Patent ID: 12244361

PREFERRED MODES

First, an outline of an example embodiment of the present invention will be given with reference to the drawings. It should be noted that the drawing reference signs in the outline are given to each element for convenience as an example to facilitate understanding and are not intended to limit the present invention to the illustrated aspects. Further, connection lines between blocks in the drawings referred to in the following description can be both bidirectional and unidirectional. A unidirectional arrow schematically shows the main flow of a signal (data) and does not exclude bidirectionality. A program is executed via a computer apparatus, and the computer apparatus is equipped with, for example, a processor, a storage device, an input device, a communication interface, and a display device if necessary. The computer apparatus is also configured to be able to communicate with devices (including computers) inside or outside the apparatus via the communication interface, whether wired or wireless. Further, although the input/output connection points of each block in the drawings have ports or interfaces, these are not illustrated. Also, in the following explanation, “A and/or B” is used in the meaning of at least one of A and B.

As shown inFIG.1, the present invention in an exemplary embodiment thereof can be realized by a spatial data creating apparatus10comprising a receiving part11, a data classifying part12, a subspace data creating part13, and a subspace data evaluating part14.

Data for estimation and data for evaluation are input to the receiving part11, respectively. The data for estimation is data for creating spatial data in which a location in a target area and a value related to that location are associated. This data for estimation is data obtained from a first group of sensors located in the target area. On the other hand, data for evaluation is actual measurement data obtained from a second group of sensors installed at a different location from the location where the first group of sensors is installed in the target area.

The data classifying part12classifies the data for evaluation based on the difference between the data for evaluation and the value of the spatial data corresponding to the acquisition location of the data for evaluation. Upper black circle(s) in a right-side balloon inFIG.2indicates the data for evaluation, which is actual measured value(s) at a distance P from a certain AP (access point). The data classifying part12classifies the data for evaluation based on the difference between this evaluation data and the value of the spatial data described above that corresponds to the acquisition location of each data for evaluation. For example, in this embodiment, as shown in a lower broken line(s) in the right-side balloon ofFIG.2, the residual is focused on as a concrete example of the difference (difference). In this case, the data classifying part12classifies the data for evaluation into two groups, one in which the residuals are largely on the + (positive) side and the other in which the residuals are largely on the − (negative) side.

The subspace data creating part13creates subspace data that forms a part of the spatial data by using the data obtained from the first sensor group selected based on the acquisition location of the data for evaluation classified by the data classifying part among the data for estimation. For example, the subspace data creating part13selects the data for estimation that is close to the acquisition location of the data for evaluation, as shown in the upper part in the right-side balloon inFIG.3. Then, the subspace data creating part13creates the subspace data using the selected estimation data, as shown in the lower part of the right-side balloon inFIG.3. As a method for creating the subspace data, a method selected from among the kriging method, IDW method, etc. can be used based on objective in use and expected accuracy of the spatial data. Of course, the subspace data may be created using a method identical to the spatial data.

The subspace data evaluating part14decides whether or not to adopt the subspace data by comparing the subspace data with the data for evaluation. For example, the subspace data evaluating part14decides whether or not to adopt the subspace data by comparing the subspace data with the data for evaluation, as shown in the right-side balloon inFIG.4. In an example shown inFIG.4, the subspace data evaluating part14decides to adopt the subspace data because a good result is obtained as a result of comparison between the subspace data and the data for evaluation. Such subspace data can be used as spatial data to supplement inaccurate spatial data. Concretely, in estimating the data at a certain location in the target area, it is possible to obtain good estimates by using the subspace data in preference to the spatial data. The estimated values can also be used for location estimation and map creation.

As described above, it is possible to improve accuracy in the creation of spatial data from data for estimation.

First Example Embodiment

The following is a detailed description of a first example embodiment of the present invention applied to the creation of spatial data for quality evaluation of wireless networks, with reference to the drawings. First, definitions are given for the terms used in the following description.Spatial data: Data configured by a set of location information indicating a location in a certain space and feature value(s) at that location.Spatial data estimation: A process of creating spatial data by estimating the distribution of feature value(s) in space. For example, typical methods include, but are not limited to, spline interpolation and kriging. For example, the IDW method described in the background is also included.Data for estimation refers to data that maps the location of the first sensor in the target area sampled to perform the spatial data estimation described above to the feature value(s).Data for evaluation is data that maps feature value(s) measured by a second sensor located in the target area to the position of the second sensor. The evaluation data is used to classify the evaluation data and to adopt or reject subspace data.Classification means to divide an entire data into a number of groups. Classification by data-for-evaluation classifying part106refers to grouping the data for evaluation. Therefore, “performing spatial data estimation independently for each classification” means that spatial data estimation is performed using the data for estimation that belongs to each classified set.

FIG.5is a drawing illustrating a configuration of a spatial data creating apparatus of the first example embodiment of the present invention. Referring toFIG.5, a spatial data creating part105, a data-for-evaluation classifying part106, a subspace data creating part107, a subspace data evaluating part108, a spatial data output part109, and a storage apparatus110are illustrated.

The storage apparatus110functions as a data-for-estimation storing part101, a data-for-evaluation storing part102, a spatial data storing part103, and a primary storing part104of subspace data.

The data-for-estimation storing part101stores data for estimation (data-for-estimation) used for the spatial data estimation described above.

For example, the radio wave reception strength at a time radio waves transmitted from each of the APs1to10located in a target area inFIG.6are received at each of the AP1to AP10positions can be used as data for estimation. A first sensor can be installed at each of these AP1to AP10locations. Any one AP transmits radio waves as a transmitting station, and the other APs measure the received signal strength (RSSI) as the first sensor. This allows us to obtain data for estimation that is associated with the location(s) of the APs. InFIG.6, B is a barrier and P is a pillar.

The spatial data storing part103stores spatial data created by the spatial data creating part105using the above data for estimation. Spatial data stored in the spatial data storing part103is, for example, illustrated inFIG.22. By creating and synthesizing such spatial data for each of AP, it is possible to create a spatial data map for evaluating the radio wave quality of the target area.

The data-for-evaluation storing part102stores data for evaluation (data-for-evaluation). As the data for evaluation, an actual measured value of the received signal strength (RSSI) at any position of the radio wave transmitted from each of APs can be used.FIG.7illustrates an example of the arrangement of measurement points LN1to LN29of the data for evaluation in the target area. The measurement points LN1to LN29can be placed uniformly in the target area, but they can also be placed with emphasis in areas where highly accurate estimates are required. Radio waves transmitted by any one AP as a transmitting station are received by these LN1-LN29as a second sensor, and by measuring the received signal strength (RSSI) as a feature value(s), data for evaluation associated with the location(s) can be obtained.

The primary storing part104of subspace data performs grouping of the above measurement points LN1to LN29and stores subspace data created using estimated data for each group. While the spatial data described above represents estimated values for entire target area, this subspace data is estimated values for a part (or parts) of the target area (which does not have to be a physically contiguous area).

The spatial data creation part105creates spatial data using the spatial data estimation described above, and stores it in the spatial data storing part103.

The data-for-evaluation classifying part106functions as a data classifying part that classifies (groups) the data for evaluation based on the difference between data for evaluation and spatial data. For example, the data-for-evaluation classifying part106classifies data for evaluation based on the residual difference (may be termed as “residual”) between spatial data (seeFIG.22) stored in the spatial data storage part103and data for evaluation measured at the measurement points LN1to LN29. Here, the residual (dBm; decibel millimeter) is an estimated value of the spatial data value of the data for evaluation. Therefore, if the residual difference is a positive value, the larger the value, the worse the actual radio wave reception strength is at that location concerned than the estimated value. Conversely, when the residual difference is negative, the larger the value (absolute value), the better the actual radio wave reception strength is than the estimated value.

FIG.8is a drawing illustrating residual difference(s) of the spatial data of AP8and the RSSIs at LN1to LN29. The “LNxx/yy” in the figure indicates that the residual at any measurement point LNxx is “yy” (xx is a natural number from 1 to 29 in this system). In the case ofFIG.8, residual differences of 3 or more are detected at locations of circles indicated by broken lines. Specifically, LN5, LN6, LN11, and LN18indicate that actual signal condition is poor.

On the other hand, if the residual difference is negative and its value (absolute value) is large, it means that the actual radio wave situation is better than the estimated value. In the case ofFIG.9, locations with residual differences of −3 or less are detected in the broken-line circles. Concretely, LN20and LN23indicate that actual radio wave conditions are better than the estimated values.

For radio waves transmitted from other APs, the data-for-evaluation classifying part106calculates residual differences with spatial data in the same way as for AP8.FIG.10is a drawing illustrating spatial data of AP10and residual differences of RSSI in LN1to LN29. In case ofFIG.10, locations with residuals of 3 or more are detected in the broken-line circles. Similarly, inFIG.11, the broken-line circles indicate that residual differences of −3 or less are detected.

The data-for-evaluation classifying part106according to this example embodiment calculates spatial data of each AP as described above and average values of residual differences of RSSI in LN1to LN29, and classifies (groups) data for evaluation based on the results. For example, as shown inFIG.12, when spatial data of each AP and average values of residual differences of RSSI in LN1to LN29are calculated, the data-for-evaluation classifying part106classifies data for evaluation in which average values of residual differences are positive and large into the first group (see broken-line circles inFIG.12). Similarly, the data-for-evaluation classifying part106classifies the data for evaluation whose residual difference average value is negative and whose absolute value is large into the second group (see the solid circle inFIG.12).

For each of the classified groups, the subspace data creating part107selects the nearest estimation data from the measurement points of data-for-evaluation, and creates subspace data using that set of estimation data. The subspace data created by the subspace data creating part107is temporarily stored in a primary storing part of subspace data104.

The subspace data evaluating part108compares data-for-evaluation with subspace data, and calculates residual differences (residual differences of subspace data). Then, the subspace data evaluating part108compares total and average values of residual differences of subspace data with total and average values of residual differences between spatial data and evaluation data calculated by the data-for-evaluation classifying part106. As a result of the above comparison, if the residual differences of the subspace data are smaller, the subspace data evaluating part108adopts the subspace data. Furthermore, the subspace data evaluating part108saves the subspace data in the spatial data storing part103.

On the other hand, if residual differences of subspace data are larger, the subspace data evaluating part108determines that the subspace data is not adopted because accuracy of the subspace data is degraded more than that of the spatial data. The subspace data evaluating part108does not save the subspace data to the spatial data storing part103.

The spatial data output part109outputs the spatial data stored in the spatial data storing part103in a predetermined manner after judgment by the subspace data evaluating part108. This predetermined configuration can be a two-dimensional radio wave map as shown inFIG.22, a three-dimensional radio wave map, or various other configurations.

In addition, various data such as data for estimation and data-for-evaluation stored in the above-mentioned storage apparatus110are input from outside via input device(s) and communication interface(s), which are omitted in figures. Therefore, in this example embodiment, these input apparatus(es) and communication interface(s) function as input part.

Next, operations of this example embodiment will be described in detail with reference to drawings.FIG.13is a flow chart illustrating operations of the spatial data creating apparatus100of the first example embodiment of present invention. Referring toFIG.13, first, the spatial data creating apparatus100reads data for estimation from the data storing part101to create spatial data for a target area (step S000).

Next, the spatial data creating apparatus100saves created spatial data in the spatial data storing part103(Step S001).

Next, the spatial data creating apparatus100calculates residual differences between the created spatial data and evaluation data in the data-for-evaluation storing part102, and classifies the data-for-evaluation based on the values. For example, a group [of LNs] with an average residual difference value of 3 dB or more inFIG.12is classified as S1group. In this case, LN10, LN11, LN18, and LN19belong to the S1group. In the same way, a group [of LNs] with a residual difference average value of −3 dB or less inFIG.12is grouped together as S2group. In this case, LN9, LN16, LN20to LN23will belong to the S2group.

Furthermore, the spatial data creating apparatus100selects data for estimation that is measured at a location closest to a location of the classified data for evaluation. This completes the classification of the data for estimation (Step S002).FIG.14illustrates an example of selecting data for estimation for LN10, LN11, LN18, and LN19in S1group. The reason for selecting the nearest data for estimation resides in that it is assumed that the closer the distance, the more similar the environment is. An algorithm to search for such nearest neighbor is the k-nearest neighbor method. Of course, if information on installation location and environment of each of data for estimation and data for evaluation is available, data for estimation that is not the nearest neighbor but is similar in environment, etc., may be selected. For example, instead of selecting AP6, which is close to LN10inFIG.12, an algorithm may be used to select AP4, whose environment is similar to LN17and LN18on the point that it is outside the area enclosed by the shield B.

Next, the spatial data creating apparatus100performs estimation of spatial data independently per each classification using classified data for estimation. The spatial data creating apparatus100saves created subspace data in the primary storing part of subspace data104(Step S003).

Next, the spatial data creating apparatus100evaluates created subspace data (Step S004). The spatial data creating apparatus100calculates residual differences of data for evaluation and the spatial data created in step S000, and residual differences of data for evaluation and the subspace data created in step S003, respectively. If it is determined that residual difference between the data for evaluation and the subspace data created in step S003is smaller, the spatial data creating apparatus100determines that the subspace data is adopted (“Adopt subspace data” in step S004).

On the other hand, if it is determined that residual difference between the data for evaluation and the subspace data created in step S003is larger, the spatial data creating apparatus100determines that the subspace data is not adopted (“Not Adopt” in step S004).

If it is determined that the subspace data is to be adopted, the spatial data creating apparatus100stores the subspace data in the spatial data storing part103(Step S005).

The spatial data creating apparatus100outputs spatial data stored in the spatial data storing part103(Step S006). At this time, if subspace data is stored in the spatial data storing part103, the spatial data creating apparatus100outputs a part(s) of spatial data that overlaps with subspace data by replacing such part(s) with contents of the subspace data. As a result, the part(s) of the spatial data for which absolute value(s) of the residual differences is determined to be large in step S002is replaced with the subspace data to improve the accuracy.

FIG.15andFIG.16are a drawing illustrating effect of this example embodiment. Black dots on the left side of figure ofFIG.15represent data-for-evaluation. Regression line in the figure shows regression line obtained from data for estimation by an appropriate method. If RSSI (Received Signal Strength Indication) according to location p (distance from a predetermined origin) of each AP is estimated with this regression line, deviation from data-for-evaluation (estimated residual differences) will occur, as shown in the left figure ofFIG.15.

On the other hand, when the present invention is applied, as shown in the figure on the right side ofFIG.15, locations at which deviation from the data for evaluation (estimated residual differences) has occurred are divided (classified), and data for estimation is selected for each divided (classified) group to create subspace data. The New Regression lines on the right side of the figure inFIG.15represent regression lines obtained as a result of this creation of subspace data. For example, if residual difference average value of the S1group mentioned above is more than 3 dB, and if residual difference average value with the subspace data is less than 3 dB, the subspace data will be adopted. This makes it possible to suppress the deviation from the data for evaluation (estimated residual differences) for locations at which deviation from the data for evaluation (estimated residual differences) has occurred.

As a result of applying the present invention, as illustrated in the left figure ofFIG.16, there is a possible case where deviation from data for evaluation (estimated residual differences) may become larger in new regression line than in the original regression lines. This is due to the fact that acquisition location of the data for estimation, i.e., placement of first sensor, is not good or inappropriate data for estimation was selected accordingly, as shown in the right figure ofFIG.16. In this case, it is possible to deal with the case by not-adopting the subspace data in step S004ofFIG.13. Of course, instead of such measures, it is also possible to reclassify the data for evaluation or re-select the data for estimation corresponding to the-data-for evaluation by changing rules (algorithms) for selecting the data for estimation.

In the examples ofFIG.15andFIG.16above, spatial data and subspace data are represented by regression line(s), but the configuration of spatial data and subspace data is not limited to this, and can be a non-linear regression model. For example, given an explanatory variable such as location, various predictive models that can estimate RSSI as an objective variable can be employed.

In the above-described example embodiment, it is explained that total and average values of residual differences between subspace data and data for evaluation are used to select subspace data, but rule for selecting subspace data is not limited to this method. For example, a criterion (upper limit) may be set for the maximum absolute value of the residual differences between subspace data and data-for-evaluation, and a rule may be used to not adopt subspace data if the maximum absolute value of the residual differences exceeds the upper limit, even if the total and average values of the residual differences are suppressed.

In the above-described example embodiment, it is explained that the data-for-evaluation classifying part106classifies (groups) the data-for-evaluation based on residual differences between the data-for-evaluation and spatial data, but the method for classifying (grouping) data for evaluation is not limited to this. However, the method of classifying (grouping) evaluation data is not limited to this. For example, various methods can be adopted, such as classifying evaluation data based on the ratio between evaluation data and spatial data value(s).

Second Example Embodiment

Next, a second example embodiment will be described with reference to drawings, which is expected to improve accuracy further than the first example embodiment.FIG.17is a drawing illustrating configuration of a spatial data creating apparatus of the second example embodiment of the present invention. The difference from the first example embodiment illustrated inFIG.5is that a re-classification directing part120is added to recursively re-classify subspace data. Since the other configurations are common to the first example embodiment, we will focus on the differences below.

The re-classification directing part120operates when the subspace data is determined to be adopted by the subspace data evaluating part108a, and directs the data-for-evaluation classifying part106ato further classify (group) the data-for-evaluation (hereinafter referred to as “re-classification directing”).

Then, when creation of subspace data is completed after the re-classification direction, the data-for-evaluation classifying part106aperforms further classification (grouping) of the classified data for evaluation based on the difference between data for evaluation and subspace data.FIG.18is a drawing of the above reclassification process of data for evaluation. For example, when evaluation data and 1st Regression line are obtained as shown in the left figure ofFIG.18, the data-for-evaluation classifying part106are-classifies the data for evaluation based on residual differences between the both (see the right figure ofFIG.18).

For each of the groups after the re-classification, the subspace data creating part107selects the nearest estimation data from measurement locations of data for evaluation and re-create subspace data using a set of estimation data. The subspace data created by the subspace data creating part107is temporarily stored in the primary storing part104of subspace data.

The subspace data evaluating part108acompares data for evaluation after reclassification with the re-created subspace data and calculates the residual differences (residual differences of subspace data). Then, the subspace data evaluating part108acompares residual differences of evaluation data against the previously calculated subspace data and residual differences of subspace data and evaluation data re-created by the subspace data creating part107. If, as a result of the comparison above, the residual difference of the subspace data is smaller, the subspace data evaluating part108adetermines that the subspace data is to be adopted, and directs the re-classification directing part120to re-classify the subspace data. Furthermore, the subspace data evaluating part108astores the subspace data after re-classification in the spatial data storing part103. The comparison of the residual differences here is between residual differences with the 1st Regression line in the left figure ofFIG.18and residual differences with the 2nd Regression line in the right figure ofFIG.18.

On the other hand, if residual differences of the subspace data after reclassification are larger, the subspace data evaluating part108adetermines that no further re-classification is to be performed. The subspace data evaluating part108adoes not store the subspace data after reclassification in the spatial data storing part103.

The spatial data output part109aoutputs spatial data and subspace data stored in the spatial data storing part103after judgment by the subspace data evaluating part108a.

Next, operations of this example embodiment will be described in detail with reference to drawings.FIG.19is a flow chart representing operations of the spatial data creating apparatus100of the second example embodiment of the present invention. Since the operations of steps S000to S002, S004, and S006ofFIG.19are the same as those of the first example embodiment, the contents of steps S003, S005, and S007will be described below.

If it is determined that subspace data is to be adopted, the spatial data creating apparatus100stores subspace data in the spatial data storing part103(Step S005) and further performs re-classification of data for evaluation (Step S007). Here, it is assumed that the data is re-classified into four regions, as shown by broken lines in the right figure ofFIG.18.

For each of the four groups after the re-classification, the subspace data creating part107selects the nearest estimation data from measurement points of the data for evaluation and re-create the subspace data using the set of estimation data (Step S003). The four regression lines inFIG.18correspond to these four subspace data. The four subspace data created by the subspace data creating part107are temporarily stored in the primary storing part104of the subspace data.

The subspace data evaluating part108acompares data-for-evaluation after re-classification with the re-created subspace data described above, and calculates the residual differences (residual differences of subspace data). Then, the subspace data evaluating part108acompares the residual differences of the evaluation data against the previously calculated subspace data with residual differences between the subspace data recreated by the subspace data creating part107and the evaluation data (Step S004). If, as a result of the comparison, total and average values of the residual differences of the subspace data are smaller, the subspace data evaluating part108adetermines that the subspace data is to be adopted, and directs the re-classification directing part120to re-classify the subspace data. Furthermore, the subspace data evaluating part108astores the subspace data after reclassification in the spatial data storing part103.

On the other hand, if total and average values of residual differences of the subspace data after re-classification are larger, the subspace data evaluating part108adetermines that no further re-classification is to be performed (“Not Adopt” in step S004). The subspace data evaluating part108adoes not store the subspace data after re-classification in the spatial data storing part103.

As described above, classification of data for evaluation is performed recursively until it is determined that no further re-classification is necessary in the subspace data evaluating part108a.

Finally, the spatial data creating apparatus100outputs spatial data saved in the spatial data storing part103(Step S006). At this time, if spatial data and multiple subspace data are stored in the spatial data storing part103, the spatial data creating apparatus100replaces the content of the subspace data with the content of the most finely classified subspace data among the spatial data and subspace data, and outputs it. As a result, among the spatial data and the subspace data, the part(s) of the spatial data and the subspace data that is judged to have a large absolute value of residual differences in steps S002and S007is replaced by the finer subspace data to improve accuracy.

As described above, this example embodiment can improve estimation accuracy in comparison with the first example embodiment. The reason for this is that evaluation data is divided recursively, and this example embodiment is configured to allow accurate estimation even when there are many models in the environment.

In this example embodiment, rules for selecting subspace data are not limited to the method described above. A standard (upper limit) can also be set for maximum absolute value of residual differences, etc., between subspace data and data for evaluation. Then, even if total and average values of residual differences are suppressed, rules such as not adopting the subspace data (not re-dividing) can be used if the maximum absolute value of residual differences exceeds the upper limit.

Third Example Embodiment

Next, a third example embodiment in which spatial data created by the present invention is applied to a radio map provision service will be described with reference to drawings.FIG.20is a drawing illustrating the configuration of the spatial data creating apparatus of the third example embodiment of the present invention. The difference from the first example embodiment illustrated inFIG.5is that a map displaying part130is added. Since the other configurations are common to the first example embodiment, we will focus on the differences below.

The map displaying part130converts subspace data output by the spatial data output part109into a radio wave map format and outputs it to a predetermined display device, etc. (not shown).

Operations of this example embodiment are described in detail with reference to drawings.FIG.21is a flow chart representing operations of the spatial data creating apparatus100baccording to the third example embodiment of the present invention. Since operations from step S000to S005inFIG.19are the same as in the first example embodiment, details of step S206will be described below.

In step S206, the map displaying part130of the spatial data creating apparatus100bcreates and outputs a radio wave map using the data received from the spatial data output part109.FIG.22is a drawing illustrating an example of output form of radio wave map. In the radio wave map inFIG.22, RSSI strength is represented by contour lines. The “H” in the figure indicates a peak of location where RSSI is high, and the “L” indicates a peak (bottom) of location where RSSI is low. Furthermore, these contour lines are not created by a single model as described in the first example embodiment, but are contour lines that have been properly classified and modified by subspace data.

Accordingly, this example embodiment makes it possible to display spatial data with improved accuracy in a form that is easy for user to grasp visually. Note that a configuration in which a map displaying part130is added to the configuration of the second example embodiment to display a radio wave map can also be naturally adopted.

Fourth Example Embodiment

Next, a fourth example embodiment in which spatial data created by the present invention is used to estimate (locate) location of a moving body will be described with reference to drawings.FIG.23is a drawing illustrating a configuration of the spatial data creating apparatus of the fourth example embodiment of the present invention. The difference from the first example embodiment illustrated inFIG.5is that the location estimating part140is added. Since the other configurations are common to the first example embodiment, we will focus on the differences below.

The location estimating part140estimates location of a moving body (or bodies) using subspace data output by the spatial data output part109, and outputs results of the location estimation to a predetermined display device, etc. (not shown).

Next, operations of this example embodiment will be described in detail with reference to the drawings.FIG.24is a flow chart representing operations of the spatial data creating apparatus100cof the fourth example embodiment of the present invention. Since the operations from step S000to S005inFIG.24are the same as these in the first example embodiment, details of step S306will be described below.

In step S306, the location estimating part140of the spatial data creating apparatus100cestimates and outputs a location corresponding to received power vectors of a moving body using data received from the spatial data output part109.FIG.25is a drawing illustrating an example of output form of location estimation information of a moving body. In the example ofFIG.25, a map of target area is shown with estimated location of the moving body indicated by code UV. And this estimated location is not created by a single model, as described in the first example embodiment, but is a location identified by spatial data that is properly classified and modified by subspace data.

Therefore, according to this example embodiment, it is possible to perform highly accurate location estimation. A configuration in which the location estimating part140is added to the configuration of the second example embodiment to estimate the location of the mobile station can naturally be adopted.

Fifth Example Embodiment

In the above first through fourth example embodiments, the description is based on the premise that evaluation data for evaluating subspace data is available in advance, but depending on environment, it may not be possible to prepare sufficient evaluation data. The fifth example embodiment, which enables evaluation of subspace data even in such a situation, is described below.

FIG.26is a drawing illustrating a configuration of a spatial data creating apparatus of a fifth example embodiment of the present invention. The difference from configuration of the first example embodiment illustrated inFIG.5is that the spatial data creating apparatus100dhas a feature value-for-evaluation storing part301and a location information estimating part302. Since the other configurations are common to the first example embodiment, the differences are mainly described below.

The feature value-for-evaluation storing part301stores information of feature values to be used as data-for-evaluation. For example, the feature value-for-evaluation storing part301stores received power vectors, which are a set of RSSI of radio waves received from each AP.

The location information estimating part302estimates location information at the timing when spatial data held in the spatial data storing part103is updated, using spatial data and information on feature values held in the feature value-for-evaluation storing part301, wherein the location information is assumed to have been used for obtaining information on feature values. This location information can be estimated using FingerPrint method or other methods. Then, the location information estimating part302creates data-for-evaluation by associating estimated location information with information on related feature values, and registers it in the data-for-evaluation storing part102.

Next, operations of this example embodiment will be described in detail with reference to drawings.FIG.27is a flow chart representing operations of the spatial data creating apparatus100dof the fifth example embodiment of the present invention. The difference from the first example embodiment is that steps S501and S502are added between steps S001and S002. The details of steps S501and S502are described below.

As in the first example embodiment, when the spatial data creating apparatus100dcreates and stores spatial data (steps S000and S001), it uses the spatial data and feature values for evaluation to estimate a location at which feature values for evaluation would have been obtained (step S501).

Next, the spatial data creating apparatus100dgenerates data for evaluation by mapping feature value(s) for evaluation to estimated location(s), and stores the data in the data-for-evaluation storing part102(Step S502).

The above-described procedure enables the spatial data creating apparatus100dto calculate residual differences between created spatial data and evaluation data in the data-for-evaluation storing part102in next step S002.

As described above, this example embodiment can improve accuracy of spatial data estimation even when it is not possible to prepare data for evaluation. The reason for this is that it is configured to use feature values for evaluation to estimate location(s) at which feature value(s) would have been acquired, and to generate data-for-evaluation that is associated to location information.

In the above description, it was assumed that the feature values for evaluation storing part301and the location information estimating part302are added to the configuration of the first example embodiment. In this case, as illustrated inFIG.28, in addition to creation of the data for evaluation in steps S501and S502, a step of updating location of data for evaluation may be added between steps S005and S007. By making this preparation, it is possible to update location information of data for evaluation in the procedure of recursive classification of data for evaluation, thereby improving accuracy.

Each of the example embodiments of the present invention is described above. However, the present invention is not limited to the embodiments described above, and further variations, substitutions, and adjustments can be made to the extent that the basic technical idea of the present invention is not deviated from. For example, the apparatus configuration, configuration of each element, displayed data, and other forms of expression shown in each drawing are examples to help understand the invention, and are not limited to the configuration illustrated in these drawings.

For example, in the example embodiments described above, it was assumed that data for estimation and data for evaluation are feature values (received signal strength) associated with location information, but the scope of application of the present invention is not limited to estimation of received signal strength and location estimation using this. For example, by using sunshine and wind power information as feature values associated with location information, it is possible to create an estimation map of sunshine and wind power with a limited number of sensors. In these cases as well, accuracy of prediction of spatial data (sunshine and wind speed) for the entire target area can be improved by classifying evaluation data by deviation from spatial data. Similarly, by using the exploratory value of mineral resources as a feature associated with location information, it is possible to create an estimated resource map with a limited number of exploratory (boring) points.

Further, the procedures illustrated in the first through fifth example embodiments described above can be realized by a program that causes a computer (9000inFIG.29) that functions as a spatial data creating apparatus to function as the same apparatus. Such a computer is exemplified by configuration with a CPU (Central Processing Unit)9010, a communication interface9020, a memory9030, and an auxiliary storage apparatus9040inFIG.29. That is, the CPU9010ofFIG.29can be used to execute domain partitioning program and spatial data creating program, and to perform update processing of each calculation parameter held in the auxiliary storage apparatus9040, etc. thereof.

That is, each part (processing method, function) of the spatial data creating apparatus shown in the first through fifth example embodiments above can be realized by a computer program that causes a processor installed in the spatial data creating apparatus to execute each of the above-mentioned processes using its hardware.

Finally, preferred modes of the present invention will be summarized.

[Mode 1]

(Refer to the spatial data creating apparatus according to the first aspect.)

[Mode 2]

The above described first sensor group and second sensor group of the spatial data creating apparatus are receiving apparatuses that receive radio wave transmitted from a prescribed transmitting station, and the data for estimation and the data for evaluation can be data that associates power reception strength measured by the receiver with the location of sensors of the first sensor group and the second sensor group.

[Mode 3]

The above described data classifying part of spatial data creating apparatus can be configured to classify the data for evaluation based on difference or ratio between the data for evaluation and the spatial data corresponding to the location at which the data for evaluation is acquired.

[Mode 4]

The above described spatial data creating apparatus may further comprise an output part that outputs the spatial data in a predetermined manner.

[Mode 5]

The above described spatial data creating apparatus may further comprise a map displaying part that visualizes and displays spatial data including subspace data adopted by the subspace data evaluating part.

[Mode 6]

The above described spatial data creating apparatus may further comprise a location estimating part that estimates location of a moving body by collating the spatial data and sensor value acquired by the moving body, and the location estimating part estimates location of the moving body using the spatial data including the subspace data adopted by the subspace data evaluating part, and provides the estimated location of the moving body as location information of the moving body.

[Mode 7]

The above described spatial data creating apparatus can be further configured to have a location information estimating part that estimates location at which feature values prepared in advance are acquired by collating the spatial data with the feature values and creates the data for evaluation.

[Mode 8]

(Refer to the spatial data creating method according to the second aspect.)

[Mode 9]

(Refer to the program according to the third aspect.)

Note that, like Mode 1, Modes 8 to 9 can be developed into Modes 2 to 7.

Note that each disclosure of Patent Literatures cited above is incorporated herein in its entirety by reference thereto, and may be used as a basis or part of the present invention as necessary. It is possible to modify and adjust the example embodiments or examples within the whole disclosure of the invention (including the claims) and based on the basic technical concept thereof. Further, it is possible to variously combine or select (or partially delete) a wide variety of disclosed elements (including the individual elements of the individual claims, the individual elements of the example embodiments or examples, and the individual elements of the individual figures) within the scope of the disclosure of the present invention. That is, it is self-explanatory that the present invention includes any types of variations and modifications to be done by a skilled person according to the whole disclosure including the Claims, and the technical concept of the present invention. Particularly, any numerical ranges disclosed herein should be interpreted that any intermediate values or subranges falling within the disclosed ranges are also concretely disclosed, even without specific recital thereof. In addition, each of the disclosures in the above-cited literatures may be used, if necessary, as part of the disclosure of the present invention according to the purpose of the present invention, in part or in whole, in combination with the descriptions in this document, and shall be deemed to be included in the disclosure of the present application.

INDUSTRIAL APPLICABILITY

This invention can be applied to applications such as consulting, design, and indoor/outdoor location-based services using spatial data obtained through spatial data estimation.

SIGNS LIST

10,100,100a-100d: spatial data creating apparatus11: receiving part12: data classifying part13: subspace data creating part14: subspace data evaluating part101: data-for-estimation storing part102: data-for-evaluation storing part103: spatial data storing part104: primary storing part of subspace data105: spatial data creating part106,106a: data-for-evaluation classifying part107: subspace data creating part108,108a: subspace data evaluating part109,109a: spatial data output part110: storage apparatus120: re-classification directing part130: map displaying part140: location estimating part301: feature value for evaluation storing part302: location information estimating partAP1-AP10: access pointLN1-LN29: measurement pointP: pillarB: barrier9000: computer9010: CPU9020: communication interface9030: memory9040: auxiliary storage apparatus