Estimator for determining an update to an estimated local state vector

Abstract

The present application relates to estimators for determining updates to an estimated local state vector, and discloses a communication network having a plurality of nodes. An individual node of the communication network includes a measurement device configured to collect local measurements, an antenna configured to wirelessly connect the individual node to cooperating nodes that are part of the communication network, one or more processors in electronic communication with the measurement device and the antenna, a memory coupled to the one or more processors, the memory storing data into a database and program code that, when executed by the one or more processors, causes the individual node to estimate an estimated local state vector for the individual node based on the local measurements. The individual node determines a cooperation update based on estimated cooperation measurements and a cooperation residual, where the cooperation update is applied to the estimated local state vector of the individual node.

Description

Technical Field

[0001] This disclosure relates to an estimator for communication networks. More specifically, this disclosure relates to an estimator for determining updates for individual nodes based on local and cooperative measurement results among other nodes that are part of the communication network. Background Technology

[0002] A cooperative positioning, navigation, and timing (PNT) system comprises a group of users interconnected via a wireless communication network. Each user can be a vehicle or an individual comprising PNT devices and sensors, with each user referred to as a node. Each node as part of the PNT system can be located in a different geographical area. Thus, some nodes may be located in locations where Global Navigation Satellite System (GNSS) signals are widely available, while other nodes may receive very limited signals or no signal at all. For example, a cluster of high-rise buildings or tall structures in a city (called an urban canyon) often obstructs GNSS signals. In another example, some nodes may be located in areas with significant radio frequency interference or congestion.

[0003] Some nodes may only be equipped with GNSS receivers, and therefore may be unable to receive signals in some situations. However, other nodes may include combined GNSS and inertial measurement units. Some other nodes may include celestial navigation systems or vision-based navigation systems, which are capable of providing PNT solutions even when GNSS signals are unavailable. However, cooperative PNT systems help mitigate some of these problems by allowing nodes to utilize not only their own information but also information available on the wireless network. Summary of the Invention

[0004] According to several aspects, a communication network with multiple nodes is disclosed. Individual nodes of the communication network include: a measuring device configured to collect local measurement results; an antenna configured to wirelessly connect the individual node to cooperating nodes as part of the communication network; one or more processors that electronically communicate with the measuring device and the antenna; and a memory coupled to the one or more processors. The memory stores data as a database and program code, which, when executed by the one or more processors, causes the individual node to estimate a local state vector for the individual node based on the local measurement results. The individual node predicts an estimated local measurement result based on the estimated local state vector and the local measurement results. A local residual is associated with the estimated local measurement result. The individual node determines a local update based on the estimated local measurement result and the local residual, wherein the local update is applied to the estimated local state vector of the individual node. The individual node predicts an estimated cooperative measurement result based on cooperative measurement results between the individual node and the cooperating nodes, wherein a cooperative residual is associated with the estimated cooperative measurement result. Finally, the individual node determines a cooperative update based on the estimated cooperative measurement result and the cooperative residual, wherein the cooperative update is applied to the estimated local state vector of the individual node.

[0005] A method for updating an estimated local state vector for an individual node is disclosed. The method includes estimating the estimated local state vector for the individual node based on local measurements collected by the individual node's measurement device. The individual node is part of a communication network with multiple nodes. The method includes determining a local error covariance matrix for the estimated local state vector by an estimator of the individual node based on the local measurements, wherein the local error covariance matrix characterizes the error of the estimated local state vector. The method further includes predicting the estimated local measurement results of the individual node by the estimator of the individual node based on the estimated local state vector and the local measurements. The method also includes determining local residuals associated with the estimated local measurement results by the estimator of the individual node. The method includes determining a local measurement sensitivity matrix by the estimator of the individual node, which represents the amount of change experienced by the local measurement results based on corresponding changes in the local state vector. The method also includes determining a local measurement variance matrix by the estimator of the individual node, which represents the uncertainty in the local measurement results. The method further includes determining a local residual covariance matrix by an estimator of an individual node based on a local error covariance matrix and a local measurement variance matrix. The method also includes determining a local gain matrix by an estimator of an individual node based on the local residual covariance matrix, the local error covariance matrix, and a local measurement sensitivity matrix. The method further includes combining the local gain matrix with the local residuals to create a local update. The method also includes applying the local update to the estimated local state vector and the local residual covariance matrix of the individual node. The method further includes predicting an estimated cooperative measurement result by an estimator of an individual node based on cooperative measurement results between the individual node and cooperative nodes, wherein the cooperative nodes are part of a communication network and communicate wirelessly with the individual nodes. The method includes determining cooperative residuals associated with the estimated cooperative measurement result by an estimator of an individual node. The method also includes determining a cooperative measurement sensitivity matrix by an estimator of an individual node, which represents the amount of change experienced by the cooperative measurement result based on the corresponding change in the local state vector of the individual node. The method includes determining a composite covariance matrix by an estimator of individual nodes, which characterizes the uncertainty in the cooperative measurement outcome when the influence of one or more states of the cooperative nodes is modeled as random noise. The method also includes determining a cooperative error covariance matrix of the cooperative residuals by an estimator of individual nodes, based at least on the cooperative measurement sensitivity matrix and the composite covariance matrix. The method further includes determining a cooperative gain matrix by an estimator of individual nodes based on the cooperative error covariance matrix of the cooperative residuals. Finally, the method includes combining the cooperative gain matrix with the cooperative residuals to create a cooperative update.

[0006] The features, functions, and advantages already discussed can be implemented individually in various embodiments or combined in other embodiments, further details of which can be seen in the following description and figures. Attached Figure Description

[0007] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of this disclosure in any way.

[0008] Figure 1 This is a schematic diagram of a public communication network having multiple nodes that can wirelessly communicate with each other, according to an exemplary embodiment.

[0009] Figure 2 This is a schematic diagram of a communication network according to an exemplary embodiment, which has a Local and Direct Neighbor (LWIN) approach for updating the estimated state vector of individual nodes.

[0010] Figure 3 This is a schematic diagram of an individual node that updates the estimated state vector based on local measurement results and cooperative measurement results using the LWIN approach according to an exemplary embodiment.

[0011] Figure 4 A cooperative positioning, navigation, and timing (PNT) system according to an exemplary embodiment is shown;

[0012] Figure 5A This illustrates an example of collaborative measurement results between individual nodes and collaborating nodes, which includes relative distance measurements combined with relative line-of-sight (LOS) measurements, according to an exemplary implementation.

[0013] Figure 5B This illustrates another example of collaborative measurement results between individual nodes and collaborating nodes, including relative LOS orientation measurement results, according to an exemplary embodiment.

[0014] Figure 5C This illustrates yet another example of collaborative measurement results between individual nodes and collaborating nodes, including relative range measurement results, according to an exemplary embodiment;

[0015] Figures 6A to 6B A process flowchart according to an exemplary embodiment is shown, illustrating a method for determining an update of the estimated state vector for an individual node based on the LWIN approach;

[0016] Figure 7 This is a schematic diagram of a communication network based on an Integrated Total Network Solution (ITNS) approach for updating estimated state vectors, according to an exemplary embodiment.

[0017] Figure 8This is based on an exemplary embodiment of determining local updates and cooperative updates for each node that is part of a communication network. Figure 7 A schematic diagram of the centered portion of the estimator shown;

[0018] Figures 9A to 9B A process flowchart according to an exemplary embodiment is shown, illustrating a method for determining updates to the estimated state vectors for individual nodes based on the ITNS approach; and

[0019] Figure 10 It is a computational system for a disclosed estimator according to an exemplary embodiment. Detailed Implementation

[0020] This disclosure relates to an estimator for determining corrections or updates to the estimated local state vectors applied to individual nodes. The updates are based on local measurements as well as collaborative measurements between the individual node and collaborating nodes. This disclosure describes two different approaches for updating the estimated local state vectors for individual nodes. In a first example, the individual node includes its own estimator, and the estimated local state vectors are updated based on local measurements as well as collaborative measurements between the individual node and collaborating nodes. This decentralized approach is called the Local and Direct Neighbor (LWIN) approach. In the second approach, a portion of the local update for each node is performed locally, while the collaborative update for each pair of collaborating nodes is performed at a centralized portion of the estimator. This is a more centralized approach and is therefore called the Integrated Total Network Solution (ITNS) approach. Both approaches improve the accuracy of the estimated local state vectors for each node because the updates are based not only on local measurements but also on collaborative measurements between nodes.

[0021] The following description is exemplary in nature and is not intended to limit this disclosure, application, or use.

[0022] Reference Figure 1An exemplary communication network 10 with multiple nodes 18 is illustrated. The nodes 18 communicate wirelessly with each other via network connection 20. Each node 18, as part of the communication network 10, is configured to collect local measurement results and collaborative measurement results measured between two cooperating nodes 18. Some examples of local measurement results and collaborative measurement results include, but are not limited to, time transfer and synchronization, three-dimensional images, range measurement results between nodes 18, line-of-sight (LOS) measurement results between nodes 18, angle of arrival (AOA) measurement results between nodes 18, or direction of arrival (DOA) measurement results between nodes 18. Nodes 18 represent any device configured to estimate local state vectors, such as, for example, a machine, vehicle, or individual such as a soldier. For example, in one embodiment, nodes 18 may each represent a machine, wherein nodes 18 are located in a manufacturing facility. As explained below and in Figure 4 As shown, in another embodiment, each node 18 represents a user as part of the Cooperative Positioning, Navigation, and Timing (PNT) network 26.

[0023] Reference Figure 2 and Figure 3 In one approach, individual node 18i includes an estimator 40 that determines an update to the estimated local state vector. The update is determined based on local measurements and cooperative measurements taken between individual node 18i and cooperative nodes 18j. This approach is called the Local and Direct Neighbor (LWIN) approach because all computations are performed locally at individual node 18i. Alternatively, in... Figure 7 and Figure 8 In the illustrated embodiment, the communication network 10 includes a distributed approach. Specifically, this approach includes estimators 140 distributed throughout the communication network 10. Specifically, a portion 98 of the estimator 140 is locally located at each node 18 that is part of the communication network 10 and determines some local updates. A centralized portion 100 of the estimator 140 determines the remainder of the local updates for each node 18 that is part of the communication network 10, and determines collaborative updates based on collaborative measurements between two cooperating nodes 18. This approach is called Integrated Total Network Solution (ITNS) because some computation is performed at the centralized portion 100 of the estimator 140.

[0024] Return to Figure 2 In the LWIN approach, each node 18 wirelessly communicates with at least one other neighboring node 18 that is part of the communication network 10. For the purposes of this disclosure, a neighboring node 18 refers to a logical or topological relationship between two nodes 18. Furthermore, although... Figure 2The diagram illustrates that nodes 18i and 18j communicate electronically with each other, nodes 18k and 18n communicate electronically with each other, and nodes 18j and 18n communicate electronically with each other. However, it should be understood that each node 18 can communicate with all the remaining nodes 18 that are part of the communication network 10. Specifically, if there are n nodes that are part of the communication network 10, then each node 18 can communicate wirelessly with up to n-1 nodes.

[0025] Each node 18 includes a computing system 30, a measurement device 32, a transceiver 34, and an antenna 36, ​​wherein the nodes 18 communicate wirelessly with each other via their respective antennas 36. The computing system 30 communicates electronically with the measurement device 32, the transceiver 34, and the antenna 36. The measurement device 32 refers to any device or combination of devices configured to collect local and collaborative measurement results for the respective node 18. For example, in one embodiment, if the local measurement result is a three-dimensional image, the measurement device 32 includes one or more cameras. In another embodiment, the measurement device 32 is a PNT system that determines the position of the respective node 18 in a Earth reference frame. It should be understood that the PNT system is not limited to a particular type of system. For example, some nodes 18 as part of the communication network 10 may include only a Global Navigation Satellite System (GNSS) receiver as a PNT system. However, other nodes 18 may include a GNSS receiver combined with an inertial measurement unit as a PNT system. Alternatively, in another approach, some nodes 18 may include a vision-based navigation system or an astronomical navigation system as a PNT system.

[0026] Figure 3 It includes estimator 40. Figure 2 A schematic diagram of the computing system 30 for individual node 18i shown, wherein the estimator 40 is configured to be based on the measurement device 32 of individual node 18i. Figure 2 The local measurement results collected determine the local update 46. The local measurement results represent information based solely on individual node 18i. The estimator 40 is also configured to determine the collaborative update 48 (e.g., based on collaborative measurement results between individual node 18i and collaborating node 18j) based on collaborative measurement results between individual node 18i and collaborating node 18j. Figure 2 (As shown in the diagram). The cooperation measurement results between individual node 18i and cooperating node 18j are measured relative to individual node 18i.

[0027] The estimator 40 includes a local state propagation block 50, a local measurement block 52, and a local update block 54 for determining the local update 46. The local state propagation block 50 determines multiple local state propagators. These propagators include an estimated local state vector for individual node 18i and a local error covariance matrix of the estimated local state vector. The local state propagation block 50 receives the local state vector x. i(k) Deterministic input vector u i (k) and local measurement result vector z i (k) is used as input, where k represents a specific time point. Local state vector x i (k) Deterministic input vector u i (k) and local measurement result vector z i (k) Based on local measurement results collected from the measurement device 32 of individual node 18i ( Figure 2 ).

[0028] The local state propagation block 50 includes a local state propagation block 60 and a local error covariance block 62. The local state propagation block 60 is based on the local state vector x according to Equation 1. i (k) and deterministic input vector u i (k) Estimate the local state vector for individual node 18i, as shown in Equation 1 below:

[0029]

[0030] in It is the local state vector for estimating individual node 18i up to time point (k-1), and f i The function represents the dynamic behavior of the communication network 10 at individual node 18i. Figure 1 Modeling.

[0031] Local error covariance block 62 determines the local state vector used for estimation. The local error covariance matrix. The local error covariance matrix represents the estimated local state vector. The error. The local error covariance matrix is ​​based on local measurement results and estimated local state vectors. To determine this, and to express it in Equation 2:

[0032]

[0033] Where P i (k|k-1) is the local error covariance matrix when time is equal to (k-1), based on measurements up to time point (k-1). i (k-1|k-1) is the covariance matrix for individual node 18i at time k, based on measurements up to time point (k-1). i It is the state transition matrix used for node 18i, Φ i T Let Q represent the transpose state transition matrix, and Q... i (k) is the process noise covariance matrix for node 18i. It should be understood that the state transition matrix Φ...i The process noise covariance matrix Q i (k) are all block diagonal matrices, which can lead to a reduction in computational cost.

[0034] Local measurement results of the estimated local measurement of individual node 18i predicted by local measurement block 52. and local residual μ i Specifically, the local measurement block 52 includes a local measurement prediction block 64, which is based on the deterministic input vector u according to Equation 3. i (k) Local measurement result vector z i (k) and the local state vector for individual node 18i The estimation is used to predict the local measurement results of individual node 18i. Equation 3 is as follows:

[0035]

[0036] Where g i The representation function represents the local measurement model at individual node 18i. Local residual μ i Local measurement results estimated with individual node 18i Relatedly, the local measurement block 52 includes a local residual block 66, which is based on the local measurement result vector z. i (k) and estimated local measurement results The difference between them determines the local residual μ used for individual node 18i. i And it is expressed in Equation 4 as:

[0037]

[0038] Local update block 54 includes a local measurement sensitivity block 68, a local residual covariance matrix block 70, and a local gain matrix block 72. Local update block 54 determines a local update 46, which is applied to the local covariance matrix P for individual node 18i. i (k-1|k-1) and the estimated local state vector Specifically, the local measurement sensitivity block 68 first determines the local measurement sensitivity matrix H. zi (k) and the local measurement variance matrix R zi (k). Local measurement sensitivity matrix H zi (k) indicates that the local measurement results are based on the local state vector x. i The amount of change experienced by the change in (k). Local measurement variance matrix R zi (k) represents the uncertainty in the local measurement result, where uncertainty can also be called error. Local measurement sensitivity matrix H zi(k) is expressed in Equation 5 as:

[0039]

[0040] in Represents the local state vector x i The partial derivative of (k). It should be understood that the local measurement sensitivity matrix H... zi (k) and the local measurement variance matrix R zi (k) Both are block-diagonal, which simplifies the calculation. Local residual covariance matrix block 70 determines the local residual covariance matrix. It represents μ used for individual node 18i i Uncertainty. Local residual μ i residual covariance matrix Based on the local error covariance matrix P i (k|k-1) and the local measurement variance matrix R zi (k) is used to determine this, and is expressed in Equation 6 as follows:

[0041]

[0042] in Represents the partial mapping matrix. Local gain matrix block 72 is based on the covariance matrix. Local error covariance matrix P i (k|k-1) and the local measurement sensitivity matrix H zi (k) Determine the local gain matrix K zi (k), which is expressed in Equation 7 as:

[0043]

[0044] Where the covariance matrix The inverse matrix P μi -1 Expressed as B μi Local gain matrix K zi (k) and local residual μ i Combining these elements to create a local update 46. Specifically, the local update 46 is the local gain matrix K. zi (k) and local residual μ i The product of the local state vector and the local state propagation block 60 and the local residual covariance matrix block 70. Specifically, the local state propagation block 60 adds the local update 46 to the estimated local state vector. To determine the updated estimated local state vector It is expressed in Equation 8 as:

[0045]

[0046] Local update 46 is also applied to the local residual covariance matrix. Specifically, the local residual covariance matrix block 70 adds local update 46 to the local residual covariance matrix. To determine the updated residual covariance matrix P i (k|k), which is expressed in Equation 9 as:

[0047]

[0048] Where I represents the identity matrix.

[0049] The collaborative update 48 for individual node 18i is now described. It should be understood that the estimator 40 can determine the local error covariance matrix P for individual node 18i within the local error covariance block 62. i The cooperative update 48 is determined immediately after (k|k-1), or alternatively, the local gain matrix K is determined in the local gain matrix block 72. zi (k) The cooperative update 48 is determined immediately afterward. However, if the estimator 40 determines the cooperative update 48 after the local update 46, the covariance matrix is ​​updated first. Then the estimator 40 can determine the collaborative measurement results.

[0050] The estimator 40 includes a collaborative measurement block 76 and a collaborative update block 78 for determining the collaborative update 48. The collaborative measurement block 76 includes a collaborative measurement prediction block 80 and a collaborative residual block 82. As explained below, the collaborative measurement prediction block 80 predicts the estimated collaborative measurement result based on the collaborative measurement results between individual node 18i and collaborative node 18j. It should be understood that the measurement results between individual node 18i and collaborating node 18j are relative to the measurement of individual node 18i. Collaborating node 18j can communicate via communication network 10 ( Figure 1 The local measurement results of the individual node 18j can be sent to the individual node 18i, or alternatively, the measurement device 32 of the individual node 18i can collect the local measurement results of the cooperating node 18j.

[0051] Collaborative measurement prediction block 80 is based on the collaborative measurement result vector y ij (k) Local state vector x of individual node 18i i (k) and the local state vector y of cooperative node 18j i (k), the predicted estimated collaborative measurement results And it can be expressed by Equation 10 as follows:

[0052]

[0053] Where h ijThis represents a function that models the collaborative measurement results at individual node 18i, such as those obtained by collaborative nodes 18i through nodes 18i and 18j. The collaborative measurement result vector is y. ij (k) represents the collaborative measurement result between individual node 18i and collaborating node 18j.

[0054] Collaborative residual υ and estimated collaborative measurement results Related. Specifically, collaborative residual block 82 determines the collaborative residual υ. The collaborative residual υ represents the collaborative measurement result vector y. ij (k) and estimated collaborative measurement results The difference between them, and expressed according to Equation 11, is:

[0055]

[0056] The cooperative update block 78 includes a cooperative measurement sensitivity block 84, a cooperative covariance matrix block 86, and a cooperative gain matrix block 88. The cooperative measurement sensitivity block 84 determines the cooperative measurement sensitivity matrix H. yij (k) and the composite covariance matrix Collaborative measurement sensitivity matrix H yij (k) represents the collaborative measurement result based on the local state vector x of individual node 18i. i The amount of change experienced by the corresponding change in (k). When the influence of one or more states of cooperative node 18j is modeled as random noise, the composite covariance matrix Characterizes the uncertainty in collaborative measurement results. Collaborative measurement sensitivity matrix H yij (k) Based on estimated collaborative measurement results To determine this, it is expressed in Equation 12 as:

[0057]

[0058] The cooperative measurement sensitivity block 84 is also based on the cooperative measurement variance matrix R. yij (k) determines the composite covariance matrix This indicates uncertainty in the state, and The uncertainty of noise is represented, and is expressed in Equation 13 as:

[0059]

[0060] Block 86 of the cooperative covariance matrix determines the cooperative error covariance matrix P of the cooperative residual υ. υij Cooperative error covariance matrix P υij Based on the cooperative measurement sensitivity matrix H yij (k), Cooperative measurement variance matrix R yij (k) Composite covariance matrix The cooperative error covariance matrix P of individual node 18i ii The cooperative error covariance matrix P of (k|k-1) and cooperative node 18j jj (k|k-1) is used to determine this, and it is expressed in Equation 14 as follows:

[0061]

[0062] The cooperative gain matrix block 88 is based on the covariance matrix P of the cooperative residual υ. υij , Covariance matrix of cooperative error P ii (k|k-1) and cooperative measurement sensitivity matrix H yij (k) determines the cooperative gain matrix K yij (k), which is expressed in Equation 15 as:

[0063]

[0064] Wherein the cooperative covariance matrix P υij The inverse P υij -1 Expressed as B υij Cooperative gain matrix K yij (k) is combined with the cooperative residual υ to create a cooperative update 48. Specifically, the cooperative update 48 is the cooperative gain matrix K. yij The product of (k) and the cooperative residual υ. Then the local state propagation block 60 applies the cooperative update 48 to the estimated local state vector. Specifically, the local state propagation block 60 adds the cooperative update 48 to the estimated local state vector. To determine the updated estimated local state vector It is expressed in Equation 16 as:

[0065]

[0066] The local residual covariance matrix block 70 determines the updated residual covariance matrix P based on local update 46. i (k|k), which is expressed in Equation 17 as:

[0067]

[0068] It should be understood that this is used for composite covariance matrices. The estimator is decoupled. In other words, each node 18i, 18j is associated with a unique state vector. It should also be understood that the estimation error of the cooperative node 18j is modeled as noise. Specifically, the cooperative measurement result vector y ij (k) is expressed in Equation 18 as:

[0069]

[0070] Where s ij (k) represents the random measurement noise vector for individual node 18i at time k when cooperating with cooperative node 18j. Finally, it should be understood that the cooperative measurement result vector y ij (k) is a unique measurement result that is not locally independent. In other words, all the remaining measurement results described above are local measurement results specific to individual node 18i or cooperative node 18j.

[0071] Figure 4 This is an exemplary diagram of a PNT network 26, where each node 18 represents a user. In... Figure 4 In the exemplary embodiment shown, node 18 represents a land vehicle, helicopter, or aircraft. However, it should be understood that node 18 can also represent an individual. For example, in one embodiment, one or more nodes 18 represent an individual such as a soldier carrying a PNT system. (See also...) Figure 2 and Figure 4 The measurement device 32 for each node 18 of the PNT network 26 is a PNT system that determines the position 90 of the corresponding node 18 in a geocentric reference frame. An example of a geocentric reference frame is a geocentric, fixed-on-the-earth (ECEF) reference frame. Alternatively, in another embodiment, latitude, longitude, and altitude may be used instead. In the illustrated embodiment, the dashed lines between nodes 18 represent wireless communication connections 92A. The thin solid lines between nodes 18 represent wireless communication connections 92B that include time transfer and range measurement. The thick solid lines between nodes 18 represent wireless communication connections 92C that include time transfer, range measurement, and line-of-sight (LOS) measurement.

[0072] Figures 5A to 5C It shows the basis Figure 4 The exemplary collaborative measurement results of the PNT network 26 shown are illustrated. Figure 5A In the illustrated embodiment, the collaboration measurement results between individual node 18i and collaborating node 18j include a combination of the relative distance measurement results between individual node 18i and collaborating node 18j and the relative LOS measurement results between individual node 18i and collaborating node 18j. For example... Figure 5A As seen, the relative distance measurement result is the first relative distance r measured between individual node 18i and cooperative node 18j, as measured by individual node 18i. ij And the second relative distance r between individual node 18i and cooperative node 18j, as measured by cooperative node 18j. ji To represent. Relative LOS measurement results include, for example, the first relative LOS measurement results measured by individual node 18i. It is represented by the first unit vector pointing from individual node 18i to cooperative node 18j, where the superscript B i The ontology reference frame of individual node 18i is indicated. The relative LOS measurement results further include a second relative LOS measurement result, such as that measured by cooperative node 18j. It is represented by the second unit vector pointing from the cooperating node 18j to the individual node 18i, where the superscript B j Indicates the ontology reference frame of cooperative node 18j.

[0073] Equation 19 expresses the position of individual node 18i in the ECEF reference frame. E R i Furthermore, Equation 20 expresses the position of the cooperative node 18j in the ECEF reference frame. E R j :

[0074]

[0075]

[0076] in This represents the direction cosine matrix used to transform the vector from the body reference frame of individual node 18i to the ECEF reference frame, and This represents the direction cosine matrix used to transform the vector from the ontology reference frame of cooperative node 18j to the ECEF reference frame. It should be understood that the cooperative measurement results between individual node 18i and cooperative node 18j for the LWIN path are node-centric. In other words, the cooperative measurement results between individual node 18i and cooperative node 18j are measured relative to either individual node 18i or cooperative node 18j.

[0077] In such Figure 5B In another embodiment shown, the collaborative measurement result between individual node 18i and collaborating node 18j is a relative LOS direction measurement result, indicating an angle measurement result relative to either individual node 18i or collaborating node 18j. In one example, the collaborative measurement result is centered on individual node 18i and includes the position of individual node 18i. E R i The pose r of individual node 18i in the ontology reference frame Bi First relative LOS direction measurement results u ij And the first LOS angle Ψ LOSi It should be understood that these measurements involve two LOS angles; however, for simplicity, only a single angle is shown. The first relative LOS direction measurement between individual node 18i and cooperative node 18j is shown relative to individual node 18i. ijAnd relative to individual node 18i and the first relative LOS direction measurement result u ij Measure the first LOS angle Ψ LOSi Equation 21 can be used to determine the pose r of individual node 18i. Bi and location E R i And expressed as:

[0078]

[0079] Where z iij This represents the measurement result for individual node 18i, as measured by individual node 18i through collaboration between individual node 18i and collaborating node 18j, and v iij This represents all measurement noise. In another example, instead of node-centric measurements, the location of cooperative node 18j is used. E R j The pose r of cooperative node 18j in the ontology reference frame Bj Second relative LOS direction measurement results u ji The second relative LOS direction measurement result u between individual node 18i and cooperative node 18j is measured relative to cooperative node 18j. ji The measurement results u relative to cooperative node 18j and the second relative LOS direction ji Measure the second LOS angle Ψ LOSj The location of collaborative node 18j E R j The pose r of collaborative node 18j Bj Second relative LOS direction measurement results u ji Through communication network 10 ( Figure 1 The data is transmitted to individual node 18i. Equation 22 can be used to determine the pose r of individual node 18i. Bi and location E R i And expressed as:

[0080]

[0081] Where z iji This represents the measurement result for individual node 18i, as measured by cooperative node 18j through collaboration between cooperative node 18j and individual node 18i, and v iji This represents all measurement noise.

[0082] In such Figure 5CIn another embodiment shown, the collaborative measurement result between individual node 18i and collaborating node 18j is a relative range measurement result between individual node 18i and collaborating node 18j relative to either individual node 18i or collaborating node 18j. Specifically, Figure 5C The first relative range measurement result d between individual node 18i and cooperative node 18j, as measured relative to individual node 18i, is shown. ij And the second relative range measurement result d between individual node 18i and cooperative node 18j, as measured relative to cooperative node 18j. ji In one example, such as the first relative range measurement result d between individual node 18i and collaborating node 18j, measured relative to individual node 18i. ij This is expressed in equation 23 as follows:

[0083]

[0084] When the first relative range measurement result d ij Linearization to δd ij When equation 23 becomes equation 24, the equation is:

[0085]

[0086] in This represents an estimate of the measurement results for the first relative range. Represents the vector magnitude, δR i Let δR represent the linearized position of individual node 18i, and δR i This indicates the linearized position of the collaborating node 18j. If the first relative LOS measurement result... This is expressed in Equation 25 as:

[0087]

[0088] The linearized first relative range measurement result δd ij This can be expressed in Equation 26 as:

[0089]

[0090] Figures 6A to 6B An exemplary process flowchart is shown, illustrating the process of updating the local state vector estimated for individual node 18i. Method 200. (Refer to...) Figure 2 , Figure 3 and Figure 6AMethod 200 begins at box 202. In boxes 202-220, estimator 40 determines local updates 46. Specifically, in box 202, local state propagation block 60 estimates the estimated local state vector for individual node 18i based on local measurement results. Then method 200 can proceed to box 204.

[0091] In box 204, the local error covariance block 62 determines the local state vector used for estimation based on local measurement results. The local error covariance matrix P i (k|k-1). Local error covariance matrix P i (k|k-1) represents the estimated local state vector. The error. Then method 200 can proceed to box 206.

[0092] In box 206, the local measurement prediction block 64 is based on the local state vector. The estimation and local measurement results predict the local measurement results of individual node 18i. The local residual μ used for individual node 18i i Compared with the estimated local measurement results Related. Then method 200 can proceed to box 208.

[0093] In box 208, local residual block 66 determines the local residual μ for individual node 18i. i Local residual μ for individual node 18i i Represents the estimated local measurement results and local measurement result vector z i The difference between (k). Then method 200 can proceed to box 210.

[0094] In box 210, local measurement sensitivity block 68 determines the local measurement sensitivity matrix H. zi (k) represents the local measurement result based on the local state vector x. i The amount of change experienced by the change in (k). Then method 200 can proceed to box 212.

[0095] In box 212, the local measurement sensitivity block 68 determines the local measurement variance matrix R. zi (k), which represents the uncertainty in the local measurement results. Method 200 can then proceed to box 214.

[0096] In box 214, the local residual covariance matrix block 70 is based on the local error covariance matrix P. i (k|k-1) and the local measurement variance matrix R zi(k), determine the local residual μ i Local residual covariance matrix Then method 200 can proceed to box 216.

[0097] In box 216, the local gain matrix block 72 is based on the local residual covariance matrix. Local error covariance matrix P i (k|k-1) and local measurement sensitivity matrix H zi (k), determine the local gain matrix K zi (k). Then method 200 can proceed to box 218.

[0098] In box 218, local gain matrix block 72 will use local gain matrix K zi (k) and local residual μ i Combine the methods to create a partial update 46. Then method 200 can proceed to box 220.

[0099] In box 220, local update 46 is applied to the estimated local state vector of individual node 18i. and local residual covariance matrix Then method 200 can proceed to box 222.

[0100] Figure 6B Boxes 222-238 are shown, where the estimator 40 determines the collaborative update 48. Specifically, in box 222, the collaborative measurement prediction block 80 predicts the estimated collaborative measurement result based on the collaborative measurement result between individual node 18i and collaborative node 18j. The cooperative residual υ is related to the estimated cooperative measurement result. Related. Then method 200 can proceed to box 224.

[0101] In box 224, the cooperative residual block 82 determines the cooperative residual υ, which represents the cooperative measurement result vector y. ij (k) and estimated collaborative measurement results The difference between them. Then method 200 can proceed to box 226.

[0102] In box 226, cooperative measurement sensitivity block 84 determines the cooperative measurement sensitivity matrix H. yij (k) represents the collaborative measurement result based on the local state vector x of individual node 18i. i The amount of change experienced by the corresponding change in (k). Then method 200 can proceed to box 228.

[0103] In box 228, collaborative measurement sensitivity block 84 further determines the composite covariance matrix. When the influence of one or more states of the collaborating node 18j is modeled as random noise, the composite covariance matrix characterizes the uncertainty in the collaborating measurement results. Method 200 can then proceed to box 230.

[0104] In box 230, the cooperative covariance matrix block 86 is based at least on the cooperative measurement sensitivity matrix H. yij (k) and the composite covariance matrix Determine the cooperative error covariance matrix P of the cooperative residual υ υij Then method 200 can proceed to box 232.

[0105] In box 232, the cooperative gain matrix block 88 is based on the covariance matrix P of the cooperative residual υ. υij Determine the cooperative gain matrix K yij (k). Then method 200 can proceed to box 234.

[0106] In box 234, the cooperative gain matrix block 88 determines the cooperative update 48, which is based on the estimated cooperative measurement results. And the cooperative residual υ. Specifically, the cooperative gain matrix K yij (k) is combined with the collaborative residual υ to create a collaborative update 48. Then method 200 can proceed to box 236.

[0107] In box 236, collaborative update 48 is applied to the estimated local state vector of individual node 18i. Then method 200 can terminate or return to box 202.

[0108] Figure 7 This is a schematic diagram of multiple nodes 18i, 18j, 18k, and 18n that communicate wirelessly with the centralized part 100 of the estimator 140 based on the ITNS approach. In such... Figure 7 In the non-limiting embodiment shown, the communication network 10 includes a distributed estimator 140, wherein a portion 98 of the estimator 140 is located at each node 18. Specifically, this portion 98 of the estimator 140 at each node 18 includes a local state propagation block 60, a local error covariance block 62, and a local residual block 66. The estimator 140 also includes a centralized portion 100, which wirelessly communicates with all nodes 18 as part of the communication network 10. However, in an alternative embodiment, the communication network 10 includes multiple centralized portions 100 that wirelessly communicate with a sub-network or portion of the total nodes 18 as part of the communication network 10. Figure 7In the non-limiting embodiment shown, the centralized portion 100 of the estimator 140 is a separate component. In other words, the centralized portion 100 of the estimator 140 is not part of any node 18. However, in an alternative embodiment, the centralized portion 100 of the estimator 140 is included in one of the plurality of nodes 18 of the communication network 10.

[0109] The communication network 10 also includes one or more pairs of cooperating nodes 18i, 18j. For example, in the non-limiting embodiment shown, nodes 18i and 18j communicate wirelessly with each other, and there are cooperative measurement results between the pair of cooperating nodes 18i, 18j. It should be understood that, for the sake of brevity and ease of explanation, Figure 7 Only one pair of cooperating nodes 18i and 18j is shown. Each node 18 that is part of the communication network 10 can cooperate with each of the remaining nodes 18 that are part of the communication network 10. In other words, if there are n nodes 18 that are part of the communication network 10, there can be up to n*(n-1) pairs of cooperating nodes 18 included in the communication network 10.

[0110] Continue to refer to Figure 7 In the ITNS approach, multiple nodes 18 each determine their own estimated local state vectors. Estimated local measurement results And local residual μ. In other words, each node 18 includes a corresponding local state propagation block 60, a corresponding local measurement prediction block 64, and a corresponding local residual block 66. Each node 18 transmits its corresponding estimated local state vector through the communication network 10. Corresponding estimated local measurement results The corresponding local residual μ and the corresponding local measurement result are sent to the centralization section 100 of the estimator 140. The centralization section 100 of the estimator 140 receives the corresponding estimated local state vector from each of the plurality of nodes 18 that are part of the communication network 10. Corresponding estimated local measurement results The corresponding local residual μ and local measurement results. As explained below, the centralization portion 100 of the estimator 140 is based on the corresponding estimated local state vector. Corresponding estimated local measurement results The corresponding local residual μ and the corresponding local measurement results determine the local update 146 for each node 18. The centralized portion 100 of the estimator 140 also determines the cooperative update 148 for each pair of cooperative nodes 18i, 18j that are part of the communication network 10.

[0111] Figure 8This is a block diagram of the centralization portion 100 of the estimator 140. The centralization portion 100 of the estimator 140 includes a total error covariance block 162, a local measurement sensitivity block 168, a local residual covariance matrix block 170, and a local gain matrix block 172. The total error covariance block 162 is based on the data used as a communication network 10 (…). Figure 7 The estimated local state vector of each of the multiple nodes 18 in a part of ) Determine the total error covariance matrix for the entire communication network 10. The total error covariance matrix characterizes the error for the entire communication network 10. That is, the total error covariance matrix characterizes the local state vector based on the estimate for each node 18, which is part of the communication network 10. The error. The total error covariance matrix is ​​expressed in Equation 27 as:

[0112]

[0113] Where P(k|k-1) is the total error covariance matrix based on the measurement results up to point (k-1) when time equals (k-1), P(k-1|k-1) is the covariance matrix for the entire communication network 10 based on the measurement results up to point (k-1) when time equals k, and Φ is the state transition matrix for each node 18 in the communication network 10. T Let Q(k) represent the transpose state transition torque, and Q(k) be the process noise covariance matrix for the entire communication network 10.

[0114] Local measurement sensitivity block 168 determines the corresponding local measurement sensitivity matrix H z (k) and used as a communication network 10 ( Figure 7 The local measurement variance matrix R of a specific node 18 in part of ) z (k). As described above, the corresponding local measurement sensitivity matrix H z (k) represents the amount of change experienced by the local measurement result for a specific node 18 based on the change in the corresponding local state vector x(k), and the corresponding local measurement variance matrix R z (k) represents the uncertainty in the local measurement results for a specific node 18. The local residual covariance matrix block 170 of the estimator 140 is based on the total error covariance matrix P for the entire communication network 10. i (k|k-1) and the local measurement variance matrix R zi (k) Determine the corresponding local residual covariance matrix P for a specific node 18. μ It is expressed in Equation 6 above.

[0115] Then, the local gain matrix block 172 of the centralized portion 100 of the estimator 140 is based on the corresponding local residual covariance matrix P for a particular node 18. μ The total error covariance matrix P(k|k-1) for the entire communication network 10 and the corresponding local measurement sensitivity matrix H for a specific node 18. z (k), determine the corresponding local gain matrix K for a specific node 18. z (k). Corresponding local gain matrix K z (k) is combined with the local residual μ to create a local update 146 for a specific node 18. Specifically, the local update 146 for a specific node 18 is the corresponding local residual μ and the corresponding local gain matrix K for each node 18 that is part of the communication network 10. z The product of (k). Therefore, based on the total error covariance matrix P(k|k-1) for the entire communication network 10 and the local residual μ, the local update 146 is determined.

[0116] Reference Figure 7 and Figure 8 Local update 146 is applied to the estimated local state vector of a specific node 18 that is part of the communication network 10. To determine the corresponding updated estimated local state vector. Local update 146 is also applied to the covariance matrix P of a specific node 18. μ It should be understood that the centralized portion 100 of the estimator 140 determines a unique local update 146 for each of the plurality of nodes 18 in the communication network 10. In other words, the centralized portion 100 of the estimator 140 is configured to determine n local updates 146, wherein each local update 146 corresponds to a specific node among the nodes 18 that are part of the communication network 10.

[0117] Now, let's describe the collaborative update 148. There can be any number of pairs of collaborative nodes 18i, 18j, which are part of the communication network 10. Therefore, there can be up to n*(n-1) collaborative updates 148 determined by the centralization portion 100 of the estimator 140. Furthermore, the centralization portion 100 of the estimator 140 can determine the collaborative update 148 immediately after the total error covariance matrix P(k|k-1) is determined by the total error covariance block 162, or alternatively immediately after the local update 146 is applied by the local gain matrix block 172. However, if the centralization portion 100 of the estimator 140 determines the collaborative update 148 after the local update 146, the total error covariance matrix P(k|k-1) is updated first, and then the centralization portion 100 of the estimator 140 can determine the collaborative measurement result.

[0118] For illustrative purposes, nodes 18i and 18j ( Figure 7 ) represents a pair of cooperating nodes. Specifically, node 18i represents an individual node, and node 18j represents a cooperating node, performing cooperative measurements relative to the individual node. However, it should be understood that each node 18 as part of the communication network 10 can cooperate with each remaining node 18 as part of the communication network 10 to determine the cooperative measurement results. The centralized portion 100 of the estimator 140 includes a cooperative measurement block 176 and a cooperative update block 178. The cooperative measurement block 176 includes a cooperative measurement prediction block 180 and a cooperative residual block 182. The cooperative measurement prediction block 180 is based on the local state vector x of the cooperating nodes 18i and 18j. i (k), y i (k) and the collaborative measurement result vector y ij (k), the predicted estimated collaborative measurement results And expressed by Equation 10 above. With the estimated collaborative measurement results The associated collaboration residual υ represents the collaboration measurement result and the estimated collaboration measurement result. The difference between them is determined based on Equation 11 above.

[0119] The cooperative update block 178 includes a cooperative measurement sensitivity block 184, a cooperative covariance matrix block 186, and a cooperative gain matrix block 188. The cooperative measurement sensitivity block 184 determines the cooperative measurement sensitivity matrix H. yij (k) and the composite covariance matrix Collaborative measurement sensitivity matrix H yij (k) represents the cooperative measurement result based on the local state vector x of node 18i. i The change experienced by the corresponding change in (k), where the cooperative measurement is performed relative to node 18i. When the influence of one or more states of cooperative node 18j of a pair of cooperative nodes 18i, 18j is modeled as random noise, the composite covariance matrix is... Characterizes the uncertainty in collaborative measurement results. Collaborative measurement sensitivity matrix H yij (k) is represented in Equation 12 above, and the composite covariance matrix This is expressed in Equation 13 above.

[0120] Block 186 of the cooperative covariance matrix determines the cooperative error covariance matrix P of the cooperative residual υ. υij And determined based on Equation 14 above. The cooperative gain matrix block 188 is based on the cooperative error covariance matrix P of the cooperative residual υ. υij Determine the cooperative gain matrix K yij (k). Based on Equation 15 above, the cooperative gain matrix K is determined. yij (k). Cooperative gain matrix Kyij (k) is combined with the cooperative residual υ to create a cooperative update 148 for a pair of cooperative nodes 18i, 18j. Specifically, the cooperative update 148 is the cooperative gain matrix K. yij The product of (k) and the cooperative residual υ. (Refer to...) Figure 7 and Figure 8 Collaborative update 48 is applied to the estimated local state vectors of nodes 18i and 18j of a pair of collaborating nodes. Collaborative update 148 is also applied to the total error covariance matrix P(k|k-1) of communication network 10.

[0121] In one embodiment, the communication network 10 including the centralized portion 100 of the estimator 140 is a PNT network 26. Figure 4 Part of ). Therefore, in such Figure 5A In the illustrated embodiment, the collaborative measurement result between individual node 18i and collaborating node 18j includes a combination of the relative distance measurement result between individual node 18i and collaborating node 18j and the relative LOS measurement result between individual node 18i and collaborating node 18j. As described above, the relative distance measurement result is a first relative distance r between individual node 18i and collaborating node 18j, as measured by individual node 18i. ij And the second relative distance r between individual node 18i and cooperative node 18j, as measured by cooperative node 18j. ji To represent. Relative LOS measurement results include, for example, the first relative LOS measurement results measured by individual node 18i. It is represented by a first unit vector pointing from individual node 18i to cooperative node 18j. The relative LOS measurement results further include a second relative LOS measurement result as measured by cooperative node 18j. It is represented by the second unit vector pointing from the cooperating node 18j to the individual node 18i.

[0122] Reference Figure 5B In another embodiment, the collaborative measurement result between individual node 18i and collaborating node 18j is a relative LOS direction measurement result, indicating an angle measurement result, such as that measured relative to individual node 18i or collaborating node 18j. In one example, the collaborative measurement result includes the position of individual node 18i. E R i The pose r of individual node 18i in the ontology reference frame Bi Measurement results of the first relative LOS direction u ij Equation 28 can be used to determine the pose r of individual node 18i. Bi and location E R i And expressed as:

[0123]

[0124] Where z ij This represents the measurement results for individual node 18i through collaboration between individual nodes 18i. In another example, the collaboration measurement results include the location of collaborating node 18j. E R j The pose r of cooperative node 18j in the ontology reference frame Bj Second relative LOS direction measurement results u ji Equation 29 can be used to determine the pose r of individual node 18i. Bi and location E R i And expressed as:

[0125]

[0126] Where z ji This represents the measurement results for the collaborating node 18j through the collaboration between the collaborating node 18j and the individual node 18i, and v iji This represents all measurement noise.

[0127] In such Figure 5C In another embodiment shown, the collaborative measurement result is a relative range measurement result between individual node 18i and collaborative node 18j, measured relative to individual node 18i or collaborative node 18j. Specifically, Figure 5C The first relative range measurement result d between individual node 18i and cooperative node 18j, as measured relative to individual node 18i, is shown. ij And the second relative range measurement result d between individual node 18i and cooperative node 18j, as measured relative to cooperative node 18j. ji .

[0128] Figures 9A to 9B An exemplary process flowchart is shown, illustrating a method 300 for updating estimated local state vectors for multiple nodes 18 that are part of a communication network 10. (Refer to...) Figure 7 , Figure 8 and Figure 9A Method 300 begins at block 302. In blocks 302-316, the centralization portion 100 of the estimator 140 determines the local update 146. Specifically, in block 302, the centralization portion 100 of the estimator 140 receives the corresponding estimated local state vector from each of the plurality of nodes 18 that are part of a communication network. Corresponding estimated local measurement results And the corresponding local residual μ. Then method 300 can proceed to box 304.

[0129] In block 304, the total error covariance block 162 of the centralized portion 100 of estimator 140 is based on the local state vector of the corresponding estimate for each of the plurality of nodes 18 that are part of the communication network 10. Determine the total error covariance matrix P(k|k-1) for communication network 10. Then method 300 can proceed to box 306.

[0130] In box 306, the overall measurement sensitivity block 168 determines the corresponding local measurement sensitivity matrix H. z (k), which represents the amount of change experienced by the local measurement result for a specific node 18 based on the change in the corresponding local state vector x(k). Method 300 can then proceed to block 308.

[0131] In box 308, the local measurement sensitivity block 168 determines the corresponding local measurement variance matrix R. z (k), which represents the uncertainty in the local measurement results for a specific node 18. Method 300 can then proceed to box 310.

[0132] In block 310, the local residual covariance matrix block 170 is based on the total error covariance matrix P(k|k-1) for the entire communication network 10 and the corresponding local measurement variance matrix R. z (k) determines the corresponding local residual covariance matrix P for each of the plurality of nodes 18 that are part of the communication network 10. μ Then method 300 can proceed to box 312.

[0133] In box 312, the local gain matrix block 172 is based on the corresponding local residual covariance matrix P for a particular node 18. μ The total error covariance matrix P(k|k-1) for the entire communication network 10 and the corresponding local measurement sensitivity matrix H for a specific node 18. z (k), determine the corresponding local gain matrix K for a specific node 18. z (k). Then method 300 can proceed to box 314.

[0134] In box 314, the cooperative update block 178 updates the local gain matrix K. z (k) is combined with the corresponding local residual μ of a specific node 18 to create a corresponding local update 146 for that specific node 18. Specifically, the local update 146 for a specific node 18 is the corresponding local residual μ and the corresponding local gain matrix K for each node 18 that is part of the communication network 10. zThe product of (k). Then method 300 can proceed to box 316.

[0135] In box 316, local update 146 is applied to the estimated local state vector of a specific node 18 that is part of the communication network 10. To determine the corresponding updated estimated local state vector. Local update 146 is also applied to the cooperative error covariance matrix P of a specific node 18. μ Then method 300 can proceed to box 318.

[0136] Figure 9B Boxes 318-332 are shown, in which the cooperative update 148 is defined. Specifically, in box 318, the cooperative measurement prediction block 180 predicts the estimated cooperative measurement result based on the cooperative measurement result between a pair of cooperative nodes 18i, 18j, which are part of the communication network 10. The collaborative residuals are correlated with the estimated collaborative measurement results. Specifically, as described above, the estimated collaborative measurement results... Based on the local state vector x of cooperative nodes 18i and 18j i (k), y i (k) and the collaborative measurement result vector y ij (k). Then method 300 can proceed to box 320.

[0137] In box 320, the cooperative residual block 182 defines the cooperative residual υ, which represents the cooperative measurement result vector y. ij (k) and estimated collaborative measurement results The difference between them. Then method 300 can proceed to box 322.

[0138] In box 322, cooperative measurement sensitivity block 184 determines the cooperative measurement sensitivity matrix H. yij (k) represents the cooperative measurement result based on the local state vector x of node 18i. i The amount of change experienced by the corresponding change in (k), where the cooperative measurement is performed relative to node 18i. Then method 300 can proceed to box 324.

[0139] In box 324, the collaborative measurement sensitivity block 184 determines the composite covariance matrix. When the influence of one or more states of a pair of cooperating nodes 18i and 18j is modeled as random noise, the composite covariance matrix characterizes the uncertainty in the cooperative measurement results. Method 300 can then proceed to box 326.

[0140] In box 326, the cooperative covariance matrix block 186 is based at least on the cooperative measurement sensitivity matrix H. yij(k) and the composite covariance matrix Determine the cooperative error covariance matrix P of the cooperative residual υ υij Then method 300 can proceed to box 328.

[0141] In box 328, the cooperative gain matrix block 188 is based on the cooperative error covariance matrix P of the cooperative residual υ. υij and cooperative measurement sensitivity matrix H yij (k), determine the cooperative gain matrix K yij (k). Then method 300 can proceed to box 330.

[0142] In box 330, the cooperative gain matrix block 188 will combine the cooperative gain matrix K yij (k) is combined with the collaborative residual υ to create collaborative update 148. Therefore, it should be understood that collaborative update 148 is based on the estimated collaborative measurement results. Collaborative measurement results of collaborative residual estimation To confirm, method 300 can then proceed to box 332.

[0143] In box 332, the cooperative update 148 is applied to the corresponding estimated local state vectors of the two nodes 18i and 18j in a pair of cooperative nodes. Collaborative update 148 is also applied to the total error covariance matrix P(k|k-1) of communication network 10. Then method 300 can terminate or return to box 302 (in Figure 9A (as shown in the image).

[0144] Referring generally to the accompanying drawings, this disclosure provides various technical effects and benefits. Specifically, this disclosure describes an estimator that determines both local updates and cooperative updates of the estimated local state vector applied to individual nodes. The local updates are determined based on local measurements, while the cooperative updates are based on cooperative measurements between individual nodes and cooperating nodes. In a decentralized approach, the estimator can be included as part of each node that is part of a communication network. This approach may require less computational power compared to a centralized approach. Alternatively, in another approach, estimation is performed at a centralized location, which can lead to improved accuracy. However, this approach may require additional computational power compared to a decentralized approach. Both centralized and decentralized approaches can improve the accuracy of the estimated local state vector for each node because the updates are based not only on local measurements but also on cooperative measurements between nodes.

[0145] Reference Figure 10 Computing system 30 ( Figure 2 ) and the centralized portion 100 of the estimator 140 ( Figure 7This is implemented on one or more computer devices or systems, such as the exemplary computer system 1030. Computer system 1030 includes a processor 1032, memory 1034, mass storage device 1036, input / output (I / O) interface 1038, and human-machine interface (HMI) 1040. Computer system 1030 is operatively coupled to one or more external resources 1042 via network 1026 or I / O interface 1038. External resources may include, but are not limited to, servers, databases, mass storage devices, peripherals, cloud-based network services, or any other suitable computer resources that can be used by computer system 1030.

[0146] Processor 1032 includes one or more devices selected from microprocessors, microcontrollers, digital signal processors, microcomputers, central processing units, field-programmable gate arrays, programmable logic devices, state machines, logic circuits, analog circuits, digital circuits, or any other device that manipulates signals (analog or digital) based on operating instructions stored in memory 1034. Memory 1034 includes a single memory device or multiple storage devices, including but not limited to read-only memory (ROM), random access memory (RAM), volatile memory, non-volatile memory, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, cache memory, or any other device capable of storing information. Mass storage memory device 1036 includes data storage devices such as hard disk drives, optical disk drives, magnetic tape drives, volatile or non-volatile solid-state devices, or any other device capable of storing information.

[0147] Processor 1032 operates under the control of operating system 1046 residing in memory 1034. Operating system 1046 manages computer resources, enabling computer program code, embodied as one or more computer software applications (such as application 1048 residing in memory 1034), to have instructions executable by processor 1032. In an alternative example, processor 1032 may directly execute application 1048, in which case operating system 1046 may be omitted. One or more data structures 1049 also reside in memory 1034 and can be used by processor 1032, operating system 1046, or application 1048 to store or manipulate data.

[0148] I / O interface 1038 provides a machine interface that operatively couples processor 1032 to other devices and systems, such as network 1026 or external resource 1042. Application 1048 thereby communicates with network 1026 or external resource 1042 via I / O interface 1038 to provide various features, functions, applications, processes, or modules, including those exemplified by this disclosure. Application 1048 also includes program code executed by one or more external resources 1042, or otherwise dependent on functionality or signals provided by other system or network components external to computer system 1030. Indeed, given the virtually limitless possibilities of hardware and software configurations, those skilled in the art will understand that examples of this disclosure can include applications located outside computer system 1030, applications distributed across multiple computers or other external resources 1042, or applications provided by computing resources (hardware and software) offered as services (such as cloud computing services) via a network.

[0149] HMI 1040 is operably coupled to processor 1032 of computer system 1030 in a known manner to allow a user to interact directly with computer system 1030. HMI 1040 may include a video or alphanumeric display, a touchscreen, speakers, and any other suitable audio and video indicators capable of providing data to the user. HMI 1040 also includes input devices and controls, such as an alphanumeric keypad, a pointing device, a keypad, buttons, control knobs, a microphone, etc., capable of accepting commands or input from the user and transmitting typed input to processor 1032.

[0150] Database 1044 may reside on mass storage device 1036 and may be used to collect and organize data used by the various systems and modules described herein. Database 1044 may include data and supporting data structures for storing and organizing the data. In particular, database 1044 may be arranged with any database organization or structure, including but not limited to relational databases, hierarchical databases, network databases, or combinations thereof. A database management system in the form of a computer software application that executes as instructions on processor 1032 may be used to access information or data stored in records in database 1044 in response to queries, wherein the queries may be dynamically determined and executed by operating system 1046, other application 1048, or one or more modules.

[0151] Furthermore, this disclosure includes embodiments pursuant to the following provisions:

[0152] Clause 1. A communication network (10) having multiple nodes (18), wherein the individual nodes (18i) of the communication network (10) include:

[0153] Measuring device (32), configured to collect local measurement results;

[0154] Antenna (36), which is configured to wirelessly connect the individual node (18i) to a cooperating node that is part of the communication network (10);

[0155] One or more processors (1032) that communicate electronically with the measuring device (32) and the antenna (36);

[0156] A memory (1034), coupled to the one or more processors (1032), stores data as a database (1044) and program code, which, when executed by the one or more processors (1032), causes the individual node (18i) to:

[0157] Based on the local measurement results, the local state vector for the estimation of the individual node (18i) is estimated;

[0158] Based on the estimated local state vector and the local measurement results, the estimated local measurement results of the individual node (18i) are predicted, wherein the local residuals are associated with the estimated local measurement results;

[0159] Based on the estimated local measurement results and the local residuals, a local update (46) is determined, wherein the local update (46) is applied to the estimated local state vector of the individual node (18i);

[0160] Based on the collaborative measurement results between the individual node (18i) and the collaborating node (18j), an estimated collaborative measurement result is predicted, wherein the collaborative residual is correlated with the estimated collaborative measurement result; and

[0161] Based on the estimated cooperative measurement results and the cooperative residual, a cooperative update (48) is determined, wherein the cooperative update (48) is applied to the estimated local state vector of the individual node (18i).

[0162] Clause 2. The communication network (10) pursuant to Clause 1, wherein the one or more processors (1032) execute instructions to:

[0163] Based on the cooperative measurement result vector, the local state vector of the individual node (18i) and the local state vector of the cooperative node (18j), the estimated cooperative measurement result between the individual node (18i) and the cooperative node (18j) is predicted.

[0164] Clause 3. The communication network (10) as described in Clause 2, wherein the cooperative residual represents the difference between the cooperative measurement result vector and the estimated cooperative measurement result.

[0165] Clause 4. The communication network (10) pursuant to Clause 1, wherein the one or more processors (1032) execute instructions to:

[0166] Determine the cooperative measurement sensitivity matrix, which represents the amount of change experienced by the cooperative measurement result based on the corresponding change in the local state vector of the individual node (18i);

[0167] A composite covariance matrix is ​​determined to characterize the uncertainty in the cooperative measurement results when the influence of one or more states of the cooperative nodes (18j) is modeled as random noise; and

[0168] The cooperative error covariance matrix of the cooperative residual is determined based at least on the cooperative measurement sensitivity matrix and the composite covariance matrix.

[0169] Clause 5. The communication network (10) pursuant to Clause 4, wherein the one or more processors (1032) execute instructions to:

[0170] Based on the cooperative error covariance matrix and the cooperative measurement sensitivity matrix of the cooperative residuals, the cooperative gain matrix is ​​determined; and

[0171] The cooperative gain matrix is ​​combined with the cooperative residual to create the cooperative update (48).

[0172] Clause 6. The communication network (10) pursuant to Clause 5, wherein the cooperative update (48) is the product of the cooperative gain matrix and the cooperative residual.

[0173] Clause 7. The communication network (10) pursuant to Clause 1, wherein the one or more processors (1032) execute instructions to:

[0174] Based on the local measurement results, a local error covariance matrix is ​​determined for the estimated local state vector, wherein the local error covariance matrix characterizes the error of the estimated local state vector.

[0175] Clause 8. The communication network (10) pursuant to Clause 7, wherein the one or more processors (1032) execute instructions to:

[0176] Determine a local measurement sensitivity matrix, which represents the amount of change experienced by the local measurement result based on the corresponding change in the local state vector;

[0177] Determine the local measurement variance matrix, which represents the uncertainty in the local measurement results; and

[0178] Based on the local error covariance matrix and the local measurement variance matrix, the local residual covariance matrix is ​​determined.

[0179] Clause 9. The communication network (10) pursuant to Clause 8, wherein the one or more processors (1032) execute instructions to:

[0180] Based on the local residual covariance matrix, the local error covariance matrix, and the local measurement sensitivity matrix, the local gain matrix is ​​determined; and

[0181] The local gain matrix is ​​combined with the local residual to create the local update (46).

[0182] Clause 10. The communication network (10) according to Clause 9, wherein the local update (46) is the product of the local gain matrix and the local residual.

[0183] Clause 11. The communication network (10) according to Clause 1, wherein the local residual for the individual node (18i) represents the difference between the estimated local measurement result and the local measurement result vector.

[0184] Clause 12. The communication network (10) according to Clause 1, wherein the measuring device (32) of the individual node (18i) is a cooperative positioning, navigation and timing system, i.e., a PNT system (26).

[0185] Clause 13. The communication network (10) according to Clause 12, wherein the cooperative measurement results between the individual node (18i) and the cooperative node (18j) include the relative distance measurement results between the individual node (18i) and the cooperative node (18j) combined with the relative line-of-sight measurement results between the individual node (18i) and the cooperative node (18j), i.e., the relative LOS measurement results.

[0186] Clause 14. The communication network (10) according to Clause 13, wherein the relative distance measurement result is represented by a first relative distance, as measured by the individual node (18i), between the individual node (18i) and the cooperating node (18j), and a second relative distance, as measured by the cooperating node (18j), between the individual node (18i) and the cooperating node (18j).

[0187] Clause 15. The communication network (10) as described in Clause 13, wherein the relative LOS measurement results include:

[0188] A first relative LOS measurement result represented by a first unit vector pointing from the individual node (18i) to the cooperative node (18j), wherein the first relative LOS measurement result is measured by the individual node (18i); and

[0189] The second relative LOS measurement result is represented by a second unit vector pointing from the cooperative node (18j) to the individual node (18i), wherein the second relative LOS measurement result is measured by the cooperative node (18j).

[0190] Clause 16. The communication network (10) according to Clause 12, wherein the cooperative measurement results include a first relative range measurement result, as measured relative to the individual node (18i), between the individual node (18i) and the cooperative node (18j), and a second relative range measurement result, as measured relative to the cooperative node (18j), between the individual node (18i) and the cooperative node (18j).

[0191] Clause 17. A method (200) for updating an estimated local state vector of an individual node (18i), the method (200) comprising:

[0192] Based on the local measurement results collected by the measurement device (32) of the individual node (18i), a local state vector for estimation is estimated for the individual node (18i), wherein the individual node (18i) is part of a communication network (10) having multiple nodes (18).

[0193] Based on the local measurement results, the estimator (40) of the individual node (18i) determines the local error covariance matrix for the estimated local state vector, wherein the local error covariance matrix characterizes the error of the estimated local state vector;

[0194] The estimator (40) of the individual node (18i) predicts the estimated local measurement results of the individual node (18i) based on the estimated local state vector and the local measurement results;

[0195] The estimator (40) of the individual node (18i) determines the local residuals associated with the estimated local measurement results;

[0196] The local measurement sensitivity matrix is ​​determined by the estimator (40) of the individual node (18i), the local measurement sensitivity matrix representing the amount of change experienced by the local measurement result based on the corresponding change in the local state vector;

[0197] The local measurement variance matrix is ​​determined by the estimator (40) of the individual node (18i), the local measurement variance matrix representing the uncertainty in the local measurement results;

[0198] The estimator (40) of the individual node (18i) determines the local residual covariance matrix based on the local error covariance matrix and the local measurement variance matrix;

[0199] The estimator (40) of the individual node (18i) determines the local gain matrix based on the local residual covariance matrix, the local error covariance matrix, and the local measurement sensitivity matrix;

[0200] The local gain matrix is ​​combined with the local residual to create the local update (46);

[0201] The local update (46) is applied to the estimated local state vector and the local residual covariance matrix of the individual node (18i);

[0202] The estimator (40) of the individual node (18i) predicts the estimated cooperative measurement result based on the cooperative measurement result between the individual node (18i) and the cooperative node (18j), wherein the cooperative node is part of the communication network (10) and communicates wirelessly with the individual node (18i).

[0203] The estimator (40) of the individual node (18i) determines the cooperative residual associated with the estimated cooperative measurement result;

[0204] The cooperating measurement sensitivity matrix is ​​determined by the estimator (40) of the individual node (18i), the cooperating measurement sensitivity matrix representing the amount of change experienced by the cooperating measurement result based on the corresponding change in the local state vector of the individual node (18i);

[0205] The composite covariance matrix is ​​determined by the estimator (40) of the individual node (18i), and the composite covariance matrix characterizes the uncertainty in the cooperative measurement results when the influence of one or more states of the cooperative node (18j) is modeled as random noise.

[0206] The estimator (40) of the individual node (18i) determines the cooperative error covariance matrix of the cooperative residual based at least on the cooperative measurement sensitivity matrix and the composite covariance matrix;

[0207] The estimator (40) of the individual node (18i) determines the cooperative gain matrix based on the cooperative error covariance matrix of the cooperative residual; and

[0208] The cooperative gain matrix is ​​combined with the cooperative residual to create a cooperative update (48).

[0209] Clause 18. The method (200) according to Clause 17 further comprises:

[0210] The collaborative update (48) is applied to the estimated local state vector of the individual node (18i).

[0211] Clause 19. The method (200) according to Clause 18, said method further comprising:

[0212] The collaborative update (48) is applied to the local residual covariance matrix of the individual node (18i).

[0213] Clause 20. The method (200) according to Clause 17, wherein the cooperative residual represents the difference between the cooperative measurement result vector and the estimated cooperative measurement result.

[0214] The description in this disclosure is exemplary in nature only, and any changes that do not depart from the spirit and scope of this disclosure are intended to fall within its scope. Such changes should not be considered as a departure from the spirit and scope of this disclosure.

Claims

1. A communication network (10) having multiple nodes (18), wherein the individual nodes (18i) of the communication network (10) include: Measuring device (32), configured to collect local measurement results; Antenna (36), which is configured to wirelessly connect the individual node (18i) to a cooperating node that is part of the communication network (10); One or more processors (1032) that communicate electronically with the measuring device (32) and the antenna (36); A memory (1034), coupled to the one or more processors (1032), stores data as a database (1044) and program code, which, when executed by the one or more processors (1032), enables the individual node (18i) to: Based on the local measurement results, the local state vector for the estimation of the individual node (18i) is estimated; Based on the estimated local state vector and the local measurement results, the estimated local measurement results of the individual node (18i) are predicted, wherein the local residuals are associated with the estimated local measurement results; Based on the estimated local measurement results and the local residuals, a local update (46) is determined, wherein the local update (46) is applied to the estimated local state vector of the individual node (18i); Based on the cooperative measurement results between the individual node (18i) and the cooperative node (18j), an estimated cooperative measurement result is predicted, wherein the cooperative residual is associated with the estimated cooperative measurement result, wherein the cooperative measurement result includes at least one of the following: the line-of-sight (LOS) measurement result between the individual node (18i) and the cooperative node (18j), the angle of arrival (AOA) measurement result between the individual node (18i) and the cooperative node (18j), and / or the direction of arrival (DOA) between the individual node (18i) and the cooperative node (18j); and Based on the estimated cooperative measurement results and the cooperative residual, a cooperative update (48) is determined, wherein the cooperative update (48) is applied to the estimated local state vector of the individual node (18i).

2. The communication network (10) according to claim 1, wherein the one or more processors (1032) execute instructions to: Based on the cooperative measurement result vector, the local state vector of the individual node (18i) and the local state vector of the cooperative node (18j), the estimated cooperative measurement result between the individual node (18i) and the cooperative node (18j) is predicted.

3. The communication network (10) according to claim 2, wherein the cooperative residual represents the difference between the cooperative measurement result vector and the estimated cooperative measurement result.

4. The communication network (10) according to claim 1, wherein the one or more processors (1032) execute instructions to: Determine the cooperative measurement sensitivity matrix, which represents the amount of change experienced by the cooperative measurement result based on the corresponding change in the local state vector of the individual node (18i); A composite covariance matrix is ​​determined, which characterizes the uncertainty in the cooperative measurement results when the influence of one or more states of the cooperative node (18j) is modeled as random noise. and The cooperative error covariance matrix of the cooperative residual is determined based at least on the cooperative measurement sensitivity matrix and the composite covariance matrix.

5. The communication network (10) according to claim 4, wherein the one or more processors (1032) execute instructions to: Based on the cooperative error covariance matrix and the cooperative measurement sensitivity matrix of the cooperative residuals, the cooperative gain matrix is ​​determined; and The cooperative gain matrix is ​​combined with the cooperative residual to create the cooperative update (48).

6. The communication network (10) according to claim 5, wherein the cooperative update (48) is the product of the cooperative gain matrix and the cooperative residual.

7. The communication network (10) according to any one of claims 1 to 6, wherein the one or more processors (1032) execute instructions to: Based on the local measurement results, a local error covariance matrix is ​​determined for the estimated local state vector, wherein the local error covariance matrix characterizes the error of the estimated local state vector.

8. The communication network (10) according to claim 7, wherein the one or more processors (1032) execute instructions to: Determine a local measurement sensitivity matrix, which represents the amount of change experienced by the local measurement result based on the corresponding change in the local state vector; Determine the local measurement variance matrix, which represents the uncertainty in the local measurement results; and Based on the local error covariance matrix and the local measurement variance matrix, the local residual covariance matrix is ​​determined.

9. The communication network (10) according to claim 8, wherein the one or more processors (1032) execute instructions to: Based on the local residual covariance matrix, the local error covariance matrix, and the local measurement sensitivity matrix, the local gain matrix is ​​determined; and The local gain matrix is ​​combined with the local residual to create the local update (46).

10. The communication network (10) according to claim 9, wherein the local update (46) is the product of the local gain matrix and the local residual.

11. The communication network (10) according to claim 1, wherein the local residual for the individual node (18i) represents the difference between the estimated local measurement result and the local measurement result vector.

12. The communication network (10) according to claim 1, wherein the measuring device (32) of the individual node (18i) is a cooperative positioning, navigation and timing system, i.e., a PNT system (26).

13. The communication network (10) according to claim 12, wherein the cooperative measurement result between the individual node (18i) and the cooperative node (18j) includes the relative distance measurement result between the individual node (18i) and the cooperative node (18j) combined with the relative line-of-sight measurement result between the individual node (18i) and the cooperative node (18j), i.e., the relative LOS measurement result.

14. The communication network (10) according to claim 13, wherein the relative distance measurement result is represented by a first relative distance measured by an individual node (18i) between the individual node (18i) and the cooperating node (18j) and a second relative distance measured by the cooperating node (18j) between the individual node (18i) and the cooperating node (18j).

15. The communication network (10) according to claim 13, wherein the relative LOS measurement result includes: A first relative LOS measurement result represented by a first unit vector pointing from the individual node (18i) to the cooperative node (18j), wherein the first relative LOS measurement result is measured by the individual node (18i); as well as The second relative LOS measurement result is represented by a second unit vector pointing from the cooperative node (18j) to the individual node (18i), wherein the second relative LOS measurement result is measured by the cooperative node (18j).

16. The communication network (10) according to claim 12, wherein the cooperative measurement result includes a first relative range measurement result measured between the individual node (18i) and the cooperative node (18j) relative to the individual node (18i), and a second relative range measurement result measured between the individual node (18i) and the cooperative node (18j) relative to the cooperative node (18j).

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