JP2018146473A - Estimation method and estimation device using the same - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a technique for estimating the position of a radio device to be a transmission source even without information of transmission power.SOLUTION: A first acquisition part 12a acquires a TOA and an RSS. A TDOA sample measurement part 14 derives a TDOA from the TOA. A first factor graph processing part 16 uses the TDOA to derive a first estimation value of position coordinates of a radio device. A specification part 18 specifies at least four sensors 10 arranged near the first estimation value. A second acquisition part 12b acquires RSSs of a training signal. A selection part 20 selects an RSS to at least the four sensors 10 that are specified among the RSSs acquired in the second acquisition part 12b. A coefficient calculation part 22 calculates a coefficient on the basis of the selected RSS. A second factor graph processing part 24 derives a DRSS from the RSS acquired in the first acquisition part 12a, and derives a second estimation value of the position coordinates of the radio device while using the derived DRSS and the coefficient.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an estimation technique, and relates to an estimation method for estimating the position of an unknown source and an estimation apparatus using the estimation method.

  In order to estimate the position of the radio, TOA (Time Of ARIval), RSS (Receive Signal Strength), etc. are used, but there is no line-of-sight and the use of RSS for indoor environment that is a multipath environment. Is more suitable. In order to simplify the processing by RSS, hyperplane equations and factor graphs are also used (see, for example, Non-Patent Document 1).

Chin-Tseng Huang, Cheng-Hsuan Wu, Yao-Nan Lee and Junn-TSair Chen, “A Novel Ind RSS EW E 8, NO. 6, p. 3050-3058

  When estimating the position of a wireless device that is a signal transmission source based on RSS, information on transmission power in the wireless device is required. However, if the wireless device to be estimated is an illegal wireless station or an unknown transmission source, it is difficult to acquire transmission power information. Therefore, estimation is performed based on DRSS (Differential RSS) derived by subtracting RSS in each of the two sensors. DRSS requires at least four observation spots that surround a wireless device.

  The present invention has been made in view of such circumstances, and an object of the present invention is to provide a technique for estimating the position of a wireless device serving as a transmission source even without transmission power information.

  In order to solve the above-described problem, an estimation device according to an aspect of the present invention is configured such that a signal from a target wireless device is received by each of a plurality of sensors, and a TOA (Time Of Arrival) of a sample at each sensor. And a first acquisition unit that acquires RSS (Receive Signal Strength), a TDOA sample measurement unit that derives a TDOA (Time Difference of Arrival) between samples from the TOA of the sample acquired in the first acquisition unit, and a TDOA sample A first factor graph processing unit that derives a first estimate of the position coordinates of the wireless device by performing a Tyoa based factor graph process of Pythagoras using the TDOA of the sample between the sensors derived in the measurement unit; , First factor graph processing unit A specific unit for identifying at least four sensors arranged in the vicinity of the first estimated value derived in the above, and a training signal from a known transmitter is received by each of the plurality of sensors, and a sample at each sensor is received. Based on the second acquisition unit that acquires the RSS and the selection unit that selects the RSS for at least four sensors specified in the specification unit among the RSS acquired in the second acquisition unit, and the RSS selected in the selection unit A coefficient calculation unit for calculating a coefficient, a DRSS (Differential RSS) of the sample between sensors is derived from the RSS of the sample acquired in the first acquisition unit, and the derived DRSS and the coefficient calculated in the coefficient calculation unit are used. However, by performing DRSS-based factor graph processing, the position coordinates of the wireless device Comprising a second factor graph processing unit for deriving an estimated value.

  Another aspect of the present invention is an estimation method. In this method, a signal from a target wireless device is received by each of a plurality of sensors, and a sample TOA (Time Of Alignment) and RSS (Receive Signal Strength) of each sensor are obtained and obtained. Deriving a sample TDOA (Time Difference Of Arrival) between sensors from the sample TOA and performing a Pythagorean TDOA-based factor graph process using the derived TDOA of samples between the sensors. Deriving a first estimate of the position coordinates of the wireless device; identifying at least four sensors located near the derived first estimate; and a training signal from a known transmitter is a plurality of sensors Each of A sample RSS for a training signal at each sensor, a step of selecting an RSS for at least four specified sensors among the sample RSSs for the training signal, and the selected RSS. Based on the step of calculating the coefficient, the DRSS (Differential RSS) between the sensors is derived from the RSS of the sample for the signal from the target wireless device, and the derived DRSS and the calculated coefficient are used. However, a step of deriving a second estimated value of the position coordinates of the wireless device by performing DRSS-based factor graph processing is provided.

  It should be noted that any combination of the above-described constituent elements and a conversion of the expression of the present invention between a method, an apparatus, a system, a recording medium, a computer program, etc. are also effective as an aspect of the present invention.

  According to the present invention, it is possible to estimate the position of a wireless device that is a transmission source without the transmission power information.

It is a figure which shows the structure of the estimation apparatus which concerns on the Example of this invention. It is a flowchart which shows the procedure of the PTDOA based factor graph process by the estimation apparatus of FIG. It is a flowchart which shows the procedure of the downlink process by the estimation apparatus of FIG. It is a flowchart which shows the procedure of the uplink process by the estimation apparatus of FIG. It is a flowchart which shows the procedure of the calculation process by the estimation apparatus of FIG. It is a flowchart which shows the procedure of the DRSS based factor graph process by the estimation apparatus of FIG.

  Before describing the present invention specifically, an outline will be given first. Embodiments of the present invention relate to an estimation device that estimates the position of an illegal radio station or an unknown source (hereinafter referred to as “radio device”). A recent trend in geolocation is to establish low cost, high accuracy and simplicity, but this geolocation is also being used to estimate the position of unknown wireless devices. Here, “unknown” indicates that the TOA and transmission power of the target wireless device are unknown. Wireless geolocation is defined as the process of calculating the geographic coordinates of a wireless device by observing the nature of electromagnetic waves. In order to estimate the geographical location of a wireless device, the properties of electromagnetic waves are observed in TOA, RSS, TDOA (Time Difference Of Arrival), and combinations thereof.

  So far, combinations of RSS and TOA measurements and RSS and DOA (Direction Of Arrival) measurements have been used for geolocation. By combining these, the accuracy is improved as compared with the case of only RSS, TOA, and DOA. However, additional configuration is required to obtain the directional angle. Also, with the combination of RSS and TOA, the position of an unknown wireless device cannot be estimated without the absolute transmission power and time stamp information for the unknown target.

  In this embodiment, in order to estimate the position of an unknown wireless device, a PTDOA (Pythagorean Time Difference Of Arrival) factor graph and a DRSS factor graph are combined. In the algorithm structure of the factor graph, the global function is decomposed into a plurality of local functions. As a local function, the factor graph includes two types of nodes. The first is a factor node, which calculates an updated soft information (SI) message. The second is a variable node, which executes the sum product algorithm and passes a value to the factor node. Between the factor node and the variable node, the average value and the variance value of the measurement data are exchanged.

  In the geolocation technique, the factor graph performs a sum product algorithm to estimate the position of the target using TOA. Only the mean value and the variance value of the TOA measurement results assumed to be Gaussian are exchanged between the nodes. Each sensor measures the TOA, and an average value and a variance value are calculated. However, factor graph based TOA requires complete time synchronization of the target, which is not possible if the target is unknown. Therefore, Pythagorean TDOA based on a factor graph is introduced in order to eliminate the need for time stamp knowledge. Pythagorean TDOA based on a factor graph is a modification of TOA based on a factor graph, and a node is added to the factor graph. In the added node, the TOA data is subtracted between the two sensors.

  RSS based on factor graphs is also used in geolocation technology. Transmission signal sequences are transmitted from several observation spots, and RSS is acquired in watts. RSS is converted into dB units and input to the factor graph. RSS information reference data obtained by training is used to generate a PDP (Power Decay Profile) by interpolation. Here, local linearity is used to estimate the position of the target. This is highly accurate indoors.

  In order to obviate the knowledge of absolute transmission power in unknown wireless devices, DRSS based on factor graphs is proposed. DRSS is a technology that modifies RSS by subtracting RSS in each of the two sensors. This subtraction requires the addition of new nodes to the factor graph. However, DRSS requires RSS information from at least four appropriate observation spots. The reason is that since the DRSS algorithm operates in the difference region, the linear equation is degenerated and the position cannot be determined unless there are four or more observation spots. Note that the degeneracy of the linear equation can be said to reduce the rank of the matrix by one.

  This observation spot has a position close to the target and must be arranged so as to surround the target. In a DRSS factor graph, such a suitable observation spot is known and the target should be in the grid of observation spots. In practice, many observation spots are arranged, and another algorithm for selecting an appropriate observation spot from among them is required. Another such algorithm is the RADAR algorithm, which requires knowledge of the target transmit power. Therefore, a PTDOA factor graph is used here instead of the RADAR algorithm. The PTDOA factor graph is used to select observation spots for the DRSS factor graph in the outdoor environment.

  FIG. 1 shows the configuration of the estimation apparatus 100. The estimation device 100 includes a first sensor 10a, a second sensor 10b, a third sensor 10c, an Nth sensor 10n, an acquisition unit 12, a TDOA sample measurement unit 14, a first factor graph processing unit 16, which are collectively referred to as a sensor 10. A unit 18, a selection unit 20, a coefficient calculation unit 22, and a second factor graph processing unit 24. The acquisition unit 12 includes a first acquisition unit 12a and a second acquisition unit 12b. The first factor graph processing unit 16 includes an Euclidean factor node processing unit 40, an Euclidean variable node processing unit 42, an Euclidean distance estimation factor node processing unit 44, a thumb product algorithm variable node processing unit 46, a Pythagoras factor node processing unit 48, and a Pythagoras variable. A node processing unit 50, a conversion factor node processing unit 52, a thumb product algorithm processing unit 54, a determination unit 56, and an output unit 58 are included. The second factor graph processing unit 24 includes an RSS factor node processing unit 80, an RSS variable node processing unit 82, a DRSS factor node processing unit 84, a DRSS variable node processing unit 86, a linear plane factor node processing unit 88, and an output unit 90. . The sum product algorithm variable node processing unit 46 includes a downstream processing unit 60 and an upstream processing unit 62, the Pythagorean factor node processing unit 48 includes a downstream processing unit 64 and an upstream processing unit 66, and the Pythagoras variable node processing unit 50 includes: The downlink processing unit 68 and the upstream processing unit 70 are included, and the conversion factor node processing unit 52 includes a downstream processing unit 72 and an upstream processing unit 74.

N sensors 10 are arranged at coordinates (X _h , Y _h ). Here, h indicates an index of the sensor 10. This is a 1000 × 1000 m ² area with a 100 m mesh construction grid. Of the N sensors 10, four sensors 10 are observation spots. The four observation spots are arranged so as to surround the target, and the coordinates of the observation spot are indicated as (x _m , y _m ). Here, m represents an index of the observation spot. Here, the position of the sensor 10 is known to the estimation device 100. In addition, the coordinates of the target are indicated as (x, y). The target is a static unknown wireless device and a target wireless device. A signal transmitted from the target is received by the sensor 10.

ここで、右辺の前者はセンサ１０が信号を受信した受信時間であり、後者はターゲットが信号を送信した送信時間である。未知のターゲットに対して送信時間は取得できないが、受信時間は取得できる。第１取得部１２ａは、サンプルのＴＯＡをＴＤＯＡサンプル測定部１４に出力する。 The first acquisition unit 12a acquires a sample TOA based on a signal received by each sensor 10 and transmitted from the target. The TOA indicates a time difference between signals transmitted from the transmission device and received by the reception device. Here, the transmission device corresponds to the target, and the reception device corresponds to the sensor 10. The TOA is shown as follows:

Here, the former on the right side is a reception time when the sensor 10 receives a signal, and the latter is a transmission time when the target transmits a signal. Although the transmission time cannot be obtained for an unknown target, the reception time can be obtained. The first acquisition unit 12 a outputs the sample TOA to the TDOA sample measurement unit 14.

ＴＤＯＡの導出はすべてのセンサ１０に対してなされる。 The TDOA sample measurement unit 14 receives the TOA of the sample from the first acquisition unit 12a, and derives the TDOA of the sample between the sensors 10 from the TOA of the sample. This is to make the knowledge of the transmission time unnecessary. Specifically, the TDOA is derived by subtracting the TOA between the sensors 10 as follows.

The TDOA is derived for all sensors 10.

  The first factor graph processing unit 16 uses the TDOA of the samples between the sensors 10 derived in the TDOA sample measurement unit 14 to perform PTDOA-based factor graph processing, thereby first estimating the position coordinates of the wireless device. Deriving a value. Below, the 1st factor graph process part 16 is demonstrated in detail.

The Euclidean factor node processing unit 40 in the first factor graph processing unit 16 receives TDOA from the TDOA sample measurement unit 14. The Euclidean factor node processing unit 40 converts the TDOA of the sample into a difference in Euclidean distance by multiplying TDOA by the propagation speed of the electromagnetic wave. In particular, E _{i, j} of the Euclidean factor node processing unit 40 converts K TDOAs into Euclidean distance differences, and calculates an average value and a variance value. These are messages exchanged in the factor graph.

ここで、「ｍ」が「平均値」を示し、「σ^２」が「分散値」を示し、矢印は、因子グラフのノード間におけるメッセージの進む方向を示す。 Only two factor nodes are directly connected to the Euclidean variable node processing unit 42. The Euclidean variable node processing unit 42 directly passes a message from E _{i, j} of the Euclidean factor node processing unit 40 to D _{i, j of the} Euclidean distance estimation factor node processing unit 44. This message is shown in the form of an average value as in equation (3) and a variance value as in equation (4).

Here, “m” indicates “average value”, “σ ² ” indicates “variance value”, and an arrow indicates a direction in which a message advances between nodes of the factor graph.

D _{i, j} of the Euclidean distance estimation factor node processing unit 44 converts the Euclidean distance difference into an equivalent Euclidean distance as follows.

R _h of sum product algorithm variable node processing unit 46, the sum product algorithm, D _i of the Euclidean distance estimation factor node processing unit _44, and updates the message from the C _h of _j and Pythagoras factor node processing unit 48. The subscript h indicates an index that does not relate to either the first sensor 10 or the second sensor 10. Also, the thumb product algorithm variable node processing unit 46 advances the updated message to the destination node.

ここで、ｈは、ｈ＝ｉであるかｈ＝ｊである。また、ｈ＜ｎに対してａ＝Ｄ_ｈ，ｎ→ｒ_ｈであり、ｈ＞ｎに対してａ＝Ｄ_ｎ，ｈ→ｒ_ｈである。ノードｒ_ｈとＤ_ｉ，ｊとの間のメッセージの交換は、タイムスタンプの知識の必要性を除去するための重要なステップである。 Sum product algorithm variable node update message from r _h in the downlink processing unit 60 of the processor 46 to C _h Pythagorean factors node processing unit 48 is shown as formula (9) and (10). On the other hand, D _i of the sum product algorithm variable node Euclidean distance from r _h in the uplink processing part 62 of the processing unit 46 estimates factor node processing unit _44, update message to _j is represented by the equation (11) and (12) It is. However, the first iteration of this step requires an initial value of the average value and variance value indicating a message from the C _h Pythagorean factors node processing unit 48.

Here, h is h = i or h = j. In addition, a = D _{h, n} → r _h for h <n, and a = D _{n, h} → r _h for h> n. Exchange of messages between the node r _h and D _{i, j} is an important step to eliminate the need for knowledge of the time stamp.

ここで、ｍ^２ _{Δｘｈ→Ｃｈ}、ｍ^２ _{Δｙｈ→Ｃｈ}、δ^２ _{Δｘｈ→Ｃｈ}、δ^２ _{Δｙｈ→Ｃｈ}の初期値は、最初の繰り返しにおいて「０」に設定される。 C _h Pythagorean factors node processing unit 48 executes a Pythagorean calculation to the message. The downstream processing unit 64 of the Pythagorean factor node processing unit 48 derives a message having a relative distance between the variable nodes Δx _h and Δy _h , as indicated by equations (13) to (16). Up processing unit Pythagorean factors node processing unit 48 66, replaces the message _{r h} as shown in equation (17) and equation (18).

Here, the initial values of m ² _{Δxh → Ch} , m ² _{Δyh → Ch} , δ ² _{Δxh → Ch} , and δ ² _{Δyh → Ch} are set to “0” in the first iteration.

ここで、εは限定目的のパラメータであり、経験的に選択される。また、上り処理部６６は、次の２つの制限を加える。

ここで、δも限定目的のパラメータであり、経験的に選択される。 Furthermore, in order to maintain the stability of the messages exchanged in C _h Pythagorean factors node processing unit 48, a downlink processing part 64 adds the following restrictions.

Here, ε is a limited purpose parameter and is selected empirically. The upstream processing unit 66 adds the following two restrictions.

Here, δ is also a limited purpose parameter and is selected empirically.

ここで、平均値と分散値は、最初の繰り返しにおけて、初期値「０」にされる。 Since the Pythagorean variable node processing unit 50, like the Euclidean variable node processing unit 42, has only two nodes that are directly connected to the relative distance nodes Δx _i and Δy _i , messages are directly exchanged between them. The That is exchanged between the C _h Pythagorean factors node processing unit 48 and A _h and B _h of the conversion factors node processing unit 52. The processing of the downstream processing unit 68 of the Pythagorean variable node processing unit 50 is represented by Equation (19) and Equation (20), and the processing of the upstream processing unit 70 is represented by Equation (21) and Equation (22).

Here, the average value and the variance value are set to the initial value “0” in the first iteration.

A _h and B _h of the conversion factor node processing unit 52 convert a relative distance message in order to estimate the position of the target calculated by the variable nodes x _init and y _init . The processing of the downlink processing unit 72 of the conversion factor node processing unit 52 is represented by Equation (23) and Equation (24), and the processing of the uplink processing unit 74 is represented by Equation (25) and Equation (26).

ｘ_ｉｎｉｔに対する式（２７）と式（２８）は、ｙ_ｉｎｉｔにも同様に適用される。 The sum product algorithm processing unit 54 adds a message to the variable nodes x _init and y _init according to the sum product algorithm shown in the equations (27) and (28) in the iterative processing.

Equations (27) and (28) for x _init apply to y _init as well.

The determination unit 56 determines whether the processing result of the conversion factor node processing unit 52 has converged. Here, the amount of change between the calculation result of Expression (27), the calculation result of Expression (28), the calculation result of Expression (27), and the calculation result of Expression (28) derived so far is the threshold value. If it becomes smaller than the value, it is determined that the convergence has occurred. The determination of convergence may be made for y _init and may be made based on either the average value or the variance value. If not converged, the determination unit 56 returns the processing result of the sum product algorithm processing unit 54 to A _h and B _h of the upstream processing unit 74 of the conversion factor node processing unit 52.

ｙ_ｉｎｉｔに対する演算は、式（２９）と式（３０）と同様である。（ｍ_{ｘｉｎｉｔ}，ｍ_{ｙｉｎｉｔ}）の最終的な値は、ＰＴＤＯＡ因子グラフで提供されるターゲットの荒い位置推定値を示し、前述の第１推定値に相当する。 In the output unit 58, when the determination unit 56 determines that the iteration has converged, the variable nodes x _init and y _init add all incoming messages in the same manner as in the equations (27) and (28).

The calculation for y _init is the same as in equations (29) and (30). The final value of (m _xinit , my _yinit ) indicates a rough target position estimated value provided in the PTDOA factor graph, and corresponds to the first estimated value described above.

The specifying unit 18 specifies the four sensors 10 arranged near the first estimated value derived by the first factor graph processing unit 16 as observation spots. That is, the result of the PTDOA factor graph estimation algorithm at the variable nodes (x _init , y _init ) is used as the initial position of the DRSS factor graph, and a rough estimate of the target position to select the four most appropriate observation spots. become.

  The second acquisition unit 12b receives a training signal from a known transmission device in each of the plurality of sensors 10, and acquires the RSS of the sample in each sensor 10. A known transmitter is placed at a known position coordinate. The second acquisition unit 12b performs an averaging process on the sample RSS. The averaging period is set to be sufficiently long and a sufficient amount of sample. This is to reduce the influence of shadowing and instantaneous fading, so that only path loss fading remains and error free. The second acquisition unit 12 b outputs the averaged RSS of the sample to the selection unit 20. The selecting unit 20 selects the RSS for the four sensors 10 identified by the identifying unit 18 among the RSSs acquired by the second acquiring unit 12b, that is, the observation spot.

Following the processing of the RTDOA factor graph thus far, processing of the DRSS factor graph is executed. In the DRSS factor graph, the position estimate of the target (x, y) is obtained via the relationship between the power of the received signal and the PDP. Here, the power of the received signal is obtained by the sensor 10 and the first acquisition unit 12a, and the PDP is obtained from the training signal described above. In the second acquisition unit 12b, the training signals received at the observation spot are averaged. All RSS samples, including the training signal and the RSS of the signal from the target, follow Gaussian assumptions. This is therefore modeled by a path loss model with only a sufficiently long average. This is because, as described above, a sufficiently long average excludes instantaneous fading fluctuations and shadowing. The result is shown as follows.

Here, P indicates the exponent of the path loss, and the unit is dB. d, d ₀ , f _c , f, n are the Euclidean distance (in meters) from the target or observation spot to the h-th sensor 10, the reference distance (in meters) of the path loss exponent model, and the carrier frequency (in Hz) , The speed of light (m / s), and the coefficient of the exponent of the path loss, respectively.

  The first acquisition unit 12a acquires the RSS of the sample based on the signal received by each sensor 10 and transmitted from the target. The first acquisition unit 12a averages the RSS of the sample over a sufficiently long period in order to remove instantaneous fading fluctuation and shadowing. Therefore, the RSS of the sample is represented by a path loss exponential model because the measurement error is modeled by Gaussian noise that does not include “0” by collecting many independent factors.

  The second factor graph processing unit 24 derives the DRSS of the sample between the sensors 10 from the RSS of the sample acquired by the first acquisition unit 12a, and executes the DRSS-based factor graph processing to thereby calculate the position coordinates of the wireless device. A second estimated value is derived. Note that, when executing the DRSS-based factor graph processing, DRSS and the coefficients calculated by the coefficient calculation unit 22 are used. The coefficients calculated by the coefficient calculation unit 22 will be described later. Below, the 2nd factor graph process part 24 is demonstrated in detail.

H _ph of the RSS factor node processing unit 80 of the second factor graph processing unit 24 converts RSS samples from watts to dB units. _{N ph} of RSS variable node processing unit 82 sends a message from the _{H ph} of RSS factors node processing section 80 _{G pi} of DRSS factors node processing unit _84, to _j.

これは、次のように示してもよい。

ＤＲＳＳ因子ノード処理部８４は、ＤＲＳＳ変数ノード処理部８６のＦ_ｐｉ，ｊでの繰り返し処理へのＤＲＳＳ入力メッセージとして使用するために次のような平均値と分散値を計算する。ここで、平均値と分散値は、測定値誤差がガウス分布にしたがうと想定されることによって導出される。

G _{pi, j} of the DRSS factor node processing unit 84 subtracts the RSS sample between the two sensors 10 to obtain the DRSS sample as follows.

This may be shown as follows.

The DRSS factor node processing unit 84 calculates the following average value and variance value for use as a DRSS input message for the iterative processing at F _{pi, j} of the DRSS variable node processing unit 86. Here, the average value and the variance value are derived by assuming that the measurement value error follows a Gaussian distribution.

N _{pi, j} of the DRSS variable node processing unit 86 directly passes the message from G _{pi, j} of the DRSS factor node processing unit 84 to F _{pi, j} of the linear plane factor node processing unit 88 as follows.

係数計算部２２は、４つの適切な観測スポットから送られたトレーニング信号をもとに、線形平面最小２乗（ＬＳ）方程式を解くことによってＰＤＰを生成する。線形平面最小２乗（ＬＳ）方程式は次のように示される。

ここで、ａ_ｘｉ，ｊ、ａ_ｙｉ，ｊ、ａ_Ｐｉ，ｊは、平面方程式の係数である。ｘとｙはターゲットの位置を示す。 The coefficient calculation unit 22 calculates a coefficient based on the RSS selected by the selection unit 20. More specifically, the coefficient calculation unit 22 derives the DRSS of the sample between the sensors 10 from the RSS of the training signals sent from four appropriate observation spots as follows.

The coefficient calculator 22 generates a PDP by solving a linear plane least squares (LS) equation based on training signals sent from four appropriate observation spots. The linear plane least squares (LS) equation is shown as follows:

Here, a _{xi, j} , a _{yi, j} , and a _{Pi, j} are coefficients of a plane equation. x and y indicate the position of the target.

その結果、ＬＳ解は次のように示される。

その結果、係数計算部２２において導出される係数は次の通りである。

The coefficient vector a, the observation spot coordinates, the DRSS matrix B, and the constant matrix C are expressed as follows.

As a result, the LS solution is shown as follows.

As a result, the coefficients derived by the coefficient calculation unit 22 are as follows.

F _{pi, j} of the linear plane factor node processing unit 88 generates a linear plane to approximate the target position. The linear plane factor node processing unit 88 inputs an average value and a variance value from the DRSS variable node processing unit 86 and also inputs a coefficient from the coefficient calculation unit 22. The linear plane factor node processing unit 88 derives the average value and the variance value of the position coordinates x and y of the wireless device based on the input average value, variance value, and coefficient as follows.

式（４６）、式（４７）は、ｘに対するサムプロダクトアルゴリズムを示すが、ｙに対するサムプロダクトアルゴリズムも同様に示される。線形平面因子ノード処理部８８は、サムプロダクトアルゴリズムの実行結果のメッセージが収束するまで、処理を繰り返し実行する。収束についての条件は前述の通りであるので、ここでは説明を省略する。 Further, the linear plane factor node processing unit 88 executes a sum product algorithm on the derived average value and variance value.

Equations (46) and (47) show the sum product algorithm for x, but the sum product algorithm for y is also shown. The linear plane factor node processing unit 88 repeatedly executes the processing until the sum product algorithm execution result message converges. Since the conditions for convergence are as described above, a description thereof is omitted here.

式（４８）、式（４９）は、ｘに対するサムプロダクトアルゴリズムを示すが、ｙに対するサムプロダクトアルゴリズムも同様に示される。繰り返し回数が終了しなければ、線形平面因子ノード処理部８８に戻される。繰り返し回収が終了した場合、平均値が、ターゲットの位置（ｘ，ｙ）とされる。このターゲットの位置が第２推定値に相当する。 The output unit 90 inputs the average value and the variance value converged in the linear plane factor node processing unit 88. The output unit 90 executes the sum product algorithm on the input average value and variance value.

Equations (48) and (49) show the sum product algorithm for x, but the sum product algorithm for y is also shown. If the number of repetitions does not end, the process returns to the linear plane factor node processing unit 88. When the repeated collection is completed, the average value is set as the target position (x, y). This target position corresponds to the second estimated value.

  This configuration can be realized in terms of hardware by a CPU, memory, or other LSI of any computer, and in terms of software, it can be realized by a program loaded in the memory, but here it is realized by their cooperation. Draw functional blocks. Accordingly, those skilled in the art will understand that these functional blocks can be realized in various forms by hardware only, software only, or a combination thereof.

  The operation of the estimation apparatus 100 having the above configuration will be described. FIG. 2 is a flowchart showing a procedure of PTDOA-based factor graph processing by the estimation apparatus 100. Expressions (1) and (2) are executed (S10). Expressions (3) and (4) are executed (S12). Expressions (5) to (8) are executed (S14). The first factor graph processing unit 16 executes a downlink process (S16). If converged (Y of S18), Formula (29) and Formula (30) will be performed (S20). If the repetition is completed (Y in S22), the first estimated value is output (S26). If it has not converged (N in S18), or if the repetition has not ended (N in S22), the first factor graph processing unit 16 executes an upstream process (S24). Return to step 14.

FIG. 3 is a flowchart showing a procedure of downlink processing by the estimation apparatus 100. Expressions (9) and (10) are executed (S50). If _{mrh → Ch} <ε is not satisfied (N in S52), Expressions (13) to (16) are executed (S56). If m _{rh → Ch} <ε (Y in S52), m _{rh → Ch} = ε is set (S54), and Expressions (13) to (16) are executed (S56). Expressions (19) and (20) are executed (S58). Expressions (23) and (24) are executed (S60). Expressions (27) and (28) are executed (S62).

FIG. 4 is a flowchart showing the procedure of the uplink processing by the estimation apparatus 100. Expressions (25) and (26) are executed (S100). Expressions (21) and (22) are executed (S102). When m ² _{rh → Ch} −m _{Δxh → Ch} <0 (Y in S104), m _{Δxh → Ch} = sign (m _{Δxh → Ch} ) × (abs (m ² _{rh → Ch} ) −δ) ( S106). This is also done for y. If m ² _{rh → Ch} −m _{Δxh → Ch} <0 is not satisfied (N in S104), step 106 is skipped. Expressions (17) and (18) are executed (S108). When σ ² _{Ch → rh} > max (σ ² _{Dh → rh} ) (Y in S110), σ ² _{Ch → rh} = max (σ ² _{Dh → rh} ) is set (S112). If σ ² _{Ch → rh} > max (σ ² _{Dh → rh} ) is not satisfied (N in S110), step 112 is skipped. Expressions (11) and (12) are executed (S114). An observation spot is specified (S116).

  FIG. 5 is a flowchart showing a procedure of calculation processing by the estimation apparatus 100. Expression (37) is executed (S150). Expressions (38) and (39) are executed (S152). Expression (40) is executed (S154). Expression (41) is executed (S156).

  FIG. 6 is a flowchart showing a procedure of DRSS-based factor graph processing by the estimation apparatus 100. Expression (32) is executed (S200). Expressions (33) to (35) are executed (S202). Expression (36) is executed (S204). Expressions (42) to (45) are executed (S206). Expressions (46) and (47) are executed (S208). If converged (Y of S210), Formula (48) and Formula (49) will be performed (S212). If the repetition is completed (Y in S214), the second estimated value is output (S216). If not converged (N in S210), or if the repetition has not ended (N in S214), the process returns to Step 206.

  According to the embodiment of the present invention, since PTDOA is used, knowledge of the time stamp can be made unnecessary. Moreover, since PTDOA is executed, the target position can be roughly estimated. In addition, since the target position is roughly estimated, four observation spots can be selected from a plurality of sensors. Also, since four observation spots surrounding the target are selected, DRSS can be executed. Moreover, since the process is executed by deriving the DRSS of the samples between the sensors based on the RSS of the samples at each sensor, a relative value can be used. Moreover, since it uses relatively, the information of absolute transmission power can be made unnecessary. Also, since absolute transmission power information is not required, the position of a target serving as a transmission source can be estimated based on RSS even without transmission power information. In addition, since the position of the target serving as the transmission source is estimated, illegal radio stations and unknown transmission sources can be searched.

  In the above, this invention was demonstrated based on the Example. This embodiment is an exemplification, and it will be understood by those skilled in the art that various modifications can be made to combinations of the respective constituent elements, and such modifications are also within the scope of the present invention.

  In the present embodiment, the specifying unit 18 specifies four sensors 10 as observation spots. However, the present invention is not limited to this. For example, the specifying unit 18 may specify four or more sensors 10 as observation spots. According to this modification, the estimation accuracy can be improved.

  DESCRIPTION OF SYMBOLS 10 sensor, 12 acquisition part, 14 TDOA sample measurement part, 16 1st factor graph process part, 18 specification part, 20 selection part, 22 coefficient calculation part, 24 2nd factor graph process part, 40 Euclidean factor node process part, 42 Euclidean variable node processing unit, 44 Euclidean distance estimation factor node processing unit, 46 sum product algorithm variable node processing unit, 48 Pythagoras factor node processing unit, 50 Pythagoras variable node processing unit, 52 transform factor node processing unit, 54 sum product algorithm processing Unit, 56 determination unit, 58 output unit, 60 downstream processing unit, 62 upstream processing unit, 64 downstream processing unit, 66 upstream processing unit, 68 downstream processing unit, 70 upstream processing unit, 72 downstream processing unit, 74 upstream processing unit, 80 RSS factor Node processing unit, 82 RSS variable node processing unit, 84 DRSS factor node processing unit, 86 DRSS variable node processing unit, 88 linear plane factor node processing unit, 90 output unit, 100 estimation device.

Claims (2)

A first acquisition unit that receives a signal from a target wireless device in each of the plurality of sensors, and acquires a sample TOA (Time Of Arrival) and an RSS (Receive Signal Strength) of each sensor;
A TDOA sample measurement unit for deriving a TDOA (Time Difference Of Arrival) between samples from the TOA of the sample acquired in the first acquisition unit;
A first factor graph for deriving a first estimated value of a position coordinate of a wireless device by performing a TDOA-based factor graph process of Pythagoras using a TDOA of samples between sensors derived in the TDOA sample measurement unit A processing unit;
A specifying unit for specifying at least four sensors arranged near the first estimated value derived in the first factor graph processing unit;
A training signal from a known transmission device is received at each of the plurality of sensors, and a second acquisition unit that acquires the RSS of the sample at each sensor;
Of the RSS acquired in the second acquisition unit, a selection unit that selects RSS for at least four sensors specified in the specifying unit;
A coefficient calculation unit for calculating a coefficient based on the RSS selected in the selection unit;
A DRSS-based factor graph process is performed by deriving a DRSS (Differential RSS) between samples from the RSS of the sample acquired by the first acquisition unit and using the derived DRSS and the coefficient calculated by the coefficient calculation unit. A second factor graph processing unit for deriving a second estimated value of the position coordinates of the wireless device by executing
An estimation apparatus comprising: A signal from a target wireless device is received by each of the plurality of sensors, and a sample TOA (Time Of Alignment) and RSS (Receive Signal Strength) of each sensor are obtained;
Deriving a TDOA (Time Difference of Arrival) of samples between sensors from the acquired TOA of the samples;
Deriving a first estimate of the position coordinates of the wireless device by performing a Pythagoras TDOA-based factor graph process using the derived TDOA between the derived sensors;
Identifying at least four sensors located near the derived first estimate;
A training signal from a known transmitter is received at each of the plurality of sensors, and obtaining a sample RSS for the training signal at each sensor;
Selecting RSS for at least four identified sensors of the sample RSS for the training signal;
Calculating a coefficient based on the selected RSS;
A DRSS (Differential RSS) between the sensors is derived from the RSS of the sample for the signal from the target wireless device, and DRS-based factor graph processing is executed using the derived DRSS and the calculated coefficients. Thereby deriving a second estimate of the position coordinates of the wireless device;
An estimation method comprising:

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