CN116661021B - Maneuvering detection method based on model probability matching - Google Patents
Maneuvering detection method based on model probability matching Download PDFInfo
- Publication number
- CN116661021B CN116661021B CN202310608347.3A CN202310608347A CN116661021B CN 116661021 B CN116661021 B CN 116661021B CN 202310608347 A CN202310608347 A CN 202310608347A CN 116661021 B CN116661021 B CN 116661021B
- Authority
- CN
- China
- Prior art keywords
- model
- probability
- maneuver
- matching
- vector
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V20/00—Geomodelling in general
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geophysics (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
一种基于模型概率匹配的机动检测方法，所述方法为：分析高超声速再入飞行器出现机动突变时的不同特征参数，构建机动模型描述矩阵，确定机动模型；定义影响因子，设计模型匹配度函数；对不同机动模型的特征参数设置阈值，综合当前系统的实际特征参数计算当前系统与每个预设模型的匹配向量；将先验影响因子和解算的匹配向量带入模型匹配度函数，推算模型匹配概率；根据当前时刻预设的不同机动模型与真实状态相符的概率计算模型概率修正序列，最后修正模型概率向量；由模型概率向量判断是否满足预设的模型切换条件。本发明根据不同机动突变的特征设定不同机动模型，适应了高超声速再入飞行器具有极强未知机动特性的特点。
A maneuver detection method based on model probability matching, the method is: analyzing different characteristic parameters when a hypersonic reentry vehicle has a maneuver mutation, constructing a maneuver model description matrix, and determining a maneuver model; defining an influencing factor and designing a model matching function; setting thresholds for characteristic parameters of different maneuver models, and calculating the matching vector between the current system and each preset model by integrating the actual characteristic parameters of the current system; bringing the priori influencing factor and the solved matching vector into the model matching function to infer the model matching probability; calculating the model probability correction sequence according to the probability that the different maneuver models preset at the current moment are consistent with the actual state, and finally correcting the model probability vector; judging whether the preset model switching condition is met by the model probability vector. The present invention sets different maneuver models according to the characteristics of different maneuver mutations, and adapts to the characteristics of hypersonic reentry vehicles with extremely strong unknown maneuver characteristics.
Description
Technical Field
The invention relates to a real-time maneuver detection method capable of realizing hypersonic reentry of an aircraft, in particular to a maneuver detection method based on model probability matching.
Background
The hypersonic reentry vehicle has the advantages of large operational radius, high flying speed, strong maneuverability and the like, the emerging threat is treated by the powerful development interception technology, and the rapid and accurate detection and estimation of the target motion state are further accurately predicted as the precondition for high-efficiency interception. However, the reentry track of the hypersonic reentry vehicle has extremely strong unknown maneuver characteristics, so that researches on maneuver characteristics and corresponding maneuver detection technologies are needed to judge the maneuver state of the target so as to provide real-time information for track prediction, realize high-precision prediction on the target track and meet the requirement of interception fight.
Disclosure of Invention
The invention aims to solve the problem that the reentry track of the hypersonic reentry vehicle has extremely strong unknown maneuver characteristics, provides a maneuver detection method based on model probability matching, according to the method, states of different known maneuver models are distinguished by using maneuver model description matrixes, and hypersonic reentry vehicle real-time maneuver detection is realized by a model probability matching-based method.
The invention aims at realizing the following technical scheme:
a maneuver detection method based on model probability matching, the method comprising the steps of:
analyzing different characteristic parameters of the hypersonic reentry vehicle when maneuver mutation occurs, constructing a maneuver model description matrix according to the characteristic parameters, and determining a maneuver model;
Defining an influence factor according to the maneuver model description matrix, and designing a model matching degree function;
setting threshold values for characteristic parameters of different maneuvering models, and calculating a matching vector of the current system and each preset model by integrating actual characteristic parameters of the current system;
step four, bringing the prior influence factors and the calculated matching vectors into a model matching degree function, and calculating model matching probability;
Calculating a model probability correction sequence according to the probability that different maneuver models preset at the current moment coincide with the real state, and finally correcting a model probability vector;
And step six, judging whether a preset model switching condition is met or not by the model probability vector, if so, performing model switching, and resetting the model probability vector according to a model selection result, and if not, not performing model switching, wherein the maneuver detection result model is used as prior information of the follow-up track forecast.
Further, analyzing the change of different characteristic parameters when the hypersonic reentry aircraft has a maneuver mutation according to task requirements, assuming five maneuver models to be preset, and selecting seven quantities of Δax, Δay, Δaz, A K,ΔAk, Δepsilon and Δeta as model characteristic parameters to describe the five models, wherein Δax is an x-axis acceleration difference value, Δay is a y-axis acceleration difference value, Δaz is a z-axis acceleration difference value, A K is an acceleration direction, ΔA k is an acceleration direction difference value, Δepsilon is a speed sign change and Δeta is an acceleration sign change, so as to obtain a maneuver model matrix G;
Each row in G beta represents a possible model, wherein GB1 corresponds to a non-maneuver model, GB2, GB3, GB4, and GB5 correspond to four common maneuver models, and in practical application, the corresponding maneuver models can be designed according to maneuver characteristics of the aircraft.
Further, in the second step, in order to realize the detection of maneuver, a model matching degree function is defined,
gi(t)＝KaxiΒi(Δax)+KayiΒi(Δay)+KaziΒi(Δaz)+KAiΒi(AK)
+KAΔiΒi(ΔAk)+KεiΒi(Δε)+KηiΒi(Δη),i=1...5
Wherein K ji is the influence factor of each parameter in the five models, i=1..5 represents five different models, j=1..7 corresponds to seven different parameters Δax, Δay, Δaz, a K,ΔAk, Δε, Δη, where Δax is the x-axis acceleration difference, Δay is the y-axis acceleration difference, Δaz is the z-axis acceleration difference, a K is the acceleration direction, Δa k is the acceleration direction difference, Δε is the velocity sign change, Δη is the acceleration sign change, and the value of the matching degree function g i (t) of the current system for the five preset models is calculated from the matching vector of each model at the current moment i (t) and the corresponding influence factor K ji.
Further, the method ,Bi(t)＝[Βi(Δax),Βi(Δay),Βi(Δaz),Βi(AK),Βi(ΔAk),Βi(Δε),Βi(Δη)]|t.
Further, in the third step, the specific method of the matching vector is as follows:
step three, setting corresponding threshold values for each model characteristic parameter:
Let the threshold vector h= [ H (Δax), H (Δay), H (Δaz), H (a K),H(ΔAk), H (Δε), H (Δη) ];
step three, solving the current system description vector G beta (t):
Comparing the estimated values of all model characteristic parameters with corresponding items in the threshold value vector, taking 0 when the estimated values are smaller than the threshold value, and taking 1 when the estimated values are larger than or equal to the threshold value, so as to obtain corresponding current system description vectors G beta (t), wherein G beta (t) is a seven-dimensional vector with the values of 0 and 1;
Step three, the matching vector BETA i (t) is obtained:
And comparing each row of the G BETA (t) with each row of the G BETA (t) according to the bit, taking 1 when the row is the same and taking 0 when the row is different, and obtaining a matching vector BETA i (t) of the current system and each preset model.
Further, in the fifth step, a model probability vector n= [ p 1p 2 p3 p4 p5] T is defined, the vector represents the probability that five types of models preset at the current moment coincide with a real model, that is, p1 represents the probability that a GB1 model preset at the current moment coincides with the real model, p2 represents the probability that a GB2 model preset at the current moment coincides with the real model, p3 represents the probability that a GB3 model preset at the current moment coincides with the real model, p4 represents the probability that a GB4 model preset at the current moment coincides with the real model, and p5 represents the probability that a GB5 model preset at the current moment coincides with the real model, so as to satisfy Σpi=1, i=1, 2..5;
A probability correction sequence { dp1, dp2, dp3, dp4, dp5}, wherein dp1 represents a correction amount of p1, dp2 represents a correction amount of p2, dp3 represents a correction amount of p3, dp4 represents a correction amount of p4, dp5 represents a correction amount of p5, and the probability correction sequence functions to correct the model probability vector based on the estimation result of each step of simulation.
Further, in the step six, the specific mode of model switching is as follows:
step six, giving an initial value of a model probability vector N, setting a related threshold value and a model switching condition;
Step six, judging the model characteristic parameters after each step of filtering, and calculating a model matching function;
step six, determining the matching degree sequence of the current system and the preset five models according to the size sequence of g i (t);
Setting values of probability correction sequences { dp1, dp2, dp3, dp4, dp5} according to the model matching sequence;
Step six, adding the probability correction sequence to the model probability vector N for correction;
Judging whether a preset model switching condition is met for a filtering algorithm requiring a specific maneuver model, if so, performing model switching, and resetting a model probability vector N according to a model selection result, and if not, not performing model switching;
and seventhly, repeating the process until the filtering is finished.
Compared with the prior art, the invention has the beneficial effects that:
(1) And carrying out mathematical modeling and quantification on the detection of the maneuver characteristic and maneuver mutation of the hypersonic reentry vehicle by introducing a maneuver model description matrix, an influence factor, a model matching degree function, a model matching vector and a model probability vector.
(2) The target actual state quantity is converted into model matching probability through designing a model matching degree function, different maneuver models can be set according to the characteristics of different maneuver mutations, and the hypersonic reentry vehicle has the characteristic of extremely strong unknown maneuver characteristics.
(3) And (3) designing a model switching condition, converting the maneuver detection result into a determined maneuver model or a determined maneuver model probability, and adapting to the requirement of subsequent track forecast.
Drawings
FIG. 1 is a diagram of a maneuver detection method based on model probability matching according to the present invention.
Detailed Description
The following description of the present invention is provided with reference to the accompanying drawings and examples, but is not limited to the following description, but is intended to cover all modifications and equivalents of the present invention without departing from the spirit and scope of the present invention.
Example 1:
The invention provides a maneuver detection method based on model probability matching, which comprises the steps of introducing a maneuver model description matrix, an influence factor, a model matching degree function, a model matching vector and a model probability vector, carrying out mathematical modeling and quantization on maneuver characteristics and maneuver mutation detection of a hypersonic reentry vehicle, converting a target actual state quantity into model matching probability through a design model matching degree function, and finally obtaining a determined maneuver model or a determined maneuver model probability result by a design model switching condition. The method comprises the following specific steps:
Step 1, analyzing the change of different characteristic parameters when the hypersonic reentry vehicle has maneuver mutation according to task requirements, assuming five maneuver models to be preset, and selecting seven quantities of Deltaax, deltaay, deltaaz, A K,ΔAk, deltaepsilon and Deltaeta as model characteristic parameters to describe the five models, wherein Deltaax is an x-axis acceleration difference value, deltaay is a y-axis acceleration difference value, deltaaz is a z-axis acceleration difference value, A K is an acceleration direction, deltaA k is an acceleration direction difference value, deltaepsilon is a speed sign change and Deltaeta is an acceleration sign change, so that a maneuver model matrix G can be obtained.
Each row of G beta represents one possible class of models.
And 2, defining an influence factor according to the maneuver model description matrix, and designing a model matching degree function. To achieve detection of maneuver, a model matching degree function is defined as follows,
gi(t)＝KaxiΒi(Δax)+KayiΒi(Δay)+KaziΒi(Δaz)+KAiΒi(AK)+KAΔiΒi(ΔAk)+KεiΒi(Δε)+KηiΒi(Δη),i＝1…5
Where K ji is the influencing factor for each parameter in the five classes of models, i=1..5 represents five different models, j=1..7 corresponds to seven different parameters Δax, Δay, Δaz, a K,ΔAk, Δε, Δη. The value of the matching function g i (t) of the current system to the five types of preset models can be calculated by using the BETA i (t) of each model at the current time and the corresponding influence factor K ji. In the simulation process, K ji is given in advance according to the characteristics of the model, and BETA i (t) is the matching vector of the current system and the ith model at the moment t.
Step 3, setting threshold values for the characteristic parameters of different maneuvering models, calculating the matching vector of the current system and each preset model by integrating the actual characteristic parameters of the current system, and setting Bi(t)＝[Βi(Δax),Βi(Δay),Βi(Δaz),Βi(AK),Βi(ΔAk),Βi(Δε),Βi(Δη)]|t.
And 4, bringing the prior influence factors and the calculated matching vectors into a model matching degree function, and calculating the model matching probability.
And 5, calculating a model probability correction sequence according to the probability that different maneuver models preset at the current moment are consistent with the real state, and finally correcting the model probability vector.
A model probability vector n= [ p1 p2 p3 p4 p5] T is defined, and this vector represents the probability that five types of models preset at the current moment coincide with the real model.
A probability correction sequence { dp1, dp2, dp3, dp4, dp5} is defined, and the probability correction sequence functions to correct the model probability vector according to the estimation result of each step of simulation. The values of { dp1, dp2, dp3, dp4, dp5} determine how fast the model is determined. When { dp1, dp2, dp3, dp4, dp5} is too large, it can cause "jitter" in the system.
And 6, judging whether a preset model switching condition is met or not by the model probability vector, if so, performing model switching, and meanwhile resetting the model probability vector according to a model selection result, and if not, not performing model switching, wherein the maneuver detection result model is used as prior information of the follow-up track forecast.
Further, the specific method of matching vectors in step 3 is as follows:
Step 3.1, setting corresponding threshold values for each model characteristic parameter:
Let the threshold vector h= [ H (Δax), H (Δay), H (Δaz), H (a K),H(ΔAk), H (Δε), H (Δη) ].
Step 3.2, obtaining the current system description vector G beta (t):
And comparing the estimated values of all the model characteristic parameters with corresponding items in the threshold value vector, taking 0 when the estimated values are smaller than the threshold value and taking 1 when the estimated values are larger than or equal to the threshold value, and obtaining corresponding current system description vector G beta (t), wherein G beta (t) is a seven-dimensional vector with the values of 0 and 1.
Step 3.3, obtaining a matching vector BETA i (t):
And comparing each row of the G BETA (t) with each row of the G BETA (t) according to the bit, taking 1 when the same row is used, and taking 0 when the same row is used, so as to obtain a matching vector BETA i (t) of the current system and each preset model. For example, when g_beta (t) = [ 1010 100 ], the result of the comparison with the first row of the matrix g_beta is b 1 (t) = [ 010 0011 ].
Further, the specific mode of the step 6 model switching is as follows:
and 6.1, giving an initial value of a model probability vector N, setting a related threshold value and a model switching condition.
And 6.2, judging the model characteristic parameters after each step of filtering, and calculating a model matching function.
And 6.3, determining the matching degree sequence of the current system and the preset five types of models according to the size sequence of g i (t).
And 6.4, setting values of probability correction sequences { dp1, dp2, dp3, dp4 and dp5} according to the model matching sequence.
And 6.5, adding the probability correction sequence to the model probability vector N to correct the model probability vector N.
And 6.6, judging whether a preset model switching condition is met for a filtering algorithm requiring a specific maneuver model, if so, performing model switching, resetting a model probability vector N according to a model selection result, and if not, not performing model switching, and directly taking the maneuver model probability as maneuver detection output for an IMM interactive multi-model filtering algorithm requiring each maneuver model probability.
And 6.7, repeating the process until the filtering is finished.
Claims (6)
1. A maneuver detection method based on model probability matching is characterized by comprising the following steps:
Analyzing different characteristic parameters of the hypersonic reentry vehicle when maneuver mutation occurs, constructing a maneuver model description matrix according to the characteristic parameters, and determining a maneuver model; analyzing the change of different characteristic parameters when the hypersonic reentry vehicle has a maneuver mutation according to task requirements, presetting five maneuver models, and selecting seven quantities of Deltaax, deltaay, deltaaz, A K,ΔAk, deltaepsilon and Deltaeta as model characteristic parameters to describe the five models, wherein Deltaax is an x-axis acceleration difference value, deltaay is a y-axis acceleration difference value, deltaaz is a z-axis acceleration difference value, A K is an acceleration direction, deltaA k is an acceleration direction difference value, deltaepsilon is a speed sign change, deltaeta is an acceleration sign change, and obtaining a maneuver model matrix G beta;
Each row in the G BETA represents a model, wherein GB1 corresponds to a non-maneuver model, and GB2, GB3, GB4 and GB5 correspond to four maneuver models;
Defining an influence factor according to the maneuver model description matrix, and designing a model matching degree function;
setting threshold values for characteristic parameters of different maneuvering models, and calculating a matching vector of the current system and each preset model by integrating actual characteristic parameters of the current system;
step four, bringing the prior influence factors and the calculated matching vectors into a model matching degree function, and calculating model matching probability;
Calculating a model probability correction sequence according to the probability that different maneuver models preset at the current moment coincide with the real state, and finally correcting a model probability vector;
And step six, judging whether a preset model switching condition is met or not by the model probability vector, if so, performing model switching, and resetting the model probability vector according to a model selection result, and if not, not performing model switching, wherein the maneuver detection result model is used as prior information of the follow-up track forecast.
2. The method for detecting maneuver based on model probability matching as defined in claim 1, wherein in step two, for realizing detection of maneuver, a model matching degree function is defined,
gi(t)＝KaxiΒi(Δax)+KayiΒi(Δay)+KaziΒi(Δaz)+KAiΒi(AK)+KAΔiΒi(ΔAk)+KεiΒi(Δε)+KηiΒi(Δη),i＝1…5
Wherein K ji is the influence factor of each parameter in the five models, i=1..5 represents five different models, j=1..7 corresponds to seven different parameters Δax, Δay, Δaz, a K,ΔAk, Δε, Δη, where Δax is the x-axis acceleration difference, Δay is the y-axis acceleration difference, Δaz is the z-axis acceleration difference, a K is the acceleration direction, Δa k is the acceleration direction difference, Δε is the velocity sign change, Δη is the acceleration sign change, and the value of the matching degree function g i (t) of the current system for the five preset models is calculated from the matching vector of each model at the current moment i (t) and the corresponding influence factor K ji.
3. A maneuver detection method based on model probability matching as defined in claim 2 wherein ：Bi(t)＝[Βi(Δax),Βi(Δay),Βi(Δaz),Βi(AK),Βi(ΔAk),Βi(Δε),Βi(Δη)]|t.
4. The maneuver detection method based on model probability matching as defined in claim 1 or 2, wherein in the third step, the specific solution of the matching vector is:
step three, setting corresponding threshold values for each model characteristic parameter:
Let the threshold vector h= [ H (Δax), H (Δay), H (Δaz), H (a K),H(ΔAk), H (Δε), H (Δη) ];
step three, solving the current system description vector G beta (t):
Comparing the estimated values of all model characteristic parameters with corresponding items in the threshold value vector, taking 0 when the estimated values are smaller than the threshold value, and taking 1 when the estimated values are larger than or equal to the threshold value, so as to obtain corresponding current system description vectors G beta (t), wherein G beta (t) is a seven-dimensional vector with the values of 0 and 1;
Step three, the matching vector BETA i (t) is obtained:
And comparing each row of the G BETA (t) with each row of the G BETA (t) according to the bit, taking 1 when the row is the same and taking 0 when the row is different, and obtaining a matching vector BETA i (t) of the current system and each preset model.
5. The maneuver detection method based on model probability matching of claim 1 or 3, wherein in step five, a model probability vector N= [ p1 p2p 3 p4 p5] T is defined, the vector represents the probability that five types of models preset at the current moment coincide with a real model, namely, p1 represents the probability that a GB1 model preset at the current moment coincides with the real model, p2 represents the probability that a GB2 model preset at the current moment coincides with the real model, p3 represents the probability that a GB3 model preset at the current moment coincides with the real model, p4 represents the probability that a GB4 model preset at the current moment coincides with the real model, and p5 represents the probability that a GB5 model preset at the current moment coincides with the real model, thereby meeting Σpi=1, i=1, 2..5;
A probability correction sequence { dp1, dp2, dp3, dp4, dp5}, wherein dp1 represents a correction amount of p1, dp2 represents a correction amount of p2, dp3 represents a correction amount of p3, dp4 represents a correction amount of p4, dp5 represents a correction amount of p5, and the probability correction sequence functions to correct the model probability vector based on the estimation result of each step of simulation.
6. The maneuver detection method based on model probability matching as defined in claim 1, wherein in step six, the specific mode of model switching is as follows:
step six, giving an initial value of a model probability vector N, setting a related threshold value and a model switching condition;
Step six, judging the model characteristic parameters after each step of filtering, and calculating a model matching function;
step six, determining the matching degree sequence of the current system and the preset five models according to the size sequence of g i (t);
Setting values of probability correction sequences { dp1, dp2, dp3, dp4, dp5} according to the model matching sequence;
Step six, adding the probability correction sequence to the model probability vector N for correction;
Judging whether a preset model switching condition is met for a filtering algorithm requiring a specific maneuver model, if so, performing model switching, and resetting a model probability vector N according to a model selection result, and if not, not performing model switching;
and seventhly, repeating the process until the filtering is finished.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310608347.3A CN116661021B (en) | 2023-05-27 | 2023-05-27 | Maneuvering detection method based on model probability matching |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310608347.3A CN116661021B (en) | 2023-05-27 | 2023-05-27 | Maneuvering detection method based on model probability matching |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116661021A CN116661021A (en) | 2023-08-29 |
| CN116661021B true CN116661021B (en) | 2025-07-04 |
Family
ID=87709077
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310608347.3A Active CN116661021B (en) | 2023-05-27 | 2023-05-27 | Maneuvering detection method based on model probability matching |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116661021B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111783307A (en) * | 2020-07-07 | 2020-10-16 | 哈尔滨工业大学 | A State Estimation Method for Hypersonic Vehicles |
| CN112784506A (en) * | 2021-01-29 | 2021-05-11 | 中国人民解放军空军工程大学 | Reentry maneuvering trajectory target tracking algorithm based on variable structure multi-model |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7848905B2 (en) * | 2000-12-26 | 2010-12-07 | Troxler Electronic Laboratories, Inc. | Methods, systems, and computer program products for locating and tracking objects |
| CN106324631B (en) * | 2016-07-28 | 2018-08-07 | 北京空间飞行器总体设计部 | A kind of remote sensing satellite Energy Sources Equilibrium constraint analysis system and method |
| CN113283161A (en) * | 2021-04-28 | 2021-08-20 | 江西核工业测绘院集团有限公司 | Landslide deformation displacement prediction method for improving BP neural network |
-
2023
- 2023-05-27 CN CN202310608347.3A patent/CN116661021B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111783307A (en) * | 2020-07-07 | 2020-10-16 | 哈尔滨工业大学 | A State Estimation Method for Hypersonic Vehicles |
| CN112784506A (en) * | 2021-01-29 | 2021-05-11 | 中国人民解放军空军工程大学 | Reentry maneuvering trajectory target tracking algorithm based on variable structure multi-model |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116661021A (en) | 2023-08-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hewing et al. | Cautious nmpc with gaussian process dynamics for autonomous miniature race cars | |
| Hernandez | Optimal sensor trajectories in bearings-only tracking | |
| Rubies-Royo et al. | A classification-based approach for approximate reachability | |
| Qi et al. | Stable indirect adaptive control based on discrete-time T–S fuzzy model | |
| CN112348223A (en) | Missile flight trajectory prediction method based on deep learning | |
| Peng et al. | A new likelihood ratio method for training artificial neural networks | |
| CN113534679A (en) | System monitoring model generation method, processor chip and industrial system | |
| Choi et al. | Hardware-friendly logarithmic quantization with mixed-precision for mobilenetv2 | |
| CN111325776A (en) | A PHD Multi-target Tracking Method Based on Variational Bayesian T-distributed Kalman Filter | |
| CN116300801A (en) | Vehicle control parameter calibration method and device and nonvolatile storage medium | |
| CN116661021B (en) | Maneuvering detection method based on model probability matching | |
| Zhu et al. | An adaptive interactive multiple-model algorithm based on end-to-end learning | |
| CN113030940B (en) | A multi-satellite convex extended target tracking method under turning maneuvers | |
| US8190536B2 (en) | Method of performing parallel search optimization | |
| CN112925200B (en) | Iterative learning control method based on Anderson acceleration | |
| CN115019150B (en) | Target detection fixed point model establishing method and device and readable storage medium | |
| Bajelani et al. | Data-driven safety filter: An input-output perspective | |
| Manetsch | Transfer function representation of the aggregate behavior of a class of economic processes | |
| Wang et al. | Adaptive dynamic surface control for servo system driven by twin motors with unknown actuator failures | |
| Luo et al. | Novel Varying‐Parameter ZNN Schemes for Solving TVLEIE Under Prescribed Time With UR5 Manipulator Control Application | |
| Chen et al. | On a new approach to the design of tracking controllers for nonlinear dynamical systems | |
| Takano et al. | A deep neural network with module architecture for model reduction and its application to nonlinear system identification | |
| Buschermöhle et al. | Disturbance feedback-based model predictive control in uncertain dynamic environments | |
| Li et al. | Identification of a class of multi-signal based neuro-fuzzy wiener systems | |
| CN113985900B (en) | Four-rotor unmanned aerial vehicle attitude dynamic characteristic model, identification method and self-adaptive soft prediction control method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |