DRSS-based distributed low-interaction positioning method special for unmanned cluster

Abstract

The invention discloses a DRSS-based distributed low-interaction positioning method special for an unmanned cluster, and relates to the technical field of passive positioning, the method is suitable for an unmanned cluster cooperative positioning scene comprising an unmanned cluster and a target signal source, and the advantages of a large number of unmanned cluster nodes, high moving speed and the like are comprehensively considered. A multi-node mutual test DRSS and single-node self test DRSS combined positioning framework is provided, two energy domain positioning parameters of I-DRSS and M-DRSS are jointly utilized, a WLS-based single-moment positioning solving algorithm is provided, and a new unmanned cluster self-cooperation and mutual cooperation positioning solving thought is constructed. According to the invention, the dependence of a traditional positioning technology on real-time transmission of a large amount of original sampling data can be effectively overcome, and target positioning can be carried out in a low-interaction environment, a denial environment, a weak network signal and other communication link blocking conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of passive positioning, and in particular relates to a distributed low-interaction positioning method dedicated to unmanned clusters based on DRSS (Difference of Received Signal Strength). Background Art

[0002] Passive positioning technology has always been an important research topic in the field of signal processing. It is a technology that obtains the target position by detecting the electromagnetic signals radiated by the target and adopting a series of signal processing methods. It is the core link that associates "signal" and "target". It has the advantages of good concealment, long detection distance, and strong applicability. It has received extensive attention from domestic and foreign scholars in the fields of radar, wireless sensor networks, etc.

[0003] In response to the positioning problem under low-interaction conditions, some scholars have studied the positioning mechanism based on received signal strength (RSS), which uses multiple receivers to independently detect signal strength and aggregate information to the fusion processing center to reversely calculate the distance of signal propagation and determine the target location. A major advantage of this RSS positioning method is that it does not require the return of detailed original sampling data, but only requires the transmission of processed spectrum information, so it is suitable for low-interaction scenarios. On this basis, relevant scholars proposed a positioning method based on the received signal gain ratio (GROA), which can obtain its position when the signal radiation power is unknown. However, in the existing research on GROA, most of them only characterize GROA as a linear function of distance, and do not consider the actual situation that RSS decays with the exponential power of distance, and GROA is often used in conjunction with TDOA (Time Difference of Arrival), and the algorithm that only uses GROA to achieve positioning is still missing.

[0004] In recent years, domestic and foreign scholars have focused their research on DRSS. DRSS considers a more accurate signal propagation model and introduces high-order exponential powers of distance into the process of target positioning analysis, further improving the accuracy and stability of positioning. The DRSS parameters mentioned in existing literature are essentially multi-node mutual measurement DRSS (Mutual DRSS, M-DRSS), which is obtained by differentiating the RSS measured by each node with the RSS measured by the reference node. However, if only M-DRSS is used for positioning, the positioning accuracy will be reduced when the communication link is blocked in low-interaction environment, denied environment, weak network signal, etc., resulting in the positioning method not being suitable for low-interaction environment, denied environment, weak network signal, and other communication link blocked situations. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a DRSS-based unmanned cluster dedicated distributed low-interaction positioning method. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0006] The present invention provides a DRSS-based distributed low-interaction positioning method for unmanned clusters, which is applicable to an unmanned cluster collaborative positioning scenario including an unmanned cluster and a target signal source, wherein the unmanned cluster is composed of multiple unmanned aerial vehicles, and the method comprises:

[0007] Step 1: constructing an I-DRSS measurement equation and an M-DRSS measurement equation of the unmanned cluster at the current positioning moment according to the signal power of the target signal source received by each UAV node in the unmanned cluster;

[0008] Step 2: Obtain a first pseudo linear equation about the position of the target signal source according to the I-DRSS measurement equation and the M-DRSS measurement equation;

[0009] Step 3: Solve the first pseudo linear equation using the WLS algorithm to obtain a rough position estimate of the target signal source at the current positioning moment;

[0010] Step 4: Based on the rough position estimate, construct a second pseudo-linear equation about the position of the target signal source, and solve the second pseudo-linear equation using the WLS algorithm to obtain a precise position estimate of the target signal source at the current positioning moment.

[0011] Compared with the prior art, the present invention has the following beneficial effects:

[0012] The DRSS-based distributed low-interaction positioning method for unmanned clusters of the present invention is applicable to unmanned cluster collaborative positioning scenarios including unmanned clusters and target signal sources. Taking into account the advantages of large number of nodes and fast moving speed of unmanned clusters, a joint positioning framework of multi-node mutual detection DRSS (M-DRSS) and single-node self-detection DRSS (I-DRSS) is proposed. By jointly utilizing the two energy domain positioning parameters of I-DRSS and M-DRSS, a single-time positioning solution algorithm based on WLS is proposed, and a new positioning solution idea of ​​"self-collaboration" and "inter-collaboration" for unmanned clusters is constructed. The present invention can effectively overcome the dependence of traditional positioning technology on the real-time transmission of a large amount of original sampling data, and can perform target positioning in low-interaction environments, denied environments, weak network signals and other communication link obstructions.

[0013] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following specifically cites a preferred embodiment and describes it in detail with the accompanying drawings as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a schematic diagram of an unmanned cluster collaborative positioning scenario provided by an embodiment of the present invention;

[0015] Figure 2 It is a flow chart of a DRSS-based unmanned cluster-specific distributed low-interaction positioning method provided by an embodiment of the present invention;

[0016] Figure 3 It is a schematic diagram of the coordinates and relative position relationship of an unmanned cluster and a target signal source provided by an embodiment of the present invention;

[0017] Figure 4 is a performance curve diagram of a single-time positioning algorithm when the measurement error changes provided by an embodiment of the present invention;

[0018] Figure 5 is a performance curve diagram of a single-time positioning algorithm when the scale of an unmanned cluster changes, provided by an embodiment of the present invention;

[0019] Figure 6 It is a performance curve diagram of a single-time positioning algorithm when the aperture of an unmanned cluster changes provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following is a detailed description of a DRSS-based unmanned cluster-specific distributed low-interaction positioning method proposed in accordance with the present invention, in combination with the accompanying drawings and specific implementation methods.

[0021] The above and other technical contents, features and effects of the present invention are clearly presented in the following detailed description of the specific implementation modes in conjunction with the accompanying drawings. Through the description of the specific implementation modes, the technical means and effects adopted by the present invention to achieve the predetermined purpose can be more deeply and specifically understood. However, the attached drawings are only for reference and explanation purposes and are not used to limit the technical solutions of the present invention.

[0022] The embodiment of the present invention provides a DRSS-based distributed low-interaction positioning method for unmanned clusters, which is applicable to unmanned cluster collaborative positioning scenarios including unmanned clusters and target signal sources, where the unmanned cluster consists of multiple drones. Figure 1 , Figure 1is a schematic diagram of an unmanned cluster collaborative positioning scenario provided by an embodiment of the present invention, such as Figure 1 As shown in the unmanned cluster collaborative positioning scenario, there is an unmanned cluster consisting of N drones and a target signal source. The spatial position of the i-th drone node is u i =(u x,i ,u y,i ,u z,i ) T , i=1,2,…,N, the position of the target signal source is s=(s x ,s y ,s z ) T , the signal power radiated by the target signal source is P t , the signal center frequency is f, and the signal bandwidth is B. Considering the large-scale spatial range of unmanned cluster reconnaissance scenarios, the distance between the drone nodes of the unmanned cluster and the target signal source is far, and the radio wave propagation loss is mainly affected by the propagation distance. The distance between the drone node and the target signal source is d i =||u i -s||2. According to the free space propagation model, the signal power of the target signal source received by the drone node is:

[0023]

[0024] Among them, K i (dB) represents the front-end RF receiver gain of each drone node. The gain of different devices is slightly different. t (dB) represents the transmitter power of the target signal source.

[0025] Considering the change of the received power caused by the change of the position of the drone nodes in the unmanned cluster at different times, the signal power received by the i-th drone node from the target signal source at time t is defined as P i r [t].

[0026] See also Figure 2 , Figure 2 is a flow chart of a DRSS-based unmanned cluster-specific distributed low-interaction positioning method provided by an embodiment of the present invention, such as Figure 2 As shown, the DRSS-based unmanned cluster dedicated distributed low-interaction positioning method of this embodiment includes the following steps:

[0027] Step 1: According to the signal power of the target signal source received by each drone node in the unmanned cluster, the I-DRSS measurement equation and M-DRSS measurement equation of the unmanned cluster at the current positioning moment are constructed.

[0028] In an optional embodiment, step 1 includes the following steps:

[0029] Step 1.1: According to the signal power of the target signal source received by each drone node in the unmanned cluster at the current positioning moment and the previous positioning moment and the set I-DRSS measurement error, the I-DRSS measurement equation of the unmanned cluster at the current positioning moment is constructed.

[0030] In this embodiment, the I-DRSS measurement error obeys additive Gaussian distribution.

[0031] Optionally, step 1.1 includes:

[0032] Step 1.11: Based on the signal power of the target signal source received by each drone node in the unmanned cluster at the current positioning moment and the previous positioning moment, the theoretical I-DRSS measurement value of the drone node at the current positioning moment is obtained.

[0033] In this embodiment, for the i-th UAV node and the target signal source in the unmanned cluster, at the current positioning time t1 and the previous positioning time t0, after the UAV node receives the signal from the target signal source, the theoretical I-DRSS measurement value of the UAV node at the current positioning time can be obtained as follows:

[0034]

[0035] Where i represents the i-th drone node, i = 1, 2, ..., N, N is the number of drones in the unmanned cluster, t1 is the current positioning time, t0 is the previous positioning time, is the theoretical I-DRSS measurement value of the i-th UAV node at the current positioning moment, P i r [t1] is the signal power of the target signal source received by the i-th UAV node at the current positioning time, P i r [t0] is the signal power of the target signal source received by the i-th UAV node at the previous positioning moment, d i [t0] is the distance between the i-th UAV node and the target signal source at the previous positioning moment, d i [t1] is the distance between the i-th UAV node and the target signal source at the current positioning moment.

[0036] In this embodiment, for From the above formula, it can be seen that when a single UAV node measures I-DRSS, the ratios of receiver gains cancel each other out.

[0037] Step 1.12: Based on the theoretical I-DRSS measurement value and the I-DRSS measurement error, obtain the actual I-DRSS measurement value of the drone node at the current positioning moment.

[0038] When each drone node measures I-DRSS, the measurement value will produce random errors due to the inevitable thermal noise in the digital circuit. Without loss of generality, assuming that the I-DRSS measurement error obeys the additive Gaussian distribution with a mean of 0, then the actual I-DRSS measurement value of each drone node at the current positioning moment is expressed as:

[0039]

[0040] In the formula, ρ i [t1] is the actual I-DRSS measurement value of the i-th UAV node at the current positioning moment, Δρ i [t1] is the I-DRSS measurement error of the i-th UAV node at the current positioning moment, Δρ i The variance of [t1] is

[0041] Step 1.13: Based on the actual I-DRSS measurement value and I-DRSS measurement error of the drone node, construct the I-DRSS measurement equation of the unmanned cluster at the current positioning moment.

[0042] In this embodiment, the I-DRSS measurement equation of the unmanned cluster at the current positioning time is expressed as:

[0043] ρ[t1]=(ρ1[t1],ρ2[t1],…,ρ N [t1]) T ;

[0044] Δρ[t1]=[Δρ1[t1],Δρ2[t1],…,Δρ N [t1]] T N×1 ;

[0045] Where ρ[t1] is the actual I-DRSS measurement value of the unmanned cluster at the current positioning moment, Δρ[t1] is the I-DRSS measurement error, and T represents the matrix transpose.

[0046] The covariance matrix of the I-DRSS measurement error Δρ[t1] is

[0047] Step 1.2: According to the signal power of the target signal source received by each group of drone nodes in the unmanned cluster at the current positioning time and the set M-DRSS measurement error, the M-DRSS measurement equation of the unmanned cluster at the current positioning time is constructed.

[0048] In this embodiment, the M-DRSS measurement error obeys Gaussian distribution.

[0049] Optionally, step 1.2 includes:

[0050] Step 1.21: Based on the signal power of the target signal source received by each group of drone nodes in the unmanned cluster at the current positioning moment, the theoretical M-DRSS measurement value of each group of drone nodes at the current positioning moment is obtained.

[0051] In this embodiment, for the current positioning time t1, after a group of UAV nodes (the i-th UAV node and the q-th UAV node) receive the signal from the target signal source, the theoretical M-DRSS measurement value of the group of UAV nodes at the current positioning time can be obtained as follows:

[0052]

[0053] Where i is the i-th UAV node, i = 1, 2, ..., N, q is the q-th UAV node, q = 1, 2, ..., N, q ≠ i, N is the number of UAVs in the UAV cluster, t1 is the current positioning time, is the theoretical M-DRSS measurement value of the i-th UAV node and the q-th UAV node as a group of UAV nodes at the current positioning moment, P i r [t1] is the signal power of the target signal source received by the i-th UAV node at the current positioning moment, is the signal power of the target signal source received by the qth UAV node at the current positioning moment, d i [t1] is the distance between the i-th UAV node and the target signal source at the current positioning moment, d q [t1] is the distance between the qth UAV node and the target signal source at the current positioning moment, κ i,q is the ratio of the gains of the front-end receivers of the i-th UAV node and the q-th UAV node,

[0054] Step 1.22: Based on the theoretical M-DRSS measurement value and the M-DRSS measurement error, obtain the actual M-DRSS measurement value of each group of UAV nodes at the current positioning moment.

[0055] The actual M-DRSS measurement value of each group of UAV nodes at the current positioning moment is expressed as:

[0056]

[0057] In the formula, γ i,q [t1] is the actual M-DRSS measurement value of the i-th UAV node and the q-th UAV node as a group of UAV nodes at the current positioning moment, Δγ i,q[t1] is the M-DRSS measurement error of the i-th UAV node and the q-th UAV node as a group of UAV nodes at the current positioning moment, Δγ i,q [t1] follows a Gaussian distribution with a mean of 0 and a variance of

[0058] Step 1.23: Based on the actual M-DRSS measurement value and M-DRSS measurement error of each group of drone nodes, construct the M-DRSS measurement equation of the unmanned cluster at the current positioning moment.

[0059] In this embodiment, the M-DRSS measurement equation of the unmanned cluster at the current positioning time is expressed as: γ[t1]=(γ 1,2 [t1],…,γ 1,N [t1],γ 2,1 [t1],γ 2,3 [t1],…,γ 2,N [t1],…,γ N,1 [t1],γ N,2 [t1],…,γ N,N-1 [t1]) T ; Δγ[t1]=(Δγ 1,2 [t1],…,Δγ 1,N [t1],Δγ 2,1 [t1],Δγ 2,3 [t1],…,Δγ 2,N [t1],…,Δγ N,1 [t1],Δγ N,2 [t1],…,Δγ N,N-1 [t1]) T ;

[0060] Where γ[t1] is the actual M-DRSS measurement value of the unmanned cluster at the current positioning moment, Δγ[t1] is the M-DRSS measurement error, and T represents the matrix transpose.

[0061] The covariance matrix of the M-DRSS measurement error Δγ[t1] is:

[0062]

[0063] Step 2: According to the I-DRSS measurement equation and the M-DRSS measurement equation, a first pseudo linear equation about the position of the target signal source is obtained.

[0064] In an optional embodiment, step 2 includes: transforming the I-DRSS measurement equation and the M-DRSS measurement equation into forms related to the position of the target signal source and then integrating them to obtain a first pseudo-linear equation.

[0065] In this embodiment, the I-DRSS measurement equation is first processed, and d i [t0]=||u i [t0]-s||2 and d i [t1]=||u i [t1]-s||2 is brought in In the above example, multiply the left and right sides by We can get:

[0066]

[0067] The unknown quantity, i.e., the position s of the target signal source, is combined with similar terms, and the I-DRSS measurement error Δρ i [t1] is moved to the right side of the equation, and we get:

[0068] (ρ i [t1]-1)s T s+2(u i [t0] T -ρ i [t1]u i [t1] T )s+ρ i [t1]u i [t1] T u i [t1]-u i [t0] T u i [t0] = d i [t1] 2 Δρ i [t1].

[0069] The M-DRSS measurement equation is processed by converting d i [t1]=||u i [t1]-s||2 and d q [t1]=||u q [t1]-s||2 is brought in In the above example, multiply the left and right sides by Combining the unknown quantity, i.e. the position s of the target signal source, with similar terms, we can obtain:

[0070]

[0071] The N actual I-DRSS measurement values ​​of all UAV nodes for the target signal source at the current positioning time t1 given by the processed I-DRSS measurement equation and the M-DRSS measurement equation, as well as the N(N-1) actual M-DRSS measurement values ​​are integrated into a matrix form, that is, the first pseudo-linear equation is obtained, which is expressed as: P1[t1]-G1[t1]S=D1[t1]Δθ[t1].

[0072] in,

[0073]

[0074]

[0075] Where t1 is the current positioning time, t0 is the previous positioning time, and u i ,i=1,2,…,N, represents the spatial position of the i-th UAV node, ρ i [t1],i=1,2,…,N, represents the actual I-DRSS measurement value of the i-th UAV node at the current positioning time, κ i,q ,i=1,2,…,N,q=1,2,…,N,q≠i,represents the ratio of the gain of the front-end receiver of the i-th UAV node and the q-th UAV node, γ i,q [t1], i = 1, 2, ..., N, q = 1, 2, ..., N, q ≠ i, represents the actual M-DRSS measurement value of the i-th UAV node and the q-th UAV node as a group of UAV nodes at the current positioning time, d i [t1], i = 1, 2, ..., N, represents the distance between the i-th UAV node and the target signal source at the current positioning time, diag() represents the construction of a diagonal matrix function, I N-1 represents the unit matrix of size N-1, S is the first position vector, S = [s, s T s] T 4×1 , s is the position coordinate of the target signal source, T represents the matrix transpose, Δθ[t1] is the error vector, Δρ[t1] is the I-DRSS measurement error, Δγ[t1] is the M-DRSS measurement error, and N is the number of drones in the unmanned cluster.

[0076] Step 3: Use the WLS algorithm to solve the first pseudo-linear equation to obtain a rough position estimate of the target signal source at the current positioning moment.

[0077] In an optional embodiment, step 3 includes the following steps:

[0078] Step 3.1: Based on the first pseudo-linear equation, the optimization problem is constructed using the minimum mean square error criterion. The optimization problem is expressed as:

[0079]

[0080] Where W1[t1] is the first weighting matrix.

[0081] Step 3.2: Use the WLS algorithm to solve the optimization problem to obtain the estimated value of the first position vector. The estimated value of the first position vector is expressed as:

[0082]

[0083] When the weighting matrix is ​​a positive definite matrix, the error matrix of the weighted least squares estimation can be minimized, then,

[0084] W1[t1]=E[(D1[t1]Δθ[t1])(D1[t1]Δθ[t1]) T ] -1 =[D1[t1]Q[t1](D1[t1]) T ] -1 ,

[0085] Where E[] represents the expectation, Q[t1] is the covariance matrix of the additive Gaussian error, Q I [t1] is the covariance matrix of I-DRSS measurement error, Q M [t1] is the covariance matrix of the M-DRSS measurement error.

[0086] The estimated value of the first position vector The covariance matrix of the estimated error relative to the true value is

[0087] Step 3.3: Obtain a rough position estimate of the target signal source at the current positioning moment according to the estimate of the first position vector.

[0088] In this embodiment, the estimated value of the first position vector The first three elements of are the rough estimated position values ​​of the target signal source at the current positioning moment, denoted as

[0089] Step 4: Based on the rough position estimate, construct a second pseudo-linear equation about the position of the target signal source, and use the WLS algorithm to solve the second pseudo-linear equation to obtain the precise position estimate of the target signal source at the current positioning moment.

[0090] In this embodiment, considering that s and s in the first position vector S T The correlation between s is again estimated and solved using the WLS algorithm.

[0091] Optionally, step 4 includes:

[0092] Step 4.1: Construct a second pseudo linear equation for the position of the target signal source according to the estimated value of the first position vector.

[0093] Let the estimated value of the first position vector be The error relative to the corresponding true value is ΔS[t1], then the estimated value of the first position vector is It can be expressed as

[0094] Rewrite the above equation into a matrix form and square both ends, obtain the Schur product by element-by-element multiplication, and ignore the quadratic term ΔS[t1] of the position estimation error 2 , then we can get:

[0095]

[0096] Rewriting the above formula into matrix form, we can obtain the pseudo linear equation containing the unknown variable S, that is, the second pseudo linear equation, which is expressed as:

[0097] H2[t1]-G2V[t1]≈D2[t1]ΔS[t1].

[0098] in,

[0099]

[0100] In the formula, is the rough position estimate of the target signal source at the current positioning moment,

[0101] The product of the transpose of the rough position estimate of the target signal source at the current positioning moment and the rough position estimate of the target signal source, ⊙ represents the Schur product, I3 is the unit matrix of size 3, V[t1] is the second position vector, V[t1] = [S[t1](1:3)⊙S[t1](1:3)] 3×1 , S[t1](1:3) is the true position value of the target signal source at the current positioning moment, and ΔS[t1] is the estimated error of the estimated value of the first position vector relative to the true value.

[0102] Step 4.2: Use the WLS algorithm to solve the second pseudo linear equation to obtain an estimated value of the second position vector.

[0103] The estimated value of the second position vector is expressed as:

[0104]

[0105] Where W2[t1] is the second weighting matrix, is the covariance matrix of the estimated error of the estimated value of the first position vector relative to the true value.

[0106] Step 4.3: According to the estimated value of the second position vector and the estimated value of the first position vector, obtain the precise estimated value of the position of the target signal source at the current positioning moment.

[0107] The precise position estimate of the target signal source at the current positioning moment is expressed as:

[0108]

[0109] In the formula, Indicates the estimated value of the first position vector is extracted to ensure the positive and negative and have the same positive and negative properties, is the first row element of the estimated value of the second position vector, is the second row element of the estimated value of the second position vector, is the third row element of the estimated value of the second position vector.

[0110] The covariance matrix of the estimated error of the precise position estimate of the target signal source relative to the true value at the current positioning moment is:

[0111] The DRSS-based distributed low-interaction positioning method for unmanned clusters of the present invention is applicable to unmanned cluster collaborative positioning scenarios including unmanned clusters and target signal sources. Taking into account the advantages of large number of nodes and fast moving speed of unmanned clusters, a joint positioning framework of multi-node mutual detection DRSS (M-DRSS) and single-node self-detection DRSS (I-DRSS) is proposed. By jointly utilizing the two energy domain positioning parameters of I-DRSS and M-DRSS, a single-time positioning solution algorithm based on WLS is proposed, and a new positioning solution idea of ​​"self-collaboration" and "inter-collaboration" for unmanned clusters is constructed. The present invention can effectively overcome the dependence of traditional positioning technology on the real-time transmission of a large amount of original sampling data, and can perform target positioning in low-interaction environments, denied environments, weak network signals and other communication link obstructions.

[0112] Furthermore, the effect of the DRSS-based unmanned cluster-specific distributed low-interaction positioning method of the present invention is illustrated through simulation experiments.

[0113] The maximum likelihood estimation (MLE) algorithm based on Taylor iteration is used as the benchmark algorithm, and the WLS algorithm proposed in the present invention is compared with the MLE algorithm from the three influencing factors of measurement error, unmanned cluster scale, and unmanned cluster aperture. The initial point of the iteration of the MLE algorithm based on Taylor iteration is the true position of the target, which can be regarded as an unbiased estimation algorithm. In the single-time positioning scenario, the algorithm performance is mainly reflected in the root mean square error (RMSE) and bias, where the theoretical limit of RMSE is measured by the positioning error theoretical bound CRLB, which gives the lower limit of the variance of any unbiased estimator. CRLB is defined as the inverse matrix of the Fisher Information Matrix (FIM).

[0114] Take o=[0,0,0] T m is the origin to build the local coordinate system. The target signal source is fixed and the coordinates are s = (10, 10, 10). T m, the signal power P radiated by the target signal source t =200W, signal center frequency f=800MHz, signal bandwidth B=250kHz. The background noise power spectrum density is -118dBm / Hz, and the receiver gain is set to 30dB. There are N drone nodes in the unmanned cluster. The x and y coordinates of each node are evenly distributed on a circle with the target as the center and r1 as the radius. 2r1 can be regarded as the aperture of the drone nodes in the unmanned cluster relative to the target on the x and y plane. The z coordinates of the drone nodes in the unmanned cluster are evenly distributed in [100m, 500m]. Then, the initial position of the drone nodes in the unmanned cluster under this motion mode is:

[0115]

[0116] Consider a movement of an unmanned cluster. Each drone node moves at the same speed on the x and y planes and remains constant in the z-axis direction. Set the moving speed v of the drone node o =(20,20,0) T m / s.

[0117] In terms of positioning parameter measurement error, RSS represents the specific power value, while I-DRSS and M-DRSS are the ratios of received power, which only have numerical meanings and no specific units. In practice, the distance of the unmanned cluster to the target signal source is at the same order of magnitude, and the values ​​of I-DRSS and M-DRSS are close to 1. Therefore, the standard deviation of the measurement error of I-DRSS (σ i ) and the standard deviation of the measurement error of M-DRSS (ω i,q) are equal and are between -40dB and -20dB. Assuming that the gain of the front-end receivers of the drone nodes in the unmanned cluster is the same, then

[0118] Set the number of drone nodes in the unmanned cluster N = 10, and the distance between the drone node and the target signal source r1 = 5000m. Consider the unmanned cluster moving for 1 moment, and perform a single positioning of the target signal source at this moment. The coordinates and relative position relationship between the unmanned cluster and the target signal source are as follows: Figure 3 As shown, Figure 3 It is a schematic diagram of the coordinates and relative position relationship of an unmanned cluster and a target signal source provided by an embodiment of the present invention.

[0119] See also Figure 4 , Figure 4 1 is a performance curve diagram of the single-time positioning algorithm when the measurement error changes provided by the embodiment of the present invention. (a) is a comparison diagram of the RMSE of the target positioning results of the proposed WLS algorithm and the MLE algorithm. (b) is a comparison diagram of the Bias of the target positioning results of the proposed WLS algorithm and the MLE algorithm. i and ω i,q When it gradually increases from -40dB to -20dB, Figure 4 As shown in Figure (a), the RMSE performance of the WLS algorithm is generally close to CRLB and MLE. When the measurement error is large, there is a deviation. Secondly, from the relative position of the unmanned cluster and the target signal source, it can be seen that the unmanned cluster is located above the target signal source as a whole, so it loses the ability to estimate the elevation of the target signal source (i.e., the Z-axis direction), resulting in the overall deviation of the WLS algorithm and CRLB in the three-dimensional measurement case. In contrast, the two-dimensional positioning is relatively stable. When the measurement error is -20dB, the accuracy of the unmanned cluster for the two-dimensional positioning of the target signal source is about 5.86m. Considering the distance between the unmanned cluster and the target signal source, the positioning accuracy reaches 0.12%r1. Figure 4 Figure (b) shows the deviation (Bias) of the target positioning results of the proposed WLS algorithm and the MLE algorithm. It can be seen from the figure that the deviation of the two-dimensional positioning result is close to that of the MLE algorithm, while there is a large deviation in the three-dimensional positioning result. This is because it is difficult for the unmanned cluster to accurately estimate the elevation of the target signal source, which is also the main reason for the increase of RMSE in Figure (a).

[0120] See also Figure 5 , Figure 5The performance curve of the single-time positioning algorithm when the scale of the unmanned cluster changes provided by the embodiment of the present invention, (a) is a comparison of the RMSE of the target positioning results of the proposed WLS algorithm and the MLE algorithm, and (b) is a comparison of the Bias of the target positioning results of the proposed WLS algorithm and the MLE algorithm. The measurement error σ of I-DRSS and M-DRSS i and ω i,q Both are set to -40dB. Figure 5 It can be seen that, firstly, as the size of the unmanned cluster increases, both CRLB and the RMSE of the proposed algorithm show a downward trend. This is because the number of unmanned clusters directly determines the amount of measurement information. In the framework of the weighted least squares algorithm, the more information, the better the positioning performance. Secondly, the RMSE and Bias of the two-dimensional positioning results of the proposed algorithm are close to those of the MLE algorithm, and the RMSE is also close to CRLB. Furthermore, in Figure 5 In Figure (a), due to the lack of target signal source elevation positioning capability, the RMSE and CRLB of the three-dimensional positioning results of the proposed WLS algorithm increase. Figure 5 As can be seen from Figure (b), the increase in RMSE of the WLS algorithm mainly comes from the increase in Bias.

[0121] See also Figure 6 , Figure 6 The performance curve of the single-time positioning algorithm when the aperture of the unmanned cluster changes provided by the embodiment of the present invention, (a) is a comparison of the RMSE of the target positioning results of the proposed WLS algorithm and the MLE algorithm, and (b) is a comparison of the Bias of the target positioning results of the proposed WLS algorithm and the MLE algorithm. The measurement error σ of I-DRSS and M-DRSS i and ω i,q All are set to -40dB, and the number of drone nodes in the unmanned cluster is set to 10. As can be seen from the figure, as the distance of the unmanned cluster relative to the target signal source increases, the positioning error increases. There are mainly two reasons: First, as the distance increases, the RSS measurement values ​​of the target signal source of each drone node in the unmanned cluster are close to the same, and the M-DRSS is close to 1. Compared with the close distance, the differentiation between M-DRSS becomes weaker, and it is more sensitive to measurement errors. Second, the Fisher information matrix is ​​inversely proportional to the fourth power of the distance. As the distance increases, the positioning information of the target signal source decreases sharply, resulting in an increase in the positioning error.

[0122] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants are intended to cover non-exclusive inclusion, so that the article or device including a series of elements includes not only those elements, but also other elements that are not explicitly listed. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the article or device including the elements. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The orientation or position relationship indicated by "up", "down", "left", "right", etc. is based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention.

[0123] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A DRSS-based distributed low-interaction positioning method for unmanned clusters, characterized in that: Applicable to an unmanned cluster collaborative positioning scenario including an unmanned cluster and a target signal source, wherein the unmanned cluster is composed of multiple drones, and the method comprises: Step 1: constructing an I-DRSS measurement equation and an M-DRSS measurement equation of the unmanned cluster at the current positioning moment according to the signal power of the target signal source received by each UAV node in the unmanned cluster; Step 2: Obtain a first pseudo linear equation about the position of the target signal source according to the I-DRSS measurement equation and the M-DRSS measurement equation; Step 3: Solve the first pseudo linear equation using the WLS algorithm to obtain a rough position estimate of the target signal source at the current positioning moment; Step 4: Based on the rough position estimate, construct a second pseudo-linear equation about the position of the target signal source, and solve the second pseudo-linear equation using the WLS algorithm to obtain a precise position estimate of the target signal source at the current positioning moment.

2. The DRSS-based unmanned cluster dedicated distributed low-interaction positioning method according to claim 1 is characterized in that: The step 1 comprises: Step 1.1: construct an I-DRSS measurement equation of the unmanned cluster at the current positioning moment according to the signal power of the target signal source received by each UAV node in the unmanned cluster at the current positioning moment and the previous positioning moment and the set I-DRSS measurement error; the I-DRSS measurement error obeys additive Gaussian distribution; Step 1.2: According to the signal power of the target signal source received by each group of drone nodes in the unmanned cluster at the current positioning time and the set M-DRSS measurement error, the M-DRSS measurement equation of the unmanned cluster at the current positioning time is constructed; the M-DRSS measurement error obeys Gaussian distribution.

3. The DRSS-based unmanned cluster dedicated distributed low-interaction positioning method according to claim 2 is characterized in that: The step 1.1 comprises: Step 1.11: According to the signal power of the target signal source received by each drone node in the unmanned cluster at the current positioning moment and the previous positioning moment, the theoretical I-DRSS measurement value of the drone node at the current positioning moment is obtained; the theoretical I-DRSS measurement value of each drone node at the current positioning moment is expressed as: Where i represents the i-th drone node, i = 1, 2, ..., N, N is the number of drones in the unmanned cluster, t1 is the current positioning time, t0 is the previous positioning time, is the theoretical I-DRSS measurement value of the i-th UAV node at the current positioning moment, P i r [t1] is the signal power of the target signal source received by the i-th UAV node at the current positioning time, P i r [t0] is the signal power of the target signal source received by the i-th UAV node at the previous positioning moment, d i [t0] is the distance between the i-th UAV node and the target signal source at the previous positioning moment, d i [t1] is the distance between the i-th UAV node and the target signal source at the current positioning moment; Step 1.12: According to the theoretical I-DRSS measurement value and the I-DRSS measurement error, the actual I-DRSS measurement value of the drone node at the current positioning moment is obtained; the actual I-DRSS measurement value of each drone node at the current positioning moment is expressed as: In the formula, ρ i [t1] is the actual I-DRSS measurement value of the i-th UAV node at the current positioning moment, Δρ i [t1] is the I-DRSS measurement error of the i-th UAV node at the current positioning moment, Δρ i The variance of [t1] is Step 1.13: According to the actual I-DRSS measurement value of the drone node and the I-DRSS measurement error, the I-DRSS measurement equation of the unmanned cluster at the current positioning time is constructed; the I-DRSS measurement equation of the unmanned cluster at the current positioning time is expressed as: ρ[t1]=(ρ1[t1],ρ2[t1],…,ρ N [t1]) T ; Δr[t1]=[Δr1[t1],Δr2[t1],…,Δr N [t1]] T N×1 ; Where ρ[t1] is the actual I-DRSS measurement value of the unmanned cluster at the current positioning moment, Δρ[t1] is the I-DRSS measurement error, and T represents the matrix transpose.

4. The DRSS-based unmanned cluster dedicated distributed low-interaction positioning method according to claim 2 is characterized in that: The step 1.2 comprises: Step 1.21: According to the signal power of the target signal source received by each group of drone nodes in the unmanned cluster at the current positioning time, the theoretical M-DRSS measurement value of each group of drone nodes at the current positioning time is obtained; the theoretical M-DRSS measurement value of each group of drone nodes at the current positioning time is expressed as: Where i is the i-th UAV node, i = 1, 2, ..., N, q is the q-th UAV node, q = 1, 2, ..., N, q ≠ i, N is the number of UAVs in the UAV cluster, t1 is the current positioning time, is the theoretical M-DRSS measurement value of the i-th UAV node and the q-th UAV node as a group of UAV nodes at the current positioning moment, P i r [t1] is the signal power of the target signal source received by the i-th UAV node at the current positioning moment, is the signal power of the target signal source received by the qth UAV node at the current positioning moment, d i [t1] is the distance between the i-th UAV node and the target signal source at the current positioning moment, d q [t1] is the distance between the qth UAV node and the target signal source at the current positioning moment, κ i,q is the ratio of the gains of the front-end receivers of the i-th UAV node and the q-th UAV node; Step 1.22: According to the theoretical M-DRSS measurement value and the M-DRSS measurement error, the actual M-DRSS measurement value of each group of drone nodes at the current positioning time is obtained; the actual M-DRSS measurement value of each group of drone nodes at the current positioning time is expressed as: In the formula, γ i,q [t1] is the actual M-DRSS measurement value of the i-th UAV node and the q-th UAV node as a group of UAV nodes at the current positioning moment, Δγ i,q [t1] is the M-DRSS measurement error of the i-th UAV node and the q-th UAV node as a group of UAV nodes at the current positioning moment, Δγ i,q [t1] follows a Gaussian distribution with a mean of 0 and a variance of Step 1.23: According to the actual M-DRSS measurement value of each group of drone nodes and the M-DRSS measurement error, the M-DRSS measurement equation of the unmanned cluster at the current positioning time is constructed; the M-DRSS measurement equation of the unmanned cluster at the current positioning time is expressed as: γ[t1]=(γ 1,2 [t1],…,γ 1,N [t1],γ 2,1 [t1],γ 2,3 [t1],…,γ 2,N [t1],…,γ N,1 [t1],γ N,2 [t1],…,γ N,N-1 [t1]) T ; Δγ[t1]=(Δγ 1,2 [t1],…,Δγ 1,N [t1],Δγ 2,1 [t1],Δγ 2,3 [t1],…,Δγ 2,N [t1],…,Δγ N,1 [t1],Δγ N,2 [t1],…,Δγ N,N-1 [t1]) T ; Where γ[t1] is the actual M-DRSS measurement value of the unmanned cluster at the current positioning moment, Δγ[t1] is the M-DRSS measurement error, and T represents the matrix transpose.

5. The DRSS-based unmanned cluster dedicated distributed low-interaction positioning method according to claim 1 is characterized in that: The step 2 comprises: The I-DRSS measurement equation and the M-DRSS measurement equation are respectively transformed into the form of the position of the target signal source and then integrated to obtain the first pseudo linear equation; the first pseudo linear equation is expressed as: P1[t1]-G1[t1]S=D1[t1]Δθ[t1]; in, Where t1 is the current positioning time, t0 is the previous positioning time, and u i ,i=1,2,…,N, represents the spatial position of the i-th UAV node, ρ i [t1],i=1,2,…,N, represents the actual I-DRSS measurement value of the i-th UAV node at the current positioning time, κ i,q ,i=1,2,…,N,q=1,2,…,N,q≠i,represents the ratio of the gain of the front-end receiver of the i-th UAV node and the q-th UAV node, γ i,q [t1], i = 1, 2, ..., N, q = 1, 2, ..., N, q ≠ i, represents the actual M-DRSS measurement value of the i-th UAV node and the q-th UAV node as a group of UAV nodes at the current positioning time, d i [t1], i = 1, 2, ..., N, represents the distance between the i-th UAV node and the target signal source at the current positioning time, diag() represents the construction of a diagonal matrix function, I N-1 represents the unit matrix of size N-1, S is the first position vector, S = [s, s T s] T 4×1 , s is the position coordinate of the target signal source, T represents the matrix transpose, Δθ[t1] is the error vector, Δρ[t1] is the I-DRSS measurement error, Δγ[t1] is the M-DRSS measurement error, and N is the number of drones in the unmanned cluster.

6. The DRSS-based unmanned cluster-specific distributed low-interaction positioning method according to claim 5 is characterized in that: The step 3 comprises: Step 3.1: Based on the first pseudo linear equation, an optimization problem is constructed using the minimum mean square error criterion. The optimization problem is expressed as: Where W1[t1] is the first weighting matrix; Step 3.2: Solve the optimization problem using the WLS algorithm to obtain an estimated value of the first position vector, where the estimated value of the first position vector is expressed as: W1[t1]=E[(D1[t1]Δθ[t1])(D1[t1]Δθ[t1]) T ] -1 =[D1[t1]Q[t1](D1[t1]) T ] -1 ; Where E[] represents the expectation, Q[t1] is the covariance matrix of the additive Gaussian error, Q I [t1] is the covariance matrix of I-DRSS measurement error, Q M [t1] is the covariance matrix of M-DRSS measurement error; Step 3.3: Obtain a rough position estimate of the target signal source at the current positioning moment according to the estimate of the first position vector.

7. The DRSS-based unmanned cluster dedicated distributed low-interaction positioning method according to claim 6, characterized in that: The step 4 comprises: Step 4.1: construct a second pseudo linear equation about the position of the target signal source according to the estimated value of the first position vector, wherein the second pseudo linear equation is expressed as: H2[t1]-G2V[t1]≈D2[t1]ΔS[t1]; in, In the formula, is the rough position estimate of the target signal source at the current positioning moment, The product of the transpose of the rough position estimate of the target signal source at the current positioning moment and the rough position estimate of the target signal source, ⊙ represents the Schur product, I3 is the unit matrix of size 3, V[t1] is the second position vector, V[t1] = [S[t1](1:3)⊙S[t1](1:3)] 3×1 , S[t1](1:3) is the true position value of the target signal source at the current positioning moment, ΔS[t1] is the estimated error of the estimated value of the first position vector relative to the true value; Step 4.2: Solve the second pseudo linear equation using the WLS algorithm to obtain an estimated value of the second position vector; Step 4.3: Obtain a precise position estimate of the target signal source at the current positioning moment according to the estimated value of the second position vector and the estimated value of the first position vector.

8. The DRSS-based unmanned cluster-specific distributed low-interaction positioning method according to claim 7, characterized in that: The estimated value of the second position vector is expressed as: Where W2[t1] is the second weighting matrix, is the covariance matrix of the estimated error of the estimated value of the first position vector relative to the true value.

9. The DRSS-based unmanned cluster dedicated distributed low-interaction positioning method according to claim 7, characterized in that: The precise position estimate of the target signal source at the current positioning moment is expressed as: In the formula, Indicates the estimated value of the first position vector is extracted The positive and negative is the first row element of the estimated value of the second position vector, is the second row element of the estimated value of the second position vector, is the third row element of the estimated value of the second position vector.

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