ACADEMY OF MILITARY SCIENCE AND TECHNOLOGY PHAM DUC THOA RESEARCH ON THE CONSTRUCTION OF AN ALGORITHM FOR IMPROVING THE QUALITY OF THE PROCESS OF HEIGHT MEASUREMENT SIGNALS FOR A CLASS
Trang 1ACADEMY OF MILITARY SCIENCE AND TECHNOLOGY
PHAM DUC THOA
RESEARCH ON THE CONSTRUCTION OF AN ALGORITHM FOR IMPROVING THE QUALITY OF THE PROCESS OF HEIGHT MEASUREMENT SIGNALS FOR A CLASS OF MARINE CRUISE MISSILES BASED
ON THE MODERN CONTROL THEORY
Major: Control Engineering and Automation Code : 9 52 02 16
SUMMARY OF TECHNICAL DOCTORAL DISSERTATION
HA NOI – 2019
Trang 21 Pham Duc Thoa, Nguyen Quang Vinh, Nguyen Xuan Can, (2016),
”Building an algorithm for information processing in the compound altimeter of a flying vehicle”, Journal of Military Science and
Technology, Academy of military science and technology, (9/2016)
p.116-122
2 Pham Duc Thoa, Nguyen Quang Vinh, Nguyen Xuan Can, Tran Ngoc
Huong, (2018), “Application of the self-organnized algorithm for improving the signal processing quality of the linked high measuaring system”, Confference: “Apply high technology into practice”, Journal of
Military Science and Technology, Academy of military science and technology, (8/2018) p.382-390
3 Nguyen Quang Vinh, Pham Duc Thoa, (2018), “Improving high quality in
combination processing the high measurement signals”, The 7th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA2018); 29-30 November
4 Pham Duc Thoa, To Ba Thanh, Nguyen Quang Vinh, Bui Minh Tuan,
(2019), “The construction of a self-organizing algorithm for choosing a
model of extrapolation in the combined process of signal of the height measurement”, Journal of Military Science and Technology, Academy of
military science and technology, (3/2019), p269-278
5 Pham Duc Thoa, Nguyen Quang Vinh, Tran Ngoc Huong, (2019),
“Evaluating the influence of the observability level to the exactness of
processing signals in the combination of the inertia height meter and the radio height meter”, Journal of Military Science and Technology, Academy of
military science and technology, (4/2019), p.73-80
ACADEMY OF MILITARY SCIENCE AND TECHNOLOGY
Supervisors:
1 Dr Nguyen Quang Vinh
2 Dr Nguyen Xuan Can
Review 1: Assoc Prof Dr Pham Trung Dung
Military Technical Academy
Review 2: Prof Dr Nguyen Doan Phuoc
Hanoi University of Science and Technology
Review 3: Assoc Prof Dr Tran Duc Thuan
Academy of Military Science and Technology
The dissertation was defended in front of the Doctoral Evaluating Committee
at Academy level held at Academy of Military Science and Technology
at …/…, 2019
More information on the dissertation can found from:
- Academy of Military Science and Technology
- National Library
Trang 31 The necessity of the dissertation
In terms of current combat operations, Electronic combat systems have superior features such as high combat capability both in mobility, the ability to suppress the operation of the system to lead the enemy in the form of different types of noise On the lead of many modern cruise missiles equipped with inertial navigation system (INS), due to large cumulative errors, from there, this error will cause a large error in the control
Due to the characteristics of cruise missiles with the condition of low orbit in many altitude ranges, long flight times, under the influence of different types of noise, changing some special parameters corresponding
to each combination of high measurement Resulting in high quality signal processing when combining each high measuring set with inertial height measuring kit at each flight condition is different The dissertation
“Research on the construction of an algorithm for improving the quality
of the process of height measurement signals for a class of marine cruise missiles based on the modern control theory” studying the solution of
combining high-measuring set and signal processing algorithm to optimize the structure of the high-measurement combination, ensuring that the height information of cruise missiles is continuous and accurate; Construction of an extrapolation model in the combined process of height measurement by using a self-organization algorithm
2 Research targets for the dissertation
Applying modern control theory on the basis of self-organizing algorithm (SOA) and standard of observing the observed level of variables in the state space to build intelligent high-quality measurement, improve quality in the combination of high measurement signals
3 Some main contents of the dissertation
- Construction of an algorithm for the combined process of height measurement signals and choice of the structure of the combined height meter based on the evaluation of the observably level
- Construction of an extrapolation model in the combined process of height measurement by using a self-organization algorithm with the
Trang 4condition of an in consonant observably level of status variable
4 Object and scope of the dissertation research
Modern high-altitude measuring system in the high-channel control system of a class of maritime cruise missiles
5 Approaches of study
Combining method of theoretical research and using simulation techniques to test and evaluate algorithms
6 Scientific and practical benefits of this project
Use the standard of observable level and SOA to build an extrapolation model to optimize signal processing in high measurement combination Overcoming the limitations that the Kalman filter cannot solve during processing combined with high measurement signals The results of the thesis can be used for designing and improving the control system-stabilizing the height for a class of cruise missiles, adding methodologies and knowledge to serve the training and teaching activities Teaching and researching in research institutes, Academies, Schools in the Army
7 The layout of the dissertation
The whole thesis consists of 128 pages presented in 4 chapters with the Introduction, Conclusion, List of published scientific works, References and Appendixes
Chapter 1 OVERVIEW OF HIGH MEASUREMENT METHODS AND PROCESSING OF SIGNALS OF HIGH CHANNEL OF
CRUISE MISSILE 1.1 Overview of high measurement methods
On general flight vehicles, to measure the altitude of a flying device typically uses two measurement systems: The high measurement system does not use magnetic (no-radio) electromagnetic waves and high measurement systems using electromagnetic (radio) waves
1.2 The situation of research on processing and combining high measuring signals
1.2.1 Research situation in the world
The application of SOA for the construction of the model used to extrapolate the error according to the horizontal channel INS [48], [53],
Trang 5[54] yet clearly mentioned explicitlyin the form of algorithms, evaluation results also improved qualitative characteristics, especially not the proposal was time of application algorithms The application of SOA for combined processing problems in high measuring combination when
changing flight conditions has not been resolved
1.2.2 Research situation in our country
The research published in the country on the construction of algorithms to combine high measurement signals is performed by Kalman filtering algorithm and given certain results on improving the quality of processing and combining high measuring signals However, these studies have not fully addressed the limitations and remedies that result in high combined measurement errors when using Kalman filters From the analysis of domestic and foreign research situation, the thesis raises the problem to be solved
- For high-measurement combinations with many high-altitude measurement units combined with different flight conditions, the algorithm-based optimization selects a high-order measurement structure combined in a high-measurement combination in one thing Specific flight packages are needed
- In the case of the observation of improper state variables, the Kalman filter works ineffectively for a certain period of time, using a model-building algorithm to extrapolate an alternative state error, resulting in Adjusting the error of INS state
1.3 The problem of combining high measurement signals on cruise missiles
When processing signals in a combined high-level measurement, compensation or correction methods are often used The synthetic results are treated with various filters such as Kalman filter, adaptive filter after the filter receives the error estimates of the high-altitude signal treated with markedly improved quality in the flight of the missiles
1.4 Application of Kalman filtering algorithm and the problem of selecting structure in high measurement combination
1.4.1 The Kalman filter algorithm treats the combination of high measurement signals
Trang 61.4.2 Select a high-gauge set structure in combination with the observation standard
In the combination of high measurement the high combined meters are measuring a generic parameter is the elevation, to consider the remaining components of the state vector to assess whether to calculate (the observed) and calculate exactly how (the level observed) through the component is measured directly as a height
Selection, search out the combined height structure consistent with the specific flight conditions in order to improve the quality of the processed signal height combinations when the combination of high measurement are more combined height, use standard reviews the level of observed status variables Interms of specific flights, the quality of the measuring signal match processors willbe increased markedly, when the combined height level of the larger states respectively, will choosing to handle the high measuring signal matching
1.5 The instability of Kalman filtering algorithm and application
of self-organization algorithm to improve processing quality combined with high measurement signal
1.5.1 The instability of Kalman filtering algorithm and method of constructing extrapolation model
In order to overcome the decomposition of the Kalman filter, many methods are given as the offset method, the Kalman filter structure method, the method of constructing the Kalman filter structure is more appropriate, however, when initial prognosis is incorrect (mathematical modeling, input interference, measuring noise etc.), the use of the above methods does not bring high efficiency Alternative methods can then be used instead: neural networks, self-organizing algorithms, genetic algorithms [54], [63], [64]…
That at some time (tAtB) to use the model construction algorithms extrapolate (figure 1.8) then get estimates before time tA the set sample value zi = z1, z2, z3, …,zN updated results matching measuring signal processing of high, the algorithm will use this data to evaluate new construction extrapolation models from the base models
Trang 71.5.2 Self-organizing algorithm in processing high-signal matching
When the high-level detectors combine signal processing using Kalman filter, it is not allowed to evaluate the system state accurately enough with high measuring intensity Then, the criteria for assessing the observed level of state variables for the evaluation value do not exceed the observation threshold, the signal processing results with large errors for the measurement At this point, correcting the high measurement errors of the base measurement system needs to use a new algorithm to model the extrapolation of their status errors instead The thesis proposes
to use SOA to solve this problem
1.6 Conclusion chapter 1
On the basis of analyzing domestic and foreign research works related
to the problem of combining high measurement signals, given the limitations of the Kalman filter algorithm associated with the combined high gauge structure, it shows that it is necessary to solve the problem using the observation level evaluation criteria to optimize the structure in the high measuring assembly (selection) Choose the appropriate combination plan - chapter 2) Application of self-organizing algorithm
to model extrapolation of state errors when Kalman filtering algorithm is not stable (chapter 3)
Chapter 2 ALGORITHMS FOR INFORMATION
PROCCESSING AND SELECTION OF COMBINED HIGH
MEASUREMENT SET STRUCTURE 2.1 Diagram of structure adjustment parameter of high channel state
Figure 1.8 Overview of constructing models of extrapolating:
BH- The base height; MCA- Model construction algorithms; PA- Prediction algorithm
Trang 8When connecting the circuit of feedback to the corresponding point in the structure IHM, Kalman form status equation the following discrete [56]:
xk k,k 1xk 1 uk 1 k 1wk 1 ; (2.1)
k,k-1 is the matrix system; k,k-1 is the input interference matrix; elements in the matrix k,k-1, k,k-1 received from the mathematical model
of the associated high input signal error; wk-1 is the input noise vector;
uk-1 is a vector of calibration signals, constructed from optimal estimates of the filter [56]
2.2 Build kinematic model of high measurement error
2.2.1 Differential kinematic model on high channel of inertial navigation system
The model error of the IHM will be described by differential equations simpler than [56]:
accelerometers under the vertical channel δa(t) = δay(t) and the error due
to the attractive uncertainty g(t)
2.2.2 Kinetic model of error of the radio height meter
The process error Hvt(t) be performed as standard Mackop process
-+
+ +
-Figure 2.1 The scheme of IHM with the feedbacks of the
cumulative error
g
Trang 9satisfy the differential equation a [56]:
2.2.3 Kinetic model of error of the micro-barometer
The cause of the error is primarily the impact of the noise sourcedue
to the fluctuations of motion speed cruise missiles, satisfy the quadratic linear differential equation for [56]
2
B e (2.13)
2.4 Develop standards for assessing the observed level for combined high measuring sets
2.4.1 Observed and controlled according to Kalman standards
On the level observed by В.Н Афанасьев and К.А Неусыпинthere
is a specific concept, considered the precision of approximation of the status vectors and analyzed the measurement noise as well: the observability level defines the variance ratio of an arbitrary status element and the variance of the status vector is measured directly considering the variance of the measurement noise
2.4.2 Develop standards for assessing the level of observation in the high-altitude meter IHM-RHM
When considering the exact characteristics of the high measuring IHM-RHM need to transfer the persistent Kalman filtering algorithm to discrete, we have the equation and state equations in discrete form for
Trang 10measuring the combined height IHM-RHM (2.19), (2.20)
the case Hk = [1 0 … -1] Measurement of equation z(xk) in scalar
form, with the size of the matrix system of n = 5 would be:
The coefficient of i j ,k(j 1, 2, , 5)is the row of a matrix O* at time
tk Calculate the variance of the error estimate of the state variables
at time k according to the formula (2.26)
n 2 i,k 2
k 1 i,k
in that: i = 1,2,3,4 corresponds to the element status H,V,a,g
For arbitrary elements of the statusvector, vector measurement of
Trang 11derivatives *k O v* *kin scalar form corresponding (2.27):
R is the variance of the original measured vk
The expression on standards to rate the status element:
2 i,k
2 ij,k
variance is the status vector measure directly
From (2.30) which in turn determines the level of the state variables
of the IHM The expression evaluates the level ofobserved status vector component of velocity (2.31), of the acceleration (2.32), of gravity (2.33)
- Standard construction the criterion for evaluating the observability level
in the high measure of IHM-AHM according to the measurement equation (2.35)
- Standard construction the criterion for evaluating the observability level in the high measure of IHM-RHM-AHM according to the measurement equation (2.37)
2.5 The algorithm selects a high measurement set structure in combination with the use of a standard of observation
The basic steps of the algorithm choiceusing standard structure the criterion for evaluating the observability level :
- Form measurements from the combined height (2.25),(2.35), (2.37)
- The variance of the error estimate of the status vector element in the set of matching height:
Trang 12
n 2 i,k
in that: i = 1,2,3,4 corresponding to the state variables of the ĐCQT
H,V,a,g; j = 1, 2, 3 is the index correspond to the sets of measures combining IHM-RHM, IHM-AHM, IHM-RHM-AHM
- Magazine variance measure corresponding to each element in the status vector measure combines high:
- The level of the state variables in the set of matching height Choosing the appropriate height structure on the basis of reviews the level of observed status variables
in that: i = 2,3,4 respectively with the status vector element V,a,g; m
= 5,6,7- the size of system matrix correspond to the sets of measures combining IHM- RHM, IHM- AHM, IHM- RHM-AHM; j = 1,2,3- the index correspond to the sets of measures combining IHM-RHM, IHM-AHM, IHM-RHM-AHM; k the time calculated at time tk
2.6 The conclusions chapter 2
In this chapter, the error model of a number of high measuring sets has been developed, constructing the structure of the high-measurement assembly to select the combined high-gauge structure Research and standard analysis assess the level of observation of state variables in combined information processing
Applying the research results, developing algorithms to select the appropriate combination of high gauges by assessing the degree of observation of state variables to improve the quality of processing high measuring signals for the selected combination of high gauges when considering characteristic parameters corresponding to specific flight
Trang 13conditions
Proposing measures to overcome the limitations of the algorithm in case the ability to observe state variables does not exceed the observed threshold, the initial a priori information is not sufficient for Kalman filtering algorithm
Research results of chapter 2 will be proved by simulation in chapter
4 and shown on works [3], [5] of the author
Chapter 3 BUILDING EXTRAPOLATING MODEL IN HANDLING HIGH-QUALITY SIGNALS ALGORITHMS APPLICATION SELF-
ORGANIZATION
3.1 Basic principles when implementing SOA
3.2 The structure of the SOA
The construction of self-organizing algorithm consists of the following basic steps:
3.2.1 Enter input data base
Fact based on the experimental process of research subjects, the data about the technical characteristics of measuring complex high on a particular cruise missile classand level, the capacity estimated by the design of variable trend status error will choose the basis functions and limits the number of basis functions properly and considering it is the wrong model of simplified base accepted General form of the basis functions arespecified in section 1.5.2.1;
3.2.2 Organize improving the quality of the model
The method of finding the coefficient for the linear model [67]
Data is divided nto 2 parts: A- school section; B- the test section is described on figure 3.2 To time tm in the study A will for a value y(tm)
To find the coefficients for the linear format model, using the method
Figure 3.2 Performing split model to construct and model reviews