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Introduction
The telemetry system is an important part of modern aircraft and aviation weapon launch tests [1]. Over the years, with the continuous running-in and improvement of test tasks, a set of telemetry data processing procedures based on multi-station measurement and control has been formed [5]. As a key technology in the processing process, data fusion mainly selects the best multi-channel data to improve the reliability and accuracy of the whole process [3, 4].
The traditional method of fusion technology is to manually count the errors of a piece of data after the fact, and select the best for splicing [19]. Based on the development of telemetry/computer systems, in 2009, the automatic processing of fusion technology use the “three judgments and two principles”, which is a byte-by-byte comparison algorithm for full-frame data (F-frame). Meanwhile, it also exposed a series of problems such as transmission delay, frame loss, and bit errors in the processing process. Establish a standard and unified data quantification method based on the literature [17]. In 2014, literature [13] used theoretical ballistics to characterize the data transmission delay, and divided the fusion technology into two parts: alignment and optimization. Since then, various algorithms in the field of fusion technology have been proposed one after another [7,8,9,10,11,12,13,14,15,16,17], aiming to overcome the difficulties encountered in practical tasks and to develop in a more versatile, efficient and precise direction.
At present, many results have been achieved in the research of fusion technology, but the research content is relatively scattered and fragmented. Therefore, the research starts from the two parts of alignment and optimization involved in data fusion, summarizes the current research status of real-time and after-the-fact fusion processing methods, and elaborates the key algorithms involved. Strive to provide feasible research ideas for the development of data fusion processing technology.
Telemetry data
Telemetry data is usually measured using the PCM system, and the sampling period is milliseconds. The parameters in the same cycle are collected as a frame of PCM data stream, collected again after a certain time interval, and cyclically form the final PCM telemetry data stream, which is recorded in a binary data stream file, and the amount of data is relatively huge.
The processing flow of telemetry data is shown in Figure 1. The Ground Stations (GS) will send the received ciphertext data (C-data) to the command center in real time. Firstly, Passing the decryption pre-processing equipment (DPE) to complete the data encryption independently in parallel, and output the plaintext data (P-data), Then, through the data fusion processing server (DFPS), the multiple channels of plaintext data are clipped to form an optimal whole data stream (O-data), Finally, The central computer system divides the parameters of the whole measurement data (WM-data) flow, calculates, processes and displays the results data(R-data).
In the data fusion processing, affected by the spatial geographic location of the GS, the data sent by the target at the same time is parsed to the DFPS at a different time, that is the transmission delay. Therefore, the first priority of data fusion is the alignment operation, to match the same-origin frame data sent by different ground stations to receive targets. The main difficulty of alignment lies in the real-time dynamic change of the distance between the target and the GS during the flight, which causes the change of the transmission delay and the possible frame loss and error during data transmission. After alignment, it is necessary to screen the parts with better record quality and complete the whole WM-data splicing, which is called optimization. The key is how to accurately evaluate the quality of the same-origin frames from different GS.
In summary, the data volume of telemetry data is huge, the information processing process is cumbersome, and it is accompanied by frame loss and error and transmission delay. The problems faced by data alignment and optimization processing are complex, and the algorithm is slightly delayed, there will be a cumulative waiting phenomenon, and the fusion time will double, which undoubtedly brings severe challenges to the alignment and optimization of fusion technologies.
Real-time Alignment technology
Real-time alignment processing must make timely judgments and choices on the currently received limited telemetry data, and the alignment algorithm requires high real-time and reliability. Real-time alignment is often rough, mainly including flag alignment (FA), time code matching alignment (TCMA), and error control alignment (ECA). The following is a key analysis of the real-time alignment algorithm.
Flag alignment (FA)
The most classic algorithm in FA is the alignment method based on frame count proposed in Literature [21]. Frame counting is a part of telemetry parameters, has continuity and unity at the same time, and its calculation amount is relatively small, the algorithm time complexity is relatively low, and it has become the first choice for real-time alignment technology. But the frame count error is fatal to the algorithm.
Literature [8] optimizes the receiving buffer and the judgment conditions when the frame count is wrong, and reduces the use of computer resources. When the frame count is inconsistent, the smaller frame count is selected as the alignment result data. However, when frame count errors occur continuously in multiple channels of data, this method will cause accumulated frame count errors in the fusion result. As shown in Table I, the frame counts of No. 1 and No. 4 of GS1 have errors, and GS2 Frame count consecutive errors, including data frames No. 2,3, and 4. When frame count alignment is used, the fusion result is 28586, 65467, 65356, 65523 cumulative errors.
Accumulative error alignment process
Frame number
GS1
GS2
Fusion result
Correct
1
28586
65530
28586
65530
2
65531
65467
65467
65531
3
65532
65356
65356
65532
4
65523
65533
65523
65533
Time code matching alignment (TCMA)
The first step of real-time TCMA is performs time code correction, and then, uses the time code matching to align S-frame or F-frame. Reference [21] revises other stations with reference to the time code of the master station frame. This method is easy to implement, but because the time delay of the master station time code in the data transmission process is not considered, the time accuracy of the data alignment result is lost.
Error control alignment (ECA)
The main idea of real-time ECA is to calculate the time error range in the frame data transmission process, which is called the time delay error range. Based on the frame time code of any station, the corresponding data of the frame time code within the time delay error range is determined as the frame at the same time. As shown in formula:
T + \Delta t > T > T - \Delta t
T is the reference time, Δt is the time delay error range.
Literature [14] uses theoretical ballistics as equation (2) to accurately calculate the radio wave transmission delay.
\Delta {t_i} = {{{R_i}} \over C}
C is the speed of light. Ri is the distance between the target at the time of ti and the ground station, Δt is the time delay of the electric wave transmission at ti which realizes the alignment of the F-frame data. The time code differences of adjacent S-frame of the same F-frame at different stations are all less than the calculation delay, and the S-frame cannot be uniquely aligned.
Literature [2] determines the delay error of the S-frame period through the telemetry equipment indicators and the code rate technical indicators. The S-frame time difference within this range can be considered as the S-frame from the same time, and the maximum utilization of the S-frame is realized. This method relies too much on device index values and fails to solve the actual problems caused by frame loss and error codes.
Table II summarizes and analyzes the existing problems of the real-time alignment algorithm according to the document serial number. At present, the alignment algorithm can complete data alignment with different accuracy, but different alignment algorithms still have corresponding problems. The engineering needs to be further combined with actual needs. Analysis and optimization.
Literature correspondence alignment algorithm analysis
The post-alignment processing is aimed at the entire telemetry data file record, and the processing process is fine. Researchers pay more attention to post-processing methods. The existing post-alignment methods include: Post-event flag bit alignment (P-FA), post-event time code matching alignment (P-TCMA), post-event error control alignment (P-ECA).
P-FA
Compared with the real-time method, it pays more attention to the error correction of the flag bit. The literature [13] uses the time difference of the F-frame (S-frame) frame header divided by the S-frame sampling period to obtain the difference in the number of sub-frames between the F-frames (S-frame count difference). ); Starting from the first frame, the frame count is accumulated frame by frame, and the frame count is restored. It overcomes the situation that the frame count and frame data are not one-to-one corresponding to the frame count caused by the clearing of the frame count and the error code. However, when a frame loss occurs in the data record file of one of the measurement stations, the F-frame count difference at the position of the lost frame will increase exponentially, and the alignment algorithm cannot solve the problem of matching the frame data and the frame count at this time.
P-TCMA
The focus of P-TCMA is timecode refinement correction. Literature [19] first selects two data streams of the same length, combines the characteristics of the sensor signal, and calculates the delay using the third-order mutual cumulant estimation method. To accurate time delay estimation. Assuming p is the expected maximum delay, Delay D as integer, The measurement signal y (n) is the AR(p) process. The calculation method satisfies:
\left\{{\matrix{{y\left( n \right) = \sum\limits_{i = - p}^p {a\left( i \right)x\left( {n - i} \right) + w\left( n \right)}} \hfill \cr {a\left( i \right) = 0,\,i \ne D,\,a\left( D \right) = 1} \hfill \cr}} \right.
Where a(i) is the coefficient of AR, w(n) is Gaussian white noise, When it is maximum of |a(i)|, the i is the required delay. The specific formula is calculated as follows:
\left\{{\matrix{{{c_{yxx}}\left( {\tau,\,\rho} \right) = E\left\{{y\left( n \right)x\left( {n + \tau} \right)x\left( {n + \rho} \right)} \right\}} \hfill \cr {{c_{xxx}}\left( {\tau,\,\rho} \right) = E\left\{{x\left( n \right)x\left( {n + \tau} \right)x\left( {n + \rho} \right)} \right\}} \hfill \cr {{c_{yxx}}\left( {\tau,\,\rho} \right) = \sum\limits_{i = - p}^p {a\left( i \right){c_{xxx}}\left( {\tau + i,\,\rho + i} \right)}} \hfill \cr {{C_{xxx}}a = {C_{yxx}}} \hfill \cr}} \right.
This method is relatively cumbersome to calculate, the algorithm is difficult to implement, and the time complexity is high, which is not conducive to popularization and application. Literature [18] uses the transmit zero time plus a multiple of the number of data positions to refill the frame time code. As shown in formula: Ti = T0 + i * Δt, Where T0 is the moment when zero occurs. Δt is the number of data positions, Ti is the time corresponding to the data of the first frame. There are three ways to calculate the number of data positions. One is that the program reads the data of the same number of bits to find the corresponding number of data positions based on the same number of bits occupied by the frame data; The difference is divided by the frame period to obtain the number of data positions. I.e. formula (Ti − T0) / Δt. This centralized method reduces the computational complexity of the algorithm, but when frame loss occurs, the problem of the same number of data positions and different data contents has not been resolved. Literature [12] provides a local frame time code correction method, which uses the frame time interval to correct the time code. The specific process is: taking four adjacent frames in the same data recording file, using the principle of “the time difference between adjacent frames is the same”, and correcting time codes with different differences. This partial correction method overcomes the problem of frame counting errors, but obviously does not consider the data transmission delay between multiple stations.
P-ECA
In the A. P-ECA, the calculation of the delay error range is the most critical problem to be solved. The traditional calculation method is to use the difference between the frame time code of the reference station and the time codes of the adjacent frames before and after other stations. Reference [16] sets its size based on the target test model. However, the relevant values are often not given in practice. Reference [10] calculates the current F-frame theoretical time according to formula:
{T_n} = {T_0} + \left( {{C_n} \times P} \right)
Among them T0 is the time zero point, Cn is the frame count value, and P is the frame period. The time delay error range is 20 milliseconds by analyzing the time delay of the time system link and the time difference between the data demodulated by the telemetry station. The calculation of this method relies on the frame count value. When there is a bit error, the theoretical time will be calculated incorrectly, resulting in data loss. Literature [4] determines the allowable error of S-frame sampling according to the code rate technical index, which is used as the time delay error range. Obviously, only the influence of the time difference of the ground station on the data delay is considered, and the transmission delay is not considered. The theoretical trajectory of literature [3] estimates the time delay and corrects the time code as shown in formula, and corrects and number the adjacent F-frame time codes. The F-frame with the same number is the aligned data.
Table III summarizes and analyzes the alignment algorithm after the fact according to the document serial number from the accuracy of the algorithm, the advantages of the algorithm, the existing problems, and whether to consider the transmission delay and frame error. The current alignment algorithm can complete data alignment with different accuracy., But different alignment algorithms still have corresponding problems, and the engineering needs to be further analyzed and optimized in combination with actual needs.
Literature correspondence alignment algorithm analysis
Approach
Problem
Literature number
P-FA
The problem of matching the frame data and the frame count when the frame is lost is not solved
The core of the real-time optimization technology is the QEA. The difficulty of the QEA is to reduce the complexity as much as possible on the premise of ensuring the accuracy of the evaluation. The exploration of the algorithm from selection to F-frame and S-frame marks the inevitable trend of the optimization technology to leap to refinement. The exploration of real-time quality assessment algorithms is the most challenging research problem of real-time data fusion. The existing real-time quality assessment algorithms are based on F-frame and S-frame, which will be described in detail below.
Based on F-frame
F-frame QEA (FF-QEA) was proposed and tested in the literature [14]. The specific process is: first check whether the frame time code is continuous and whether the frame synchronization code is correct as the basis for priority selection, then, compare it byte by byte According to the data, the best F-frame is evaluated according to the method selected by the three-judgment principle. Obviously, the system overhead of this method is relatively large, and as the amount of data increases, the problem that the information is too late to process is prone to appear. Moreover, in the process of implementing the three-judgment-two principle, when the three frame byte data in the multi-channel F-frame are all different, the algorithm only relies on the frame synchronization code for quality evaluation, and the accuracy is low.
In order to improve the processing efficiency of the FF-QEA, literature [8] sets a delay buffer window and improves the evaluation strategy. In practical applications, use the frame counter number and sampling correlation value (signal-to-noise ratio) with a small amount of data calculation for quality evaluation, and accurately calculate the buffer size by formula:
{{{L_{FIFO}}} \over {{t_r} - {t_w}}} > {D \over {{t_w}}}
LFIFO is the depth of the FIFO buffer, tw is the bit rate at which data is written to the fusion processing server, tr is the rate at which the optimal algorithm reads the buffer, and D is the amount of data with the size of the transmission delay. This method effectively reduces the time and space complexity of the algorithm, but when a frame count error occurs, the signal-to-noise ratio alone cannot accurately evaluate the F-frame quality.
Based on S-frame
S-frame QEA (SF-QEA) improves the degree of refinement of data processing and maximizes utilization of S-frame. Literature [2] extracts S-frame from the F-frame; prioritizes the algorithm evaluation of S-frame normality, integrity, action period, and characteristic parameters; selects preferred S-frame according to the priority S-frame by S-frame. The algorithm time complexity of this method increases, but it provides a more accurate, reliable and efficient quality evaluation method for the SF-QEA.
Table IV summarizes and analyzes the real-time quality evaluation algorithm according to the document serial number. The current algorithm achieves quality evaluation with different accuracy based on the F-frame and S-frame. The time and resource overhead required for its operation are different. The engineering can be based on different actual conditions. Need to select and improve the appropriate quality assessment algorithm.
Literature Corresponding QEA Analysis
Approach
Problem
Literature number
FF-QEA
The system overhead is large, and when the amount of data increases, the information is too late to process.
The ex post selection technology focuses on more accurate quality assessment, which provides a reference for real-time selection. The following is a specific introduction to the post-selection technology based on segment selection (PS-QEA), F-frame (PFF-QEA), and subframe (PSF-QEA).
PSF-QEA
PSF-QEA is also known as multi-site selection and splicing method [20] or time reference method [17]. The purpose of algorithm evaluation is to find the connection point of the selection. The specific method is to select the docking point in the critical point (T1, T2, T3) of the measurement area of the ground station and compare and verify the N frames of data before and after. Literature [18] selects two stations to check the following 10 subframes before and after the node: (1) Check whether the frame length meets the predetermined size; (2) Whether the BCD code sequence meets the maximum allowable error range of the sampling period. This method requires a large amount of calculation, and data errors do not affect the frame length, but cause quality misjudgments. Literature [6] puts forward the concept of S-frame loss-of-lock rate [6], that is, the docking point is determined by the S-frame synchronization code error rate in a period of time. As shown in formula: E = M / N × 1000‰, where M is the number of S-frames in the selection, and N is the number of S-frame data synchronization code errors. This method greatly improves the efficiency of selecting butt joints.
PFF-QEA
PFF-QEA was first proposed in the literature [20]. Compared with the method of segment selection, it obviously improves the utilization rate of the F-frame data and improves the accuracy of the processing result. However, the actual received data format is changeable, and there are errors and frame loss. The adaptability of this method is relatively poor. The classic quality evaluation method is proposed in [10], that is, the integrity of all subframe synchronization codes in the F-frame is used as the basis for evaluation. This not only guarantees the reliability of the quality assessment, but also improves the calculation efficiency of the quality assessment. Literature [3] uses the classic quality evaluation method, the difference is that a necessity check is performed, as shown in Figure 9: before the quality evaluation, the number of F-frames participating in the evaluation is judged. If there is only one F-frame, select it directly without performing quality evaluation. In this way, under the premise of ensuring the reliability of the algorithm, the calculation amount of the algorithm is reduced.
PSF-QEA
Literature [5] uses 3 kinds of constraint conditions to select the F-frame, and the F-frame that meets the constraint conditions will be determined as qualified. The constraints are calculated as follows:
\left\{{\matrix{{\left| {{T_{ki}} - T_j^g} \right| < \varepsilon} \hfill \cr {C_j^g = {C_{ki}} + \Delta C} \hfill \cr {{C_{ki}} = {C_{k\left( {i - 1} \right)}} + 1} \hfill \cr}} \right.
k is the station number, Tki is the F-frame BCD time code,
T_j^g
is the F-frame BCD time code of the j-th period, ɛ is allowable error for F-frame header time,
C_j^g
is the global frame count after the j-th period is corrected, Cki is frame count of the ith F-frame, ΔC is the correction value caused by frame count overflow or clearing, Ck(i−1) is the frame count for the i-1th F-frame. This algorithm has high requirements for data preprocessing, discarding incomplete F-frame data, which is not conducive to full use of data. The S-frame-based post-mortem quality evaluation algorithm is characterized by a high degree of refinement and a large computational complexity. Literature [11] formatted the S-frame data as shown in the formula:
D = \left( {T,a,A,F'} \right)
The T vector represents the time code, the a vector represents the S-frame data, the A vector represents the S-frame synchronization code, and the F represents the identifier; the quality evaluation method is as follows:
a) Check calculation for subframe structure: F data frame error evaluation value is 1, the synchronization code is normal synchronization code is 0, the synchronization code is inverted code is −1, and its value is assigned to the structure check vector value of δ1;
b) Check the subframe count: the difference between the frame counts is 1, and the frame count check evaluation value is 1, otherwise it is 0. Assign the evaluation value in the entire matrix to the frame count check vector of δ2;
c) S-frame time code verification: the time code difference between adjacent S-frames is 0 within the allowable error range of the time code; otherwise, it is 1. The verification result is recorded as a vector of δ3;
d) Inverted code period check: the length between the positions where adjacent inverted codes appear is equal to the length of the whole frame, which is 0, otherwise it is unqualified and its value is 1. The inverted code period check result is recorded as a vector of δ4;
e) Quality evaluation value: assign weight to the above four check vectors (w1, w2, w3, w4), Calculate the overall evaluation value. As shown in the formula:
\delta = {w_i} \bullet {\delta_{ij}}
The literature [7] evaluates the S-frame quality based on the principle of nearest neighbor clustering. The better the S-frame quality is mapped to the higher the similarity, the closer the distance from the cluster center. First, the S-frame data at the same time is subjected to a standardized metric value to reflect the degree of dispersion of the S-frame data, that is, the standard metric value formula is obtained by the average value of the absolute deviation:
\left\{{\matrix{{{S_{vi}}} \hfill & = \hfill & {{1 \over N}\sum\limits_{n - 1}^N {\left| {v_n^i - {m_{vi}}} \right|}} \hfill \cr {Z_{vi}^n} \hfill & = \hfill & {{{v_n^i - {m_{vi}}} \over {{S_{vi}}}}\left( {n = 1,\,2,\, \cdots,\,N} \right)} \hfill \cr}} \right.
Among them, i is the station number, and N is the length of the subframe. Then calculate the Manhattan distance of the normalized metric of the subframe data:
{d_{ij}} = \sum\limits_{n - 1}^N {\left| {v_n^j - v_n^i} \right|}
dij is the difference value of the S-frame data between station i and station j. Finally, set the cluster center radius, classify the corresponding S-frame data, and select the S-frame closest to the cluster center as the optimal result. This similarity-based method provides a new idea for the quality evaluation algorithm, which is worthy of attention and in-depth study by researchers.
Table V summarizes and analyzes the real-time and post-event quality evaluation algorithms according to the document number. Among them, the complexity of the algorithm principle and the accuracy of the algorithm processing results include 5 levels from high to low, high, normal, low, and low. The resource overhead used by the algorithm is divided into large, large, general, small, and small from large to small. The current algorithm achieves quality evaluation with different accuracy based on selection, F-frame, and subframe. The time and resource overhead required for its operation are different. In engineering, suitable quality evaluation algorithms can be selected and improved according to different actual needs.
Literature correspondence algorithm analysis table
Approach
Problem
Literature number
PSF-QEA
Data error does not affect the frame length, causing quality misjudgment.
At present, it is difficult for fusion technology to take into account the intricacies of the actual situation at the same time. The research on generalized, high-efficiency, and high-precision processing methods for data fusion has increased the difficulty, and there are problems that need to be further studied and improved.
Data frame loss is an inevitable interference factor that affects fusion accuracy, and is a key issue faced by alignment and optimization algorithms. Once frame loss occurs, the algorithm will run delayed alignment, wrong alignment, and invalid selection. It will definitely affect the calculation time and accuracy of the fusion result. Researchers use a method based on pseudo-S-frames (PS-frame) to fill in the missing frame data and solve the related difficulties of the alignment technology [7]. However, there are few frame parameters in the PS-frame, which are quite different from the actual frame signal, and when participating in the optimization, the accuracy of the resultant data is reduced. In subsequent research, the method of pattern recognition and parameter estimation can be used to predict the S-frame data [25], so that the data participating in the selection is closer to the actual value.
The design of the key algorithms for alignment and optimization in the fusion technology mainly uses specific parameters such as frame count, time code, synchronization word, and the phenomenon of bit errors in this part of the data is bound to have a certain impact on the operation of the algorithm, especially for those that rely too much on specific parameters [21]. Algorithms are often fatal. Although fusion processing uses related technologies to repair specific parameters [3], the repair method will also fail when encountering more complex situations. Therefore, in the selection technique, researchers try to measure the similarity by calculating the Manhattan distance metric based on the standard metric value of the entire frame of data to achieve the purpose of selection [8]. Based on this idea, we can learn from the machine learning method for time series data mining technology [23, 24], accurately calculate the similarity, and match the entire frame of data to achieve the purpose of fusion.
Conclusion
Continuously improving the key algorithms of the fusion technology in practice is an effective way to ensure the accuracy and reliability of the test data. The study introduced the structural characteristics of telemetry data and the information processing flow, analyzed the actual problems of transmission delay and frame loss and error encountered in data processing, and pointed out the technical difficulties in data fusion. Starting from actual engineering requirements and technical points, the development process of real-time alignment and selection is explained, and the alignment algorithms and QEA involved are classified and analyzed in detail. According to the shortcomings of the existing methods, the next step of the algorithm will focus on the direction of frame loss prediction and the matching of the whole frame, and design pattern recognition and machine learning algorithms to improve the accuracy of the fusion process. With the continuous advancement of computer technology, real-time data fusion technology is bound to burst into new vitality.
Acknowledgment
Thank teacher for his careful guidance in thesis reading and summary writing. This work is supported by the research and development project of wireless network and Intelligent System Laboratory of Xi'an Technological University and real-time fusion of telemetry data.
References
Yu, K. (2017) The development and trend of real-time telemetry data ground station., 15(08):15–17.YuK.201715081517Jia, H.Y., Wang, S.H. (2020) Research on real-time fusion method of multi-station telemetry data based on the best sub-frame quality. J. Electronic Measurement Technology., 43(10):74–77.JiaH.Y.WangS.H.2020Research on real-time fusion method of multi-station telemetry data based on the best sub-frame quality. J43107477Yang, J., Zhang, D. (2019) Multi-station telemetry data fusion method based on full frame optimization. J. Electronic Measurement Technology., 42(17):101–105.YangJ.ZhangD.2019Multi-station telemetry data fusion method based on full frame optimization. J4217101105Yu, C.H., Xu, S.T., Shi, Y.H. (2018) Research on Data Fusion Method in Multi-station Telemetry Data Processing. J. Journal of Telemetry, Tracking and Command., 39(01):47–52+56.YuC.H.XuS.T.ShiY.H.2018Research on Data Fusion Method in Multi-station Telemetry Data Processing. J39014752+56Lu, Z.G. (2017) Intelligent multi-station telemetry data processing system. J. Journal of Telemetry, Tracking and Command., 38(04):9–19.LuZ.G.2017Intelligent multi-station telemetry data processing system. J3804919Zhang, J., Zhang, X.X., Li, Z.F. (2017) Design and Realization of Accurate Mosaic Method of Multi-station Telemetry Data. J. Journal of Telemetry, Tracking and Command., 38(02):22–26.ZhangJ.ZhangX.X.LiZ.F.2017Design and Realization of Accurate Mosaic Method of Multi-station Telemetry Data. J38022226Zhu, X.F. (2016) Multi-station telemetry data fusion method based on nearest neighbor cluster analysis. J. Journal of Ballistics., 28(02):93–96.ZhuX.F.2016Multi-station telemetry data fusion method based on nearest neighbor cluster analysis. J28029396Wu, Y., Huo, J.H., Guo, S.W. (2016) Real-time telemetry data selection technology for multi-site networking. J. Measurement & Control Technology., 35(06):60–63+67.WuY.HuoJ.H.GuoS.W.2016Real-time telemetry data selection technology for multi-site networking. J35066063+67Han, N. (2015) Improved telemetry data docking method and error elimination. J. Measurement & Control Technology., 22(03):14–16.HanN.2015Improved telemetry data docking method and error elimination. J22031416Du, P. (2015) Design and Realization of Software for Fast Fusion of Multi-station Telemetry Data. J. Computer Measurement & Control., 23(06):2218–2219+2240.DuP.2015Design and Realization of Software for Fast Fusion of Multi-station Telemetry Data. J230622182219+2240Lu, N. (2015) Research on the technology of telemetry transmission and ground data fusion for high dynamic aircraft. D. National University of Defense Technology.LuN.2015National University of Defense TechnologyCheng, H.Y., Shu, C.H., Cui, J.F. (2015) Methods to improve the accuracy of telemetry through data processing. J. Radio Engineering., 45(08):10–14.ChengH.Y.ShuC.H.CuiJ.F.2015Methods to improve the accuracy of telemetry through data processing. J45081014Shu, C.H., Sun, X., Cui, J.F., Cheng, H.Y. (2014) Method for automatic connection of telemetry multi-station data based on the best single frame quality. J. Journal of Telemetry, Tracking and Command., 35(06):50–55+66.ShuC.H.SunX.CuiJ.F.ChengH.Y.2014Method for automatic connection of telemetry multi-station data based on the best single frame quality. J35065055+66Liu, G.S., Gao, S., Gui, Y., He, J.J., Li, T.B. (2014) Research on real-time docking method of multi-station telemetry data. J. Radio Engineering., 44(11):34–37.LiuG.S.GaoS.GuiY.HeJ.J.LiT.B.2014Research on real-time docking method of multi-station telemetry data. J44113437Jia, H.Y., Yu, R.N. (2020) Multi-task telemetry data real-time processing system. J. Journal of Detection & Control., 42(05):102–106.JiaH.Y.YuR.N.2020Multi-task telemetry data real-time processing system. J4205102106Liu, Y.N., Chen, L., Chang, S.L., Dai, Y.C. (2012) Design and Realization of Telemetry Data Fusion Software. J. Modern Electronics Technique., 35(04):136–138+144.LiuY.N.ChenL.ChangS.L.DaiY.C.2012Design and Realization of Telemetry Data Fusion Software. J3504136138+144Zhu, X.F. (2011) Precise docking method for multi-station telemetry raw data. J. Tactical Missile Technology., (06):112–115.ZhuX.F.2011Precise docking method for multi-station telemetry raw data. J06112115Zhang, D., Wu, X.L. (2011) Research on the Method of Missile Telemetry Data Preprocessing. J. Information Technology., 30(03):65–68.ZhangD.WuX.L.2011Research on the Method of Missile Telemetry Data Preprocessing. J30036568Zhu, X.F., Han, N. (2009) Time delay estimation method of telemetry data based on mutual cumulant. J. Journal of Telemetry, Tracking and Command., 30(03):65–68.ZhuX.F.HanN.2009Time delay estimation method of telemetry data based on mutual cumulant. J30036568Xu, H.Z., Liang, H., Han, C.Z. (2009) Research on data extraction of telemetry preprocessing under multi-station measurement system. J. Information Technology., 33(02):26–29.XuH.Z.LiangH.HanC.Z.2009Research on data extraction of telemetry preprocessing under multi-station measurement system. J33022629Liang, H., Chen, L., Li, Y.L. (2007) Research on the Fusion Mechanism of Real-time Telemetry Data. J. Journal of Telemetry, Tracking and Command., 30(03):65–68.LiangH.ChenL.LiY.L.2007Research on the Fusion Mechanism of Real-time Telemetry Data. J30036568Ding, F., Jiang, Q.X., Zhang, N. (2007) Review and Prospect of the Development of Multi-sensor Data Fusion. J. Shipboard Electronic Countermeasure., (03):52–55+73.DingF.JiangQ.X.ZhangN.2007Review and Prospect of the Development of Multi-sensor Data Fusion. J035255+73Chen, H.Y., Liu, C.H., Sun, B. (2017) A Survey of Similarity Measures in Time Series Data Mining. J. Control and Decision., 32(01):1–11.ChenH.Y.LiuC.H.SunB.2017A Survey of Similarity Measures in Time Series Data Mining. J3201111Wu, Y., Liang, J., Peng, Y. (2021) Similarity based telemetry data recovery for enhancing operating reliability of satellite., J. Microelectronics Reliability., 0026–2714WuY.LiangJ.PengY.2021Similarity based telemetry data recovery for enhancing operating reliability of satellite., J00262714Cui, G.L. (2017) Research on Spacecraft Telemetry Data Prediction Algorithm Based on Time Series., D. Xi’an Technological University.CuiG.L.2017Xi’an Technological University
Yu, K. (2017) The development and trend of real-time telemetry data ground station., 15(08):15–17.YuK.201715081517Jia, H.Y., Wang, S.H. (2020) Research on real-time fusion method of multi-station telemetry data based on the best sub-frame quality. J. Electronic Measurement Technology., 43(10):74–77.JiaH.Y.WangS.H.2020Research on real-time fusion method of multi-station telemetry data based on the best sub-frame quality. J43107477Yang, J., Zhang, D. (2019) Multi-station telemetry data fusion method based on full frame optimization. J. Electronic Measurement Technology., 42(17):101–105.YangJ.ZhangD.2019Multi-station telemetry data fusion method based on full frame optimization. J4217101105Yu, C.H., Xu, S.T., Shi, Y.H. (2018) Research on Data Fusion Method in Multi-station Telemetry Data Processing. J. Journal of Telemetry, Tracking and Command., 39(01):47–52+56.YuC.H.XuS.T.ShiY.H.2018Research on Data Fusion Method in Multi-station Telemetry Data Processing. J39014752+56Lu, Z.G. (2017) Intelligent multi-station telemetry data processing system. J. Journal of Telemetry, Tracking and Command., 38(04):9–19.LuZ.G.2017Intelligent multi-station telemetry data processing system. J3804919Zhang, J., Zhang, X.X., Li, Z.F. (2017) Design and Realization of Accurate Mosaic Method of Multi-station Telemetry Data. J. Journal of Telemetry, Tracking and Command., 38(02):22–26.ZhangJ.ZhangX.X.LiZ.F.2017Design and Realization of Accurate Mosaic Method of Multi-station Telemetry Data. J38022226Zhu, X.F. (2016) Multi-station telemetry data fusion method based on nearest neighbor cluster analysis. J. Journal of Ballistics., 28(02):93–96.ZhuX.F.2016Multi-station telemetry data fusion method based on nearest neighbor cluster analysis. J28029396Wu, Y., Huo, J.H., Guo, S.W. (2016) Real-time telemetry data selection technology for multi-site networking. J. Measurement & Control Technology., 35(06):60–63+67.WuY.HuoJ.H.GuoS.W.2016Real-time telemetry data selection technology for multi-site networking. J35066063+67Han, N. (2015) Improved telemetry data docking method and error elimination. J. Measurement & Control Technology., 22(03):14–16.HanN.2015Improved telemetry data docking method and error elimination. J22031416Du, P. (2015) Design and Realization of Software for Fast Fusion of Multi-station Telemetry Data. J. Computer Measurement & Control., 23(06):2218–2219+2240.DuP.2015Design and Realization of Software for Fast Fusion of Multi-station Telemetry Data. J230622182219+2240Lu, N. (2015) Research on the technology of telemetry transmission and ground data fusion for high dynamic aircraft. D. National University of Defense Technology.LuN.2015National University of Defense TechnologyCheng, H.Y., Shu, C.H., Cui, J.F. (2015) Methods to improve the accuracy of telemetry through data processing. J. Radio Engineering., 45(08):10–14.ChengH.Y.ShuC.H.CuiJ.F.2015Methods to improve the accuracy of telemetry through data processing. J45081014Shu, C.H., Sun, X., Cui, J.F., Cheng, H.Y. (2014) Method for automatic connection of telemetry multi-station data based on the best single frame quality. J. Journal of Telemetry, Tracking and Command., 35(06):50–55+66.ShuC.H.SunX.CuiJ.F.ChengH.Y.2014Method for automatic connection of telemetry multi-station data based on the best single frame quality. J35065055+66Liu, G.S., Gao, S., Gui, Y., He, J.J., Li, T.B. (2014) Research on real-time docking method of multi-station telemetry data. J. Radio Engineering., 44(11):34–37.LiuG.S.GaoS.GuiY.HeJ.J.LiT.B.2014Research on real-time docking method of multi-station telemetry data. J44113437Jia, H.Y., Yu, R.N. (2020) Multi-task telemetry data real-time processing system. J. Journal of Detection & Control., 42(05):102–106.JiaH.Y.YuR.N.2020Multi-task telemetry data real-time processing system. J4205102106Liu, Y.N., Chen, L., Chang, S.L., Dai, Y.C. (2012) Design and Realization of Telemetry Data Fusion Software. J. Modern Electronics Technique., 35(04):136–138+144.LiuY.N.ChenL.ChangS.L.DaiY.C.2012Design and Realization of Telemetry Data Fusion Software. J3504136138+144Zhu, X.F. (2011) Precise docking method for multi-station telemetry raw data. J. Tactical Missile Technology., (06):112–115.ZhuX.F.2011Precise docking method for multi-station telemetry raw data. J06112115Zhang, D., Wu, X.L. (2011) Research on the Method of Missile Telemetry Data Preprocessing. J. Information Technology., 30(03):65–68.ZhangD.WuX.L.2011Research on the Method of Missile Telemetry Data Preprocessing. J30036568Zhu, X.F., Han, N. (2009) Time delay estimation method of telemetry data based on mutual cumulant. J. Journal of Telemetry, Tracking and Command., 30(03):65–68.ZhuX.F.HanN.2009Time delay estimation method of telemetry data based on mutual cumulant. J30036568Xu, H.Z., Liang, H., Han, C.Z. (2009) Research on data extraction of telemetry preprocessing under multi-station measurement system. J. Information Technology., 33(02):26–29.XuH.Z.LiangH.HanC.Z.2009Research on data extraction of telemetry preprocessing under multi-station measurement system. J33022629Liang, H., Chen, L., Li, Y.L. (2007) Research on the Fusion Mechanism of Real-time Telemetry Data. J. Journal of Telemetry, Tracking and Command., 30(03):65–68.LiangH.ChenL.LiY.L.2007Research on the Fusion Mechanism of Real-time Telemetry Data. J30036568Ding, F., Jiang, Q.X., Zhang, N. (2007) Review and Prospect of the Development of Multi-sensor Data Fusion. J. Shipboard Electronic Countermeasure., (03):52–55+73.DingF.JiangQ.X.ZhangN.2007Review and Prospect of the Development of Multi-sensor Data Fusion. J035255+73Chen, H.Y., Liu, C.H., Sun, B. (2017) A Survey of Similarity Measures in Time Series Data Mining. J. Control and Decision., 32(01):1–11.ChenH.Y.LiuC.H.SunB.2017A Survey of Similarity Measures in Time Series Data Mining. J3201111Wu, Y., Liang, J., Peng, Y. (2021) Similarity based telemetry data recovery for enhancing operating reliability of satellite., J. Microelectronics Reliability., 0026–2714WuY.LiangJ.PengY.2021Similarity based telemetry data recovery for enhancing operating reliability of satellite., J.00262714Cui, G.L. (2017) Research on Spacecraft Telemetry Data Prediction Algorithm Based on Time Series., D. Xi’an Technological University.CuiG.L.2017Xi’an Technological University