A reliable multi-center relative navigation method suitable for communication resource constraint

Abstract

The application provides a reliable multi-center relative navigation method suitable for a communication resource limitation, and belongs to the technical field of navigation. The method solves the problems of a large communication burden and a packet loss influence of an existing relative navigation method based on measurement sharing. The method comprises the following steps: establishing a total error state model; establishing a multi-source measurement model; designing an indirect TOA measurement sharing strategy; designing a data link communication mechanism and a data packet format based on time division multiple access rotation broadcasting; performing packet loss detection based on a shared broadcast serial number; performing time updating in each filtering period, and adopting a partial updating strategy to update only a sub-state related to a current measurement when a relative measurement is received; when a barometric altimeter measurement is obtained, an absolute state subspace is extracted, an absolute measurement partial updating is performed and is mapped back to an original state space, and relative position of all unmanned aerial vehicles in a relative coordinate system and other relative navigation results are calculated. The method is used in a large-range detection field.

Description

Technical Field

[0001] This invention belongs to the field of navigation technology, and in particular relates to a reliable multi-center relative navigation method suitable for communication resource constraints. Background Technology

[0002] Unmanned aerial vehicle (UAV) swarms can efficiently complete tasks such as large-scale reconnaissance and parallel operations, achieving adaptation to complex dynamic environments and efficient and reliable execution of complex tasks. Accurate and reliable relative positioning is a key prerequisite and important guarantee for the successful collaborative operation of the swarm. Currently, most swarm systems still rely on the Global Navigation Satellite System (GNSS) to obtain accurate absolute navigation information. However, in complex environments such as indoors, cities, forests, and canyons, GNSS signals may be completely lost or intermittently lost, making it difficult for this method to reliably achieve relative navigation. Relative navigation (RN), which measures the round-trip time (RTT) and time of arrival (TOA) of signals to achieve accurate relative clock and relative distance measurements, is an effective solution for scenarios where GNSS is completely or intermittently denied. Combined with inertial navigation systems (INS), altimeters, and other autonomous navigation sensors, it can achieve relative positioning and timing of swarm nodes, thereby ensuring the system's swarm operation capability. To improve relative navigation accuracy, researchers have proposed a measurement-sharing-based relative navigation method. In this method, each node in the cluster uses time-division multiple access (TDMA) communication, achieving ordered broadcasting through pre-defined time slot planning. Each node only packages its own navigation estimate, clock information (forming a TOA measurement at the receiver), and all shared TOA measurements from the most recent round (including the corresponding navigation estimate) into a data packet at a fixed assigned broadcast time. This packet is then shared with other nodes in the network, enabling them to utilize these measurements for information fusion. To further reduce communication overhead, existing methods only share the receiver's own navigation state estimate corresponding to the TOA measurement, as the broadcaster's navigation state estimate has already been broadcast and stored by other UAVs. This method increases the number and types of TOA measurements each node can acquire, effectively improving the observability and estimation accuracy of the relative navigation state. However, this existing measurement-sharing-based technology still has significant drawbacks, severely impacting its reliability and practicality. First, the problem of limited communication resources is prominent: at a specific moment, nodes need to simultaneously broadcast TOA measurements and related navigation estimation data from all UAVs, which leads to a large consumption of communication bandwidth, causing network congestion and transmission delays. For example, in large clusters, each node needs to send a large number of data packets each time it broadcasts, increasing the burden on the communication system, reducing real-time performance, and potentially causing data conflicts due to channel contention, affecting the stability of the entire collaborative operation. Second, existing methods cannot effectively handle data packet loss: due to the inherent characteristics of TDMA communication, data packet loss is difficult to avoid, but receiving members lack a mechanism to determine whether packet loss has occurred. For example, when the navigation state estimation broadcast by a node is lost in transmission, other nodes will still use the stored outdated or erroneous state estimation for calculation when subsequently fusing and sharing TOA measurements, leading to a mismatch between measurement and state, and thus causing relative position estimation failure. This error will accumulate and propagate, ultimately damaging the navigation accuracy of the entire cluster, which is particularly serious in dynamic environments. Summary of the Invention

[0003] In view of this, the present invention aims to propose a reliable multi-center relative navigation method suitable for communication resource constraints, so as to solve the problems of high communication burden and packet loss in existing relative navigation methods based on measurement sharing.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a reliable multi-center relative navigation method suitable for communication resource constraints, the method comprising: Step S1: Establish a full-error state model for the nodes in the cluster; Step S2: Establish a multi-source measurement model for the nodes in the cluster; Step S3: Design an indirect TOA measurement sharing strategy based on efficient communication; Step S4: Design a data link communication mechanism and data packet format based on time division multiple access and round-robin broadcasting to realize the transmission of the original TOA measurement and the filtered state estimate between nodes; Step S5: Perform packet loss detection based on the shared broadcast sequence number, and match the broadcast sequence number of the shared TOA measurement with the corresponding shared sequence number of the stored navigation estimate; Step S6: Perform time updates within each filtering cycle, and when a relative measurement is received, adopt a partial update strategy to update only the sub-states related to the current measurement; Step S7: When the local machine or other machine obtains barometric altimeter measurements, extract the absolute state subspace, perform absolute measurement part update in the subspace and map it back to the original state space, and then calculate the relative position and other relative navigation results of all UAVs in the relative coordinate system.

[0005] Furthermore, a preferred method is proposed, wherein establishing the full error state model in step S1 includes: For by N A cluster consisting of nodes, the navigation state of node i. Based on its own navigation state and others N The navigation error states of -1 nodes are constituted, specifically in the following form:

[0006] in, This represents the navigation error state of node 1. For nodes Navigation error status, The navigation error state of node N includes: longitude error, latitude error, altitude error, velocity error, attitude misalignment angle, data link clock error, and GNSS receiver clock error.

[0007] Using a full-error state-space modeling method based on the right-invariant error of the Lie group, the velocity error is redefined as:

[0008] in, Indicates speed error, , and Indicates the attitude misalignment angle. This represents the traditional velocity error vector. This represents the inertial navigation system solution result. Indicates drone In its The eastward, northward, and celestial velocities of the system.

[0009] Based on nodes Based on the modeled state variables and noise variables, construct the global error discrete-time propagation equation:

[0010] in, Represents a node In time The state vector, Represents a node In time The state vector, Indicates time The state transition matrix, Indicates time Noise-driven array, The variance is The system's white noise.

[0011] Furthermore, a preferred embodiment is proposed, wherein step S2 includes: Constructing a barometric altimeter measurement model :

[0012] in, Indicates drone In the The state at each measurement time The corresponding actual height value; It is an altitude estimate based on INS indications; It is the white noise from the altimeter measurement.

[0013] Constructing a GNSS pseudorange measurement model :

[0014] in, Indicates drone In the The state at each measurement time The corresponding actual GNSS pseudorange value; Represents a node exist The position below; Indicates that the satellite is in The position below; Represents the speed of light; This indicates white noise in GNSS pseudorange measurements. This indicates the clock phase error of the GNSS receiver.

[0015] Constructing an RTT bidirectional ranging clock synchronization measurement model :

[0016] in, Indicates based on drones and state variables and The theoretical value of RTT measurement obtained; This represents white noise measured in RTT. This indicates the data link receiver clock phase error; This indicates the clock phase error of the data link transmitter.

[0017] Constructing a direct TOA ranging measurement model :

[0018] in, Indicates based on drones and state variables and The theoretical value of direct TOA measurement obtained; This represents white noise measured directly at TOA. Represents a node exist The position below.

[0019] Furthermore, a preferred embodiment is proposed, wherein the indirect TOA measurement sharing strategy in step S3 includes at least one of sequential sharing strategy, fixed sharing strategy and random sharing strategy, for selectively sharing TOA measurements under limited communication resources; Indirect TOA measurement models include: Set nodes At any moment Obtain a message about the node TOA measurement, and at time The measurement and related information will be broadcast together; node After receiving the message, TOA measurement at time and current time If a correspondence is established between the states, then indirect TOA measurement... Represented as: , in, Indicates by At any given moment , The theoretical value of indirect TOA measurement determined by the state quantity; This represents white noise from indirect TOA measurements; express Time node exist Position vector in coordinate system; express Time node Clock deviation; express Time node Clock deviation; express Time node For nodes of System location estimate; express Time node For nodes The global coordinate system position estimate; Represents a node Corresponding node The horizontal Jacobian matrix of the position (used for position deviation linearization). Represents the time node at time k For nodes Horizontal position deviation; Represents a node Corresponding node The horizontal Jacobian matrix of the position; express Time node For nodes Horizontal position deviation; express Time node For nodes The clock skew estimate; express Time node For nodes The clock skew estimate; Represents a node Corresponding node High Jacobian matrix (used for high deviation linearization); Indicate Time node For nodes Height deviation; express Time node For nodes Height deviation; express Time node For nodes The height deviation.

[0020] Among them, the sequential sharing strategy selects shared measurements by cyclically moving the window, the fixed sharing strategy always broadcasts measurements of a fixed node group, and the random sharing strategy randomly selects a subset of measurements.

[0021] Furthermore, a preferred approach is proposed, wherein the sequential sharing strategy specifically includes: For node j, define an ordered tuple. It includes N-1 TOA measurements collected:

[0022] in, For node 1 and node TOA measurement values ​​between For node 2 and node TOA measurement values ​​between For nodes With nodes TOA measurement values ​​between For nodes With nodes TOA measurement values ​​between For nodes With nodes The TOA measurement values ​​between.

[0023] During the k-th broadcast, the shared measurement set By drone index set Decide:

[0024] in, It is the starting index of the selected window during the k-th broadcast. Indicates the quantity of TOA measurements. This is the index offset parameter, used to generate different indices to select the measurement to be shared. The number of TOA measurements shared during the k-th broadcast; The obtained shared indirect TOA measurement set The specific format is as follows:

[0025] in, ordered tuples The Middle The TOA measurement value corresponding to each location.

[0026] Furthermore, a preferred approach is proposed, wherein the fixed sharing strategy includes: Node j shares a fixed UAV index set Corresponding measurements:

[0027] The obtained shared indirect TOA measurement set The specific format is as follows:

[0028] in, For nodes With nodes The TOA measurement values ​​between.

[0029] Furthermore, a preferred method is proposed, wherein the packet loss detection in step S5 includes: Upon receiving a shared TOA measurement, the broadcast sequence number is compared with the stored navigation estimate broadcast sequence number; When the sequence numbers match, the shared TOA measurement is merged; If the serial numbers do not match, the fusion is rejected.

[0030] Furthermore, a preferred embodiment is proposed, wherein step S6, measurement update, includes: Relative measurement update: When receiving RTT clock synchronization measurement, TOA measurement or shared TOA measurement, only the sub-states directly related to the measurement are updated, and the other sub-states retain their prior values. Absolute measurement update: When barometric altimeter or GNSS measurements are obtained, an update is performed in the absolute state subspace and then mapped back to the original state space.

[0031] Based on the same inventive concept, the present invention also proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes a reliable multi-center relative navigation method applicable to communication resource-constrained conditions as described in any of the preceding claims.

[0032] Based on the same inventive concept, the present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of a reliable multi-center relative navigation method applicable to communication resource-constrained conditions as described in any of the above-mentioned embodiments.

[0033] Compared with the prior art, the beneficial effects of the present invention are: Existing relative navigation methods based on measurement sharing typically employ a full broadcast mode, sharing all collected TOA measurements and related navigation states at each broadcast moment. While this increases observability, it leads to excessive consumption of communication resources and sensitivity to packet loss. In contrast, the method proposed in this invention designs an indirect TOA measurement model, allowing nodes to collect TOA measurements during broadcast intervals and then share only a subset of measurements instead of all, thus significantly reducing data transmission volume. Specifically, the principle includes three sharing strategies: a sequential sharing strategy that systematically selects a subset of measurements through a cyclically moving window, ensuring all measurements are shared evenly over time; a fixed sharing strategy that consistently broadcasts measurements related to a fixed UAV group, simplifying the communication mode; and a random sharing strategy that introduces unpredictability by randomly selecting a subset of measurements to enhance adaptability. The principle behind these strategies is to dynamically optimize communication resource allocation, rather than static full broadcasting, fundamentally differentiating them from the rigid sharing methods of existing technologies.

[0034] Furthermore, the packet loss detection principle of this invention addresses the shortcomings of existing technologies in handling data packet loss. In existing technologies, the receiving member lacks a mechanism to determine packet loss, leading to the potential use of outdated navigation state estimates during the fusion of shared TOA measurements, resulting in error accumulation. This invention implements packet loss detection by comparing broadcast sequence numbers: measurement fusion can only proceed when the broadcast sequence number of the shared TOA measurement matches the stored shared navigation estimate sequence number; otherwise, fusion is rejected to prevent mismatches. This principle is based on the sequence characteristics of time-division multiple access communication, ensuring data consistency and the accuracy of state estimation through simple and efficient sequence number verification. In addition, the full-error state modeling and multi-source measurement fusion principles of this invention enhance the completeness of state estimation; for example, by redefining velocity error using the Lie group right-invariant error method, the stability of the model in dynamic environments is improved. Attached Figure Description

[0035] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of the overall process of the reliable multi-center relative navigation algorithm applicable to communication resource constraints as described in this invention. Figure 2 This is the trajectory diagram used in the test described in this invention; Figure 3 This is a schematic diagram of the parameters used in this invention; Figure 4 This is a schematic diagram of the three TOA measurement sharing strategies described in this invention; Figure 5 This is a schematic diagram illustrating the relative position estimation accuracy under different sharing strategies described in this invention. Figure 6 This refers to the relative position estimation accuracy under GNSS complete rejection environment as described in this invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other, and the described embodiments are only some embodiments of the present invention, not all embodiments.

[0037] Implementation Method 1: This implementation method addresses the problems of high communication burden and susceptibility to packet loss in existing relative navigation methods based on measurement sharing. It proposes a reliable multi-center relative navigation method suitable for situations with limited communication resources. The method includes: Step S1: Establish a full-error state model for the nodes in the cluster; Step S2: Establish a multi-source measurement model for the nodes in the cluster; Step S3: Design an indirect TOA measurement sharing strategy based on efficient communication; Step S4: Design a data link communication mechanism and data packet format based on time division multiple access and round-robin broadcasting to realize the transmission of the original TOA measurement and the filtered state estimate between nodes; Step S5: Perform packet loss detection based on the shared broadcast sequence number, and match the broadcast sequence number of the shared TOA measurement with the corresponding shared sequence number of the stored navigation estimate; Step S6: Perform time updates within each filtering cycle, and when a relative measurement is received, adopt a partial update strategy to update only the sub-states related to the current measurement; Step S7: When the local machine or another machine obtains an altimeter measurement, the absolute state subspace is extracted, the absolute measurement part is updated in the subspace and mapped back to the original state space, and then the relative position of all UAVs in the relative coordinate system and other relative navigation results are calculated.

[0038] This embodiment provides a reliable multi-center relative navigation method suitable for environments with limited communication resources, demonstrating significant technical advantages. The primary advantage is the effective reduction of communication burden. By introducing an intelligent measurement sharing strategy, it avoids the network congestion problem caused by simultaneously broadcasting all UAV TOA measurement and navigation estimation data in existing technologies, thereby improving transmission efficiency and system real-time performance. Secondly, this invention ensures the correct update of shared TOA measurements through a packet loss detection mechanism, solving the state estimation error problem caused by packet loss in existing methods, significantly improving the reliability and accuracy of relative navigation. Furthermore, while maintaining high-precision state estimation, this method possesses good versatility and can adapt to different communication conditions. For example, the data transmission volume can be flexibly adjusted through a configurable sharing strategy to ensure the collaborative operation capability of UAV swarms in GNSS-denied environments.

[0039] The method proposed in this embodiment achieves two core functions: reducing communication burden while maintaining high-accuracy state estimation and ensuring correct updates of shared TOA measurements through packet loss detection. In practical applications, simultaneously broadcasting TOA measurements and related navigation estimation data from all UAVs at a specific time can significantly burden the communication system, leading to network congestion and decreased transmission efficiency. Furthermore, because receiving members cannot determine whether packet loss has occurred, other members are prone to mismatches between received shared TOA measurements and stored P messages when fusing the shared TOA measurements from the receiving member. The proposed method can reduce communication burden while maintaining high-accuracy state estimation, enabling other members to determine whether packet loss has occurred, correctly fusing shared TOA measurements, and ensuring correct updates of shared TOA measurements, thus possessing significant practical application value.

[0040] Implementation Method 2, see below Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 and Figure 6 This embodiment describes a specific implementation of the reliable multi-center relative navigation method applicable to communication resource-constrained conditions described in Embodiment 1. The method specifically includes the following steps: Step 1: Establish a full-error state model for the nodes in the cluster; Step 1 in this embodiment includes: Step 1.1, assuming a... A multi-UAV system consisting of several UAVs. Selecting nodes. The state variables in the model, the navigation state of this node. Based on its own navigation state and others The navigation error state of each node is constituted, and its specific form is shown below:

[0041] in, Represents a node Navigation error status ( The specific form is as follows:

[0042] in, and They represent drones Longitude error, latitude error, and altitude error; and They represent drones The eastward velocity error, northward velocity error, and celestial velocity error; and Indicates the attitude misalignment angle; and These represent the data link receiver clock phase and frequency error, respectively. and These represent the clock phase and frequency error of the GNSS receiver, respectively.

[0043] Step 1.2, corresponding to the global error state described above, node The global process noise vector is defined as:

[0044] in, For nodes The process noise of self-navigation error includes random drift of inertial devices, data link clock noise, and GNSS receiver clock noise. For nodes For other nodes Equivalent process noise introduced during state modeling.

[0045] Step 1.3: In this implementation method, a full-error state-space modeling method based on the right-invariant error of the Lie group is adopted to redefine the node northeast-sky velocity error:

[0046] in, , ; This represents the traditional velocity error vector; This represents the inertial navigation system solution result. Indicates drone In its The eastward velocity, northward velocity, and celestial velocity of the system.

[0047] Step 1.4, based on nodes The state variables and noise variables in the model are used to construct the global error discrete-time propagation equation, which has the following form:

[0048] in, and Representing nodes respectively In time and time The state vector; and They represent time. The state transition matrix and the noise driving matrix; The variance is The system's white noise.

[0049] Step 2: Establish a multi-source measurement model for the nodes in the cluster; Step 2 in this embodiment includes: Step 2.1: Construct a barometric altimeter measurement model, in the following form:

[0050] in, Indicates drone In the The state at each measurement time The corresponding actual height value; It is an altitude estimate based on INS indications; It is the white noise of the altimeter measurement, which obeys... .

[0051] Step 2.2, construct the GNSS pseudorange measurement model, when the node When GNSS signals can be received, the pseudorange measurement equation can be expressed as:

[0052] in, Indicates drone In the The state at each measurement time The corresponding actual GNSS pseudorange value; Represents a node exist The position below; Indicates that the satellite is in The position below; Represents the speed of light; This represents the white noise of GNSS pseudorange measurements, which follows the... distributed.

[0053] Step 2.3: Construct a bidirectional ranging clock synchronization measurement model for RTT. RTT measurement directly reflects the clock difference between the data links of the broadcast node and the receiving node, with the time reference node NTR as a reference.

[0054] For nodes With nodes The RTT measurement between them can be expressed as a function of the round-trip time and the local clock reading, which can be simplified as follows:

[0055] in, Indicates based on drones and state variables and The theoretical value of RTT measurement obtained; The white noise representing the RTT measurement follows a certain order. distributed; This represents the clock phase error of the data link transmitter. After linearization, the error state can be obtained. The observation equation.

[0056] Step 2.4, construct the direct TOA ranging measurement model, when the node Received from node Direct TOA measurements are generated during broadcasting. Direct TOA measurement is a relative measurement, and its physical meaning is the signal propagation time, corresponding to the spatial distance between nodes.

[0057] For one-way ranging measurements based on TOA, the observed quantity can be written as:

[0058] in, Indicates based on drones and state variables and The theoretical value of direct TOA measurement obtained; This represents white noise measured directly at TOA. Represents a node exist The position below.

[0059] Step 3: Design an indirect TOA measurement sharing strategy based on efficient communication; To alleviate the problem of limited communication resources, this implementation design includes an indirect TOA measurement model and three TOA measurement sharing strategies to improve relative navigation performance.

[0060] Step 3.1, Build Nodes The indirect TOA measurement model.

[0061] Set nodes At any moment Obtain a message about the node TOA measurement, and at time The measurement and related information will be broadcast together; node After receiving the message, TOA measurement at time and current time If a correspondence is established between the states, then indirect TOA measurement can be expressed as:

[0062] in, Indicates by At any given moment , The theoretical value of indirect TOA measurement determined by the state quantity; This represents white noise from indirect TOA measurements; express Time node exist Position vector in coordinate system; express Time node Clock deviation; express Time node Clock deviation; express Time node For nodes of System location estimate; express Time node For nodes The global coordinate system position estimate; Represents a node Corresponding node The horizontal Jacobian matrix of the position (used for position deviation linearization). Represents the time node at time k For nodes Horizontal position deviation; Represents a node Corresponding node The horizontal Jacobian matrix of the position; express Time node For nodes Horizontal position deviation; express Time node For nodes The clock skew estimate; express Time node For nodes The clock skew estimate; Represents a node Corresponding node High Jacobian matrix (used for high deviation linearization); express Time node For nodes Height deviation; express Time node For nodes Height deviation; express Time node For nodes The height deviation.

[0063] Step 3.2: Design three TOA measurement sharing strategies.

[0064] Indirect TOA measurement models can be used on drones The model estimates the state of any pair of drones. After collecting TOA measurements during the broadcast interval, the drones... These TOA measurements will be shared at the next broadcast time. Other drones will integrate these shared indirect TOA measurements to improve estimation accuracy. Theoretically, as long as the TOA measurement data of all drones can be broadcast in real time, the drones... This allows us to obtain the optimal global position estimate.

[0065] For the proposed indirect TOA measurement model, instantaneously broadcasting all TOA measurement data would impose a significant communication burden. This implementation devises three measurement sharing strategies, which can be selected arbitrarily based on the actual communication situation.

[0066] (1) Sequential sharing strategy Consider a network consisting of N drones. During broadcast breaks, from other A drone was used to collect TOA (Transit of Altitude) measurements. The complete set of measurements collected is denoted as ,in Indicates when drone The TOA measurement is generated when the drone j acts as the receiver and the drone j acts as the broadcaster. In each broadcast, the drone j... From its collection Select Some measurements are shared with other drones, among which It is determined based on available communication resources, and The sequential sharing strategy ensures that all collected TOA measurements are shared systematically and cyclically over time. To introduce this strategy, an ordered tuple is defined. This includes all the data collected by drone j. Each measurement value is in the following format:

[0067] in, Represents ordered tuples The p-th element.

[0068] At the k-th broadcast time of drone j, the shared TOA measurement set It is determined by a moving selection window of size q, which cycles through... The selection window is based on the drone index set. The definition, in its specific form, is as follows:

[0069] The operation mod(a,b) generates the remainder when a is divided by b in arithmetic. It is the starting index of the selected window during the k-th broadcast, and its specific form is as follows:

[0070] The obtained shared indirect TOA measurement set The specific format is as follows:

[0071] (2) Fixed sharing strategy This strategy requires drone j to continuously broadcast measurements associated with the same group of drones. Specifically, drone j shares TOA measurements associated with the q drones immediately in front of it, and this process is repeated. A shared set of indirect TOA measurements is also provided. It remains unchanged across all broadcast times. It consists of a fixed set of drone indexes. The set is determined to be in the following specific form:

[0072] The resulting shared indirect TOA measurement set The specific format is as follows:

[0073] (3) Random sharing strategy This strategy introduces unpredictability by selecting a random subset of q TOA measurements at each broadcast. At the k-th broadcast, the shared indirect TOA measurement set... From all sizes of q Uniformly and randomly selected from the possible subsets. Set The specific format is as follows:

[0074] Among them, set The base number is q.

[0075] The three TOA measurement sharing strategies are as follows: Figure 4 As shown, you can choose any option according to the actual situation to reduce the communication burden.

[0076] Step 4: Design a data link communication mechanism and data packet format based on time-division multiple access and round-robin broadcasting to realize the transmission of raw TOA measurements and filtered state estimates between nodes, including: Establishing communication mechanisms and data packet formats: Based on a time-division multiple access (TDMA) round-robin broadcast communication mechanism, each node in the cluster sequentially occupies the channel and sends data packets according to a preset order and broadcast period. The information data packets shared by each node in the cluster are shown in Table 1. Table 1. Information data shared by each node

[0077] Step 5: Perform packet loss detection based on the shared broadcast sequence number, and match the broadcast sequence number of the shared TOA measurement with the corresponding shared sequence number of the stored navigation estimate; Due to the time-division multiple access (TDMA) communication mode, data packet loss is difficult to avoid. Receiving members cannot determine whether packet loss has occurred, and other members are prone to mismatches between the received shared TOA measurements and stored P messages when fusing the TOA measurements shared by the receiving member. The reliable multi-center relative navigation method proposed in this embodiment, applicable to communication resource-constrained environments, compares broadcast sequence numbers. If the broadcast sequence numbers are the same, it proves that the previous UAV broadcast did not result in packet loss, and the shared TOA can be fused; otherwise, if the previous member's broadcast resulted in packet loss, the shared TOA is not fused, preventing mismatches between the shared TOA measurements and stored P messages and ensuring correct updates of the shared TOA measurements.

[0078] Step 6: Perform time updates within each filtering cycle, and when a relative measurement is received, adopt a partial update strategy to update only the sub-states related to the current measurement; Step 6 in this embodiment includes: Step 6.1: Perform relative measurement updates based on the constructed measurement equations. Each node performs corresponding partial measurement updates upon receiving RTT clock synchronization measurements, TOA measurements, and shared TOA measurements. This partial update strategy only performs precise updates on sub-states directly related to the measurement; other sub-states retain their prior values, including: Based on the specific force and angular velocity output by the inertial navigation system, and combined with the continuous-time dynamic equations of the multi-center error state, the global error state is analyzed. Discretize and propagate the covariance matrix to obtain the prior estimate of the current time step. and ; RTT clock synchronization measurement update: When an RTT clock synchronization measurement between a pair of nodes is received, the corresponding observation matrix is ​​constructed. Only the sub-states of the two nodes related to the measurement are selected for the update. For other irrelevant sub-states, a partial update strategy that preserves prior values ​​is adopted, and the relevant covariance blocks are updated accordingly. TOA relative ranging measurement update: When a direct or indirect TOA relative ranging measurement is received, a relative distance observation equation is constructed based on the measurement model established in step two. After passing the packet loss test, only the position error, velocity error and other sub-states of the two nodes related to the measurement are updated using a partial update strategy. The sub-states of the remaining nodes remain unchanged with their prior values, and only the corresponding cross covariance is updated.

[0079] Step 6.2: Update the absolute measurement based on the constructed measurement equation, when the drone... When precise barometric altimeter and GNSS measurements are suddenly obtained, a partial update method similar to that used for relative measurements is employed, including: Barometric altimeter update: When a node obtains its own barometric altimeter measurement, it selects the altitude-related state components of the corresponding node from the global state vector and performs a standard EKF update according to the altitude measurement model.

[0080] Step 7: When the drone When another aircraft obtains barometric altimeter measurements, the absolute state subspace is extracted, the absolute measurement part is updated within this subspace and mapped back to the original state space, and then the relative positions of all UAVs in the relative coordinate system and other relative navigation results are calculated. In step 7 of this embodiment: Step 7.1, Node The error estimated by the filter is fed back to the inertial navigation system to correct its output absolute state information. An absolute state subspace is constructed based on the absolute state information. The absolute measurement part is updated in this subspace and mapped back to the original state space. Step 7.2, after obtaining the absolute state information of all nodes, the nodes... It can be calculated for any node The relative state, that is, the relative position of all UAVs in the relative coordinate system, and other relative navigation results.

[0081] Implementation Method 3: This implementation method proposes a computer device, including a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes a reliable multi-center relative navigation method suitable for communication resource constraints, as described in any one of Implementation Methods 1 to 2.

[0082] Implementation Method 4: This implementation method proposes a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of a reliable multi-center relative navigation method applicable to communication resource-constrained environments, as described in any one of Implementation Methods 1 to 2.

[0083] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the published pending claims.

Claims

1. A reliable multi-center relative navigation method suitable for communication resource constraints, characterized in that, The method includes: Step S1: Establish a full-error state model for the nodes in the cluster; Step S2: Establish a multi-source measurement model for the nodes in the cluster; Step S3: Design an indirect TOA measurement sharing strategy based on efficient communication; Step S4: Design a data link communication mechanism and data packet format based on time division multiple access and round-robin broadcasting to realize the transmission of the original TOA measurement and the filtered state estimate between nodes; Step S5: Perform packet loss detection based on the shared broadcast sequence number, and match the broadcast sequence number of the shared TOA measurement with the corresponding shared sequence number of the stored navigation estimate; Step S6: Perform time updates within each filtering cycle, and when a relative measurement is received, adopt a partial update strategy to update only the sub-states related to the current measurement; Step S7: When the local machine or other machines obtain barometric altimeter measurements, extract the absolute state subspace, perform absolute measurement part update in the subspace and map it back to the original state space, and then calculate the relative position and relative navigation results of all UAVs in the relative coordinate system. Step S2 includes: Constructing a barometric altimeter measurement model : in, Indicates drone In the The state at each measurement time The corresponding actual height value; It is an altitude estimate based on INS indications; It is the white noise from the altimeter measurement; Constructing a GNSS pseudorange measurement model : in, Indicates drone In the The state at each measurement time The corresponding actual GNSS pseudorange value; Represents a node exist The position below; Indicates that the satellite is The position below; Represents the speed of light; This represents white noise in GNSS pseudorange measurements. This indicates the clock phase error of the GNSS receiver; Constructing an RTT bidirectional ranging clock synchronization measurement model : in, Indicates based on drones and state variables and The theoretical value of RTT measurement obtained; This represents white noise measured in RTT. This indicates the data link receiver clock phase error; This indicates the clock phase error of the data link transmitter; Constructing a direct TOA ranging measurement model : in, Indicates based on drones and state variables and The theoretical value of direct TOA measurement obtained; This represents white noise measured directly at TOA. Represents a node exist The position below.

2. The reliable multi-center relative navigation method applicable to communication resource-constrained conditions according to claim 1, characterized in that, The step S1 of establishing the full error state model includes: For by N A cluster consisting of nodes, the navigation state of node i. Based on its own navigation state and others N The navigation error states of -1 nodes are constituted, specifically in the following form: in, This represents the navigation error state of node 1. For nodes Navigation error status, The navigation error state of node N includes: longitude error, latitude error, altitude error, velocity error, attitude misalignment angle, data link clock error, and GNSS receiver clock error. Using a full-error state-space modeling method based on the right-invariant error of the Lie group, the velocity error is redefined as: in, Indicates speed error, , and Indicates the attitude misalignment angle. This represents the traditional velocity error vector. This represents the inertial navigation system solution result. Indicates drone In its The eastward, northward, and celestial speeds under the system; Based on nodes Based on the modeled state variables and noise variables, construct the global error discrete-time propagation equation: in, Represents a node In time The state vector, Represents a node In time The state vector, Indicates time The state transition matrix, Indicates time Noise-driven array, The variance is The system's white noise.

3. The reliable multi-center relative navigation method applicable to communication resource-constrained conditions according to claim 1, characterized in that, The indirect TOA measurement sharing strategy in step S3 includes at least one of sequential sharing strategy, fixed sharing strategy and random sharing strategy, which is used to selectively share TOA measurements when communication resources are limited. Indirect TOA measurement models include: Set nodes At any moment Obtain a message about the node TOA measurement, and at time The measurement and related information will be broadcast together; node After receiving the message, TOA measurement at time and current time If a correspondence is established between the states, then indirect TOA measurement... Represented as: ， in, Indicates by At any given moment , The theoretical value of indirect TOA measurement determined by the state quantity; This represents white noise from indirect TOA measurements; express Time node exist Position vector in coordinate system; express Time node Clock deviation; express Time node Clock deviation; express Time node For nodes of System location estimate; express Time node For nodes The global coordinate system position estimate; Represents a node Corresponding node The horizontal Jacobian matrix of the position; Represents the time node at time k For nodes Horizontal position deviation; Represents a node Corresponding node The horizontal Jacobian matrix of the position; express Time node For nodes Horizontal position deviation; express Time node For nodes The clock skew estimate; express Time node For nodes The clock skew estimate; Represents a node Corresponding node A high-precision Jacobian matrix; express Time node For nodes Height deviation; express Time node For nodes Height deviation; express Time node For nodes Height deviation; Among them, the sequential sharing strategy selects shared measurements by cyclically moving the window, the fixed sharing strategy always broadcasts measurements of a fixed node group, and the random sharing strategy randomly selects a subset of measurements.

4. The reliable multi-center relative navigation method applicable to communication resource-constrained conditions according to claim 3, characterized in that, The sequential sharing strategy specifically includes: For node j, define an ordered tuple. It includes N-1 TOA measurements collected: in, For node 1 and node TOA measurement values ​​between For node 2 and node TOA measurement values ​​between For nodes With nodes TOA measurements between For nodes With nodes TOA measurement values ​​between For nodes With nodes The TOA measurement values ​​between; During the k-th broadcast, the shared measurement set By drone index set Decide: in, It is the starting index of the selected window during the k-th broadcast. Indicates the quantity of TOA measurements. For index offset parameters, The number of TOA measurements shared during the k-th broadcast; The obtained shared indirect TOA measurement set The specific format is as follows: in, ordered tuples The Middle The TOA measurement value corresponding to each location.

5. A reliable multi-center relative navigation method suitable for communication resource constraints according to claim 4, characterized in that, The fixed sharing strategy includes: Node j shares a fixed UAV index set Corresponding measurements: The obtained shared indirect TOA measurement set The specific format is as follows: in, For nodes With nodes The TOA measurement values ​​between.

6. A reliable multi-center relative navigation method suitable for communication resource constraints according to claim 1, characterized in that, The packet loss detection in step S5 includes: Upon receiving a shared TOA measurement, the broadcast sequence number is compared with the stored navigation estimate broadcast sequence number; When the sequence numbers match, the shared TOA measurement is merged; If the serial numbers do not match, the fusion is rejected.

7. A reliable multi-center relative navigation method suitable for communication resource constraints according to claim 1, characterized in that, The measurement update step S6 includes: Relative measurement update: When receiving RTT clock synchronization measurement, TOA measurement or shared TOA measurement, only the sub-states directly related to the measurement are updated, and the other sub-states retain their prior values. Absolute measurement update: When barometric altimeter or GNSS measurements are obtained, an update is performed in the absolute state subspace and then mapped back to the original state space.

8. A computer device, characterized in that: It includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes a reliable multi-center relative navigation method applicable to communication resource-constrained conditions as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of a reliable multi-center relative navigation method applicable to communication resource-constrained environments as described in any one of claims 1-7.

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