JP2011047881A5

JP2011047881A5 -

Info

Publication number: JP2011047881A5
Application number: JP2009198342A
Authority: JP
Filing date: 2009-08-28
Publication date: 2012-04-19
Anticipated expiration: 2029-08-28

Description

次いで、予測処理が実行される（ステップＳＴ１０２）。すなわち、追跡処理部２は、前回観測時のステップＳＴ１０３において算出された目標の平滑値とその共分散行列に基づいて、目標の予測値およびその共分散行列を算出する。 Next, a prediction process is executed (step ST102). That is, the tracking processing unit 2 calculates a target predicted value and its covariance matrix based on the target smooth value and its covariance matrix calculated in step ST103 at the previous observation.

次いで、平滑処理が実行される（ステップＳＴ１０３）。すなわち、追跡処理部２は、パッシブセンサ１からの目標の測角データと、ステップＳＴ１０２において算出された目標の予測値とその共分散行列に基づいて、目標の平滑値とその共分散行列を算出し、平滑値および予測値とその共分散行列を制御部３に送る。 Next, smoothing processing is executed (step ST103). That is, the tracking processing unit 2 calculates the target smooth value and the covariance matrix based on the target angle measurement data from the passive sensor 1, the target predicted value calculated in step ST102, and the covariance matrix thereof. Then, the smooth value and the predicted value and its covariance matrix are sent to the control unit 3.

次に、追跡処理部２の処理内容について、詳細に説明する。目標の運動モデルを以下のように定義する。

Next, processing contents of the tracking processing unit 2 will be described in detail. The target motion model is defined as follows.

上述したステップＳＴ１０２においては、前回観測時の平滑処理の結果を用いて、以下の式で表される予測処理が実施される。なお、以下において、ハットｘは「ｘ（＾）」と表記する。

In step ST102 described above, a prediction process represented by the following equation is performed using the result of the smoothing process at the previous observation. In the following, the hat x is expressed as “x (^)”.

また、第３の発明は、目標の角度を測定して測角データとして出力するパッシブセンサからの測角データを入力して前記目標の航跡を算出する追跡処理部と、前記追跡処理部に補正情報を出力する補正情報算出部とを備えた目標追跡装置であって、前記補正情報算出部は、外部から入力される前記目標の状態ベクトルに基づいて、前記補正情報として、前記パッシブセンサの位置における前記目標のバイアス誤差を算出して制御入力として出力し、前記追跡処理部は、前記補正情報算出部から出力される制御入力に基づいて、前記目標の予測値を算出することを特徴とする。 According to a third aspect of the present invention, there is provided a tracking processing unit that calculates the wake of the target by inputting angle measurement data from a passive sensor that measures the target angle and outputs it as angle measurement data, and corrects the tracking processing unit. A target tracking device including a correction information calculation unit that outputs information, wherein the correction information calculation unit uses, as the correction information, the position of the passive sensor based on the target state vector input from the outside. The target bias error is calculated and output as a control input, and the tracking processing unit calculates a predicted value of the target based on the control input output from the correction information calculation unit. .

また、第４の発明は、目標の角度を測定して測角データとして出力するパッシブセンサからの測角データを入力して前記目標の航跡を算出する追跡処理部と、前記追跡処理部に補正情報を出力する補正情報算出部とを備えた目標追跡装置であって、前記補正情報算出部は、外部から入力される前記目標の状態ベクトル、または、外部から入力されるプロセス雑音共分散行列を含む前記目標の状態ベクトルに基づいて、前記補正情報として、前記パッシブセンサの位置における前記目標のバイアス誤差を算出して制御入力として出力し、かつ、前記パッシブセンサから前記目標までの距離、または、前記パッシブセンサの位置における前記目標のプロセス雑音共分散行列を算出し、前記追跡処理部は、前記補正情報算出部から出力される制御入力に基づいて前記目標の予測値を算出し、かつ、前記補正情報算出部で算出された前記目標までの距離に基づいて算出したプロセス雑音共分散行列、または、前記補正情報算出部で算出されたプロセス雑音共分散行列に基づいて、前記目標の航跡を算出することを特徴とする。 According to a fourth aspect of the present invention, there is provided a tracking processing unit for calculating a wake of the target by inputting angle measurement data from a passive sensor that measures the target angle and outputs it as angle measurement data, and corrects the tracking processing unit. A target tracking device including a correction information calculation unit that outputs information, wherein the correction information calculation unit calculates the target state vector input from the outside or a process noise covariance matrix input from the outside. Based on the target state vector including, as the correction information, the bias error of the target at the position of the passive sensor is calculated and output as a control input, and the distance from the passive sensor to the target, or The target process noise covariance matrix at the position of the passive sensor is calculated, and the tracking processing unit outputs a control input output from the correction information calculation unit. Calculating a predicted value of the target on the basis of, and the correction information calculating unit calculates, based on the distance to the calculated target in the process noise covariance matrix, or calculated by the correction information calculating unit The wake of the target is calculated based on a process noise covariance matrix.

次いで、予測処理が実行される（ステップＳＴ１０８）。すなわち、前回観測時のステップＳＴ１０３において算出された目標の平滑値およびその共分散行列とステップＳＴ１０７において算出されたプロセス雑音共分散行列Ｑ_ｋ−１とに基づいて、目標の予測値およびその共分散行列を算出する。 Next, a prediction process is executed (step ST108). That is, based on the target smooth value and its covariance matrix calculated in step ST103 at the previous observation and the process noise covariance matrix Q _k−1 calculated in step ST107, the target predicted value and its covariance are calculated. Calculate the matrix.

次いで、平滑処理が実行される（ステップＳＴ１０３）。すなわち、追跡処理部２ａは、パッシブセンサ１からの目標の測角データとステップＳＴ１０８において算出された目標の予測値およびその共分散行列とに基づいて、目標の平滑値およびその共分散行列を算出し、平滑値および予測値とその共分散行列を制御部３に送る。 Next, smoothing processing is executed (step ST103). That is, the tracking processing unit 2a calculates the target smooth value and the covariance matrix based on the target angle measurement data from the passive sensor 1, the target predicted value calculated in step ST108, and the covariance matrix thereof. Then, the smooth value and the predicted value and its covariance matrix are sent to the control unit 3.

次に、補正情報算出部５ａと追跡処理部２ａの処理内容について、詳細に説明する。目標の運動モデルを以下のように定義する。

Next, processing contents of the correction information calculation unit 5a and the tracking processing unit 2a will be described in detail. The target motion model is defined as follows.

ここで、ｘ_ｋ−１、ｙ_ｋ−１、ｚ_ｋ−１は、観測時刻ｔ_ｋ−１の目標の状態ベクトルの位置のｘ、ｙ、ｚ成分、ｘ_０、ｙ_０、ｚ_０は、パッシブセンサ１の位置のｘ、ｙ、ｚ成分である。 Here, x _k−1 , y _k−1 , z _k−1 are x, y, z components , x ₀ , y ₀ , z ₀ of the position of the target state vector at the observation time t _k−1 are These are the x, y, and z components of the position of the passive sensor 1.

上述したステップＳＴ１０７では、補正情報算出部５ａからの補正情報（目標距離ｒ（＾）_ｋ−１）に基づいて、プロセス雑音共分散行列Ｑ_ｋ−１を算出する。

In step ST107 described above, the process noise covariance matrix Q _k-1 is calculated based on the correction information (target distance r (^) _k-1 ) from the correction information calculation unit 5a.

次いで、予測処理が実行される（ステップＳＴ１０８）。すなわち、追跡処理部２ｂは、前回観測時のステップＳＴ１０３において算出された目標の平滑値およびその共分散行列と補正情報算出部５ｂからの補正情報（プロセス雑音共分散行列）とに基づいて、目標の予測値およびその共分散行列を算出する。 Next, a prediction process is executed (step ST108). That is, the tracking processing unit 2b uses the target smooth value calculated in step ST103 at the previous observation, the covariance matrix thereof, and the correction information (process noise covariance matrix) from the correction information calculation unit 5b. The predicted value and its covariance matrix are calculated.

次いで、平滑処理が実行される（ステップＳＴ１０３）。すなわち、追跡処理部２ｂは、パッシブセンサ１からの目標の測角データとステップＳＴ１０８において算出された目標の予測値およびその共分散行列とに基づいて、目標の平滑値およびその共分散行列を算出し、平滑値および予測値とその共分散行列を制御部３に送る。 Next, smoothing processing is executed (step ST103). That is, the tracking processing unit 2b calculates a target smooth value and its covariance matrix based on the target angle measurement data from the passive sensor 1, the target predicted value calculated in step ST108, and its covariance matrix. Then, the smooth value and the predicted value and its covariance matrix are sent to the control unit 3.

ここで、外部装置から入力されるプロセス雑音共分散行列Ｑ^ａ _ｋ−１が以下の（３５）式で表されるとすると、変換行列Ｔ_ｋ−１は、（３６）式のようになる。なお、（ｘ_ｋ−１、ｙ_ｋ−１、ｚ_ｋ−１）、（ａ_ｋ−１、ｅ_ｋ−１）は、座標変換によって、外部装置から入力される目標の状態ベクトルをパッシブセンサ１から見た目標の位置ベクトル（直交座標系と極座標系）に変換した値である。

Here, if the process noise covariance matrix Q ^a _k−1 input from the external device is expressed by the following equation (35), the transformation matrix T _k−1 is expressed by equation (36). Note that (x _k−1 , y _k−1 , z _k−1 ) and (a _k−1 , e _k−1 ) are _obtained by converting the target state vector input from the external device into the passive sensor 1 by coordinate conversion. This is a value converted into a target position vector (orthogonal coordinate system and polar coordinate system) as seen from FIG.

次いで、予測処理が実行される（ステップＳＴ１１１）。すなわち、追跡処理部２ｃは、前回観測時のステップＳＴ１０３において算出された目標の平滑値およびその共分散行列と補正情報算出部５ｃからの補正情報（制御入力ベクトル）とに基づいて、目標の予測値およびその共分散行列を算出する。 Next, a prediction process is executed (step ST111). That is, the tracking processing unit 2c predicts the target based on the target smooth value calculated in step ST103 at the time of the previous observation, its covariance matrix, and the correction information (control input vector) from the correction information calculation unit 5c. Calculate the value and its covariance matrix.

次いで、平滑処理が実行される（ステップＳＴ１０３）。すなわち、追跡処理部２ｃは、パッシブセンサ１からの目標の測角データとＳＴ１１１において算出された目標の予測値およびその共分散行列とに基づいて、目標の平滑値およびその共分散行列を算出し、平滑値および予測値とその共分散行列を制御部３に送る。 Next, smoothing processing is executed (step ST103). That is, the tracking processing unit 2c calculates a target smooth value and its covariance matrix based on the target angle measurement data from the passive sensor 1, the target predicted value calculated in ST111, and its covariance matrix. The smooth value, the predicted value, and the covariance matrix thereof are sent to the control unit 3.

次に、補正情報算出部５ｃと追跡処理部２ｃの処理内容について、詳細に説明する。目標の運動モデルを以下のように定義する。

Next, processing contents of the correction information calculation unit 5c and the tracking processing unit 2c will be described in detail. The target motion model is defined as follows.

ここで、（ｘ_ｋ−１、ｙ_ｋ−１、ｚ_ｋ−１）、（ｘ（・）_ｋ−１、ｙ（・）_ｋ−１、ｚ
（・）_ｋ−１）、（ａ（・）_ｋ−１、ｅ（・）_ｋ−１）は、座標変換によって、外部装置
から入力される目標の状態ベクトルをパッシブセンサ１から見た目標の位置ベクトル、速度ベクトルおよび角速度ベクトルに変換した値である。 Here, (x _k−1 , y _k−1 , z _k−1 ), (x (•) _k−1 , y (•) _k−1 , z
(•) _k−1 ), (a (•) _k−1 , e (•) _k−1 ) are target positions when the target state vector input from the external device is viewed from the passive sensor 1 by coordinate transformation. A value converted into a vector , a velocity vector, and an angular velocity vector .

上述したステップＳＴ１１１では、前回観測時の平滑処理の結果と制御入力ベクトルｕ_ｋ−１を用いて、以下の式で表される予測処理を実施する。

In step ST111 mentioned above, the prediction process represented by the following formula | equation is implemented using the result of the smoothing process at the time of last observation, and control input vector uk _-1 .

次いで、予測処理が実行される（ステップＳＴ１１２）。すなわち、追跡処理部２ｄは、前回観測時のステップＳＴ１０３において算出された目標の平滑値およびその共分散行列と補正情報算出部５ｄからの補正情報（プロセス雑音共分散行列と制御入力ベクトル）とに基づいて、目標の予測値およびその共分散行列を算出する。 Next, prediction processing is executed (step ST112). That is, the tracking processing unit 2d uses the target smooth value calculated in step ST103 at the time of the previous observation, its covariance matrix, and the correction information (process noise covariance matrix and control input vector) from the correction information calculation unit 5d. Based on this, a target predicted value and its covariance matrix are calculated.

次いで、平滑処理が実行される（ステップＳＴ１０３）。すなわち、追跡処理部２ｄは、パッシブセンサ１からの目標の測角データとＳＴ１１２において算出された目標の予測値およびその共分散行列とに基づいて、目標の平滑値およびその共分散行列を算出し、平滑値および予測値とその共分散行列を制御部３に送る。

Next, smoothing processing is executed (step ST103). That is, the tracking processing unit 2d calculates a target smooth value and its covariance matrix based on the target angle measurement data from the passive sensor 1, the target predicted value calculated in ST112, and its covariance matrix. The smooth value, the predicted value, and the covariance matrix thereof are sent to the control unit 3.

Claims

A tracking processing unit that calculates angle data from a passive sensor that measures a target angle and outputs it as angle measurement data, and a correction information calculation unit that outputs correction information to the tracking processing unit A target tracking device comprising:
The correction information calculation unit calculates a distance from the passive sensor to the target as the correction information based on the target state vector input from the outside,
The tracking processing unit calculates a process noise covariance matrix based on the distance to the target calculated by the correction information calculation unit, and calculates a track of the target based on the calculated process noise covariance matrix A target tracking device characterized by:

A tracking processing unit that calculates angle data from a passive sensor that measures a target angle and outputs it as angle measurement data, and a correction information calculation unit that outputs correction information to the tracking processing unit A target tracking device comprising:
The correction information calculation unit calculates the target process noise covariance matrix at the position of the passive sensor as the correction information based on the target state vector including the process noise covariance matrix input from the outside. ,
The target tracking device, wherein the tracking processing unit calculates a track of the target based on a process noise covariance matrix calculated by the correction information calculating unit.

A tracking processing unit that calculates angle data from a passive sensor that measures a target angle and outputs it as angle measurement data, and a correction information calculation unit that outputs correction information to the tracking processing unit A target tracking device comprising:
The correction information calculation unit calculates the bias error of the target at the position of the passive sensor as the correction information based on the target state vector input from the outside, and outputs it as a control input,
The target tracking device, wherein the tracking processing unit calculates a predicted value of the target based on a control input output from the correction information calculating unit.

A tracking processing unit that calculates angle data from a passive sensor that measures a target angle and outputs it as angle measurement data, and a correction information calculation unit that outputs correction information to the tracking processing unit A target tracking device comprising:
The correction information calculation unit, as the correction information, based on the target state vector input from the outside or the target state vector including a process noise covariance matrix input from the outside, Calculating a bias error of the target at a position and outputting it as a control input, and calculating a distance from the passive sensor to the target, or a process noise covariance matrix of the target at the position of the passive sensor;
The tracking processing unit calculates a predicted value of the target based on a control input output from the correction information calculation unit, and calculates based on a distance to the target calculated by the correction information calculation unit A target tracking device that calculates a track of the target based on a process noise covariance matrix or a process noise covariance matrix calculated by the correction information calculation unit.

5. The target according to claim 1, wherein the tracking processing unit calculates a filter gain used for calculating a track of the target based on the calculated process noise covariance matrix. Tracking device.