CN113507744A - Precision-adjustable distributed wireless network target positioning method - Google Patents
Precision-adjustable distributed wireless network target positioning method Download PDFInfo
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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Abstract
The invention discloses a precision-adjustable distributed wireless network target positioning method, which comprises the following implementation steps: step 1: setting an initial target position guessStep 2: each wireless positioning node k obtains a noisy distance value by collecting signal strength RSSIAnd step 3: at the current iteration time i, each wireless positioning node k calculates the error between the node k and the targetAnd 4, step 4: at the current iteration time i, calculate it relative toGradient vector ofThereby obtaining a target estimation value at the next timeAnd 5: judgment ofIf true, the algorithm ends; step 6: setting temp as temp +1, each wireless positioning node k exchanges with neighbor node set N _ k in its communication rangeAnd 7: performing a weighted consistency operation, step 8: updating the iterationAnd step 9: if temp. is detected<Returning to the step 6 if t is not equal, otherwise, executing the step 10; step 10: and updating the iteration times and returning to the step 2. The algorithm in the invention is a distributed wireless network positioning method with adjustable precision and high consistency.
Description
Technical Field
The invention relates to the technical field of wireless sensing technology and wireless sensing network positioning, in particular to a wireless sensing network node positioning method.
Background
In a wireless network environment, target positioning has wide application and important significance. For example, the location of mobile phone signals in wireless cellular networks, and the location of various sensing objects in wireless sensor networks. In these applications, the positioning accuracy as a core index is particularly important.
The current target positioning method mainly comprises two technical schemes, one is free of ranging, and the other is based on ranging. The former does not need to measure the distance between an anchor node (a network node for positioning) and a target, and the general positioning precision is not high, but the calculation and communication costs are lower; the latter requires measuring the distance between the anchor node and the target, and has relatively high positioning accuracy and high cost of calculation and communication. With the rapid development of wireless communication and electronic computing technologies, the current target positioning algorithm has higher and higher requirements on positioning accuracy without excessive consideration of cost.
The main method for measuring the distance of the wireless Signal is to adopt a received Signal Strength rssi (received Signal Strength indicator) technology, which is greatly influenced by environmental noise. The higher the measurement accuracy when the signal to noise ratio is high. Under the extreme condition of no noise, the target can be accurately positioned only by using three anchor nodes and simple trilateral positioning. In contrast, in a high noise (low signal-to-noise ratio) environment, more anchor nodes are needed to form a wireless network to resist the influence of noise, and the target position is estimated cooperatively.
Range-based targeting can be modeled as a typical nonlinear Least mean square (NLLS) (nonlinear Least squares) convex problem with a globally optimal solution, and thus can be solved using unconstrained optimization methods. Generally, either first order gradient descent or second order Newton-like methods can be developed. However, the gradient descent method has too slow convergence rate and the second-order Newton method is too computationally intensive. Recently, the inventor develops a target positioning algorithm based on a Gauss-Newton method in a high-noise industrial environment, the algorithm exchanges estimation information with neighbor nodes, a time iteration optimization method based on local communication is adopted, and target estimation information from the neighbor nodes at the current moment is integrated in the local calculation process, so that higher estimation precision is obtained. The algorithm mainly comprises two steps of consistency communication and iterative computation, wherein in the consistency communication process, any anchor node k receives target estimation of a neighbor anchor node j at a time iAnd transmits its own target estimation valueGiving a node j and weighting and summing; and in the iterative calculation process, the Gauss-Newton of the depth fusion is implemented to descend.
The generated DiffusionGauss-Newton (DGN) algorithm is simple to implement, and obtains better comprehensive performance in the similar algorithms, but the following defects still exist:
(1) the positioning accuracy still needs to be improved;
(2) the consistency of each anchor node to the estimation is not high, namely, all the local estimation along with i → ∞cannotbe well satisfiedAll converge to the exact neighborhood of the same value;
(3) the precision is not controllable, namely the positioning algorithm cannot flexibly adjust the positioning precision according to the application requirement.
Disclosure of Invention
To overcome the above-mentioned disadvantages of the DGN algorithm, we have found that this problem can be alleviated to some extent by increasing the number of coherent communication rounds, i.e. implementing multiple rounds (1 to t, t being specified by the user) of coherent communication during each iteration, the resulting algorithm can be referred to as DGNt. When t is 1, DGNtI.e. the original DGN algorithm. When t is>1, the obtained effect is that the consistency is improved, and the local node can absorb external information more sufficiently, so that the positioning precision in each iteration process can be further improved. Meanwhile, a balance between the estimation accuracy and the communication cost can be dynamically obtained by setting different values of t, that is, the higher the accuracy requirement is, the higher the communication cost (mainly considering the energy consumption generated by signal transmission) is, and the cost can be borne by the user. Therefore, the algorithm in the invention is a distributed wireless network positioning method with adjustable precision and high consistency.
DGNtAnother significant difference from DGN is the two steps of coherent communication and iterative computationThe execution order is different. The DGN executes consistency step and then executes iterative computation; in the present invention, however, DGNtIterative computation is first performed and then consistent communication is conducted. The adjustment brings the advantage that the updated data calculated at the current moment is exchanged with the neighbor in the communication process, so that the data timeliness is better.
Precision-adjustable distributed wireless network target positioning method named DGNtThe algorithm comprises the following implementation steps:
step 1: setting an initial target position guessThe initial round value t and a threshold epsilon, the temporary variable temp 0 and the step parameter alpha e (0, 1)];
Step 2: each wireless location node k (its own three-dimensional coordinate y)kFixed and known) obtains noisy distance values to unknown targets (whose true coordinates are assumed to be x) by collecting signal strength RSSI
And step 3: at the current iteration time i, each wireless positioning node k calculates the error between the node k and the target according to the following formula
And 4, step 4: at the current iteration time i, each wireless positioning node k is according to the current errorCalculate its relative toGradient vector ofAnd the following Gauss-Newton iterative update is performed
step 6: setting temp as temp +1, each wireless positioning node k exchanges with neighbor node set N _ k in its communication range
And 7: the following weighted coherency operation is performed
Wherein the weight parameter wk,lThe conditions need to be satisfied: when in useTime w k,l0 andwkl≥0。
And step 9: if temp is less than or equal to t, returning to step 6, otherwise executing step 10;
step 10: and updating i to i +1, and returning to the step 2.
Compared with the prior art, the invention has the following beneficial effects:
through simulation experiments, compared with the DGN algorithm (namely DGN)1) DGN in the present inventiontThe algorithm is obviously improved in three aspects of positioning precision, consistency estimation and precision adjustment. The simulation parameters are set as follows: the k is 20 positioning nodes to simulate anchor nodes, and the random deployment is 100 × 100m in the region3The initially guessed positioning coordinates are unified intoRandomly selecting the real coordinate of the target; for continued observation of the experimental results, the threshold epsilon is not specified in this experiment, but a fixed number of iterations 2000 is used to terminate the iteration; the step parameter alpha is 0.01; simulating to generate Gaussian environmental noise with the average value of 0 and the standard deviation of 0.1; finally, the round number t of the coherent communication takes values of 1, 5 and 10 respectively for comparison.
As can be seen from the experimental results, firstly, from the aspect of positioning accuracy (fig. 3 is a sampling graph of 2000 iteration results, and fig. 4 is a complete graph displayed in a similarity manner in fig. 3), as the algorithm is iterated, the positioning errors obtained by the three algorithms are reduced continuously; second from steady state accuracy, DGN10Is superior to DGN5And DGN5Is superior to DGN1But DGN10Specific DGN5The advantages of (2) are not obvious, and show that the more the number of rounds of consistent communication is, the better the number is, certain compromise needs to be adopted, so that the controllability of the algorithm between the positioning accuracy and the communication cost is also reflected; finally, FIG. 5 shows the standard deviation of the positioning error between all nodes for the three algorithms over 2000 iterations, from which it can be seen that the DGN1Is the largest, and the DGN proposed in the present invention5And DGN10DGN as representedtThe algorithm has a smaller standard deviation, which means that the consistency purpose of the algorithm is better realized.
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FIG. 1 is a DGN of the present inventiontAn algorithm flow chart;
figure 2 is a schematic diagram of a handset signal location for a wireless cellular network of the present invention;
FIG. 3 is a DGN of the present inventiontPositioning accuracy sampling chart of algorithm, displaying DGNtThe algorithm applies t values of 10, 5 and 1 (i.e., DGN)10,DGN5And DGN1) Along with a positioning precision graph of algorithm iterative convergence, the horizontal coordinate is a time iteration step, and the vertical coordinate is positioning precision;
FIG. 4 is a DGN of the present inventiontThe similarity graph of the algorithm positioning accuracy is used for more clearly displaying the difference of the three algorithms in the graph 3 so as to facilitate performance comparison;
FIG. 5 is a DGN of the present inventiontThe positioning standard deviation sampling graph of the algorithm, namely the difference between the positioning accuracy of all nodes along with the operation of the algorithm, describes the index of the consistency of the algorithm.
Detailed Description
The invention is further described with reference to the accompanying drawings in which:
referring to fig. 1 and fig. 2, a specific implementation process of the algorithm is shown by taking the positioning of a mobile phone signal in a wireless cellular network as an example. The reason for taking the wireless cellular network as an example is that, with the popularization of 5G base station signals in the future, the density of 5G base stations will be greater due to the faster frequency of the 5G signal compared with the frequency of the 4G signal, and the wireless cellular network is also more suitable for the co-location among the base stations to resist the ranging noise. Of course, the present invention is suitable for all network environments that are co-located in a wireless signal ranging manner, such as the location of a sensor network to a target in various environments (e.g., an industrial environment, a logistics environment, etc.), the location of a target in a local area network multi-WIFI environment, and so on.
Step 2: a plurality of wireless base stations serve as anchor nodes to collect the RSSI (received signal strength indicator) of the target mobile phone signal strength, and noisy distance values with errors are obtained;
and step 3:at iteration time i, each wireless base station obtains a target cost function through calculation
And 4, step 4: at iteration time i, each radio base station is based onImplementing Gauss-Newton updating to obtain a new position estimation value;
and 5: each base station calculates the Euclidean distance between the position estimation of the two moments before and after and judges the magnitude of the Euclidean distance and a threshold value epsilon; if the value is less than epsilon, the algorithm is ended, otherwise, the next step is executed;
step 6: each wireless base station sends an estimation value to an adjacent base station, and simultaneously, the estimation values from all the adjacent base stations are also obtained, so that the exchange of the estimation values is realized;
And step 9: judging temp is less than or equal to t, if true, returning to the step 6, and continuing to implement a new round of consistency weighted average; otherwise, executing step 10;
step 10: and updating i to i +1, and returning to the step 2.
Claims (1)
1. A precision-adjustable distributed wireless network target positioning method is characterized by comprising a DGNtThe algorithm comprises the following implementation steps:
step 1: setting an initial target position guessInitial round value t and threshold ε, temporary variablestemp 0 and the step size parameter α e (0, 1)];
Step 2: each wireless positioning node k is known with a three-dimensional coordinate y fixed by itselfkObtaining the noisy distance value with the unknown target by collecting the signal strength RSSIThe true coordinates of the target are assumed to be x;
and step 3: at the current iteration time i, each wireless positioning node k calculates the error between the node k and the target according to the following formula
And 4, step 4: at the current iteration time i, each wireless positioning node k is according to the current errorCalculate its relative toGradient vector ofAnd the following Gauss-Newton iterative update is performed
step 6: setting temp as temp +1, each wireless positioning node k exchanges with neighbor node set N _ k in its communication range
And 7: the following weighted coherency operation is performed
Wherein the weight parameter wk,lThe conditions need to be satisfied: when in useTime wk,l0 andwkl≥0;
And step 9: if temp is less than or equal to t, returning to step 6, otherwise executing step 10;
step 10: and updating i to i +1, and returning to the step 2.
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CN108896047A (en) * | 2018-05-09 | 2018-11-27 | 上海交通大学 | Distributed sensor networks collaboration fusion and sensor position modification method |
US20190158982A1 (en) * | 2017-11-20 | 2019-05-23 | Kabushiki Kaisha Toshiba | Radio-location method for locating a target device contained within a region of space |
CN110972077A (en) * | 2019-12-04 | 2020-04-07 | 燕山大学 | Underwater target positioning method under iterative state counterfeiting attack |
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US20190158982A1 (en) * | 2017-11-20 | 2019-05-23 | Kabushiki Kaisha Toshiba | Radio-location method for locating a target device contained within a region of space |
CN108896047A (en) * | 2018-05-09 | 2018-11-27 | 上海交通大学 | Distributed sensor networks collaboration fusion and sensor position modification method |
CN110972077A (en) * | 2019-12-04 | 2020-04-07 | 燕山大学 | Underwater target positioning method under iterative state counterfeiting attack |
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