JP2012514179A - Method for applying guided disturbance deployment, guided disturbance deployment system, and computer program product - Google Patents

Abstract

  The present invention relates to a method of perturbing missiles flying toward a mother platform by applying guided perturbation deployment. The method includes predicting a number of miss distances associated with a corresponding specific decoy firing parameter set. Furthermore, the method includes selecting a decoy parameter set having an optimal misprediction distance. The method also includes transmitting the selected decoy parameter set to a firing unit that fires the decoy. Here, the predicting step includes the use of an adjoint algorithm.

Description

  The present invention is a method of applying missed disturbance deployment to disturb missiles flying towards a mother platform, evaluating several miss distances associated with a corresponding specific decoy launch parameter set; Selecting a decoy parameter set having an optimally evaluated miss distance, and transmitting the selected decoy parameter set to a firing unit that fires the decoy.

  In countering anti-ship missile threats, guided disruption is an advantageous tool, especially when dealing with attacks that cannot be completely avoided by direct interception. Guided disturbance has several advantages over direct interception. Guided perturbation deployment systems have faster response times than most direct intercept systems, are cheaper, and are associated with the risk of collateral damage or misfire from allies that can characterize the use of direct intercept systems. Danger is not involved. There are drawbacks to using an inductive disturbance system. Because the locality of the effect is lower than the direct intercept system, it can adversely affect the sensors and weapon systems of other battleships in the MTF. Also, planning a guided disturbance system and evaluating its success / failure is a rather complex task.

  Clearly, it is a meaningful aspect to improve the efficiency of induced disturbances by making accurate decisions during deployment. Apart from the obvious consequences of inaccurate deployment or insufficient number of deployments, overkill or too many deployments are completely undesirable. In addition to limited battleship resource losses, overkills can add additional load to other sensor and weapon systems on the battleship's flank, reducing their efficiency. However, improving the efficiency of guided disturbances is not an easy task due to the complexity in making accurate judgments, for example, along with the uncertainty of information that may be available regarding the path followed by the missile.

  Advanced computing power has proven to be the secret to solving complex problems in making battle decisions. In order to determine when and how to launch a guided perturbation decoy, it may be reasonable to predict the impact of the decoy on the attacking missile. To that end, a number of miss distances associated with a corresponding specific decoy firing parameter set may be predicted so that an optimal decoy parameter set with an optimally evaluated miss distance can be selected. The selected decoy parameter set can then be sent to the firing unit that fires the decoy. However, when modeling and / or simulating battle scenarios, it is necessary to process a lot of data, for example, data about threats, battleships, guided perturbation elements, and the environment.

  Clearly, the quality of the prediction will have a direct impact on the performance of the induced disturbance deployment. Computational time constraints will inevitably limit the complexity of effect predictions, and need to approximate too many factors that can affect the effectiveness of guided disturbances, thereby degrading predictions and The effectiveness is reduced.

  An object of the present invention is to apply the disturbance guidance deployment described in the preamble to a method of perturbing missiles flying towards a mother platform, wherein one of the above identified drawbacks is reduced. Is to provide. In particular, an object of the present invention is to provide a method as described in the preamble, which can obtain a miss distance prediction with a desired accuracy in a relatively short calculation time. To that end, the prediction step of the method according to the invention involves the use of an adjoint algorithm.

  By including the use of an adjoint algorithm, the calculation can be simplified, thereby leading to a fast and accurate solution. As a result, the computational effort is dramatically improved, which allows a relatively large number of decoy firing parameter sets to be evaluated, ultimately leading to an efficient selection of the optimal decoy firing parameter set.

  According to the present invention, the method can take into account the estimation of the inherent uncertainty in the prediction in the process of determining the optimal decoy firing parameter set, thereby further improving the effectiveness of the induced disturbance expansion. The method further includes calculating uncertainty data associated with the made miss distance.

  In a further embodiment according to the invention, the method further comprises the step of verifying the effect of the fired decoy, the step of verifying zero effort miss distance under platform lock-on conditions and / or decoy lock-on conditions. From the comparison sub-step, the sub-step to measure the data of the incoming missile, the sub-step to compare the measured data with one or more predicted zero-effort miss distances, and from the comparison results, the incoming missile And a sub-step for inferring which entity is locked on. By using an adjoint algorithm, performing a zero effort miss distance prediction step can provide an efficient way to perform the verification step, thereby evaluating the effect of the fired decoy. In certain embodiments, the estimating step includes the use of calculated uncertainty data corresponding to the predicted zero effort miss distance, thereby taking advantage of the adjoint algorithm once more.

  The invention also relates to a guided disturbance deployment system.

  The invention further relates to a computer program product. The computer program product may include a computer-executable instruction set stored on a data carrier such as a CD or DVD. The computer-executable instruction set can cause a programmable computer to execute the above-defined method, and may be provided by downloading from a remote server via the Internet, for example.

  Other advantageous embodiments according to the invention are described in the following claims.

  By way of example only, embodiments of the invention will now be described with reference to the accompanying drawings.

1 shows a schematic diagram of a guided disturbance deployment system according to the present invention. 2 shows a schematic perspective view of a battleship equipped with the guided disturbance deployment system of FIG. Shows the timeline. 2 shows a flowchart of an embodiment of the method according to the invention.

  It is noted that the figures merely show a preferred embodiment according to the invention. In the figures, the same reference numbers refer to equal or corresponding parts.

  FIG. 1 shows a schematic diagram of a guided disturbance deployment system 1 according to the present invention. The system 1 is provided on a mother platform such as a battleship, and includes a launch unit 2 that launches a decoy to disturb a missile flying toward the battleship. The system 1 further comprises a computer system 3 provided with a processor 4 which is configured to perform several steps to allow appropriate control of the firing unit 2. The computer system 3 has a plurality of input ports 5 that receive input data and at least one output port 6 that transmits data to the firing unit 2.

  FIG. 2 shows a schematic perspective view of a battleship 10 equipped with the guided disturbance deployment system 1. The battleship 10 is provided with a plurality of sensors such as an omnidirectional radar unit 11 for inputting data to the computer system 3 of the guided disturbance deployment system 1. FIG. 2 further shows an enemy missile 12 attacking the battleship 10. When the missile 12 remains locked on the battleship 10, the missile 12 follows the path P <b> 1 and hits the battleship 10. However, if the guided disturbance deployment system 1 works appropriately, the missile locks on the fired decoy 14 and follows another predetermined path P2 directed to the decoy 14, thereby hitting the battleship 10. do not do. In that case, the missile passes through the battleship 10 at the shortest distance, also called the miss distance M.

  The processor 4 is configured to perform several steps. First, the processor 4 notifies the incoming missile 12 with a signal. After signaling the missile 12 with a signal, the processor 4 executes the missile 12 identification step. Such an identification step may include identifying the type, position, orientation, speed, route, etc. of the missile. Sensor data is input to the computer system 3 of the guided disturbance deployment system 1 to perform the identification step as appropriate.

FIG. 3 shows a timeline. Here, the following symbols T ₀ , T _S , and T _I indicate the missile launch time, the moment when the missile was signaled, and the moment when the missile was identified, respectively.

  The processor 4 is further configured to predict a number of miss distances associated with the corresponding specific decoy firing parameter set. As an example, dozens of decoy firing parameter sets, each corresponding to a specific miss distance, can be evaluated. In the predicting step, use of an adjoint algorithm is included. The step of predicting may be based on a number of data, such as incoming missile parameter data, mother platform parameter data, corresponding decoy launch parameter sets, and / or environmental data. Further, the processor is configured to select a decoy parameter set having an optimal misprediction distance M. The selected decoy parameter is then transmitted to the firing unit 2 that fires the decoy. In addition, control commands can be generated to change the position and / or orientation of the battleship.

  Optionally, the processor 4 is further configured to perform a step of calculating uncertainty data corresponding to the predicted miss distance, for example, a probability range of a path assumed to be followed by the missile.

  In an embodiment according to the present invention, the processor 4 selects the decoy parameter set that corresponds to the largest mispredicted distance M, thereby providing the greatest offset between the battleship 10 and the missile 12. Alternatively, the processor also considers the uncertainty data in the prediction of the miss distance, thereby optionally selecting a decoy parameter set corresponding to a relatively large misprediction distance M having a relatively small uncertainty.

  Based on the selected decoy parameter set, the launch unit 2 of the decoy system 1 may, for example, flare affect any infrared lock-on device in the enemy missile and / or any radar lock-on device in the enemy missile. Fire a decoy 14 that includes a chaff that affects. The decoy 14 is intended to deviate from the original direction so that the missile leaves the battleship 10.

Referring to FIG. 3, the decoy 14 is fired at the instant _{T D.} Then, at the instant T _SD after, the missile to lock on to the decoy. In future instant T _V, decoy whether appropriate function, and / or missile 12 is whether towards the decoy 14 at present is confirmed or verified. To that end, the processor 4 is also configured to perform a sub-step of predicting a zero effort miss distance under platform lock-on conditions and decoy lock-on conditions. As a result, the zero effort miss distance is calculated using an adjoint algorithm, assuming that the missile remains locked on the battleship. Zero effort miss distance is defined as the miss distance that depends on a particular moment and will occur if the battleship's route and missile route at that particular moment remain unchanged. Similarly, the zero effort miss distance is calculated when the missile changes to a lock-on to the decoy. The processor further performs a sub-step of measuring data regarding the incoming missile and a sub-step of comparing the measured data with one or both predicted zero effort miss distances. Next, the processor infers the entity from which the missile is expected to be locked on from the comparison result. Preferably, the guessing step includes the use of calculated uncertainty data corresponding to the predicted zero effort miss distance. After verification is performed, at the instant _TCPA , the missile enters the missed distance area, which is the closest point, and moves away from the battleship.

  As a result, an optimal firing parameter set can be found using a prediction step before decoy firing. Furthermore, after launching, the effectiveness of the guided disturbance, whether the anti-ship missile is locked on to the decoy or the battleship, can be checked by comparing the predicted effects. The determination of the optimal decoy parameter set and / or judgment at the check step can be enhanced by using uncertainty data that can be provided by an adjoint algorithm.

  An adjoint algorithm, also known as an adjoint method, is based on performing a one-time simulation of a modified model called an adjoint model and identifying the effects of all perturbation sources that affect the miss distance. The adjoint model can be easily obtained from linearization of the original model by performing some simple block diagram operations. Alternatively, the adjoint model can be easily obtained from a state-space representation of the original model. When probabilistically analyzing the induction loop, which is a separate effect of the initial condition and the input of the time-varying system on the final value of the output, it is sufficient to calculate one initial value solution of the adjoint system. Can be proved mathematically. An arbitrary initial condition and an initial value solution of the input can be used to derive an expression that evaluates the final value. The adjoint model can be useful in the case of probabilistic performance analysis, but can be used with much greater advantage in the case of probabilistic performance analysis. To formulate the relevant mathematical results, it can be shown that the adjoint response can be used to calculate the variance of the output without a long Monte Carlo simulation.

  Thus, the adjoint method includes building an adjoint model and generating performance data using the adjoint model response. The adjoint model simulates a dynamic system whose response is sensitive to the input of the system to be analyzed. Thus, the adjoint algorithm is suitable for evaluating the performance of a decoy process, particularly when the process depends on many variables, is dynamic, and changes over time.

  The initial configuration consists of anti-ship guided missiles at low altitude and generally facing the battleship, a decoy cloud positioned at a given displacement from the battleship and moving freely with the wind, and a constant bow direction during combat Represented by a ship that is supposed to keep At the start of the scenario, it is assumed that the missile is locked on to the decoy using the seeker and uses the seeker data to calculate the guidance command. This assumption corresponds to the use of decoys in the scattering mode. Conversely, in the temptation mode, the missile is locked on to the battleship itself and changes the lock-on to decoy only after the decoy is activated.

  It is assumed that the missile is directed towards the decoy using proportional navigation until it passes the decoy. The missile then continues to fly without guidance until it reaches the closest point to the battleship. The wind velocity vector is assumed to be constant throughout the battle. It is also assumed that the missile speed is constant during the battle and therefore only the missile's route is changed as a result of the guidance command.

  It is assumed that the battleship is performing an avoidance action after deploying the decoy, and it may take some time to turn the battleship to the selected route.

  According to an aspect of the present invention, a nonlinear model can be linearized to obtain a linear model, and a mis-distance equation can be formulated depending on an adjoint response defined as a solution to the initial value problem. Further, the variance of the miss distance can be expressed as a function of the variance of the initial condition elements. The variance of the initial state vector coordinates in units of the original stochastic quantity can be approximated relatively easily. Post-launch verification can be performed using a statistical hypothesis testing algorithm.

  The decoy moves

Is described as

  The equation describing the movement of the battleship is

Where

_Where t _man is the time required to complete the battleship steering.

  The movement of the missile

Where τ is the missile time constant describing the missile response to the guided command represented by the commanded lateral acceleration α _{n, c} .

  For deployment planning, assume that a missile is permanently locked onto the decoy until it passes the decoy. in this case,

N _p is the navigation constant of the missile, V _{c, f} is the approach speed between the missile and the decoy, and λ _f is the angular velocity of the line of sight between the missile and the decoy. Missile velocity vector notation

If you use

It is.

  It is also necessary to consider the case where the missile remains locked on to the battleship for deployment effect testing after launch. in this case,

And the value in the expression is

It is.

In order to linearize the nonlinear model described in the previous section, the origin is the initial position of the missile, the x-axis is along the line of sight from the missile to the battleship, and the y-axis is the positive coordinate system. It is convenient to introduce a new coordinate system called the battle coordinate system in the book. In Figure 3.1, the axis of this coordinate system is represented by _{X E} and _{Y E.} The conversion between east-north coordinates and battle coordinates is

The values in the formula are as follows:

The superscript ^e indicates all numbers expressed in battle coordinates. Therefore,

The same applies to the position and velocity vectors of battleships and decoys.

During combat, for missiles route is to keep the state of near x ^e axis, the speed of the missile along the x ^e axis is approximately constant. The range between the missile and the battleship and the range between the missile and the decoy can also be approximated by their respective x-coordinate differences, while the miss distance should be approximated by their respective y-coordinate differences. Can do. By ignoring variations in missile routes,

Thus, the lateral acceleration of the missile can be approximated.

  The angular velocity of the line of sight to the decoy is

Where the relative range missile-decoy is R _{m, f} (t) = V _{c, f} (t _{miss, f} −t).

Is obtained.

  Similarly, the line of sight to the battleship is

Where R _{m, s} (t) = V _{c, s} (t _{miss, f} −t). Therefore,

It is.

From the condition that x ^e _m = x ^e _f at time t _{miss, f} ,

I guess that.

Flight time to closest point of approach of warships, from the condition ^x _e m ^{= x} _{e f,}

Can be approximated as

  In conclusion, the linearized model when the missile is locked on to the decoy is

It has the form.

  If the missile is locked on to the battleship, equation (5.10) is

Since the decoy does not affect the outcome of the battle, the equation describing the decoy's movement can obviously be skipped.

In both cases, the resulting model is a linear time-varying system on [0, t _f ].

  Miss distance to battleships

Which is a linear function of the state of the linearized model.

  To evaluate efficiency after launch, at each instant t during the battle,

Use a zero effort miss distance that can be calculated as: Note that the _miss distance is equal to z (t _miss ).

There is still a need to modify the linearized model to suit this application. In fact, it is easy to see that the model (5.10) is singular at time t = _{tmiss, f} . A simple way to eliminate this specificity while preserving the linearity of the model is to introduce a “blind time” t _b > 0, which is a short interval before the missile passes the decoy that breaks the induction loop. With this change, the commanded acceleration formula when the missile locks on the decoy is

become.

  A similar change is necessary when a missile locks on a battleship.

  Note that the introduction of “blind time” does not necessarily affect the simulation results. Most anti-ship missiles turn the seeker off when they come close to the target. This is done to avoid seeker confusing if the target is too large compared to the field of view.

  State vector

The linearized model is

And the value in the expression is

And

Can be written in matrix form, the initial state is

It is. Miss distance is

Can be written as

The adjoint response at time t _miss is the equation

The initial state is x ^adj (0) = [1 0 0 −1 0 0] ^T. Note that since the matrix B in (6.1) is an identity matrix, the state and the output adjoint response match.

  According to Proposition 2.1, the miss distance is

Can be written as

  This format is particularly interesting for deterministic performance studies. If the initial data includes uncertainty with probabilistic features, Proposition 2.2 can be used to estimate the variance of the miss distance as a function of the variance of the initial state elements.

In the formula, P _{x (0)} represents a dispersion matrix of x (0). Since the input of the system (6.1) is deterministic, the integral term in equation (2.6) does not appear in (6.11). Due to the choice of the coordinate system, y ^e _om = 0, but the variance occurs during (6.10) because the variance can be non-zero, reflecting the uncertainty of the tracking data available for the missile. Note that equation (6.11) also includes a term corresponding to no y ^e _om . Note also that the matrix form of equation (6.11) is more general because it also includes the interdispersion term.

In order to actually apply equation (6.11), it is still necessary to specify the variance of the initial state vector coordinates with the original stochastic quantities. The exact answer to this question can be difficult to obtain analytically, but fortunately it is easy to write an approximation of these variances that are actually acceptable. For example, x _om and y _om are averages

Have dispersion

Is a random variable with Then, the distribution of _yom is the matrix

Obtained as (2,2) elements.

Dispersion of error φ _m when tracking missile route

If you have

The variance of

It is.

This relationship does not take into account errors in tracking the total missile velocity. If we want to investigate the effect of decoy cloud diffusion and model it as a random perturbation in the launch direction, the variance _of y ^e _of is

Can be approximated as

  These estimates were used for numerical tests and the results were good. In general, more complex relationships may be required to evaluate the terms that occur in (6.11).

Here, it is assumed that at the instant t _d when the decoy is fired and fixed, it is necessary to determine whether the deployment has been successful, that is, whether the missile has locked on the decoy. For active missiles, there are basically two ways to perform this function (see [1]). The first method applies similarly to active and passive missiles and is based on calculating the closest point of attack missile. A second method for evaluating whether an attack missile is locked on a battleship is based on measuring radar signals that the missile uses to track the battleship using ESM system metrics. The second method is considered more reliable because the missile also works when performing a dogleg maneuver so that the apparent closest point of view can be seen quite far away. However, as mentioned above, the first method is more widely applicable, and as reported in [1], neither method surpasses the other in all conceivable situations.

  In this section, we show how an adjoint method can be used to improve the first method of assessing the success of a decoy launch based on the point of closest approach. The concept is to use an adjoint method to estimate the closest point both when a missile locks on a decoy and when the missile locks on a battleship. By comparing these estimates with the positions calculated based on tracking data and taking into account the variance of these estimates that can be identified equally using an adjoint method, it is possible to determine whether the missile has actually locked onto the decoy. It is possible to judge.

  The linearized model when the missile locks on the decoy was introduced in the previous section as equation (6.1) or less. The linearized model when the missile locks on the battleship has the same form as (6.1),

and

Where

And the initial state is

It is.

We are interested in the value of the zero effort miss distance introduced in (5.13) at time t _d, which can be written as: Clearly, the linear model has the form (2.1) where D is not zero at all. In order to apply Proposition 2.1, the adjoint response is defined as the solution of the initial value problem.

The initial state is

It is.

Have

  Since the speed of the battleship is not a random variable, the presence of the D term in the linear model has no effect on the zero effort miss distance variance equation. According to Proposition 2.2

It is.

Similarly, by applying the associated method, missiles can be estimated zero effort miss distance at time t _d in the case of lock on to decoy.

z _s and σ _s show the average and variance of miss distance assuming that the decoy was successfully launched and the missile was locked on to the decoy, and z _f and σ _f caused the decoy launch to pull the missile away from the battleship Show the mean and variance of the miss distances assuming that they have failed.

The value of the zero effort miss distance at time t _d can also be calculated based on tracking data,

Indicated by

But,

Suppose that it is normally distributed around z ₁ , which is a “true” (based on true geographic data) ZEMD with a variance σ _m that can be evaluated based on the accuracy of the measurement data involved in the calculation of.

  Using statistical hypothesis testing theory,

In this case, the best judgment is that the missile is locked on to the decoy, and if this condition is not met, the best judgment is that the missile is locked on to the battleship Z _λ (Z _s , z _f , σ _s , σ _f , σ _m ) can be provided. Optimal spacing Z _lambda is Neyman - can be obtained from Pearson lemma. For convenience, here is a summary of the main notations and results of the statistical hypothesis tests used below.

First, let us assume that the null hypothesis H ₀ is “missile locked on to decoy” and the alternative hypothesis H ₁ is “missile locked on to battleship”. In this case, I-type probability, i.e. the probability of false positives, but the hypothesis H ₁ is accepted, the H ₀ On the contrary is that it is true.

Type II probabilities, or miss probabilities, are that hypothesis H ₀ is accepted, but H ₁ is true, on the other hand.

The power of this test is

Is defined as

The problem is the determination of the decision interval Z _λ that maximizes the power of the test or minimizes the probability of a mistake so that the false positive probability takes a given value α. The following conventional results can be used to determine the optimal threshold.

  Neiman-Pearson lemma: The optimal decision to minimize the probability of a miss receiving a given false positive probability α is obtained by the following criteria based on the likelihood ratio.

Where Z represents the observed value and Λ ₀ is

Meet.

  In this application, the observed value is the calculated ZEMD

It is represented by Assuming that all prediction errors are normally distributed, using the notation introduced earlier,

and

Thus, the likelihood ratio is

It is.

After some simple processing, the condition Λ (H ₁ | H ₀ )> Λ ₀ is

be equivalent to.

The probability that this condition is satisfied is that H ₀ is true

Can be evaluated using assumptions about the conditional distribution of. According to the Neyman-Pearson lemma, this probability needs to be equal to α, and Λ ₀ can be calculated using this condition.

The solution is particularly simple when σ _f = σ _s = σ. In this case, the previous condition is

be equivalent to.

Assuming that z _s > z _f is physically the most likely case because a successful lock-on to the decoy is expected to lead to a larger ZEMD than if the missile was locked on to the battleship . Then, the previous relationship is

be equivalent to.

  here,

Can be used to evaluate conditional probabilities in the Neyman-Pearson lemma.

Where NormCDF represents the standard normal law cumulative distribution function.

From the Neiman-Pearson lemma, the value of Λ ₀ can be easily obtained by regarding Eq. (7.19) as being equal to the false positive rate α.

Is obtained.

When this equation is introduced into (7.18), the optimal criterion for accepting hypothesis H ₁ is

It is estimated that.

This baseline force can be obtained by evaluating the probability that this condition is satisfied when H ₁ is true.

Given the conditional distribution of

It is concluded that

In the general case where σ _f ≠ σ _s , it is impossible to obtain a closed-form expression for the optimal decision criterion. However, it is possible to propose a numerical algorithm that performs the decision using conditional means and variances. This algorithm will be described when σ _f > σ _s . Notation:

To the relationship (7.16)

It can be easily understood that it can be rewritten as

The problem of determining Λ ₀ that satisfies the conditions of the Neiman-Pearson lemma is

To the problem of finding C.

  Using this value of C, zero effort miss distance

If the estimate of (7) satisfies (7.27), then H ₁ is accepted, otherwise H ₀ is accepted. further,

The quadratic equation

The two solutions are

It is.

Note that these two solutions is the limit of determination interval Z _lambda. The relationship (7.28) is

Equal to or

be equivalent to.

This formula is

To solve the nonlinear equation

Where

Is a known parameter. In this expression, the function on the left side is a strictly monotonic decreasing function of x, which takes a value of 1 when x = 0 and converges to 0 when x → ∞. It has a unique positive solution for every value of A and α. In addition, the upper and lower limits of the solution are easily found. For example, if A> 0,

It is easy to see that However, each x> 0. here,

And

Assume that x ₁ and x ₂ are both positive. This assumption is generally true for small values of α. Recall that α is the false positive rate and is usually chosen very small. Use inequality (7.36) in the case of x = _{x 1,} left-hand side of (7.35) is larger than the alpha, x = the case of _{x 2,} made it clear smaller than alpha. Monotonicity ensures that the solution of (7.35) is within the interval (x ₁ , x ₂ ). Using the bisection method, the solution can be easily identified. This provides a solution C ′ of equation (7.34), which can be used to identify C from (7.30), which is then used to Based on the relationship (7.27), the criteria for determining whether the anti-ship missile has locked on the ship or the decoy has been checked. In addition, the standard power

Is

Can be calculated as

Is given by (7.32).

  Thus, prediction using an adjoint algorithm can be applied in duplicate. First, the prediction can be used to optimize deployment before launch. Second, after launch, predictions can be used to assess the success of deployment. The latter is based on two hypotheses: the missile is locked on to the decoy, i.e. the deployment is successful, and the missile is locked on to the battleship, i.e. the observation and prediction of the closest point Can be realized by comparing The ability to take into account measurement and estimation uncertainties without undue computational effort in the adjoint method is particularly advantageous when evaluating success.

  The method of applying guided perturbation deployment to perturb missiles flying towards the mother platform can be performed using dedicated hardware structures such as FPGA and / or ASIC components. In other cases, the method may also be performed using a computer program product comprising instructions that cause a processor of a computer system to perform the above steps of the method according to the invention, at least in part. All steps can in principle be performed by a single processor. However, it is noted that at least one step may be performed by a separate processor, for example, identifying enemy missiles and / or identifying missiles.

  FIG. 4 shows a flowchart of an embodiment of the method according to the invention. The method is used to perturb missiles flying towards the mother platform, applying guided perturbation deployment. The method includes predicting (100) a number of miss distances associated with a corresponding specific decoy firing parameter set, selecting a decoy parameter set having an optimal misprediction distance (110), and selecting Transmitting the set decoy parameter set to a firing unit that fires the decoy (120). The predicting step (100) involves the use of an adjoint algorithm.

  It will be appreciated that the above-described embodiments of the invention are merely exemplary, and other embodiments are possible without departing from the scope of the invention. It will be appreciated that many variations are possible.

  A guided perturbation deployment system according to the present invention may be provided with a single launch system or multiple launch systems. Furthermore, the guided disturbance deployment system according to the present invention can accommodate a single missile or multiple missiles directed at the mother platform.

  In the embodiments described above, the method according to the present invention is applied in combating anti-ship missile threats, but this method is intended for missiles aimed at other mother platforms such as missiles aimed at airplanes or ground vehicles. It can also be applied to cases corresponding to.

  Such variations will be apparent to those skilled in the art and are considered to be within the scope of the invention as defined in the following claims.

Claims (9)

Applying guided disturbance deployment to disrupt missiles flying towards the mother platform,
-Predicting several miss distances associated with a corresponding specific decoy firing parameter set;
-Selecting a decoy parameter set having an optimal misprediction distance;
-Transmitting the selected decoy parameter set to a firing unit that fires the decoy;
The step of predicting includes the use of an adjoint algorithm, and the method further includes calculating uncertainty data associated with a misprediction distance, wherein the calculated in the step of selecting the decoy parameter set A method that takes uncertainty data into account.   The method of claim 1, wherein the misprediction distance is based on incoming missile parameters, mother platform parameters, and the corresponding decoy launch parameter set.   The method according to claim 1 or 2, wherein the adjoint algorithm includes linearizing a nonlinear prediction model. Verifying the effect of the fired decoy, the verifying step further comprising:
A sub-step of predicting zero effort miss distance under platform lock-on conditions and / or decoy lock-on conditions;
-A sub-step for measuring the data of incoming missiles;
-A sub-step of comparing the measured data with one or more of the predicted zero effort miss distances;
The method according to any one of claims 1 to 3, further comprising a sub-step of inferring from the comparison result what the incoming missile is locked on.   The method of claim 4, wherein the inferring step comprises using calculated uncertainty data corresponding to a predicted zero effort miss distance.   The method according to any one of claims 1 to 5, further comprising a step of signaling the incoming missile.   7. A method according to any one of the preceding claims, further comprising the step of identifying incoming missiles signaled. -A launch unit that launches decoys to disrupt missiles flying towards the mother platform;
A computer system provided with a processor, said processor comprising:
-Predicting several miss distances associated with a corresponding specific decoy firing parameter set;
-Selecting a decoy parameter set having an optimal misprediction distance;
-Transmitting the selected decoy parameter set to a launching unit that fires the decoy; and a computer system,
The step of predicting includes the use of an adjoint algorithm, and the method further includes calculating uncertainty data associated with a misprediction distance, wherein the calculated in the step of selecting the decoy parameter set Inductive disturbance deployment system that takes uncertainty data into account. A computer program product for applying a guided disturbance deployment to disturb a missile flying toward a mother platform, the computer program product comprising:
-Predicting several miss distances associated with a corresponding specific decoy firing parameter set;
-Selecting a decoy parameter set having an optimal misprediction distance;
-Computer readable code for causing a processor to execute the selected decoy parameter set to a firing unit that fires the decoy;
The step of predicting includes the use of an adjoint algorithm, wherein the computer program product further comprises computer readable code for causing a processor to calculate uncertainty data associated with a misprediction distance; A computer program product that takes into account the calculated uncertainty data in the step of selecting.

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