Multi-unmanned aerial vehicle fusion positioning method based on set membership filtering

Abstract

The invention provides a multi-unmanned aerial vehicle fusion positioning method based on set membership filtering, and the method comprises the steps: measuring the positions of a plurality of unmanned aerial vehicles through a sensor, and obtaining the unmanned aerial vehicle position measurement data corresponding to the plurality of unmanned aerial vehicles; estimating the position information of each unmanned aerial vehicle according to the unmanned aerial vehicle position measurement data through a plurality of filters based on a set membership filtering algorithm to obtain ellipsoid estimation data; and carrying out dimension reduction and fusion on the ellipsoid estimation data to obtain an unmanned aerial vehicle fusion positioning result. Compared with the prior art, the multi-unmanned aerial vehicle fusion positioning method based on set membership filtering provided by the invention can perform real-time positioning on a plurality of unmanned aerial vehicles in a complex environment, the calculation complexity is lower, and the positioning is more accurate.

Description

Technical Field

[0001] The present invention is applicable to the technical field of unmanned aerial vehicles, and in particular relates to a multi-unmanned aerial vehicle fusion positioning method based on set membership filtering. Background Art

[0002] With the popularization of smart mobile terminals and the development of the Internet and sensor network technology, real-time positioning through sensors to obtain target location information has become a research hotspot. Highly real-time, high-precision, highly reliable and highly available target location information is the necessary foundation and important guarantee for effective real-time positioning.

[0003] At present, positioning technologies such as global navigation satellite system / inertial navigation system, laser radar-based simultaneous positioning and mapping technology, INS autonomous dead reckoning navigation technology and ultra-wideband (UWB) wireless positioning technology are widely used, among which ultra-wideband (UWB) technology is mainly used to collect position information of indoor mobile targets. Considering that the measurement process will be interfered by unknown noise, thus affecting the positioning results, it is particularly important to use filtering technology to process the target positioning results.

[0004] Compared with the existing Kalman filter algorithm, the ensemble filtering technology has the advantage of not considering the specific characteristics of noise. In the ensemble filtering algorithm, for state estimation with certain coupling relationships between multiple targets, an estimator is usually designed through system augmentation to uniformly estimate the states of all nodes. However, due to problems such as complex environment and limited space, the network node measurement values ​​obtained by the estimator are limited, and it is difficult for the estimator to collect all node measurement values; or in a system with a large number of nodes, the system dimension increases after augmentation, resulting in excessive computational complexity.

[0005] Therefore, a new multi-UAV fusion positioning method based on set membership filtering is urgently needed to solve the above technical problems. Summary of the invention

[0006] The present invention proposes a multi-UAV fusion positioning method based on set membership filtering, which can perform real-time positioning of multiple UAVs in a complex environment with lower calculation complexity and more accurate positioning.

[0007] The present invention proposes a multi-UAV fusion positioning method based on set membership filtering, comprising the following steps:

[0008] S1. Measure the positions of multiple drones through sensors to obtain drone position measurement data corresponding to the multiple drones;

[0009] S2, estimating the position information of each UAV according to the UAV position measurement data through multiple filters based on a set membership filtering algorithm to obtain ellipsoid estimation data;

[0010] S3. Reduce the dimension of the ellipsoid estimation data and fuse them to obtain the UAV fusion positioning result.

[0011] Preferably, in step S1, during the process of measuring the positions of the plurality of drones by using sensors, the measurement is performed according to the following relationship:

[0012]

[0013] y j，t =C j，t x j，t +F j，t v j，t ;

[0014] Among them, A i,t , B i,t , D i,t , C j,t and F j,t Both represent time-varying coefficient matrices, x i,t+1 ∈R 3 Indicates that at time t+1, UAV U i The state of x j,t Indicates the drone U measured by the sensor j The state at time t, w i,t Indicates drone U i The process noise at time t, ω ij represents the external coupling parameter, Γ represents the internal coupling matrix, v j,t represents the measurement noise, y j,t Indicates drone U j Position measurement data at time t.

[0015] Preferably, in step S2, the ellipsoid estimation data is defined as L k,t , which satisfies:

[0016]

[0017] Where k represents the kth filter, represents a given ellipse restriction matrix, Represents the center of the ellipsoid, with:

[0018]

[0019] in,

[0020]

[0021] in, as well as They represent the augmented time-varying coefficient matrix, represents the external coupling parameter matrix, Represents the measurement information of the kth filter at time t.

[0022] Preferably, step S3 includes the following sub-steps:

[0023] S31, deleting rows and columns with all zeros in the matrix of the ellipsoid estimation data to obtain reduced-dimensional ellipsoid estimation data;

[0024] S32, solving the minimum intersection within the estimation range of the reduced-dimensional ellipsoid estimation data to obtain the UAV fusion positioning result.

[0025] Preferably, the sensor is a UWB sensor.

[0026] Preferably, the number of filters is less than the number of drones.

[0027] Compared with the prior art, the present invention measures the positions of multiple drones through sensors to obtain drone position measurement data corresponding to the multiple drones; estimates the position information of each drone based on the drone position measurement data through multiple filters based on the set membership filtering algorithm to obtain ellipsoid estimation data; reduces the dimension of the ellipsoid estimation data and fuses them to obtain the drone fusion positioning result. The multi-drone fusion positioning method based on set membership filtering proposed in the present invention can perform real-time positioning of multiple drones in complex environments, with lower calculation complexity and more accurate positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The present invention will be described in detail below in conjunction with the accompanying drawings. The above and other aspects of the present invention will become clearer and easier to understand through the detailed description made in conjunction with the following drawings. In the accompanying drawings:

[0029] Figure 1 It is a flowchart of a multi-UAV fusion positioning method based on set membership filtering provided by an embodiment of the present invention;

[0030] Figure 2 Schematic diagram of sensor node distribution of a multi-UAV fusion positioning method based on set membership filtering provided by an embodiment of the present invention;

[0031] Figure 3 It is a schematic diagram of the UAV fusion positioning result of the multi-UAV fusion positioning method based on set membership filtering provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0033] Please refer to Figure 1-Figure 3 The present invention proposes a multi-UAV fusion positioning method based on set membership filtering, comprising the following steps:

[0034] S1. Measure the positions of multiple drones through sensors to obtain drone position measurement data corresponding to the multiple drones;

[0035] In the embodiment of the present invention, the sensor is a UWB sensor. i (i=1,2,3,4,5), the UWB ranging principle is used to collect UAV measurement information.

[0036] The system state equations corresponding to its multiple drones are:

[0037]

[0038] Among them, A i,t , B i,t is a known time-varying coefficient matrix, x i,t+1 ∈R 3 It's a drone i Status. i,t It's a drone i The process noise meets W i,t is a known positive definite matrix representing the range of unknown bounded process noise, Γ represents the internal coupling matrix, ω ij Represents the external coupling parameter.

[0039] The set of drones that can be measured by sensor node k (k = 1, 2, 3, 4) is recorded as:

[0040]

[0041] Then, the measurement information of the kth sensor node is expressed as:

[0042]

[0043] in,

[0044] y j,t =C j,t x j,t +F j,t v j,t ,

[0045] C i,t 、F i,t is a known time-varying coefficient matrix, V j,t is a known symmetric matrix representing the range of unknown bounded measurement noise. j,t express The elements in v j,t represents the measurement noise, y j,t Indicates drone U j At the time t, the position measurement data, j takes different values ​​corresponding to different elements, that is, j represents the set of drone numbers that the kth sensor can measure. Elements inside.

[0046] For multiple drones to be located, appropriate sensor nodes are set to collect valid drone measurement information within the range, such as Figure 2 As shown in Figure 1, this structure is usually a sensor receiving measurement information from multiple drones. It should be noted that the distribution of sensor nodes is not fixed and needs to be determined according to actual conditions. Figure 1 Just an example of sensor node distribution is given.

[0047] definition UAV i The augmented state is:

[0048]

[0049] in

[0050] H=[I 0].

[0051] Here, I represents the identity matrix of appropriate dimension.

[0052] S2, estimating the position information of each UAV according to the UAV position measurement data through multiple filters based on a set membership filtering algorithm to obtain ellipsoid estimation data;

[0053] In the embodiment of the present invention, based on the initial estimation of the filter k Last moment estimate and the measured values ​​obtained The following form of set membership filter can be obtained:

[0054]

[0055] in,

[0056]

[0057] Define the filter error as: The following filtering error system can be obtained:

[0058]

[0059] in,

[0060]

[0061] For ellipsoid estimation data Given an ellipse constraint matrix The initial value constraint condition at the initial time is Then when When satisfied, mathematical induction can be used to obtain

[0062]

[0063] Sufficient condition for the establishment of , thereby ensuring that the state at each moment after iteration is within the estimated range.

[0064] After deriving sufficient conditions for the filter performance (1) to be satisfied, the local optimal estimate is obtained by solving the following optimization problem.

[0065] Optimize filter parameters:

[0066] OP1: min

[0067] So as to solve the filter parameters This achieves state estimation that includes the target's true position information. The same is true for other node filters.

[0068] The number of drones that can be estimated by filter k is u k For the UAVs where filter k can be estimated like Repeat with the UAV estimated by filter r, then n k0 =1, otherwise n ko =0.

[0069] The number of drones estimated by filter k and other filters repeatedly:

[0070]

[0071] The number of drones estimated without duplication by filter k:

[0072]

[0073] In an embodiment of the present invention, the number of filters is less than the number of drones.

[0074] S3. Reduce the dimension of the ellipsoid estimation data and fuse them to obtain the UAV fusion positioning result.

[0075] In this embodiment of the present invention, step S3 includes the following sub-steps:

[0076] S31, deleting rows and columns that are all zero in the matrix of the ellipsoid estimation data to obtain reduced-dimensional ellipsoid estimation data; specifically, after obtaining the ellipsoid estimation data Then, the obtained ellipsoid estimation data Perform dimensionality reduction.

[0077] First define the matrix:

[0078] S k0 =(1-n k0 )I 5×5 ,

[0079] R k =diag{S k0 ,S k1 ,…,S kp},

[0080]

[0081] Where nr(·) and nc(·) represent the functions of deleting rows and columns of all zeros in the matrix, respectively. It is the real state The estimated value of .

[0082] S32, solving the minimum intersection within the estimation range of the reduced-dimensional ellipsoid estimation data to obtain the UAV fusion positioning result.

[0083] Specifically, solve All estimated ranges in express Then find the intersection of these estimated ranges in This means And this is a more compact interval. The ellipsoid after dimensionality reduction is represented as:

[0084] Define two containing real states A collection of:

[0085]

[0086]

[0087] Then, for filter k, the true state of the node can be estimated The possible ranges are:

[0088]

[0089] Please refer to Figure 3 , Figure 3It can be known The dark area is Ellipsoid L k-1,t , L k+1,t and L k,t represents the estimated ellipsoid after dimensionality reduction of the corresponding node, where L k-1,t and L k+1,t Yes and L k,t Ellipsoids with the same estimated object.

[0090] Due to the region It is usually irregular. To facilitate calculation, find the smallest ellipsoid that contains all possible values ​​of the true state.

[0091] The calculation of (i.e., the fusion positioning result of the UAV) can be converted into solving the following optimization problem:

[0092] OP2: min trace

[0093] st Established.

[0094] Obviously, the obtained ellipsoid Compare Smaller range ), thus ensuring better estimation performance.

[0095] Compared with the prior art, the present invention measures the positions of multiple drones through sensors to obtain drone position measurement data corresponding to the multiple drones; estimates the position information of each drone based on the drone position measurement data through multiple estimators based on the set membership filter algorithm to obtain ellipsoid estimation data; reduces the dimension of the ellipsoid estimation data and fuses them to obtain the drone fusion positioning result. The multi-drone fusion positioning method based on set membership filtering proposed in the present invention can perform real-time positioning of multiple drones in complex environments, with lower computational complexity and more accurate positioning.

[0096] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0097] The embodiments of the present invention are described above in conjunction with the accompanying drawings. What is disclosed is only the preferred embodiment of the present invention. However, the present invention is not limited to the above-mentioned specific implementation manner. The above-mentioned specific implementation manner is only illustrative rather than restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms and equivalent changes without departing from the scope of protection of the purpose of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A multi-UAV fusion positioning method based on set membership filtering, characterized in that: The following steps are involved: S1. Measure the positions of multiple drones through sensors to obtain drone position measurement data corresponding to the multiple drones; S2, estimating the position information of each UAV according to the UAV position measurement data through multiple filters based on a set membership filtering algorithm to obtain ellipsoid estimation data; S3. Reduce the dimension of the ellipsoid estimation data and fuse them to obtain the UAV fusion positioning result.

2. The multi-UAV fusion positioning method based on set membership filtering according to claim 1 is characterized in that: In step S1, during the process of measuring the positions of multiple drones by using sensors, the measurement is performed according to the following relationship: y j,t =C j,t x j,t +F j,t v j,t ; Among them, A i,t , B i,t , D i,t , C j,t and F j,t Both represent time-varying coefficient matrices, x i,t+1 ∈R 3 Indicates that at time t+1, UAV U i The state of x j,t Indicates the drone U measured by the sensor j The state at time t, w i,t Indicates drone U i The process noise at time t, ω ij represents the external coupling parameter, Γ represents the internal coupling matrix, v j,t represents the measurement noise, y j,t Indicates drone U j Position measurement data at time t.

3. The multi-UAV fusion positioning method based on set membership filtering as claimed in claim 2 is characterized in that: In step S2, the ellipsoid estimation data is defined as L k,t , which satisfies: Where k represents the kth filter, represents a given ellipse restriction matrix, Represents the center of the ellipsoid, with: in, in, as well as Both represent the augmented time-varying coefficient matrix, represents the external coupling parameter matrix, Represents the measurement information of the kth filter at time t.

4. The multi-UAV fusion positioning method based on set membership filtering as claimed in claim 1 is characterized in that: Step S3 includes the following sub-steps: S31, deleting rows and columns with all zeros in the matrix of the ellipsoid estimation data to obtain reduced-dimensional ellipsoid estimation data; S32, solving the minimum intersection within the estimation range of the reduced-dimensional ellipsoid estimation data to obtain the UAV fusion positioning result.

5. The multi-UAV fusion positioning method based on set membership filtering as claimed in claim 1 is characterized in that: The sensor is a UWB sensor.

6. The multi-UAV fusion positioning method based on set membership filtering as claimed in claim 1, characterized in that: The number of filters is less than the number of drones.

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