A dynamic space-time cooperation network positioning and navigation performance analysis method
Patent Information
- Application Number
- CN202610806033.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-05
- Publication Date
- 2026-09-18
AI Technical Summary
该框架主要基于准静态假设开展设计与分析，将节点的位置、状态视为固定不变，未考虑动态场景下移动智能体的运动特性，导致该框架在动态环境中存在显著技术缺陷
1，本发明的动态时空协作网络的定位导航性能分析方法，以等效费希尔信息与动态直接定位估计为核心原理，融合多普勒信息与节点内测量，量化锚点不确定、收发状态偏差对定位精度的影响，可完成动态时空协作导航框架重构，适用于航天器编队、卫星集群、航空飞行器集群等场景。
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Figure CN122775084A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the fields of aerospace and navigation positioning technology, specifically relating to a method for analyzing the positioning and navigation performance of dynamic spatiotemporal cooperative networks. Background Technology
[0002] Currently, position awareness is the core foundation of aerospace, satellite navigation, and spacecraft telemetry and control. Network positioning and navigation (NLN) overcomes the limitations of traditional Global Navigation Satellite Systems (GNSS) in complex environments such as deep space, dense airspace, telemetry and control blockage areas, and signal interference areas by collaborating with satellite constellations, spacecraft formations, aircraft, and mobile intelligent agents in time and space, resulting in weak signals and positioning failures. It has become a key technology for all-domain collaborative navigation in aerospace.
[0003] NLN (Network Localization and Navigation) technology relies on spatial collaboration between nodes to achieve real-time global position coupling. Combined with intra-node temporal observations, it completes cross-time-slot transmission of spatial correlation features, effectively overcoming the constraints of traditional satellite navigation on application scenarios and significantly improving positioning accuracy and system reliability in complex environments. In the paper "A Theoretical Foundation of Network Localization and Navigation" published in the July 2018 issue of *Proceedings of the IEEE*, Volume 106, Issue 7, pp. 1136-1165, it assumes that measurement noise follows an ideal Gaussian distribution and evaluates the positioning performance of NLN based on the Cramer-Rao bound (CRB). This framework is primarily designed and analyzed based on quasi-static assumptions, treating the positions and states of nodes as fixed and unchanging, without considering the motion characteristics of mobile agents in dynamic scenarios. This leads to significant technical deficiencies in dynamic environments. Specifically, this manifests in two ways: First, ignoring Doppler information leads to model errors. The quasi-static positioning model only considers signal delay and channel amplitude, neglecting the Doppler information generated by the relative motion of nodes in dynamic scenarios. Furthermore, in dynamic scenarios, the agent's position shifts at the time of signal transmission and reception, violating the symmetry assumption of round-trip measurements and further increasing positioning errors. Second, research on information coupling due to uncertain anchor point information is lacking. Existing technologies assume that anchor points provide completely accurate position information, but in actual dynamic environments, anchor points themselves have position / velocity uncertainties. The uncertain information they transmit can cause problems for the agent. The information coupling between the agents and the impact of this coupling on positioning have not been fully studied; third, the impact of the agent's transmitting and receiving state deviation has not been effectively addressed. In dynamic scenarios, there is a time difference between signal transmission and reception, and the agent's state deviates within this time difference. Moreover, the signal reception time cannot be perfectly aligned. Existing technologies have not introduced effective means to quantify and compensate for this deviation; fourth, traditional cooperative positioning methods have poor adaptability. The two-step method (2SP) cannot fully capture position information, and the traditional direct position estimation (DPE) does not incorporate dynamic Doppler information, nor does it extend spatiotemporal cooperation to dynamic scenarios, making it difficult to meet the positioning needs of dynamic networks.
[0004] In summary, existing technologies for spatiotemporal collaboration analysis are limited to quasi-static scenarios and do not take into account factors such as node movement, state deviation, and anchor point uncertainty in dynamic scenarios. Therefore, they cannot reconstruct a spatiotemporal collaboration network navigation framework suitable for dynamic environments and are difficult to guide the deployment and operation of actual dynamic networks. Summary of the Invention
[0005] In view of this, the present invention provides a method for analyzing the positioning and navigation performance of dynamic spatiotemporal cooperative networks, which can reconstruct the navigation framework of spatiotemporal cooperative networks in dynamic scenarios and provide guidance for the accurate deployment and efficient operation of dynamic networks.
[0006] To achieve the objectives of this invention, the following technical solutions are provided.
[0007] A method for analyzing the positioning and navigation performance of dynamic spatiotemporal cooperative networks includes:
[0008] A dynamic direct positioning estimation measurement model integrating Doppler information is constructed, the Doppler factor is incorporated into the wireless received signal model, a signal model under dynamic scenarios is established, and positioning parameters and interference parameters are defined. Based on equivalent Fisher information analysis, the performance of non-cooperative localization of a single agent is analyzed. The impact of non-line-of-sight, multipath reception and clock asynchrony on localization performance in dynamic scenarios is derived. The equivalent Fisher information matrix of the agent's position and velocity is obtained, and the position error square bound is defined as the localization performance evaluation index. Information coupling analysis is performed on the uncertainty of anchor point information. The uncertainty of anchor point position and velocity is extended to the parameters to be estimated. The equivalent Fisher information matrix of the agent when the anchor point information is uncertain is derived, revealing the information coupling law of the agent caused by the uncertainty of anchor point and quantifying its attenuation effect on positioning performance. We conduct a positioning impact analysis on the state deviation of the agent at the time of transmission and reception. We introduce intra-node measurements to associate the agent states at different transmission and reception times. We combine equivalent Fisher information analysis to quantify the impact of state deviation on positioning and compensate for the positioning error caused by state deviation. A dynamic spatiotemporal cooperative network navigation framework is reconstructed. Based on dynamic non-cooperative positioning analysis, spatial cooperation and temporal cooperation are integrated. Joint constraints of inter-node measurement and intra-node measurement are introduced. The equivalent Fisher information matrix of dynamic spatiotemporal cooperation is reconstructed to realize dynamic spatiotemporal cooperative positioning and navigation of multiple agents and multiple time epochs.
[0009] The construction of the dynamic direct positioning estimation measurement model integrating Doppler information includes: breaking through the quasi-static assumption, establishing a dynamic direct positioning estimation signal model for single-path propagation in dynamic scenarios, using two-dimensional positioning as the benchmark for analysis, and being able to extend to three-dimensional scenarios; wherein the positioning parameters include the position and velocity of the agent, and the interference parameters include channel amplitude, delay deviation, and Doppler deviation.
[0010] The dynamic non-cooperative localization performance analysis based on equivalent Fisher information analysis includes: defining Doppler position elements and Doppler velocity elements based on the range position elements to characterize the contribution of Doppler information to localization; deriving the equivalent Fisher information matrix of the position and velocity of a single agent when there is no parameter prior knowledge; and calculating the position error square bound of the agent at the corresponding time using the position error square bound as the core indicator of localization performance.
[0011] The information coupling analysis of the anchor point information uncertainty includes: augmenting the anchor point's position and velocity into the parameters to be estimated to construct a joint parameter vector; determining the information matrix corresponding to the anchor point uncertainty based on the anchor point uncertainty covariance matrix; and calculating the equivalent Fisher information matrix for non-cooperative positioning considering the anchor point uncertainty, where the greater the anchor point uncertainty, the more severe the positioning information attenuation.
[0012] The positioning impact analysis of the agent's state deviation at the transmission and reception times includes: introducing in-node measurements to correlate the agent's state at the signal transmission and reception times; summarizing the agent's state vectors at each reception time and introducing an in-node measurement information matrix; and obtaining the agent's positioning equivalent Fisher information matrix at the corresponding time through equivalent Fisher information analysis.
[0013] The reconstructed dynamic spatiotemporal cooperative network navigation framework includes: constructing a dynamic spatiotemporal cooperative equivalent Fisher information matrix, which includes a dynamic spatial cooperative equivalent Fisher information matrix and a dynamic temporal cooperative equivalent Fisher information matrix; the dynamic spatial cooperative equivalent Fisher information matrix is derived based on inter-node measurements between agents, and the dynamic temporal cooperative equivalent Fisher information matrix is derived based on intra-node measurements of the agent and its state association in adjacent time epochs.
[0014] In the dynamic spatial cooperative equivalent Fisher information matrix, the agent cooperative index is used to characterize the measurement relationship between different agents; in the dynamic temporal cooperative equivalent Fisher information matrix, a temporal cooperative noise coefficient is introduced to characterize the uncertainty of state transmission between adjacent time epochs of agents.
[0015] This includes performance analysis and summary steps: constructing a unified iterative optimization process based on the derived update rules, inputting full measurement data and true parameter values, calculating the position status by combining the equivalent Fisher information matrix of spatiotemporal cooperative positioning, verifying the framework performance through simulation experiments, and evaluating the impact of different signal parameters, channel parameters, and network parameters on positioning accuracy.
[0016] Beneficial effects 1. The positioning and navigation performance analysis method of the dynamic spatiotemporal cooperative network of the present invention takes equivalent Fisher information and dynamic direct positioning estimation as the core principle, integrates Doppler information and intra-node measurement, quantifies the impact of anchor uncertainty and transmission and reception state deviation on positioning accuracy, and can complete the reconstruction of dynamic spatiotemporal cooperative navigation framework. It is applicable to scenarios such as spacecraft formation, satellite cluster, and aircraft cluster.
[0017] 2. This invention breaks through the limitations of traditional quasi-static assumptions, integrates Doppler information to construct a DDPE model, captures the position and velocity characteristics of mobile intelligent agents in aerospace, breaks the assumption of symmetry in round-trip measurements, and significantly reduces the model error of aerospace dynamic scenarios; based on EFI analysis, it reveals the information coupling law of intelligent agents caused by the uncertainty of anchor point information, clarifies the quantitative relationship between uncertainty and positioning performance, and provides optimization criteria for anchor point deployment in satellite constellations and spacecraft formations; by associating the states at different transmission and reception times through intra-node measurements, it effectively compensates for state deviations and solves the problem of accuracy degradation caused by signal transmission and reception misalignment in aerospace dynamic scenarios; it reconstructs dynamic spatiotemporal collaboration EFIM to achieve multi-agent, multi-epoch joint positioning, accurately reflects information coupling relationships, and provides a theoretical benchmark for the design of aerospace collaborative positioning algorithms; using SPEB as an indicator, it quantifies the relationship between key parameters and positioning accuracy, provides precise guidance for node deployment and parameter configuration in aerospace dynamic networks, and improves the operational efficiency and positioning reliability of aerospace networks. Attached Figure Description
[0018] Figure 1 This is a schematic diagram of the spatiotemporal cooperation NLN factor of the present invention.
[0019] Figure 2 This is a schematic diagram of the state deviation of agent 1 at the time of transmission and reception in an embodiment of the present invention. Detailed Implementation
[0020] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.
[0021] Spatiotemporal collaboration NLN factor such as Figure 1 As shown, there are currently no reports, either domestically or internationally, on dynamic spatiotemporal cooperative positioning and navigation schemes that integrate Doppler information, equivalent Fisher information analysis, and intra-node measurements for NLN applications in dynamic scenarios, and that quantify the impact of anchor point uncertainty and transmit / receive state deviation.
[0022] This invention provides a method for analyzing the localization and navigation performance of dynamic spatiotemporal cooperative networks. The network under consideration consists of agents whose states need to be estimated and anchor points. The symbols involved are defined as follows: For a collection of intelligent agents, For the set of anchor points, For the set of all nodes; For discrete time epochs, For time indexing; , For node indexing; This is a subset of anchor points based on line-of-sight (LOS). The number of agents is... The number of anchor points is .
[0023] intelligent agent exist The state vector at time t is ,in For position vectors, It is the velocity vector; for The complete parameter vector of NLN at time t, where For the interference parameter vector; for The measurement vector at time, For nodes and Inter-node measurements For intelligent agents Measurements within the nodes.
[0024] Among the signal model parameters used Given the known waveform of the transmitted real signal, Its Fourier transform, The autocorrelation function of the signal; For carrier frequency, For signal propagation speed; For signal amplitude, Due to signal propagation delay, Doppler factor; For delay deviation, Due to Doppler bias, in the LOS scenario In NLOS scenarios ; It is additive white Gaussian noise, and its two-sided power spectral density is... .
[0025] The matrices and operators used For expectation operator, For matrix trace, This is the matrix transpose. for 3D identity matrix for Zero-dimensional matrix; express It is a positive semi-definite matrix; for M A dimensional identity matrix, where only the _th ... i Line number j The column element is 1, and the rest are 0; For Kronecker product; This is the Fisher Information Matrix (FIM). For parameters The equivalent Fisher information matrix (EFIM); For intelligent agents exist Squared position error bound (SPEB) at time.
[0026] The method of this invention is based on EFI analysis and dynamic direct positioning estimation (DDPE), integrating Doppler information and intra-node measurements. Starting from dynamic non-cooperative positioning analysis, it gradually reveals the impact of anchor point uncertainty and transmit / receive state deviation, and finally reconstructs a dynamic spatiotemporal cooperative network navigation framework, including the following steps: Step 1: Construct a dynamic direct positioning estimation measurement model that integrates Doppler information By breaking through the limitations of the quasi-static assumption signal model, Doppler information is integrated into the wireless receiving signal model to establish a DDPE signal model for single-path propagation in dynamic scenarios. At the same time, positioning parameters and interference parameters in dynamic scenarios are defined. Based on two-dimensional positioning, analysis can be performed directly in three-dimensional scenarios.
[0027] In dynamic scenarios, nodes Launch, Node The received wireless signal model is: (1) in, For the observation interval, the Doppler factor The relationship between the node position and velocity is as follows: (2) in, This is the distance location element (RPE). for Time Node and The included angle.
[0028] The agent's position and velocity at any given time are the core localization parameters, while channel amplitude, delay bias, and Doppler bias are the interference parameters. The complete parameter vector is: (3) In the LOS scenario In NLOS scenarios .
[0029] Step 2: Performance Analysis of Dynamic Non-cooperative Localization Based on Equivalent Fisher Information Analysis Taking the non-cooperative localization of a single agent as the research object, based on the FIM and EFI analysis methods, the impact of NLOS, multipath reception, and clock asynchrony on localization performance in dynamic scenarios is derived, the EFIM of the agent's position and velocity is obtained, and the localization performance evaluation index SPEB is defined.
[0030] In the distance location element (RPE) Based on this, the Doppler position element (DPE) is defined. And Doppler velocity element (DVE) This characterizes the contribution of Doppler information to localization. (4) Without parameterized prior knowledge, the position and velocity of a single agent (taking agent 1 as an example). The EFIM is: (5) in, For Jacobian matrices, The corrected information matrix, For time-delayed Doppler FIM, , .in , and for (6) (7) (8) in, For nodes k and j Signal-to-noise ratio measured between intervals; , and for (9) (10) (11) Using SPEB as the core indicator of positioning performance, the intelligent agent exist The SPEB at time t is: (12) Step 3: Information Coupling Analysis for Uncertain Anchor Point Information Considering the uncertainty of anchor point position / velocity in real dynamic environments, we derive the EFIM of the agent when anchor point information is uncertain through parameter augmentation and EFI quadratic analysis, revealing the information coupling law of the agent caused by anchor point uncertainty, and quantifying its attenuation effect on positioning performance.
[0031] The position and speed of the anchor point Augmented to the parameters to be estimated, constructing a joint parameter vector. The information matrix corresponding to the uncertainty of the anchor point is: , Let be the anchor point uncertainty covariance matrix. Then, considering the non-cooperative positioning EFIM with anchor point uncertainty, it is: (13) in, , EFIM with uncertain anchor points satisfies The greater the uncertainty of the anchor point, the more severe the attenuation of the positioning information.
[0032] Step 4: Analysis of the impact of agent's state deviation during transmission and reception on localization In dynamic scenarios, the state of an agent at the moment of signal transmission and reception will shift over time. A schematic diagram illustrating the state deviation of agent 1 at the moment of transmission and reception is shown below. Figure 2 As shown. Introducing intra-node measurements. By correlating agent states at different transmission and reception times through intra-node measurements, and combining this with EFI analysis to quantify the impact of state deviations on positioning, the positioning error caused by state deviations is compensated. For intelligent agents The signal transmission time For intelligent agents Receive the The time of the signal of each node ( ); For intelligent agents At launch time The state vector, For receiving time State vector ( For two-dimensional position, (For two-dimensional velocity). The sum of agent 1's states at all receiving moments is: Introducing an intra-node measurement information matrix. The localization EFIM of agent 1 was obtained through EFI analysis. (14) Step 5: Reconstruct the dynamic spatiotemporal cooperative network navigation framework Based on dynamic non-cooperative positioning analysis, spatial cooperation and temporal cooperation are integrated, and joint constraints of inter-node measurement and intra-node measurement are introduced to reconstruct dynamic spatiotemporal cooperative EFIM, realizing dynamic spatiotemporal cooperative positioning and navigation for multiple agents and multiple time epochs.
[0033] Dynamic spatiotemporal collaborative EFIM reconstruction: (15) in, Let be the state vector of all agents at all time epochs during their emission. The Dynamic Space Cooperation (EFIM) is... (16) in, Index for agent collaboration ( , ; , ); and for (17) (18) in, For intelligent agents Receive the The state at the moment of receiving signals from other intelligent agents ( )or ( ).
[0034] Dynamic Time Collaboration (EFIM) is (19) in, (20) in, For intelligent agents exist to Time-cooperative noise figure ( ).
[0035] Step 6: Performance Analysis Summary Based on the update rules derived from the above steps, a unified iterative optimization process is constructed: input all measurement data and the true values of parameters; calculate the position status by combining spatiotemporal cooperative positioning EFIM; verify the framework performance through simulation experiments and evaluate the impact of different signal, channel and network parameters on positioning accuracy.
[0036] This invention includes, but is not limited to, the above embodiments. Any equivalent substitutions or partial improvements made under the spirit and principles of this invention shall be considered within the scope of protection of this invention.
Claims
1. A method for analyzing the positioning and navigation performance of a dynamic spatiotemporal cooperative network, characterized in that, include: A dynamic direct positioning estimation measurement model integrating Doppler information is constructed, the Doppler factor is incorporated into the wireless received signal model, a signal model under dynamic scenarios is established, and positioning parameters and interference parameters are defined. Based on equivalent Fisher information analysis, the performance of non-cooperative localization of a single agent is analyzed. The impact of non-line-of-sight, multipath reception and clock asynchrony on localization performance in dynamic scenarios is derived. The equivalent Fisher information matrix of the agent's position and velocity is obtained, and the position error square bound is defined as the localization performance evaluation index. Information coupling analysis is performed on the uncertainty of anchor point information. The uncertainty of anchor point position and velocity is extended to the parameters to be estimated. The equivalent Fisher information matrix of the agent when the anchor point information is uncertain is derived, revealing the information coupling law of the agent caused by the uncertainty of anchor point and quantifying its attenuation effect on positioning performance. We conduct a positioning impact analysis on the state deviation of the agent at the time of transmission and reception. We introduce intra-node measurements to associate the agent states at different transmission and reception times. We combine equivalent Fisher information analysis to quantify the impact of state deviation on positioning and compensate for the positioning error caused by state deviation. A dynamic spatiotemporal cooperative network navigation framework is reconstructed. Based on dynamic non-cooperative positioning analysis, spatial cooperation and temporal cooperation are integrated. Joint constraints of inter-node measurement and intra-node measurement are introduced. The equivalent Fisher information matrix of dynamic spatiotemporal cooperation is reconstructed to realize dynamic spatiotemporal cooperative positioning and navigation of multiple agents and multiple time epochs.
2. The method according to claim 1, characterized in that, The construction of the dynamic direct positioning estimation measurement model integrating Doppler information includes: breaking through the quasi-static assumption, establishing a dynamic direct positioning estimation signal model for single-path propagation in dynamic scenarios, using two-dimensional positioning as the benchmark for analysis, and being able to extend to three-dimensional scenarios; wherein the positioning parameters include the position and velocity of the agent, and the interference parameters include channel amplitude, delay bias and Doppler bias.
3. The method according to claim 1, characterized in that, The dynamic non-cooperative localization performance analysis based on equivalent Fisher information analysis includes: defining Doppler position elements and Doppler velocity elements based on the range position elements to characterize the contribution of Doppler information to localization; deriving the equivalent Fisher information matrix of the position and velocity of a single agent when there is no parameter prior knowledge; and calculating the position error square bound of the agent at the corresponding time using the position error square bound as the core indicator of localization performance.
4. The method according to claim 1, characterized in that, The information coupling analysis of the anchor point information uncertainty includes: augmenting the anchor point's position and velocity into the parameters to be estimated to construct a joint parameter vector; determining the information matrix corresponding to the anchor point uncertainty based on the anchor point uncertainty covariance matrix; and calculating the equivalent Fisher information matrix for non-cooperative positioning considering the anchor point uncertainty, where the greater the anchor point uncertainty, the more severe the positioning information attenuation.
5. The method according to claim 1, characterized in that, The analysis of the positioning impact of the agent's state deviation at the transmission and reception times includes: introducing in-node measurements to correlate the agent's state at the signal transmission and reception times; summarizing the agent's state vectors at each reception time and introducing an in-node measurement information matrix; and obtaining the agent's positioning equivalent Fisher information matrix at the corresponding time through equivalent Fisher information analysis.
6. The method according to claim 1, characterized in that, The reconstructed dynamic spatiotemporal cooperative network navigation framework includes: constructing a dynamic spatiotemporal cooperative equivalent Fisher information matrix, which includes a dynamic spatial cooperative equivalent Fisher information matrix and a dynamic temporal cooperative equivalent Fisher information matrix; the dynamic spatial cooperative equivalent Fisher information matrix is derived based on inter-node measurements between agents, and the dynamic temporal cooperative equivalent Fisher information matrix is derived based on intra-node measurements of the agent and its state association in adjacent time epochs.
7. The method according to claim 6, characterized in that, In the dynamic spatial cooperative equivalent Fisher information matrix, the agent cooperative index is used to characterize the measurement relationship between different agents; in the dynamic temporal cooperative equivalent Fisher information matrix, a temporal cooperative noise coefficient is introduced to characterize the uncertainty of state transmission between adjacent time epochs of agents.
8. The method according to any one of claims 1-7, characterized in that, It also includes performance analysis and summary steps: constructing a unified iterative optimization process based on the derived update rules, inputting full measurement data and true parameter values, calculating the position status by combining the equivalent Fisher information matrix of spatiotemporal cooperative positioning, verifying the framework performance through simulation experiments, and evaluating the impact of different signal parameters, channel parameters and network parameters on positioning accuracy.