CN106792982B - Multi-target direct positioning method based on self-adaptive clustering strategy - Google Patents
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Abstract
The invention belongs to the field of signal processing, and provides a multi-target direct positioning method based on a self-adaptive clustering strategy, which is used for solving the problem of multi-target positioning.A receiver respectively receives signals transmitted by a corresponding transmitter to obtain a discrete received signal x k n of a baseband, then performs discrete received signal exchange with a receiver which belongs to the same basic unit group in the neighborhood, then calculates an approximate value of an intermediate value , performs intermediate value exchange with all receivers in the neighborhood and calculates a self-adaptive updating combination coefficient b' lk (n), and finally calculates a target position estimated value .
Description
Technical Field
the invention belongs to the field of signal processing, particularly relates to a multi-target distributed self-adaptive direct positioning method of a wireless sensor network, and particularly provides a multi-target direct positioning method based on a self-adaptive clustering strategy.
Background
The problem of passive direct positioning of radio frequency transmitters has received increasing attention as an important research direction in the field of signal processing and communication; among them, time difference of arrival (TDOA) -based positioning methods are well-sought because of their simplicity of implementation and accuracy of positioning. The traditional positioning algorithm based on time difference mainly comprises two parts, namely, firstly, a time difference value is estimated through a received signal, and then, the estimated time difference value is utilized to carry out position calculation; however, the estimation value obtained by the traditional two-step method is not the optimal estimation value, and especially under the condition of low signal-to-noise ratio, the positioning accuracy is seriously damaged.
In order to improve the positioning accuracy, a direct positioning method is proposed, and the direct positioning method can be divided into a batch processing method and a self-adaptive method. The batch processing method needs to perform two-dimensional or three-dimensional grid type search on the positioning area, has large calculation amount and does not have the target tracking capability; although the positioning accuracy of the adaptive method is slightly lower than that of the batch processing method, the calculation amount of the adaptive method is much less than that of the batch processing method, and the adaptive method has the target tracking capability.
The self-adaptive direct positioning algorithm is divided into a centralized type and a distributed type; based on a centralized architecture method, signals received by all receivers in a network are transmitted to a fusion center, and positioning operation is carried out in the fusion center; however, the centralized processing method has poor expansibility and heavy communication burden, and puts high requirements on energy supply and computing capacity of the fusion center.
The distributed adaptive direct positioning (D-ADPD) algorithm can better address the inherent drawbacks of the centralized approach. As shown in A Noise-structured Distributed Adaptive Position determination Algorithm (9Si normal Processing,2016, Wei Xia and Xinglong Xia), patent publication No. CN105137392A, and patent publication No. CN 105807257A; because a special fusion center does not exist in the distributed algorithm, each sensor in the network has the same importance, the same operation problem is processed, different from a centralized algorithm, each receiver needs to communicate with the fusion center, the sensor in the network only needs to exchange information with a neighbor sensor under the distributed architecture, and only single-hop (single-hop) transmission exists; therefore, the communication burden of the network expansion device does not increase exponentially along with the network expansion and is always in a controllable range. Similarly, in a centralized algorithm, all computation processes are performed by the fusion center, thus posing a great challenge to the computing power and energy supply of the fusion center, while in a distributed algorithm, the computation burden is evenly distributed to all sensors in the network. Thus, the distributed adaptive direct positioning algorithm achieves more desirable positioning performance at a relatively low communication cost and energy consumption.
However, in many practical applications, the sensor network is required to locate multiple transmitter targets simultaneously. In this scenario, a portion of the receivers in the network receive a signal transmitted by a transmitter and estimate the geographic location of the transmitter. While at the same time another part of the receivers receive signals transmitted by another transmitter and locate its corresponding transmitter. The positioning system does not have the number of targets of the transmitters to be positioned in advance, and the sensors in the network divide related information. Therefore, the distributed adaptive direct positioning algorithm facing the single transmitter target positioning problem cannot well deal with the problem of simultaneous positioning of multiple transmitter targets. Based on the method, the invention provides a multi-target direct positioning method based on a self-adaptive clustering strategy.
disclosure of Invention
the invention aims to provide a multi-target direct positioning method based on a self-adaptive clustering strategy, which is used for solving the problem of multi-target positioning. The invention realizes the clustering function by adaptively updating the combination coefficient among all the receivers in the network, thereby realizing the simultaneous positioning of multiple targets.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a multi-target direct positioning method based on a self-adaptive clustering strategy comprises the following steps:
Step 1: data is collected, each receiver respectively receives signals transmitted by a corresponding transmitter, and demodulates and samples the received signals to obtain discrete received signals of a baseband, which specifically comprises the following steps:
Setting upK spatially separated receivers, receiver K receiving a baseband signal xk(t) is expressed as:
xk(t)＝sk(t-τk)+vk(t),k＝1,2,…,K；
Wherein s isk(t) is the baseband signal transmitted by the transmitter corresponding to receiver k; v. ofk(t) is the measured noise of the receiver k, which can be considered as zero-mean additive white Gaussian noise with spatial mutual independence, and its power is usedrepresents; tau iskis the transmission delay of a signal from a transmitter to a receiver, and is expressed as follows:
wherein the content of the first and second substances,Representing the position of the receiver k relative to the transmitter, p being a parameter to be estimatedkRepresenting the position of the receiver k, known a priori, c is the known propagation velocity of the electromagnetic wave signal;
At time tn＝nTsn1, 2.. n.samples the noisy received signal xk(t)，TsRepresenting the sampling period, a discrete received signal is obtained:
xk[n]＝sk(nTs-τk)+vk(nTs),k＝1,2,…,K；
Step 2: for the first data exchange, each receiver performs a discrete reception of a signal x with receivers belonging to the same group of elementary units in its neighborhoodk[n]Exchanging;
And step 3: adaptive operation, each receiver calculating intermediate valuesk＝1,2,…,K，
Wherein λ iskThe step size of the iteration is indicated,Representing the estimate of the target position of receiver k at time n-1, with an initial value of iteration ofk＝1,2,…,K，Represents an intermediate estimate of receiver k at time n, parameter a'i,kSatisfies the following conditions:
Indicating the group of elementary units in which the receiver k is located,representing the neighborhood of receiver k excluding itself,a neighborhood containing itself that represents receiver k;
ei,k[n]represents the estimated error signal between receiver k and its neighbor receiver i:
Where sinc (t) sin (tt)/(tt),Representing the time difference of arrival tauk,iThe order of the delay filter is 2M + 1;
and 4, step 4:Each receiver computingApproximate values of (a):
and 5: and (3) second data exchange: each receiver performs intermediate values with all receivers in its neighborhoodExchanging;
step 6: each receiver adaptively updates the combining coefficients:
and 7: combining the operations, each receiver calculates an estimate of the target position
And 8: iterate to target position estimateAnd converging to obtain the position estimation value of the transmitter.
The working principle of the invention is as follows:
In a single-target positioning scene, all receivers in a network receive signals transmitted by the same transmitter, and the single transmitter is positioned; the formula of the distributed adaptive direct positioning algorithm is as follows:
wherein the weighting factor { b }lkCharacterizes how much the receiver k trusts the intermediate value from its neighbor receiver i, and satisfies the following conditions:
In a multi-target positioning scene, the whole sensor network needs to simultaneously position a plurality of transmitter targets; each transmitter has its own specific but unknown range of action, there is no intersection between the ranges of action of different transmitters, and the signal transmitted by each transmitter can and cannot only be received by receivers within its range of action. Based on this, the invention marks the range of action of a certain transmitter comprising receiver k as cluster Ckcluster CkAll receivers within locate the same transmitter target. In the calculation process of the multi-target positioning method, complete and accurate prior information of clusters is not needed, so that the situation that neighbor receivers belong to different clusters can occur, and the signals transmitted by different transmitters are respectively received, and the information interaction between the neighbor receivers is meaningless or even harmful to target positioning; however, information sharing and cooperation of neighbor receivers in the network is necessary in the time difference of arrival-based positioning algorithm; therefore, the concept is set in the invention: the basic unit group is composed of three or more mutually communicated receivers belonging to the same cluster, and is used for the basic unit group containing the receiver kThe representation, and the partitioning of the set of elementary units is set a priori known, the information exchange within the set of elementary units is trusted.
The invention provides a self-adaptive clustering strategy, which realizes multi-target positioning on the basis of a single-target-oriented distributed self-adaptive direct positioning method, wherein the distributed self-adaptive direct positioning algorithm formula is as follows:
Wherein, the coefficient is a'ikSatisfies the following conditions:
Adaptive binding coefficient b'lkSatisfies the following conditions:
the invention adjusts the binding coefficient b 'in real time by a minimum deviation (MSD) optimization criterion'lkthe following optimization problem with constraints is obtained:
utilizing binding coefficient b'lkthe basic property of (1) finally results, through simple mathematical transformation, in the expression MSD as shown below:
In the practical application of the method, the material is,Is a priori unknown, and adopts an approximate value in the inventionto replaceIn addition, to make the problem easier to deal with, there is a closed-form solution where the cross terms in the above equation are discarded and the mathematical expectation is approximated by instantaneous values; a simplified approximate version of the constrained optimization problem is obtained:
Solving the above problem, temporarily ignoring non-negative constraint conditions to obtain a lagrangian function corresponding to equality constraint, and further obtaining a solution of a simplified constraint optimization problem:
observing the formula, wherein the formula meets a non-negative constraint condition; thus, it is a solution to the constrained optimization problem. At the receiver k, at the nth iteration, the denominator of the above formula is a constant and does not change with the transformation of the parameter l; the numerator is the reciprocal of the distance between the transmitter position located by the receiver k and the intermediate estimation value on the receiver l; therefore, when the target position located by receiver k is greatly different from the intermediate estimation value obtained at receiver l, i.e. the reciprocal of the distance is a small value, receiver k assigns the combination coefficient b 'of receiver l'lkA smaller value will be achieved; conversely, when the target position of the receiver k positioning is very close to the intermediate estimation value obtained at the receiver l, the reciprocal of the distance is a larger value, and then the coefficient b 'is combined'lka larger value will be obtained; therefore, as the algorithm proceeds, the receiver k gradually gives a larger combination coefficient to the neighboring receivers which have the same target as the positioning target, and gives a smaller trust value to the receivers which belong to different clusters, thereby realizing the function of adaptive clustering.
Meanwhile, in order to obtain better self-adaptive clustering, the invention constructs oneReasonable approximation ofThis approximation is crucial; in order to reduce MSD deviation caused by information interaction between neighbor receivers performing different estimation tasks, the invention adoptsThe approximation is updated locally in one step,
in conclusion, the invention has the following effective effects: the method for directly positioning multiple targets based on the self-adaptive clustering strategy is provided, and clustering of a receiver network is realized through self-adaptive updating of a combination coefficient, so that simultaneous positioning of multiple targets is realized.
drawings
FIG. 1 is a schematic diagram of the workflow of the multi-target direct positioning method based on the adaptive clustering strategy of the present invention.
Fig. 2 is an exemplary diagram of a transmitter receiver network topology in accordance with an embodiment of the present invention.
fig. 3 is a diagram illustrating the estimation of the target position by each receiver in the network according to an embodiment of the present invention.
fig. 4 is a diagram illustrating the effect of adaptive clustering in a receiver network according to an embodiment of the present invention.
FIG. 5 is a comparison graph of positioning performance under different prior conditions between the distributed adaptive direct positioning method and the present invention.
Fig. 6 is a comparison graph of the positioning performance of the distributed adaptive direct positioning method and the present invention in single target positioning.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings and examples.
The embodiment provides a multi-target direct positioning method based on a self-adaptive clustering strategy, which comprises the following steps:
1. Initialization: each receiver performs initialization preparation and sets initial values of position iterationk is 1,2, …, K, wherein p0For each receiver, the iteration step λ is set to 3.2 × 10 (4950,5000)-3the actual values of the two transmitter positions are respectivelyAndWeighting coefficient ai'kIs arranged as
WhereinIndicating the scopethe number of receivers in the receiver; initial value of binding coefficientis set as follows
Wherein N iskrepresenting the neighborhood of receiver kthe number of receivers involved;
2. collecting data: each receiver simultaneously receives the signals transmitted by the corresponding transmitter, demodulates and samples the signals to obtain discrete baseband signals;
3. exchanging original data: each receiver transmits the discrete baseband signal received by the receiver to the receivers which are in the neighborhood and belong to the same basic unit group with the receiver, and simultaneously receives the information transmitted by the corresponding receivers;
4. And (3) self-adaptive operation: the receivers update the position estimate from the previous iteration to a new intermediate estimate using the exchanged information
5. Calculating an approximation: each receiver again uses the exchanged information to obtain an approximation value on the basis of the intermediate value
6. And (3) second data exchange: each receiver calculates an intermediate estimation valueTransmitting the result to a neighbor receiver of the user, and receiving the result transmitted by the neighbor receiver at the same time;
7. Updating the combination coefficient: using the approximation obtained in step 5self-adaptive updating combination coefficient b'lk(n)
8. Combining: each receiver uses the combination coefficient obtained in step 7 to weight and combine all the intermediate estimation values in the neighborhood thereof to obtain a new transmitter position estimation value of the iteration;
9. and outputting a result: and (3) judging whether the positioning iterative operation enters a steady state (convergence) or not according to a preset threshold value, if not, repeating the steps 2-8 until the steady state is reached, and outputting a transmitter position estimated value.
in this embodiment, the sensor network is formed by 30 sensors randomly distributed in a specific geographic area, and the sensors can receive signals transmitted by a transmitter, communicate with a neighboring receiver, and cooperate with the neighboring receiver to jointly complete the problem of parameter estimation target positioning, and the topology structure is shown in fig. 2; wherein the solid lines represent reliable neighbor communication links, i.e. neighbor receivers connected by the solid lines have the same positioning targets, belong to the same basic cell group, as encircled by the dashed ellipse in the figure, and the division of the basic cell group is known a priori in the simulation; the sensor network is divided into two clusters by a virtual straight line in the topological graph, each cluster corresponds to a specific transmitter target, namely two targets to be positioned exist in the simulation at the same time, and an accurate division information positioning algorithm of the clusters is unknown a priori and needs to be obtained through learning estimation; the dashed lines between receivers represent normal neighbor communication links, which may belong to the same cluster as the connected receivers, e.g., receiver 12 and receiver 13, or may be located at different targets, e.g., receiver 12 and receiver 18, from the connected receivers belonging to different clusters.
As shown in fig. 3, when two transmitter targets exist in the sensor network at the same time, the estimation result of the distributed adaptive direct positioning algorithm for a single-target scene has a large deviation without any prior knowledge, and does not converge to the position of the real target. The multi-target direct positioning algorithm based on the self-adaptive clustering strategy well solves the problem of multi-target simultaneous positioning and realizes the function of self-adaptive clustering. As shown in FIG. 4, the binding coefficients for the last 500 iterations after a single experiment entered steady state were averaged when b'lkWhen (n) < 0.0001, the communication link between the receiver k and the receiver l is not considered to be of practical significance, and is not shown in the figure. It can be known from fig. 4 that the adaptive clustering result of the algorithm is perfectly matched with the actual clustering situation, which shows that the algorithm can accurately realize the adaptive clustering function.
fig. 5 and fig. 6 compare the steady-state performance of the adaptive clustering strategy-based multi-target direct positioning method and the distributed adaptive direct positioning method in the single-target scene and the multi-target scene, respectively.
while the invention has been described with reference to specific embodiments, any feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise; all of the disclosed features, or all of the method or process steps, may be combined in any combination, except mutually exclusive features and/or steps.
Claims (2)
1. a multi-target direct positioning method based on a self-adaptive clustering strategy comprises the following steps:
step 1: collecting data, wherein each receiver respectively receives signals transmitted by a corresponding transmitter, and demodulates and samples the received signals to obtain discrete received signals of a baseband;
Step 2: for the first data exchange, each receiver and the receivers in the adjacent area which belong to the same basic unit group carry out discrete receiving signal exchange;
and step 3: adaptive operation, each receiver calculating intermediate valuesK is the total number of receivers,
wherein λ iskrepresents the iteration step length, c is the electromagnetic wave signal propagation speed, TsIs a sampling period;Representing the estimate of the target position of receiver k at time n-1, with an initial value of iteration of Represents an intermediate estimate of receiver k at time n, parameter a'i,kSatisfies the following conditions:
Indicating the group of elementary units in which the receiver k is located,representing the neighborhood of receiver k excluding itself,a neighborhood containing itself that represents receiver k;
ei,k[n]Showing and connectingEstimated error signal between receiver k and its neighbor receiver i:
Where sinc (t) sin (tt)/(tt),representing the time difference of arrival tauk,iThe order of the delay filter is 2M + 1;
And 4, step 4: each receiver computingApproximate values of (a):
Indicating the position of the receiver k corresponding to the transmitter;
And 5: and (3) second data exchange: each receiver performs intermediate values with all receivers in its neighborhoodExchanging;
Step 6: each receiver adaptively updates the combining coefficients:
And 7: combining the operations, each receiver calculates an estimate of the target position
And 8: iterate to target position estimateand converging to obtain the position estimation value of the transmitter.
2. the multi-target direct positioning method based on the adaptive clustering strategy as claimed in claim 1, wherein the specific steps of the step 1 are as follows:
Setting K space separated receivers, the receiver K receives the base band signal xk(t) is expressed as:
xk(t)＝sk(t-τk)+vk(t),k＝1,2,…,K；
Wherein s isk(t) is the baseband signal transmitted by the transmitter corresponding to receiver k; v. ofk(t) is the measurement noise of the receiver k,. taukIs the transmission delay of a signal from a transmitter to a receiver, and is expressed as follows:
Wherein the content of the first and second substances,Indicating the position of the receiver k relative to the transmitter, pkRepresenting the position of a receiver k, and c is the propagation speed of the electromagnetic wave signal;
At time tn＝nTsN1, 2.. n.samples the noisy received signal xk(t)，TsRepresenting the sampling period, a discrete received signal is obtained:
xk[n]＝sk(nTs-τk)+vk(nTs),k＝1,2,…,K。
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101251592A (en) * | 2008-03-31 | 2008-08-27 | 中国科学院计算技术研究所 | Method for locating node of wireless sensor network |
| US7574221B2 (en) * | 2006-08-03 | 2009-08-11 | Ntt Docomo, Inc. | Method for estimating jointly time-of-arrival of signals and terminal location |
| US8040859B2 (en) * | 2006-02-17 | 2011-10-18 | Honeywell International Inc. | Identification of the location of nodes distributed in ad hoc networks |
| CN103596268A (en) * | 2013-12-02 | 2014-02-19 | 哈尔滨工业大学 | Distribution type joint iteration locating method applied to network node locating of sensor |
| CN104537257A (en) * | 2015-01-12 | 2015-04-22 | 电子科技大学 | Distributed self-adaptation direct positioning method based on time difference |
| CN105137392A (en) * | 2015-07-27 | 2015-12-09 | 电子科技大学 | Distributed adaptive direct positioning method under color receiving signal |
| CN106249197A (en) * | 2016-05-03 | 2016-12-21 | 电子科技大学 | The method for self-calibrating of receiver location error in a kind of multipoint location system |
-
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- 2017-03-23 CN CN201710177436.1A patent/CN106792982B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8040859B2 (en) * | 2006-02-17 | 2011-10-18 | Honeywell International Inc. | Identification of the location of nodes distributed in ad hoc networks |
| US7574221B2 (en) * | 2006-08-03 | 2009-08-11 | Ntt Docomo, Inc. | Method for estimating jointly time-of-arrival of signals and terminal location |
| CN101251592A (en) * | 2008-03-31 | 2008-08-27 | 中国科学院计算技术研究所 | Method for locating node of wireless sensor network |
| CN103596268A (en) * | 2013-12-02 | 2014-02-19 | 哈尔滨工业大学 | Distribution type joint iteration locating method applied to network node locating of sensor |
| CN104537257A (en) * | 2015-01-12 | 2015-04-22 | 电子科技大学 | Distributed self-adaptation direct positioning method based on time difference |
| CN105137392A (en) * | 2015-07-27 | 2015-12-09 | 电子科技大学 | Distributed adaptive direct positioning method under color receiving signal |
| CN106249197A (en) * | 2016-05-03 | 2016-12-21 | 电子科技大学 | The method for self-calibrating of receiver location error in a kind of multipoint location system |
Non-Patent Citations (1)
| Title |
|---|
| "An Enhanced Distributed Adaptive Direct Position Determination";W.Xia etc.;《IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES》;20160531;第1005-1010页 * |
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