is a non-transitory computer-readable storage medium including instructions that, when executed, configure hardware processing circuitry to perform operations for estimating the location of a wireless terminal, the operations including: receiving an acceleration measurement representing motion of the wireless terminal at a first time; estimating the location of the wireless terminal at the first time based on a first mixed location probability surface; generating a predicted location probability surface of the wireless terminal based on the acceleration measurement and the first mixed location probability surface, the predicted location probability surface indicating multiple probabilities that the wireless terminal is located in corresponding multiple geographic regions at a second time after the first time; aggregating multiple location probability surfaces, each location probability surface based on signal strength of different wireless transmitters at the wireless terminal, each location probability surface indicating multiple probabilities that the wireless terminal is located in the corresponding multiple geographic regions at the second time, the aggregation generating a composite location probability surface; generating a second mixed location probability surface based on the predicted location probability surface and the composite location probability surface; and estimating a second location of the wireless terminal at the second time based on the second mixed location probability surface.

[0213] Although the above discussion describes determining the location of a wireless terminal in two-dimensional space in some cases, the above features can be equivalently applied to locating a wireless terminal in three-dimensional space. Thus, in three-dimensional space, when considering multiple three-dimensional regions, some disclosed embodiments do not determine the location of the WT in a specific cell or region, but rather the location of the WT in a three-dimensional region.

Claims

1. A system comprising: Hardware processing circuitry; One or more hardware memories storing instructions that, when executed, configure the hardware processing circuitry to perform operations, the operations including: The signal strength measurement and motion information from the wireless terminal are received from the wireless terminal, the signal being generated by the first wireless transmitter. For each of the multiple regions, determine the expected signal strength of the signal from the wireless transmitter in the corresponding region; For each of the plurality of regions, a corresponding first difference is determined between the expected signal strength and the signal strength measurement in the corresponding region; For each of the plurality of regions, a first probability that the wireless terminal is located in the corresponding region is determined based on one of the first differences corresponding to the corresponding region; Based on the motion information, generate multiple motion estimates and corresponding probabilities for the wireless terminal; Generating a predicted location probability surface includes: for each of the plurality of regions, generating a composite probability that the wireless terminal is located in the corresponding region, the composite probability of each of the plurality of regions being based on (i) the corresponding first probability that the wireless terminal is located in the corresponding region and (ii) the plurality of motion estimates of the wireless terminal and the corresponding probability of motion; and The first location of the wireless terminal at the first moment is estimated based on the predicted location probability surface.

2. The system according to claim 1, wherein each of the plurality of regions represents a two-dimensional geographic region or a three-dimensional geographic volume.

3. The system according to claim 1, wherein the operation further includes: A second signal strength measurement is performed on the wireless terminal to receive a second signal that is received by the wireless terminal and generated by the second wireless transmitter. For each of the plurality of regions, a second expected signal strength corresponding to the signal from the second wireless transmitter in the respective region is determined; For each of the plurality of regions, a corresponding second difference is determined between the second expected signal strength and the second signal strength measurement in the corresponding region; as well as For each of the plurality of regions, a corresponding second probability that the wireless terminal is located in the corresponding region is determined based on one of the second differences corresponding to the corresponding region, wherein the estimation of the first location of the wireless terminal is further based on the determined corresponding second probability that the wireless terminal is located in the corresponding region for each of the plurality of regions.

4. The system according to claim 3, wherein the operation further includes: For each of the plurality of regions, the corresponding first probability and the corresponding second probability of the wireless terminal being located in the corresponding region are aggregated, wherein the estimation of the first location is based on the aggregation of the corresponding first probability and the corresponding second probability for each of the plurality of regions.

5. The system of claim 1, wherein generating the predicted location probability surface comprises: For each of the motion estimates, if the wireless terminal exhibits motion based on the corresponding motion estimate among the plurality of motion estimates, then the resulting region in which the wireless terminal is located is determined. as well as The probabilities of motions associated with motion estimates having equivalent result regions are aggregated, wherein the composite probability of regions within the plurality of regions is at least partially based on the aggregated probability of the motions corresponding to those regions.

6. The system of claim 1, wherein the probability of motion is generated according to a predetermined distribution.

7. The system according to claim 4, The operation further includes generating a first mixed probability surface based on the predicted location probability surface and the aggregation of the corresponding first probability and the corresponding second probability for each of the plurality of regions, and... The estimation of the first position is based on the first mixed probability surface.

8. The system according to claim 7, The operation further includes estimating the location of the wireless terminal at a second time earlier than the first time. The estimation is based on a second mixed probability surface, and The predicted location probability surface is at least partially based on the second hybrid probability surface.

9. The system according to claim 8, The multiple motion estimates are determined during the second time, and The predicted location probability surface indicates the probability that the wireless terminal is located in each of the plurality of regions at the first time.

10. The system according to any one of claims 7 to 9, The operation further includes determining a region with the highest probability, indicated by the first mixed probability surface, and The estimation of the first location includes estimating the first location of the wireless terminal as being determined to have the most probable region indicated by the first mixed probability surface.

11. The system according to any one of claims 7 to 9, The operation further includes identifying a predetermined number of regions among the plurality of regions that have the highest probability, as represented by the first mixed probability surface, and The estimation of the first location is based on a weighted average, which is based on the predetermined number of regions identified and excludes other regions from the plurality of regions.

12. The system according to claim 11, The operation further includes weighting each region in the identified regions based on the associated probability of the corresponding region, and The estimation of the first position is based on the weight of the region for each identifier.

13. The system according to any one of claims 7 to 9, wherein the generation of the first mixed probability surface comprises averaging the corresponding probabilities in the predicted location probability surface and the aggregation of the corresponding first probability and the corresponding second probability for each of the plurality of regions.

14. The system of claim 13, wherein the averaging of the corresponding probabilities is a weighted average.

15. The system according to claim 8, The generation of the plurality of motion estimates is further based on a third hybrid probability surface; and The operation further includes estimating the third position of the wireless terminal at a third time earlier than the second time, the estimation of the third position being based on the third mixed probability surface.

16. A method for estimating the location of a wireless terminal, comprising: The processing circuit receives signal strength measurements of signals received by the wireless terminal and motion information from the wireless terminal, the signals being generated by the first wireless transmitter; The processing circuit determines the expected signal strength of the signal from the wireless transmitter in each of the multiple regions in the corresponding region; For each of the plurality of regions, the processing circuit determines a corresponding first difference between the expected signal strength in the corresponding region and the signal strength measurement; The processing circuit determines, for each of the plurality of regions, a first probability that the wireless terminal is located in the corresponding region based on one of the first differences corresponding to the corresponding region; The processing circuit generates multiple motion estimates and corresponding probabilities of motion for the wireless terminal based on the motion information. The processing circuit generates a predicted location probability surface, including: for each of the plurality of regions, generating a composite probability that the wireless terminal is located in the corresponding region, the composite probability of each of the plurality of regions being based on (i) the corresponding first probability that the wireless terminal is located in the corresponding region and (ii) the plurality of motion estimates of the wireless terminal and the corresponding probability of motion; as well as The processing circuit estimates the first location of the wireless terminal at a first time based on the predicted location probability surface.

17. The method of claim 16, further comprising steps corresponding to the operations described in any one of claims 2-15.

18. A non-transitory computer-readable storage medium comprising instructions that, when executed, configure hardware processing circuitry to perform operations for estimating the location of a wireless terminal, the operations including: The signal strength measurement and motion information from the wireless terminal are received from the wireless terminal, the signal being generated by the first wireless transmitter. For each of the multiple regions, determine the expected signal strength of the signal from the wireless transmitter in the corresponding region; For each of the plurality of regions, a corresponding first difference is determined between the expected signal strength and the signal strength measurement in the corresponding region; For each of the plurality of regions, a first probability that the wireless terminal is located in the corresponding region is determined based on one of the first differences corresponding to the corresponding region; Based on the motion information, generate multiple motion estimates and corresponding probabilities for the wireless terminal; Generating a predicted location probability surface includes: for each of the plurality of regions, generating a composite probability that the wireless terminal is located in the corresponding region, wherein the composite probability of each of the plurality of regions is based on (i) the corresponding first probability that the wireless terminal is located in the corresponding region and (ii) the plurality of motion estimates and corresponding probabilities of motion of the wireless terminal; as well as The first location of the wireless terminal at the first moment is estimated based on the predicted location probability surface.

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