JP6248967B2 - Target state quantity estimation device - Google Patents
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Description
この発明は、飛しょう体を目標に会合させるにあたり、目標の位置・速度・加速度等の状態量を推定するための目標状態量推定装置に関するものである。 The present invention relates to a target state quantity estimating device for estimating a state quantity such as a target position, velocity, and acceleration when a flying object is associated with a target.
従来、飛しょう体等に搭載されているシーカによって目標の状態量を推定する場合、カルマンフィルタが広く用いられてきた。シーカから得られる目標の観測結果には観測誤差が含まれており、それを取り除く手段として、目標の運動モデルに基づいて状態量を推定するカルマンフィルタは非常に有効である。カルマンフィルタには、線形カルマンフィルタ（例えば、非特許文献１のP.163〜P.184参照）や、非線形カルマンフィルタ(例えば、非特許文献１のP.373〜P.388参照)等の種類がある。また、従来、目標の状態量を推定する装置が開示されている（例えば、特許文献１参照）。 Conventionally, a Kalman filter has been widely used to estimate a target state quantity with a seeker mounted on a flying object or the like. The observation result of the target obtained from the seeker includes an observation error, and a Kalman filter that estimates the state quantity based on the target motion model is very effective as a means for removing it. The Kalman filter includes types such as a linear Kalman filter (for example, see P.163 to P.184 of Non-Patent Document 1) and a nonlinear Kalman filter (for example, see P.373 to P.388 of Non-Patent Document 1). Conventionally, an apparatus for estimating a target state quantity has been disclosed (see, for example, Patent Document 1).
カルマンフィルタの特性は、目標の運動モデルに加えて、シーカによる観測誤差の大きさを表す観測ノイズ行列と、モデル誤差の大きさを表すプロセスノイズ行列の値によって決定される。このうち観測ノイズ行列については、シーカのＳ／Ｎ比や観測誤差モデルなどによって一意に決定される。それに対してプロセスノイズ行列は、目標の運動が予測できないことから一意に決定することができず、一般的に設計パラメータとなる。 The characteristics of the Kalman filter are determined by the observation noise matrix that represents the magnitude of the observation error due to the seeker and the value of the process noise matrix that represents the magnitude of the model error, in addition to the target motion model. Of these, the observation noise matrix is uniquely determined by the Seeker S / N ratio and the observation error model. In contrast, the process noise matrix cannot be uniquely determined because the target motion cannot be predicted, and is generally a design parameter.
カルマンフィルタに求められる応答特性は、シーカの観測値に基づいて誘導を開始してから会合に至るまでの間に変化する。誘導の開始直後においては、フィルタの推定誤差による誘導弾の余分な機動が会合の妨げとなるため、定常特性が重視される。一方で会合直前においては、フィルタの時間遅れが誘導時間の不足に直結するため、過渡特性が重視される。
定常特性と過渡特性はトレードオフの関係にある。プロセスノイズ行列を設定するにあたっては、その点を考慮する必要がある。
The response characteristic required for the Kalman filter changes between the start of guidance and the meeting based on the observed value of the seeker. Immediately after the start of the guidance, the excessive movement of the guided bullet due to the estimation error of the filter hinders the association, so that steady characteristics are emphasized. On the other hand, immediately before the meeting, the time delay of the filter is directly linked to the shortage of the induction time, so the transient characteristics are important.
Steady state characteristics and transient characteristics are in a trade-off relationship. This must be taken into account when setting the process noise matrix.
レーダー反射断面積（ＲＣＳ）が大きく、観測値のＳ／Ｎ比が高い目標の場合は、観測誤差が小さいため、プロセスノイズ行列の値がある一種類に固定されていても、定常特性と過渡特性に関する性能指標を両方満たすことができる。
しかし、ステルス機のようにＲＣＳが小さく、観測値のＳ／Ｎ比が低い目標の場合は、観測誤差が大きいために、定常特性と過渡特性は大きく劣化する。この場合、プロセスノイズ行列の値がある一種類に固定されている状態では、定常特性と過渡特性に関する性能指標を同時に満たすことが難しいという課題があった。
For targets with a large radar reflection cross-section (RCS) and high S / N ratio, the observation error is small, so even if the process noise matrix value is fixed to a certain type, steady-state characteristics and transients Both performance indicators regarding characteristics can be met.
However, in the case of a target having a small RCS and a low S / N ratio of the observed value, such as a stealth machine, since the observation error is large, the steady-state characteristic and the transient characteristic are greatly deteriorated. In this case, in the state where the value of the process noise matrix is fixed to a certain type, there is a problem that it is difficult to satisfy the performance index related to the steady characteristic and the transient characteristic at the same time.
この発明は係る課題を解決するためになされたものであり、シーカの観測値のＳ／Ｎ比が低い状況にあっても、目標の運動を高精度に推定する目標運動量推定装置を提供することを目標とする。 The present invention has been made to solve such a problem, and provides a target momentum estimation device that estimates a target motion with high accuracy even in a situation where the S / N ratio of the observed value of a seeker is low. To the goal.
この発明に係る目標状態量推定装置は、目標の状態量をカルマンフィルタにより推定する目標状態量推定装置であって、飛しょう体に搭載され、目標までの距離と方向を観測した観測量と、前記観測量のＳ／Ｎ比を出力するシーカと、前記観測量と前記Ｓ／Ｎ比に基づき、前記観測量の誤差の大きさを表す観測ノイズ行列を算出し、前記観測ノイズ行列を観測更新計算装置に出力する観測ノイズ行列計算装置と、前記観測量と、前記観測ノイズ行列と、時間外挿計算装置が出力する目標の状態量（−）と誤差共分散行列（−）に基づき観測更新計算を行い、観測更新計算後の目標の状態量（＋）と誤差共分散行列（＋）を出力する観測更新計算装置と、更新計算後の前記目標の状態量（＋）と誤差共分散行列（＋）と、前記目標の運動モデルの誤差の大きさであるプロセスノイズ行列に基づき、時間外挿計算により目標の状態量（−）と誤差共分散行列（−）を算出し、出力する時間外挿計算装置と、前記時間外挿計算装置が算出した前記目標の状態量（−）に基づき、前記プロセスノイズ行列を算出し、算出した前記プロセスノイズ行列を前記時間外挿計算装置に出力するプロセスノイズ行列計算装置とから構成される。 A target state quantity estimation device according to the present invention is a target state quantity estimation apparatus that estimates a target state quantity by a Kalman filter, and is mounted on a flying object, and an observed quantity obtained by observing the distance and direction to the target, Based on the seeker that outputs the S / N ratio of the observation amount, the observation noise matrix that represents the magnitude of the observation amount error based on the observation amount and the S / N ratio, and the observation update calculation of the observation noise matrix Observation update matrix calculation device output to device, observation update calculation based on observation amount, observation noise matrix, target state quantity (-) and error covariance matrix (-) output by time extrapolation calculation device And an observation update calculation device that outputs the target state quantity (+) and error covariance matrix (+) after the observation update calculation, and the target state quantity (+) and error covariance matrix (after the update calculation) +) And the target exercise model A time extrapolation calculation device that calculates and outputs a target state quantity (−) and an error covariance matrix (−) by time extrapolation based on a process noise matrix that is the magnitude of the error, and the time extrapolation calculation The process noise matrix calculation device is configured to calculate the process noise matrix based on the target state quantity (−) calculated by the device and to output the calculated process noise matrix to the time extrapolation calculation device.
この発明に係る目標状態量推定装置によれば、シーカの観測値のＳ／Ｎ比が低い状況にあっても目標の運動を高精度に推定することができ、目標を高精度に追尾することができる。 According to the target state quantity estimation device according to the present invention, the target motion can be estimated with high accuracy even in a situation where the S / N ratio of the observed value of the seeker is low, and the target can be tracked with high accuracy. Can do.
実施の形態１．
図１は、本実施の形態に係る目標状態量推定装置１００の構成を示す図である。
目標状態量推定装置１００は、飛しょう体に搭載され、目標の距離・方向等を観測するためのシーカ１と、観測更新計算装置２と、時間外挿計算装置３と、観測ノイズ行列計算装置４と、プロセスノイズ行列計算装置５から構成される。
以下、図１を参照しながら、実施の形態１における目標状態量推定装置の動作を詳しく説明する。
Embodiment 1 FIG.
FIG. 1 is a diagram showing a configuration of target state
A target state
Hereinafter, the operation of the target state quantity estimation apparatus in the first embodiment will be described in detail with reference to FIG.
シーカ１は、目標が反射する電波を受信、あるいは目標が放射する赤外線を受信すること等により、目標の運動を観測する。
シーカ１は、観測された目標の観測量６を観測更新計算装置２へ出力する。
The seeker 1 observes the movement of the target by receiving radio waves reflected by the target or receiving infrared rays radiated by the target.
The seeker 1 outputs the observed target observation amount 6 to the observation
観測更新計算装置２は、シーカ１から得られる観測量６と、時間外挿計算装置３から得られる「状態量（−）・誤差共分散行列（−）」８を用い、観測ノイズ行列計算装置４から得られる観測ノイズ行列９の値に基づいて観測更新計算を行う。
The observation
観測更新計算の処理を下記の式（１）に示す。
シーカ１はその装置の特性上、観測量６を離散的に出力するため、観測更新計算装置２は、シーカ１から観測量６が入力されるたびに計算されるものとする。
The observation update calculation process is shown in the following equation (1).
Since the seeker 1 outputs the observation amount 6 discretely due to the characteristics of the apparatus, the observation
観測方程式：ｈ
観測量：ｚ
観測ノイズ行列：Ｒ
状態量（観測更新計算前）：ｘ−
状態量（観測更新計算後）：ｘ＋
誤差共分散行列（観測更新計算前）：Ｐ−
誤差共分散行列（観測更新計算後）：Ｐ＋
とする。
Observed quantity: z
Observation noise matrix: R
State quantity (before observation update calculation): x −
State quantity (after observation update calculation): x +
Error covariance matrix (before observation update calculation): P −
Error covariance matrix (after observation update calculation): P +
And
観測方程式ｈは、シーカの特性をあらかじめ適切に同定した数式モデルから構成される。観測ノイズ行列は、シーカから得られる観測値のＳ／Ｎ比７や観測誤差モデルなどをもとに、適切に設定されるものとする。誤差共分散行列の初期値は、あらかじめ想定される誤差共分散に応じて適切に設定されるものとする。 The observation equation h is composed of a mathematical model in which the characteristics of the seeker are appropriately identified in advance. The observation noise matrix is appropriately set based on the S / N ratio 7 of observation values obtained from the seeker, the observation error model, and the like. It is assumed that the initial value of the error covariance matrix is appropriately set according to the error covariance assumed in advance.
以上の計算で得られた状態量（観測更新計算後）と誤差共分散行列（観測更新計算後）を「状態量（＋）・誤差共分散行列（＋）」１０として時間外挿計算装置３へ出力する。 The time extrapolation calculation device 3 with the state quantity (after observation update calculation) and the error covariance matrix (after observation update calculation) obtained by the above calculation as “state quantity (+) / error covariance matrix (+)” 10 Output to.
時間外挿計算装置３は、観測更新計算装置２からの入力「状態量（＋）・誤差共分散行列（＋）」１０と、プロセスノイズ行列計算装置５からの入力「プロセスノイズ行列」１１を用いて、時間外挿計算を行う。
The time extrapolation calculation device 3 receives the input “state quantity (+) / error covariance matrix (+)” 10 from the observation
下記の式（２）に時間外挿計算の処理を示す。 The time extrapolation calculation processing is shown in the following equation (2).
運動方程式：ｆ
プロセスノイズ行列：Ｑ
現在時間：ｔｋ
とする。
Process noise matrix: Q
Current time: tk
And
運動方程式ｆは、対象をあらかじめ適切に同定した数式モデルから構成される。 The equation of motion f is composed of a mathematical model in which an object is appropriately identified in advance.
時間外挿計算の結果として得られる状態量（時間外挿計算後）・誤差共分散行列（時間外挿計算後）を「状態量（−）・誤差共分散行列（−）」８として、観測更新計算装置２とプロセスノイズ行列計算装置５へ出力する。
State quantity (after time extrapolation calculation) and error covariance matrix (after time extrapolation calculation) obtained as a result of time extrapolation calculation are observed as "state quantity (-) and error covariance matrix (-)" 8 Output to the
本実施の形態に係るプロセスノイズ行列計算装置５は、時間外挿計算装置３から得られた「状態量（−）・誤差共分散行列（−）」８を用いて、プロセスノイズ行列１１の値を計算する。
プロセスノイズ行列１１の値は、下記の式（３）に示す処理で計算される。
The process noise matrix calculation device 5 according to the present embodiment uses the “state quantity (−) / error covariance matrix (−)” 8 obtained from the time extrapolation calculation device 3 to calculate the value of the process noise matrix 11. Calculate
The value of the process noise matrix 11 is calculated by the process shown in the following formula (3).
プロセスノイズ行列：Ｑ
プロセスノイズ行列初期値：Ｑ０
ゲイン行列関数：Ｇ
とする。
Process noise matrix initial value: Q0
Gain matrix function: G
And
Ｇは、状態量に基づいてプロセスノイズ行列の各行のゲインを算出する関数である。
各時刻において、状態量（−）を基にゲイン行列を算出し、プロセスノイズ行列初期値に乗じる。このように、状態量を反映したプロセスノイズ行列Ｑを算出することで、その場に適したフィルタ特性を得ることが可能になる。
G is a function for calculating the gain of each row of the process noise matrix based on the state quantity.
At each time, a gain matrix is calculated based on the state quantity (−) and multiplied by the process noise matrix initial value. Thus, by calculating the process noise matrix Q reflecting the state quantity, it is possible to obtain filter characteristics suitable for the situation.
一例として、状態量から計算される「予想会合時間」を用いて、プロセスノイズ行列の算出を行う場合を説明する。
予想会合時間は飛しょう体が目標に会合するまでにかかると予想される時間を表し、誘導開始時点で最も大きく、誘導中は単調減少し、会合時に０となる。
そのため、ゲイン行列Ｇを予想会合時間に基づいて算出することで、（１）誘導開始直後では定常特性を重視した特性、（２）会合直前では過渡特性を重視した特性というように、その場で求められる性能指標を満足したフィルタ特性を得ることができる。
As an example, a case where the process noise matrix is calculated using “expected meeting time” calculated from the state quantity will be described.
The expected meeting time represents the time that the flying object is expected to take to meet the target, and is the largest at the start of induction, decreases monotonically during induction, and becomes zero at the meeting.
Therefore, by calculating the gain matrix G based on the expected meeting time, (1) characteristics that emphasize steady characteristics immediately after the start of guidance, and (2) characteristics that emphasize transient characteristics immediately before the meeting, on the spot. A filter characteristic satisfying the required performance index can be obtained.
なお、プロセスノイズ行列初期値Ｑ０は、目標の最大旋回加速度などをもとにあらかじめ設定される初期値である。
この計算は時間外挿計算装置３から新たな値が送信される毎に行われ、式（３）により計算されたプロセスノイズ行列Ｑは、プロセスノイズ行列１１として時間外挿計算装置３に送信される。
The process noise matrix initial value Q0 is an initial value set in advance based on a target maximum turning acceleration or the like.
This calculation is performed every time a new value is transmitted from the time extrapolation calculation device 3, and the process noise matrix Q calculated by the equation (3) is transmitted to the time extrapolation calculation device 3 as the process noise matrix 11. The
以上の数式１〜数式３の処理を繰り返し計算し、「状態量（−）・誤差共分散行列（−）」８を毎サンプリング計算する。サンプリング毎に、「状態量（−）・誤差共分散行列（−）」８を目標状態量推定装置の最終的な出力とする。 The processing of the above Equations 1 to 3 is repeatedly calculated, and the “state quantity (−) / error covariance matrix (−)” 8 is calculated every sampling. For each sampling, “state quantity (−) / error covariance matrix (−)” 8 is set as the final output of the target state quantity estimation apparatus.
このように、本実施の形態に係る目標状態量推定装置によれば、ＲＣＳが小さく、観測値のＳ／Ｎ比が低い目標の運動を推定する状況にあっても、フィルタ特性をその場に応じて変化させることができる。
その結果、例えば、誘導開始直後では定常特性を重視した特性、会合直前では過渡特性を重視した特性というように、その場で求められる性能指標を満足したフィルタ特性を得ることができる。これにより、目標の位置や速度、旋回加速度等の状態量を高精度に推定することができ、ひいてはその推定された状態量を用いて、目標を高精度に追尾することができる。
As described above, according to the target state quantity estimating device according to the present embodiment, even in a situation where the motion of a target with a small RCS and a low S / N ratio of an observed value is estimated, the filter characteristics can be placed on the spot. It can be changed accordingly.
As a result, it is possible to obtain a filter characteristic that satisfies the performance index required on the spot, such as a characteristic that emphasizes the steady characteristic immediately after the start of guidance and a characteristic that emphasizes the transient characteristic immediately before the meeting. As a result, state quantities such as the target position, speed, and turning acceleration can be estimated with high accuracy, and the target can be tracked with high accuracy by using the estimated state quantities.
なお、この目標状態量推定装置を用いて、目標追尾装置を構成することが可能となる。 In addition, it becomes possible to comprise a target tracking apparatus using this target state quantity estimation apparatus.
１ シーカ、２ 観測更新計算装置、３ 時間外挿計算装置、４ 観測ノイズ行列計算装置、５ プロセスノイズ行列計算装置、６ 観測量、７ Ｓ／Ｎ比、８ 状態量（−）・誤差共分散行列（−）、９ 観測ノイズ行列、１０ 状態量（＋）・誤差共分散行列（＋）、１１ プロセスノイズ行列、１００ 目標状態量推定装置 1 Seeker, 2 Observation update calculator, 3 Time extrapolation calculator, 4 Observation noise matrix calculator, 5 Process noise matrix calculator, 6 Observed quantity, 7 S / N ratio, 8 State quantity (-) and error covariance Matrix (−), 9 observation noise matrix, 10 state quantity (+) / error covariance matrix (+), 11 process noise matrix, 100 target state quantity estimation device
Claims (5)
飛しょう体に搭載され、目標までの距離と方向を観測した観測量と、前記観測量のＳ／Ｎ比を出力するシーカと、
前記観測量と前記Ｓ／Ｎ比に基づき、前記観測量の誤差の大きさを表す観測ノイズ行列を算出し、前記観測ノイズ行列を観測更新計算装置に出力する観測ノイズ行列計算装置と、
前記観測量と、前記観測ノイズ行列と、時間外挿計算装置が出力する目標の状態量（−）と誤差共分散行列（−）に基づき観測更新計算を行い、観測更新計算後の目標の状態量（＋）と誤差共分散行列（＋）を出力する観測更新計算装置と、
更新計算後の前記目標の状態量（＋）と誤差共分散行列（＋）と、前記目標の運動モデルの誤差の大きさであるプロセスノイズ行列に基づき、時間外挿計算により目標の状態量（−）と誤差共分散行列（−）を算出し、出力する時間外挿計算装置と、
前記時間外挿計算装置が算出した前記目標の状態量（−）に基づき、前記プロセスノイズ行列を算出し、算出した前記プロセスノイズ行列を前記時間外挿計算装置に出力するプロセスノイズ行列計算装置と、
から構成されることを特徴とする目標状態量推定装置。 A target state quantity estimation device for estimating a target state quantity by a Kalman filter,
An observable that is mounted on a flying object and that observes the distance and direction to the target, and a seeker that outputs the S / N ratio of the observable,
An observation noise matrix calculation device that calculates an observation noise matrix that represents a magnitude of an error of the observation amount based on the observation amount and the S / N ratio, and outputs the observation noise matrix to an observation update calculation device;
The observation update calculation is performed based on the observation amount, the observation noise matrix, the target state quantity (−) and the error covariance matrix (−) output by the time extrapolation calculation device, and the target state after the observation update calculation is performed. An observation update calculation device that outputs a quantity (+) and an error covariance matrix (+);
Based on the state quantity (+) and error covariance matrix (+) of the target after the update calculation, and the process noise matrix that is the magnitude of the error of the target motion model, the target state quantity ( -) And a time extrapolation calculation device that calculates and outputs an error covariance matrix (-),
A process noise matrix calculation device that calculates the process noise matrix based on the target state quantity (−) calculated by the time extrapolation calculation device, and outputs the calculated process noise matrix to the time extrapolation calculation device; ,
A target state quantity estimation device comprising:
プロセスノイズ行列：Ｑ
プロセスノイズ行列初期値：Ｑ０
ゲイン行列関数：Ｇ
である。 2. The process noise matrix is a matrix represented by the following equation, and the process noise matrix initial value Q0 is an initial value set in advance based on the target maximum turning acceleration. Target state quantity estimation device.
Process noise matrix initial value: Q0
Gain matrix function: G
It is.
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