Research on Real-Time Fusion Technology of Range Telemetry Data

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.

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.

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: (1) T+Δt>T>T−Δt 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. (2) Δti=RiC \Delta {t_i} = {{{R_i}} \over C}

C is the speed of light. R_i is the distance between the target at the time of t_i and the ground station, Δt is the time delay of the electric wave transmission at t_i 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.

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.

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: (3) {y(n)=∑i=−ppa(i)x(n−i)+w(n)a(i)=0, i≠D, a(D)=1 \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: (4) {cyxx(τ, ρ)=E{y(n)x(n+τ)x(n+ρ)}cxxx(τ, ρ)=E{x(n)x(n+τ)x(n+ρ)}cyxx(τ, ρ)=∑i=−ppa(i)cxxx(τ+i, ρ+i)Cxxxa=Cyxx \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: T_i = T₀ + i * Δt, Where T₀ is the moment when zero occurs. Δt is the number of data positions, T_i 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 (T_i − 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.

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: (5) Tn=T0+(Cn×P) {T_n} = {T_0} + \left( {{C_n} \times P} \right)

Among them T₀ is the time zero point, C_n 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.

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: (6) LFIFOtr−tw>Dtw {{{L_{FIFO}}} \over {{t_r} - {t_w}}} > {D \over {{t_w}}}

L_FIFO is the depth of the FIFO buffer, t_w is the bit rate at which data is written to the fusion processing server, t_r 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.

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.

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 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.

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: (7) {|Tki−Tjg|<εCjg=Cki+ΔCCki=Ck(i−1)+1 \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, T_ki is the F-frame BCD time code, Tjg T_j^g is the F-frame BCD time code of the j-th period, ɛ is allowable error for F-frame header time, Cjg C_j^g is the global frame count after the j-th period is corrected, C_ki is frame count of the ith F-frame, ΔC is the correction value caused by frame count overflow or clearing, C_k(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: (8) D=(T,a,A,F′) 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;

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: (10) {Svi=1N∑n−1N|vni−mvi|Zvin=vni−mviSvi(n=1, 2, ⋯, N) \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: (11) dij=∑n−1N|vnj−vni| {d_{ij}} = \sum\limits_{n - 1}^N {\left| {v_n^j - v_n^i} \right|}

d_ij 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.

遥测系统是现代飞机和航空武器发射试验中 的重要内容[1]。多年来，随着试验任务之间的 不断磨合和改进，形成了一套基于多站测控的 遥测数据处理流程[5]。数据融合作为处理流程 中的关键技术，主要通过对各站接收的多路数 据进行择优筛选，以提高全程记录可靠性和准 确性[3, 4]。

融合技术的传统方法是事后人工统计一段数 据的出错情况，择优进行拼接[19]。基于遥测/计算机系统的发展，2009 年徐洪洲等人基于 “三判二原则”，全帧数据逐字节对比算法， 实现了融合技术的自动化处理。用时，也暴漏 出了处理过程中存在的传输时延、丢帧、误码 等一系列问题。基于文献[17]建立标准、统一的数据量化方法。2014 年，刘桂生等人利用理 论弹道刻画了数据传输时延，并且，将融合技 术分为了对齐和选优两部分进行研究。, 之后 融合技术领域的各种算法相继被提出^{[7,8,9,10,11,12,13,14,15,16,17]}，旨 在克服实际任务中遇到的难题，向着更加通 用、高效、精准的方向不断发展。

目前，对于融合技术的研究已经取得较多成 果，但研究内容相对分散、不成体系。因此， 研究从数据融合主要涉及的对齐、选优两个部 分出发，归纳总结实时和事后融合处理方法的 研究现状，并对其中涉及的关键算法进行阐 述。力求为数据融合处理技术的发展提供可行 的研究思路。

遥测数据通常使用 PCM 体制进行测量，采 样周期为毫秒级。采集同一周期内的参数作为 一帧 PCM 数据流，经过一定的时间间隔再次 采集，循环往复形成最终的 PCM 遥测数据 流，记录在表 1 所示的二进制数据流文件中， 其数据量较为庞大。

遥测数据的处理流程如图 1 所示，各地面测 站将接收的密文数据实时发送到指控中心。先 经过解密预处理设备以及解密器并行独立完成 数据密，并输出明文数据；再经过数据融合处 理服务器，多路明文数据剪辑拼成一条最优的 全程数据流；最后，中心计算机系统对全程测 量数据流进行参数分路并计算处理和结果供显 示 。

图 1

数据融合处理中，受测站空间地理位置的影 响，目标同一时刻发送的数据解析到融合处理 服务器的时刻不同，即传输时延。因此，数据 融合的第一要务是对齐操作，匹配不同地面测 站接收目标发出的同源帧数据。对齐的难点主 要在于，目标飞行中与测站的距离实时动态变 化引起传输时延的变化以及数据传输过程中可 能出现的丢帧误码现象等。对齐之后，需要筛 选记录质量较好的部分，完成全程数据的拼 接，称为选优。而选优的关键，是如何对不同 测站的同源帧进行准确的质量评估。

综上所述，遥测数据的数据量庞大，信息处 理流程繁琐，且伴随丢帧误码以及传输时延。 数据对齐、选优处理面临的问题错综复杂，并 且算法稍有延时，将出现累积等待现象，融合 时间成倍增加，这无疑给融合技术的对齐、选 优带来了严峻挑战。

实时对齐处理必须对当前接收的有限帧数据 做出及时判断、选择，对齐方法实时性、可靠 性要求高。实时对齐往往较为粗略，主要包括 实时标志位对齐、实时时码匹配对齐和实时误 差控制对齐。

事后对齐处理针对的是全程遥测数据文记录 件，处理过程精细。研究者多关注于事后处理 的方法，现有的事后对齐方法有事后标志位对 齐、事后时码匹配对齐和事后误差控制对齐。

选优技术的核心是质量评估算法。质量评估 算法的难点是保证评估准确性的前提下尽可能 降低复杂程度。算法从选段到全帧和子帧的探 索，标志着选优技术向精细化跨越的必然趋 势。实时质量评估算法的探索，是数据实时融 合最具有挑战性的研究问题。现有的实时质量 评估算法基于全帧和子帧，下面进行具体的详 细介绍。

事后选优技术关注的是更为精准的质量评 估，这为实时选优提供了借鉴的依据。下面对 基于选段、基于全帧、基于子帧的事后选优技 术进行具体介绍。

目前，融合技术在面临错综复杂的实际情况 中很难同时兼顾，常常顾此失彼。对开展数据 融合通用化、高效率、高精度处理方法的研究 增加了难度，存在需要进一步研究和完善的问 题。

数据丢帧是影响融合精度不可避免的干扰因 素，是对齐、选优算法主要面临的关键问题。 一旦发生丢帧，算法运行将出现延时对齐、错 误对齐以及无效选优等。必将影响融合结果的 计算时间和精度。研究人员使用基于伪子帧的 方法，补齐丢失的帧数据，解决了对齐技术的 相关难点[7]。然而，伪子帧中帧参数较少，与 实际帧信号相差较大，参与选优时，降低了结 果数据的精度。在后续的研究中，可以采用模 式识别和参数估计的方法对子帧数据进行预 测，使得参与选优的数据更加接近实际的数 值。

融合技术中对齐、选优关键算法的设计主要 利用帧计数、时码、同步字等特定参数，而这 部分数据产生误码的现象势必对算法运行产生 一定的影响，尤其对过于依赖特定参数的算法 来说^[21]，常常是致命的。虽然融合处理使用相 关技术对特定参数进行修复，然而当遇到更为 复杂的情况时，修复方法也将失效。因此，选 优技术中，研究者试图通过整帧数据的标准度 量值计算曼哈顿距离度量进行相似度衡量，达 到选优的目的^[8]。基于这种思想，可借鉴机器 学习方法对时间序列的数据挖掘技术^[23,24]，精 确计算相似度，对整帧数据进行匹配，达到融 合的目的。

实践中不断完善融合技术关键算法，是保证 试验数据准确可靠的有效途径。研究介绍了遥 测数据的结构特点以及信息处理流程，分析数 据处理中遇到的传输时延和丢帧误码的实际问 题，指出数据融合中的技术难点。从实际工程 需求以及技术要点出发，阐述对齐、选优的发 展历程，对其中涉及的实时和事后对齐和质量 评估算法进行了详细的分类、分析。根据现有 方法存在的不足，下一步算法将针对丢帧预测 与整帧匹配的方向展开研究，设计模式识别和 机器学习算法提高融合处理的精度。随着计算 机技术的不断进步，数据融合技术必将迸发出 新的活力。

感谢老师在论文阅读和总结写作中的细心指 导。本课题研究得到了遥测数据实时融合系统 开发项目的支持。