CN104833964A - Object detection tracking integrated method and device aiming at radar/sonar system - Google Patents
Object detection tracking integrated method and device aiming at radar/sonar system Download PDFInfo
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- CN104833964A CN104833964A CN201510210466.9A CN201510210466A CN104833964A CN 104833964 A CN104833964 A CN 104833964A CN 201510210466 A CN201510210466 A CN 201510210466A CN 104833964 A CN104833964 A CN 104833964A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/415—Identification of targets based on measurements of movement associated with the target
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/66—Radar-tracking systems; Analogous systems
- G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
- G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
- G01S13/726—Multiple target tracking
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/66—Sonar tracking systems
Abstract
The invention discloses an object detection tracking integrated method and device aiming at a radar/sonar system so as to solve the radar/sonar object detection and tracking problem in a complex observation environment. The method comprises the steps of: firstly, obtaining probability measurement prior data information related to an object number, object moving modes, object types and physical observation environments; then, adopting a Bayes non-parameter modeling method to carry out unified modeling on the above prior data information; based on the above prior model, merging current observation information into the above prior model, and adopting a Bayes mechanism to establish an object state posteriori model; and finally, adopting a Monte-Carlo posteriori distribution sampling method to carrying out random sampling on object parameter posteriori distribution, and carrying out corresponding statistics deduction by utilizing extracted random samples so as to estimate the existence of objects, the number of the objects, and the moving parameters of the objects. The method and device provided by the invention can facilitate the effective solving of the radar/sonar object detection and tracking problem in the complex observation environment.
Description
Technical field
The present invention relates to a kind of target detection tracking method and device, belong to and follow the tracks of exploration engineering field.
Background technology
How to utilize signal processing method process noise observation data, and then realization is to the accurate detection of moving target and accurate tracking, being the difficult problem faced in many military and civilian fields, is also in recent years important in the world one of research direction and study hotspot.In military affairs, effective target detection tracking method can be used to detect, search for and follow the tracks of unfriendly target, as submarine, naval vessels, torpedo, submarine mine, aircraft, guided missile etc., so that we implement effectively attack or evade.(especially being supported by the military) research institutions numerous in the world and research project, just actively developing relevant subject study: as gondola NATO Undersea Research Center (NURC), the German Defense Establishment (FGAN-FKIE) of Germany, the NavalUndersea Warfare Center of the U.S., Office of Naval Research, Air Force Research Lab, Lincoln laboratory and DARPA project, the Defense Technology Center of Britain, QinetiQ and Malvern Technology Centre, Defense Science and TechnologyOrganization (DSTO) of Australia and the Maritime Operations Division of subordinate thereof.Current China faces extremely complicated international security situation, under this overall background, carries out the outstanding tool realistic meaning of the research of Advanced target detecting and tracking method.In addition, object detecting and tracking method is also widely used in the civil areas such as the locating fish, resource exploration, marine navigation, underwater operation, traffic scheduling, video monitoring, robot navigation.
In Related Research Domain such as radar, sonar, infrared acquisition, video monitorings, target detection and target following are regarded as two questions of independence usually.Target detection problems can be described as: how by processing noise observation data to differentiate whether target exists; Tracking problem is then paid close attention to: under the prerequisite that target exists, how the movement state information of extract real-time target from noise observation data.As can be seen here, the pre-process module of target detection normally target following, target following effect then depends on reliable testing result.For underwater acoustic system, the mechanism of transmission of sound wave under water in channel is more complex than the aerial propagation of electromagnetic wave far away: on the one hand, the underwater velocity of propagation of sound wave is slow, communication delay large, and it is usually little that the single underwater sound tests the test figure obtained; On the other hand, underwater acoustic channel physical characteristics is complicated, and channel multipath effect is serious, and clutter density is large, is difficult to differentiate that observation data is derived from real goal, still results from the noise of clutter or other type.Existing sonar system takes the mode of " being divided and rule " as separate modular by detection and tracking usually.This mode Problems existing comprises: target detection tracking performance is too dependent on the setting of detection threshold value; For Complex Channel such as the underwater sounds, because Doppler spread phenomenon and multipath effect are obvious, observational data statistical stationarity is poor, and detection module chooses suitable time domain segmented node to ensure the statistics stationarity of target echo in each observation time section by being difficult to; The strong pre-alerting ability of target of modernization and maneuverability can cause transient behavior and the uncertainty of target travel pattern, thus are that conventional target detection tracing mode brings severe challenge.
Summary of the invention
In order to solve the radar/sonar target detection and tracking problem under above-mentioned complicated observing environment, the invention provides a kind of target detection Tracking Integrative method and apparatus for radar/Sonar system,
Wherein, the target detection Tracking Integrative method for radar/Sonar system comprises the steps:
S1: obtain and target number, target travel pattern, signal type, physical observation environmental correclation probability measure priori data information;
S2: adopt the non-modeling method of taking part in building of Bayes to carry out unitized modeling to above-mentioned priori data information, obtain prior model, the various factors likely affecting dbjective state is unified among a model, the model describes the statistics dependence between dbjective state and priori data information;
S3: on the prior model basis that above-mentioned S2 step obtains, incorporate Current observation information, adopts Bayes's mechanism construction dbjective state posterior model;
S4: the posterior model obtained based on above-mentioned S3 step, adopts the Monte Carlo Posterior distrbutionp method of sampling to carry out statistical inference, and estimating target is with or without, target number and the parameters of target motion.
Further, the source of above-mentioned priori data information comprises historical data under the experience of radar/sonar operator and oral statement, the related experiment scene of preserving, likely affects other Various types of data information of echo signal statistical property, as weather, seawater physical characteristics etc.
Further, in step S2, the out of true comprised in prior imformation, uncertain and ambiguity factor are represented by the form of probability distribution.
Further, the detailed process of step S3 is, first defines the relation of interdependence of likelihood function descriptive model parameter and Current observation data, merges prior distribution and likelihood function, calculate the posterior probability Density Distribution of model parameter based on Bayesian formula.
Further, in step S4, adopt the Monte Carlo Posterior distrbutionp method of sampling, comprise Markov chain Monte-Carlo algorithm and various mutation thereof and sequential Monte-Carlo method and various innovatory algorithm thereof, random sampling is carried out to model parameter Posterior distrbutionp, obtains the discrete approximation based on random sample for Posterior distrbutionp.Based on random sample collection, carry out statistical inference, export the real-time estimated value to target number and target state.
Further, the binaryparameter all whether existed containing indicating target in above-mentioned prior model and posterior model and target state parameter.
Target detection Tracking Integrative device for radar/Sonar system comprises:
Priori data information and acquisition device: this device is made up of data interface module, data memory module, data interface module is responsible for receiving the priori data information of sending in all kinds of priori data source, and data memory module is responsible for priori data information to store.
The modeling of priori data information unification and indication device: be utilize Bayes's non-moduli type to carry out unitized descriptive modelling to above-mentioned various priori data information, utilize unified mathematical linguistics, namely the non-moduli type of Bayes, is described the relation between various priori data information and dbjective state.
Observation data and priori data Information Statistics fusing device: for collecting up-to-date observation data and itself and priori data information being merged, obtain the posterior model describing current target state.
Statistical inference device based on the Posterior distrbutionp method of sampling: this device is responsible for running Bayesian posterior profile samples algorithm, random sampling is carried out to dbjective state Posterior probability distribution, utilize the random sample obtained to carry out corresponding statistical inference, export target number and Target moving parameter estimation value.
By adopting technique scheme, the present invention can realize from system perspective, on all kinds of uncertain factors affecting observation signal statistical property, as echo signal type, target travel pattern, the characteristic of channel etc., unified Modeling process is carried out in the non-ginseng model framework of Bayes, the system model realizing being dominated by observation data automatically builds and upgrades, and estimates the global optimum of target information.Target detection and target following are placed in unified statistical model framework by the present invention, inherent mechanism between abundant excavation target detection and target following two link contacts, make full use of multi-source heterogeneous prior imformation, provide comparatively traditional scheme target detection tracking effect more accurately.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the inventive method.
Fig. 2 is the structural representation of apparatus of the present invention.
Embodiment
Below in conjunction with drawings and Examples, the present invention is described in further detail.
Embodiment one
As shown in Figure 1, the present embodiment is a kind of target detection Tracking Integrative method for radar Sonar system, comprises the following steps:
S1: obtain relevant priori data information, comprise the probability measure priori data information with target number, target travel pattern, signal type, physical observation environmental correclation.
S2: unitized statistical modeling is carried out to above-mentioned priori data information, obtains prior probability model.Priori building process adopts the non-non-ginseng prior probability distribution of mould construction of strategy Bayes of taking part in building of Bayes.
S3: on above-mentioned Bayes non-ginseng prior distribution basis, merge up-to-date observation data, builds the non-ginseng Posterior probability distribution of Bayes.Uncertainty relation between observation data and dbjective state parameter carrys out modeling by probability likelihood function equation.According to Bayesian inference mechanism, above-mentioned prior distribution and probability likelihood function are merged, obtain Bayes posterior probability distribution.
S4: adopt specific Monte Carlo algorithm to carry out random sampling to above-mentioned Bayes posterior probability distribution, the statistical Inference distributed based on Bayesian posterior is converted into the summation operation based on Posterior distrbutionp sample.
Embodiment two
As shown in Figure 2, the present embodiment is a kind of target detection Tracking Integrative treating apparatus for radar Sonar system, and this device is by priori data information and acquisition device, the modeling of priori data information unification and indication device, observation data and priori data Information Statistics fusing device and form based on the statistical inference device of the Posterior distrbutionp method of sampling.Wherein:
Priori data information and acquisition device are the equipment for gathering, storing relevant priori data information (comprising the probability measure priori data information with target number, target travel pattern, signal type, physical observation environmental correclation), are made up of integrated circuit and storage chip;
The modeling of priori data information unification and indication device are the data processing equipments running specific modeling method, and the priori data information collected is carried out unified statistical distribution modeling by this device;
Observation data and priori data Information Statistics fusing device are the data processing equipments running special algorithm, for on above-mentioned Bayes non-ginseng prior distribution basis, merge up-to-date observation data, the uncertainty relation built between Bayes's non-ginseng Posterior probability distribution observation data and dbjective state parameter carrys out modeling by probability likelihood function equation.According to Bayesian inference mechanism, above-mentioned prior distribution and probability likelihood function are merged, obtain Bayes posterior probability distribution;
Statistical inference device based on the Posterior distrbutionp method of sampling adopts specific Monte Carlo algorithm to carry out random sampling to above-mentioned Bayes posterior probability distribution, and the statistical Inference distributed based on Bayesian posterior is converted into the summation operation based on Posterior distrbutionp sample.
The invention is not restricted to above-described embodiment, all technical schemes adopting equivalent replacement or equivalence replacement to be formed all belong to the scope of protection of present invention.
Claims (7)
1., for a target detection Tracking Integrative method for radar/Sonar system, it is characterized in that, comprise the steps:
S1: obtain and target number, target travel pattern, signal type, physical observation environmental correclation probability measure priori data information;
S2: adopt the non-modeling method of taking part in building of Bayes to carry out unitized modeling to described priori data information, obtain prior model;
S3: on the prior model basis that above-mentioned S2 step obtains, incorporate Current observation information, adopts Bayes's mechanism construction dbjective state posterior model;
S4: the posterior model obtained based on above-mentioned S3 step, adopts the Monte Carlo Posterior distrbutionp method of sampling to carry out statistical inference, and estimating target is with or without, target number and the parameters of target motion.
2. method according to claim 1, it is characterized in that, the source of described priori data information comprises the historical data under the experience of radar/sonar operator and oral statement, the experiment scene preserved and likely affects other data message of echo signal statistical property.
3. method according to claim 2, is characterized in that, the out of true comprised in described priori data information, uncertain and ambiguity factor are represented by the form of probability distribution.
4. method according to claim 1 and 2, it is characterized in that, the detailed process of described step S3 is, first define the relation of interdependence of likelihood function descriptive model parameter and Current observation data, merge prior distribution and likelihood function based on Bayesian formula, calculate the posterior probability Density Distribution of model parameter.
5. method according to claim 4, it is characterized in that, the detailed process of step S4 is, adopt the Monte Carlo Posterior distrbutionp method of sampling, random sampling is carried out to model parameter Posterior distrbutionp, obtain the discrete approximation based on random sample for Posterior distrbutionp, then carry out statistical inference, export the real-time estimated value to target number and target state.
6. method according to claim 1 and 2, is characterized in that, the binaryparameter all whether existed containing indicating target in described prior model and posterior model and target state parameter.
7. the target detection Tracking Integrative device for radar/Sonar system, it is characterized in that, comprise priori data information and acquisition device, the modeling of priori data information unification and indication device, observation data and priori data Information Statistics fusing device and the statistical inference device based on the Posterior distrbutionp method of sampling, wherein:
Described priori data information and acquisition device are made up of data interface module and data memory module, and data interface module is responsible for receiving the priori data information of sending in all kinds of priori data source, and data memory module is responsible for priori data information to store;
The modeling of described priori data information unification and indication device utilize Bayes's non-moduli type to carry out unitized descriptive modelling to described priori data information, the relation between various priori data information and dbjective state described;
Described observation data and priori data Information Statistics fusing device, for collecting up-to-date observation data and itself and described priori data information being merged, obtain the posterior model describing current target state;
The described statistical inference device based on the Posterior distrbutionp method of sampling is responsible for running Bayesian posterior profile samples algorithm, random sampling is carried out to dbjective state Posterior probability distribution, utilize the random sample obtained to carry out statistical inference, export target number and Target moving parameter estimation value.
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| CN106443624B (en) * | 2016-09-14 | 2019-02-22 | 清华大学 | A kind of object detecting and tracking integral method |
| CN107422326A (en) * | 2017-03-30 | 2017-12-01 | 燕山大学 | Submarine target method for tracing based on Bayesian Estimation |
| CN110531362A (en) * | 2018-05-23 | 2019-12-03 | 中国科学院声学研究所 | A kind of object detection method of high-resolution moving sonar Knowledge-based |
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| CN109085571A (en) * | 2018-08-20 | 2018-12-25 | 中国人民解放军海军航空大学 | Hypersonic method for tracking target based on triple bayesian criterions |
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| CN110852019A (en) * | 2019-11-28 | 2020-02-28 | 上海无线电设备研究所 | Scattering center modeling method for radar target |
| CN112162246A (en) * | 2020-07-17 | 2021-01-01 | 中国人民解放军63892部队 | Bayesian network radar system-based complex electromagnetic environment effect analysis method |
| CN112162246B (en) * | 2020-07-17 | 2024-02-09 | 中国人民解放军63892部队 | Complex electromagnetic environment effect analysis method based on Bayesian network radar system |
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