JP3581392B2 - Integral sensing device - Google Patents

Description

【００８７】
ここで、Ｘ（ｋ）は状態ベクトルであり、所要物理量を含む時刻ｋにおける、推定量である。また、Ａは状態遷移行列、Ｂは駆動行列、Ｕ（ｋ）はブラント雑音、Ｖ（ｋ）はシステム雑音である。
【００８８】
また、各センサからの観測量と、上記状態量との関係を示す観測方程式は、次式のようになる。
【００８９】
【数２】

【００９０】
ここで、Ｙ（ｋ）は観測ベクトル、ｆは状態量との関係を示す関数式、Ｗ（ｋ）は観測雑音である。
【００９１】
上記モデルにたいして、カルマンフィルタ処理を適用すると、最適推定値Ｘ（ｋ｜ｋ）は、次の漸化式により求められる。なお、観測方程式が非線形の場合は、拡張カルマンフィルタのアルゴリズムを用いる。
【００９２】
【数３】

【００９３】
ただし、添字（ｋ｜ｋ）は、時刻ｋでの観測値に基づいたカルマンフィルタによる最適値で、（ｋ｜ｋ−１）は時刻ｋ−１での値から時刻ｋでの値を状態方程式により予測したものである。また、Ｖ（ｋ）、Ｗ（ｋ）は、それぞれ、システム雑音、観測雑音の共分散行列である。
【００９４】
また、Ｐは推定誤差の共分散行列であり、カルマンフィルタによる推定精度の目安になる。Ｋはカルマンフィルタゲインであり、観測量が得られたときに、計算され、システム雑音と観測雑音との統計量を比較し、状態方程式と観測値とのどちらに重みを掛けるかを決定する量である。
【００９５】
数３の上から２番目の式は、最適推定値を計算する式である。時刻ｋ−１における最適推定値を用いて、状態方程式に従い、時刻ｋでの値を予測する。この予測値と、時刻ｋにおける観測値のカルマンフィルタゲインＫによる内分点が、最適推定値となる。なお、行列Ｃ（ｋ）は、観測方程式の線形化に用いるｆ（Ｘ（ｋ｜ｋ−１））のヤコビアンである。
【００９６】
以上のようなカルマンフィルタ処理において、バイアス誤差測定手段４５から得られたノイズ成分の大きさをもとに、Ｖ（ｋ）、Ｗ（ｋ）もしくは、Ｐのそれに対応する成分を適宜変更することにより、その条件で最適な重み付けを行なうカルマンフィルタゲインＫを算出することができる。
【００９７】
本発明を適用した車両ナビゲーション用の積分型センシング装置の第３の実施例を説明する。
【００９８】
本実施例の積分型センシング装置は、図４に示すように、走行距離の微分量である速度を検出して、積分する第１の手段１と、走行距離を直接または間接的に取得する第２の手段２と、第１および第２の手段から取得される走行距離の値の差を計算する減算器３と、減算器３からの出力の時間変化率を算出し、推定誤差を算出するスケール誤差推定手段５２とを有する。
【００９９】
第１の手段１は、走行距離の微分量である速度を計測する車速センサ５０と、計測された速度について、以下に説明するスケール誤差補正を行なうスケール誤差補正手段５１と、補正された速度を時間積分することにより車両の走行距離を算出する時間積分手段８とを有する。
【０１００】
第２の手段２は、センサとして、すでに決定された車両位置から走行距離を算出する地図センサ６０７と、ＧＰＳ衛星信号を基に地球上の絶対位置及び走行距離を検出するＧＰＳ装置６０６との２つを有する。ここで、これらセンサ６０７、６０６は、このセンシング装置専用である必要はない。センサの出力信号は、このセンシング装置で決定する物理量、つまり、走行距離以外の決定にも使用することができる。
【０１０１】
第２の手段は、さらに、上記第１の実施例と同様に、これらセンサの組み合わせのうち、外部から入力されるセレクタ制御信号２７の指令に従い、状況に応じて最も精度の高いセンサを選択して方位を出力するセレクタ装置２３を有する。
【０１０２】
次に、本実施例の作用を説明する。
【０１０３】
第１の手段１では、例えば、車輪の回転数を定期的にカウントする車速センサ５０が車速を計測し、時間積分手段８が時間積分することにより走行距離を算出する。一方、第２の手段２では、地図センサ６０７とＧＰＳ装置６０６のうち、セレクタ装置２３により選択された、精度の高いセンサからのデータを用いて、走行距離を出力する。
【０１０４】
この第２の手段２の距離出力を基準にして、第１の手段１による距離出力の、走行距離の増加に伴う変化を、距離変化率算出手段５３が算出する。スケール誤差推定手段５２は、この時の距離変化率αを、後述する回帰分析等の統計処理により算出し、第１の手段１のスケールファクタ誤差として推定する。さらに、次式により、スケール誤差補正手段５１は、車速算出のためのスケール誤差を補正する。
【０１０５】
【数４】

【０１０６】
従って、最終的に出力される走行距離５４は、スケール誤差を補正した分だけ精度を向上させることができる。
【０１０７】
本発明を適用した積分型センシング装置の第４の実施例を、図５を用いて説明する。
【０１０８】
本実施例の積分型センシング装置は、車両の走行速度を検出するもので、図５に示すように、走行速度の微分量である加速度を検出、積分する第１の手段１と、走行速度を直接または間接的に取得する第２の手段２と、第１および第２の手段から取得される走行速度の値の差を計算する減算器３と、減算器３からの出力の時間変化率を算出して、推定誤差を算出するバイアス誤差推定手段４２とを有する。
【０１０９】
第１の手段１は、走行速度の微分量である加速度を計測する加速度センサ６０と、計測された加速度の誤差補正を行なうバイアス誤差補正手段４１と、補正された加速度を時間積分することにより、車両の走行速度を算出する時間積分手段８とを有する。
【０１１０】
第２の手段２は、センサとして、車輪の回転数をカウントして車速を算出する車速センサ５０と、ＧＰＳ衛星信号を基に地球上の絶対位置及び走行速度を検出するＧＰＳ装置６０６との２つを有する。ここで、これらセンサ５０、６０６は、このセンシング装置専用である必要はない。センサの出力信号は、このセンシング装置で決定する物理量、つまり、走行速度以外の決定にも使用することができる。
【０１１１】
第２の手段は、さらに、これらセンサの組み合わせのうち、外部から入力されるセレクタ制御信号２７に従い、状況に応じて最も精度の高いセンサを選択して方位を出力するセレクタ装置２３を有する。
【０１１２】
次に、本実施例の作用を説明する。
【０１１３】
本実施例において、第１の手段１では、加速度センサ６０が、車両の加速度を計測する。第２の手段２は、車速センサ５０と、衛星信号を基に地球上の移動体の速度を検出するＧＰＳ装置６０６の２つを組み合わせ、セレクタ装置２３により、その状況で最も精度の高いセンサを選択して、そのセンサからの出力を、第２の手段２の車速出力として出力する。
【０１１４】
時間変化率算出手段４３は、この第２の手段２の車速出力を基準にして、加速度センサ６０の出力による車速の時間変化を算出する。バイアス誤差推定手段４２は、その時の時間変化率を、前記した回帰分析等の統計処理により算出し、それを加速度センサ６０のバイアス誤差として出力する。最後に、バイアス誤差補正手段４１は、計測された加速度のバイアス誤差を補正する。
【０１１５】
従って、最終的に出力される走行速度６１は、バイアス誤差を補正した分だけ精度を向上させることができる。
【０１１６】
本実施例では、車両の加速度のバイアス誤差を推定して補正したが、例えば、上記第３の実施例（図４参照）における車両走行速度に対する推定および補正のように、検出された加速度におけるスケール誤差を推定して補正する構成としても、本実施例と同様に、精度を向上させることができる。
【０１１７】
本発明を適用した積分型センシング装置の第５の実施例を、図１３を用いて説明する。本実施例は、上記第３の実施例（図４参照）と上記第４の実施例（図５参照）とを組み合わせたもので、上記第４の実施例の出力である走行速度６１を、上記第３の実施例の車速計測値として入力させるように構成したものである。
【０１１８】
すなわち、本実施例は、図１３に示すように、車両の加速度を検出して走行速度を出力する第１の手段１と、同じく走行速度を検出する第２の手段２と、第１の手段１および第２の手段２により検出された値の差からバイアス誤差を推定するバイアス誤差推定手段４２とを有する。
【０１１９】
さらに、本実施例は、第１の手段から出力される走行速度６１を受け入れて、時間積分して走行距離を検出する第１の手段１’と、同じく走行距離を検出する第２の手段２’と、第１の手段１’および第２の手段２’により検出された値の差からスケール誤差を推定するスケール誤差推定手段５２とを有する。
【０１２０】
本実施例の作用は、上記第３及び第４の実施例と同じであるため、ここでの説明は省略する。
【０１２１】
本実施例によれば、第１の手段１の加速度センサ６０を用いて検出される加速度量を２回積分して、走行距離５４を算出する際、各積分段階で発生する誤差を、各々、第２の手段２および第２の手段２’のセンサデータにより補正することができるので、結果として、最終的に得られる走行距離５４の精度を向上させることができる。
【０１２２】
本実施例では、各積分段階でそれぞれ、第２の手段との比較により誤差補正が行われてきたが、この誤差補正は、どちらか一方の段階だけで行うような構成としても良い。また、この時、行う誤差補正としては、スケール誤差およびバイアス誤差のうちいずれか一方を行う構成とする。また、最初の積分段階（第１の手段１の出力）でスケール誤差補正を行い、次の積分段階（第１の手段１’）でバイアス誤差の補正をする構成としても良い。
【０１２３】
以上、実施例を数例説明したが、本発明は、車両のナビゲーションに係る積分型センシング装置に共通して適用できるもので、上記例に示す構成には限定されない。
【０１２４】
したがって、本発明によれば、車両のナビゲーションに係わる所要とする物理量の微分量を計測して、その後、その物理量を積分して所要量を算出する方式において、計測された微分量に含まれる誤差、例えば、バイアス誤差等を推定して、補正することが可能となる。したがって、時間積分によって、本来累積するはずであった誤差を小さくすることができる。
【０１２５】
すなわち、センサによる計測信号そのものの誤差を小さくできるため、例えば、従来のナビゲーション装置における、地図マッチング方式の誤差補正のような最終段の補正とは異なり、補正した後に、同様な誤差がまたすぐに発生するようなことはない。従って、本発明は、精度の高いセンサを用いていることと等価になり、システム的な信頼性も向上させることができる。
【０１２６】
【発明の効果】
本発明の積分型センシング装置によれば、センサによる計測信号そのものの誤差を減少させることができるとともに、誤差補正した後に、また、同様な誤差がすぐに発生するようなことがない。したがって、本発明は、精度の高いセンサを用いていることと等価になり、システム的な信頼性も向上させることができる。
【０１２７】
【図面の簡単な説明】
【図１】本発明を適用した、走行方位を検出する積分型センシング装置の一実施例の構成を示すブロック線図。
【図２】車両用ナビゲーション装置における従来の積分型センシング装置の一例を説明するためのブロック線図。
【図３】本発明を適用した、走行方位を検出する積分型センシング装置の他の一実施例の構成を示すブロック線図。
【図４】本発明を適用した、走行距離を検出する積分型センシング装置の他の一実施例の構成を示すブロック線図。
【図５】本発明を適用した、走行速度を検出する積分型センシング装置の他の一実施例の構成を示すブロック線図。
【図６】図１の実施例の積分型センシング装置を備えた車両用ナビゲーション装置の全体作用を示すフローチャート。
【図７】図６中の走行ベクトル積分割り込み処理のフローチャート。
【図８】図６中の方位演算割り込み処理のフローチャート。
【図９】図６中のバイアス誤差演算割り込み処理（短期）のフローチャート。
【図１０】図６中のバイアス誤差演算割り込み処理（長期）のフローチャート。
【図１１】図１の実施例の積分型センシング装置を備えたナビゲーション装置のハードウエア構成の一例を示すブロック図。
【図１２】本発明におけるバイアス誤差の求め方の一例を示す説明図。
【図１３】本発明を適用した、走行距離を検出する積分型センシング装置の他の一実施例の構成を示すブロック線図。
【符号の説明】
１…第１の手段、２…第２の手段、３…加算器（減算器）、８…時間積分手段、１１…フィルタゲイン算出手段、１２…所要物理量推定手段、１３…カルマンフィルタ、２１…地図センサ、２２…地磁気センサ、２３…セレクタ装置、２４…フィルタ、２７…セレクタ制御信号、４１…バイアス誤差補正手段、４２…バイアス誤差推定手段、４３…時間変化率算出手段、４４…走行方位、４５…バイアス誤差測定手段、５０…車速センサ、５１…スケール誤差補正手段、５２…スケール誤差推定手段、５３…距離変化率算出手段、５４…走行距離、６０…加速度センサ、６１…走行速度、６０１…距離センサ、６０２…地磁気センサ、６０３…ジャイロセンサ、６０４…コントローラ、６０５…表示装置、６０６…ＧＰＳ装置、６０７…地図センサ、６０８…入力装置、６１０…地図データ記憶装置、６１１…地図演算手段。[0001]
[Industrial applications]
The present invention relates to a vehicle-mounted integral sensing device that calculates a physical quantity by measuring a differential value of a physical quantity related to navigation of a vehicle and integrating the measured value.
[0002]
[Prior art]
2. Description of the Related Art When measuring a physical quantity related to navigation of a vehicle, an integral sensing device that calculates a physical quantity by measuring a differential value of the physical quantity and integrating the measured value is used for in-vehicle navigation.
[0003]
Hereinafter, a description will be given of a conventional technique of an integral sensing device provided in a vehicle-mounted navigation device.
[0004]
2. Description of the Related Art Conventionally, a method for detecting the position of a vehicle traveling at an arbitrary point in a road traffic network includes a distance sensor, a direction sensor, and a processing device that performs necessary processing on output signals from both of these sensors. In addition, dead reckoning navigation that obtains the current position data of the vehicle while accumulating the distance change amount and the azimuth change amount caused by the running of the vehicle is used. However, there is a problem in that the errors that the distance sensor and the azimuth sensor necessarily have are accumulated with the traveling distance, and the errors included in the obtained current position data are also accumulated.
[0005]
In particular, in the azimuth sensor, conventionally, by using a combination of a geomagnetic sensor capable of detecting the absolute azimuth of the vehicle and a gyro sensor capable of detecting a relative azimuth change of the vehicle, the traveling azimuth of the vehicle is determined. Detected. However, each has an error characteristic peculiar to the sensor.
[0006]
For example, a geomagnetic sensor generates short-term noise in a place with a bad magnetic field environment due to the influence of a high-voltage line, a building, or the like, but outputs a correct direction in the long term. On the other hand, the gyro sensor is not affected by the magnetic field environment at all, and outputs the correct relative azimuth change in the short term, but the azimuth error increases with time due to the drift (fluctuation of the angular velocity bias) in the long term. Cumulative.
[0007]
As a method for solving such a problem, there is a method of filtering a sensor signal disclosed in Japanese Patent Application Laid-Open No. 61-258112.
[0008]
This method uses the integral sensing device shown in FIG. In this example, the differential odometer 20 is a sensor that detects a vehicle rotation angle from a rotation difference between left and right wheels, and has an error characteristic similar to that of a gyro sensor. A geomagnetic sensor 22 such as a compass is a representative absolute direction sensor, and a map sensor (map) 21 is a digital road database mounted on a vehicle. The direction can be output as the vehicle direction.
[0009]
The difference between the azimuth from the geomagnetic sensor 22 or the map sensor 21 and the azimuth from the differential odometer 20 is calculated by a subtractor 25. A low-pass filter 24 is applied to the value to remove high-frequency noise included in the geomagnetic sensor 22 and the like (compass). The signal is subtracted from the output of the differential odometer 20 by a subtractor 26 to remove a bias error included in the differential odometer 20 and the like (gyro sensor). Therefore, the azimuth signal finally output is a signal obtained by correcting the error component of each sensor.
[0010]
In addition, there is an example of Japanese Patent Application Laid-Open No. 3-188316 in which a Kalman filter is applied by a similar filtering method.
[0011]
Another solution is to compare current position data obtained based on the dead reckoning with road traffic network data stored in a memory in advance, assuming that the vehicle travels on a road. There is also. From this comparison data, the amount of deviation of the current position data from the road was calculated as a cumulative error, the current position data was corrected for the cumulative error, and the current position data was matched with the road data, for example. There is a map matching method as disclosed in JP-A-63-148115.
[0012]
[Problems to be solved by the invention]
However, in the case of an integrating sensing device that calculates a physical quantity by measuring a differential value of a physical quantity related to vehicle navigation and integrating the same, as in the above-described conventional example, an error included in the differential value is an integral value. Accumulated by operation. Therefore, even if the error is minute, it may cause a large error.
[0013]
In addition, in the filtering-type integral sensing device included in the in-vehicle navigation device, for example, when traveling on a continuous structure such as a highway, a steady error occurs in the output of the geomagnetic sensor. Therefore, it is difficult to accurately correct the bias of the gyro sensor based on the information. Further, in the map matching method, only the final display shown to the driver is corrected, and it does not become a fundamental solution to the occurrence of a sensor error.
[0014]
For example, when a rate sensor such as a gyro that measures an angular velocity is used as the direction sensor, if the measured angular velocity includes a bias error, the angular error increases with time. This means that even if the final angle display is corrected, a similar error will occur thereafter.
[0015]
Further, for example, when a vehicle speed sensor that periodically measures the number of rotations of the wheel is used as the distance sensor, a scale factor for calculating the vehicle speed from the number of rotations of the wheel changes due to a change in tire air pressure or the like, and the distance error indicates the distance traveled. It increases with the increase. Even in this case, even if the position is corrected by the above-described map matching method, a similar error occurs immediately thereafter.
[0016]
The present invention has been made in view of the above problems, and provides an integrated sensing device that can reduce the integration error of a physical quantity as much as possible in measuring the differential value of the physical quantity related to vehicle navigation. It is aimed at.
[0017]
[Means for Solving the Problems]
The above object is achieved by a first means and a second means for independently obtaining one physical quantity related to navigation of a vehicle, and a first and a second means in an integral sensing device mounted on a vehicle. Error estimating means for estimating an error generated by the first means from the obtained difference between the respective values of the physical quantity, wherein the first means detects a differential quantity which is a time change rate of the physical quantity. A first sensor unit including a sensor, an error correction unit for correcting the detected differential amount based on the error estimated by the error estimating unit, and an integrating unit for integrating the corrected differential amount over time to obtain the physical quantity The error estimating means includes a subtraction unit that calculates a difference between the respective values of the physical quantities obtained by the first and second means, and a change rate calculation that obtains a change rate of the calculated difference by calculation. Means, That the out rate of change with the estimated error calculating means for obtaining an estimated error of the detected differential quantity in the first means is achieved by integral sensing apparatus according to claim.
[0018]
The above object is also achieved by a first means and a second means for independently obtaining one physical quantity relating to the navigation of the vehicle in the integral sensing device mounted on the vehicle, and the first and second means. Error measuring means for obtaining an error component of the physical quantity obtained by the means, filter gain calculating means for calculating a Kalman filter gain from the obtained error component, and outputs of the first and second means. Required physical quantity estimating means for estimating the physical quantity by performing a weighting process based on the Kalman filter gain, wherein the first means includes a sensor for detecting a differential quantity which is a time change rate of the physical quantity. , And integrating means for integrating the detected differential amount with time to obtain the physical quantity. The error measuring means includes first and second A subtractor for calculating a difference between the respective values of the physical quantities obtained by the means, a change rate calculating means for obtaining a change rate of the calculated difference by calculation, and first and second values based on the obtained change rate. And an error obtaining means for obtaining an error component in the means.
[0019]
The above object is also achieved by a first means for obtaining one physical quantity related to navigation of a vehicle in an integral sensing device mounted on a vehicle, and at least one of the physical quantity and a differential quantity which is a time change rate thereof. A second means for obtaining one of them, and an error estimating means for estimating an error generated by the first means based on the values obtained by the first and second means, wherein the first means comprises: A first sensor unit including a sensor for detecting a second derivative, which is a temporal change rate, of a differential amount, which is a temporal change rate of the physical quantity, and a second differential amount output from the first sensor unit. First integrating means for obtaining the differential amount by integrating, second integrating means for integrating the differential amount output from the first integrating means and outputting the physical quantity, and a first sensor unit And between the first integrating means and the first An error correction unit that is provided in at least one of the dividing unit and the second integrating unit, corrects an amount input from the preceding stage based on the error estimated by the error estimating unit, and outputs the corrected amount to the subsequent stage. And an integrated sensing device.
[0020]
[Action]
With the integral sensing device according to the present invention, one physical quantity related to vehicle navigation is measured by independent first and second means, respectively, and is generated by the first means based on those values. The error is estimated by the error estimating means.
[0021]
In the first means, a sensor included in the first sensor unit detects a differential amount, which is a time change rate of the physical quantity, and the error correction means determines the differential quantity based on the error estimated by the error estimating means. After the correction, the integrating means integrates the corrected differential amount over time to obtain the physical amount. Further, in the error correction means, the subtraction unit calculates the difference between the respective measured values of the physical quantity obtained by the first and second means, and the change rate calculation means calculates the change rate of the calculated difference. Then, the estimation error calculating means obtains an estimation error of the differential amount detected by the first means from the change rate.
[0022]
Further, in the integral sensing device of the present invention, the first and second means each independently obtain one physical quantity related to the navigation of the vehicle, and the error measuring means is provided by the first and second means. An error component of the obtained physical quantity is obtained. The filter gain calculating means calculates a Kalman filter gain from these acquired error components, and the required physical quantity estimating means performs a weighting process on the outputs of the first and second means based on the calculated Kalman filter gain. Then, the physical quantity is estimated. Here, the first means has the same action as described above.
[0023]
According to the present invention, for example, when a change rate of a difference between physical quantities related to navigation of a vehicle is set as a time change rate, a bias error of a measured differential amount can be estimated based on the time change rate. That is, when the change of the physical quantity difference with respect to time is represented by a regression line, a constant slope of the regression line can be regarded as a constant bias error of the measured differential amount in the first means.
[0024]
Specifically, the physical quantity is stored in a road azimuth of the vehicle, the first means is stored as a driving azimuth detecting means using a gyro sensor fixed to the vehicle body, and the second means is stored in a road map memory mounted on the vehicle. Azimuth detecting means for detecting a road azimuth angle corresponding thereto as a traveling azimuth angle, azimuth detecting means by a GPS device for detecting an absolute azimuth on the earth based on a GPS satellite signal, and a geomagnetic sensor for detecting an earth magnetic field. When any one of the azimuth detecting means is used, according to the present invention, it is possible to calculate a bias error in the angular velocity of the running azimuth, and to obtain a highly accurate running azimuth by correcting the error.
[0025]
Further, for example, the physical quantity is a traveling distance of the vehicle, the first means is a traveling distance detecting means for periodically measuring the number of rotations of the wheels, and the second means is further stored in a road map memory mounted on the vehicle. According to the present invention, when any one of the mileage means for detecting the stored corresponding road length as the mileage and the mileage detection means based on a GPS satellite signal is used, the first means The scale factor error of the measured differential amount in the first means is calculated from the rate of change of the difference between the physical quantities obtained by the second means and the physical quantity according to the traveling distance, and the first factor is calculated based on the scale factor error. It is possible to correct the measured differential amount in the means.
[0026]
Further, for example, the physical quantity is a traveling speed of the vehicle, the first means is a traveling speed detecting means using an acceleration sensor fixed to the vehicle body, and the second means is a traveling speed for periodically measuring the number of rotations of the wheels. Even in the case of using the detecting means, it is possible to achieve exactly the same improvement in accuracy as described above.
[0027]
【Example】
Hereinafter, an embodiment of an integral type sensing device to which the present invention is applied will be described with reference to the drawings.
[0028]
First Embodiment A navigation device provided with an in-vehicle integral sensing device according to the present invention will be described as a first embodiment. FIG. 11 shows one embodiment of the hardware configuration in this embodiment.
[0029]
As shown in FIG. 11, the navigation device according to the present embodiment includes a photoelectric sensor or an electromagnetic sensor that outputs a pulse signal proportional to the number of rotations of the vehicle wheels. A distance sensor 601 that generates a signal indicating the traveling direction of the vehicle, a geomagnetic sensor 602 that outputs a signal of an absolute direction corresponding to the traveling direction of the vehicle, and a signal that is proportional to the direction of the vehicle or the amount of change in the direction according to the traveling direction of the vehicle. And a rate-type gyro sensor 603.
[0030]
The present embodiment further includes, as a sensor, a GPS device 606 that receives a GPS signal from a GPS satellite, and when the current position is specified on a road, outputs a road direction or a travel distance as a vehicle direction in the same manner as the sensor. A so-called map sensor 607 and a map data storage device 610 for storing map data including digital road data.
[0031]
The map sensor 607 includes a map calculation unit 611 that calculates the direction of the road from the road data from the map data storage device 610 and the already determined vehicle position.
[0032]
The present embodiment further includes a controller 604 that calculates a current position of the vehicle by receiving a signal from the sensor and outputs the vehicle position and map data of an area including the position. And a display device 605 for displaying the vehicle position output on the map data output from the controller 604, and an input device 608 for performing various settings of the controller 604. Here, the controller 604 includes, for example, a CPU, a ROM / RAM, and an input / output unit.
[0033]
The display device 605 updates and displays the current position of the vehicle on the basis of the position data on the two-dimensional coordinates that change every moment obtained by the controller 604. A T display device, a liquid crystal display device, or the like is used.
[0034]
In the present embodiment, an example is shown in which the integral sensing device of the present invention is applied to the detection of the traveling direction. Here, as shown in FIG. 1, the integral type sensing device is composed of a part of a plurality of sensors included in the navigation device of the present embodiment shown in FIG. Be composed.
[0035]
Here, the integral type sensing device of the present invention is not limited to the example shown below. For example, the integral type sensing device described in Japanese Patent Application No. 5-17421 filed by the same inventor as the present application. Can be used for navigation of a vehicle.
[0036]
The integral sensing device according to the present embodiment detects the angular velocity, which is a differential amount of the running azimuth 44, which is a physical quantity to be acquired, and obtains the first means 1 for integration and the running azimuth directly or indirectly. A second means 2, a subtractor 3 for calculating a difference between the values of the traveling azimuth obtained from the first and second means, and a time change rate of an output from the subtractor 3 to calculate an estimation error. And a bias error estimating unit 42 for calculating and feeding back to the first means 1.
[0037]
The first means 1 includes a gyro sensor 603 for measuring an angular velocity which is a differential amount of the traveling azimuth 44, a bias error correcting means 41 for correcting an error of the measured angular velocity, and a time integration of the corrected angular velocity. And a time integrating means 8 for calculating the traveling direction 44 of the vehicle.
[0038]
The second means 2 includes, as sensors, a map sensor 607 for calculating a running direction from a vehicle position which has been determined, a geomagnetic sensor 602, and a GPS device for detecting an absolute position and an absolute direction on the earth based on a GPS signal. 606. Here, these sensors 607, 602 and 606 need not be dedicated to this sensing device. The output signal of the sensor can also be used to determine a physical quantity determined by the sensing device, that is, a value other than the traveling direction 44.
[0039]
The second means further receives a selector control signal 27 input from the outside of the combination of these sensors, selects an output signal from the sensor with the highest accuracy according to the situation, and outputs the traveling direction. It has a selector device (SEL) 23.
[0040]
The selector device 23 is, for example, one of the addresses indicating the memory locations where the output signals of the map sensor 607, the geomagnetic sensor 602, and the GPS device 606 are temporarily stored, according to the selector control signal 27. This is an address selector for selecting. The selector control signal 27 is, for example, a flag signal indicating the sensor that evaluates the reliability of each of the above sensors and is selected as the most reliable sensor as a result.
[0041]
The bias error estimating unit 42 includes a time change rate calculating unit 43 that calculates a time change of the azimuth output from the first unit 1 based on the azimuth output of the second unit 2.
[0042]
Here, in the configuration of the present embodiment described above, each unit other than the sensors 603, 607, 602, and 606 is achieved as a partial function of the controller 604.
[0043]
First, an outline of the overall operation of the navigation device according to the present embodiment will be described.
[0044]
In the present embodiment, the controller 604 counts the number of pulse signals from the distance sensor 601 to detect the traveling distance of the vehicle, and also detects the traveling distance of the vehicle based on the azimuth signal output from the geomagnetic sensor 602 and the gyro sensor 603. Detect orientation. Further, the controller 604 obtains the position on the two-dimensional coordinates for each unit traveling distance of the vehicle by calculation according to the detection results, and outputs the result to the display device 605.
[0045]
In this embodiment, the traveling azimuth of the vehicle is detected by the integral sensing device.
[0046]
That is, in the sensing device, in the first means 1, the gyro sensor 603 measures the angular velocity of the vehicle, and the time integration means 8 integrates the angular velocity with respect to time to calculate the traveling direction of the vehicle. On the other hand, in the second means 2, the map sensor 607, the geomagnetic sensor 602, and the GPS device 606 are combined, and the selector device 23 receives the selector control signal 27 input from the outside, and adjusts the running condition of the vehicle. Accordingly, an output is selected from the sensor with the highest accuracy, and is output to the subtractor 3 as the traveling azimuth.
[0047]
The subtractor 3 calculates a difference in azimuth based on the output of the gyro sensor 603 with reference to the azimuth output of the second means 2. The bias error estimating means 42 calculates the time change in the signal output from the subtractor 3 by the time change rate calculating means 43. Here, this time change rate is calculated by statistical processing such as regression analysis described later. The bias error estimator 42 estimates the bias error of the gyro sensor 603 using the calculation result as an input. Finally, using the estimated error, the bias error correction unit 41 corrects the angular error bias error measured by the gyro sensor 603.
[0048]
As described above, the accuracy of the finally output traveling azimuth 44 can be improved by an amount corresponding to the correction of the bias error.
[0049]
Next, the operation of the navigation device according to the present embodiment will be described in more detail with reference to the flowcharts of FIGS.
[0050]
FIG. 6 shows an overall flow of the navigation device of the present embodiment. When the navigation device is started by an external command, as shown in FIG. 6, an initial process is first performed (step 100), and the current location is set by an input device 608 such as a keyboard (step 102). Then, under the control of the controller 604, the road data is read from the map data storage device 610, and a map of the current location and the surroundings of the vehicle is displayed on the display device 605 (step 104).
[0051]
Next, in step 106, interrupts in and after step 108 are permitted, and the process enters the following main loop. In this main loop, the current vehicle position and the map are updated by integrating and adding the travel amounts for each unit distance travel, the travel vector integration interrupt processing (step 108), the azimuth calculation interrupt processing (step 110), and the bias error. If the current position has been moved by the calculation interruption process (step 112) (Yes in step 114), the vehicle current position and the surrounding map are updated and displayed (step 116).
[0052]
FIG. 7 is a flowchart showing the processing means of the running vector integration interrupt processing in step 108 described above. Here, this process is executed every time the vehicle travels a predetermined distance ΔD and a vehicle speed pulse is generated each time.
[0053]
That is, first, at step 200, the vehicle direction θ is read. Is detected and stored by the processing of step 110 described later. As a result, the X-direction component and the Y-direction component of the distance ΔD are obtained (step 202), and each component is added to the X-direction cumulative distance and the Y-direction cumulative distance to update the cumulative distance (step 204).
[0054]
When the running vector integration interrupt processing shown in FIG. 7 is performed, the coordinate position indicated by the latest X-direction integrated distance and the Y-direction integrated distance in the processing of the navigation device of the present embodiment shown in FIG. Is treated as the current traveling position of the vehicle.
[0055]
The vehicle running azimuth θ used in step 108 is performed by the azimuth calculation interrupt processing in step 110. This process is a timer interrupt process performed every time a predetermined time Δt elapses. This processing will be described with reference to the flowchart of FIG.
[0056]
In the azimuth calculation interrupt processing, first, the angular velocity ω of the vehicle is read from the gyro sensor 603 (step 300). Since a voltage proportional to the angular velocity ω is output from the gyro sensor 603, this is obtained by multiplying the voltage by a predetermined coefficient after A / D conversion. In step 302, the bias error of the gyro sensor 603 is read. This is calculated in step 112 described later, and the bias error b is set to 0 until it is calculated.
[0057]
As described above, using the read angular velocity ω, bias error b, and predetermined time Δt, the azimuth change amount Δθg from the previous processing to the current processing is calculated by the calculation in step 304. Then, after outputting the other azimuths and filtering in step 306, the driving azimuth is output.
[0058]
Next, the processing procedure of the bias error calculation interruption processing shown in step 112 will be described with reference to FIGS.
[0059]
FIG. 9 shows a process for calculating a bias error of the gyro sensor 603 in a relatively short time (several tens of seconds), and is a timer interrupt process started every time a predetermined time T elapses.
[0060]
First, in step 402, the number of interrupts is counted (n). Next, in steps 404 and 406, the current outputs from the geomagnetic sensor 602 and the gyro sensor 603 are read, and the running azimuth is calculated by adding the gyro rotation angle (gyro azimuth) based on the output of the geomagnetic sensor 602. I do. In step 408, the direction of the road on which the current position is located is read from the map sensor 607.
[0061]
Next, in step 410, the angular velocities ωg and ωm are calculated by taking the differences from the previous values for each of the gyro azimuth and the road azimuth. Further, the gyro angle difference based on the road orientation at the present time and the gyro angular speed difference based on the road angular velocity are calculated as Δθ and Δω, respectively. At step 412, it is determined whether or not a predetermined number of interrupts N has been exceeded, and only when the number has been exceeded, the following processing is performed.
[0062]
In step 414, the memory position is shifted one by one for the data up to the previous time, and new data is input Nth. In step 416, a case where the absolute values of the gyro angular velocity ωg and the road angular velocity ωm are small is selected. Here, when these are large, it means that the turning angle of the vehicle is large, and errors due to scale errors rather than bias errors become dominant.
[0063]
In step 418, a case where Δθ has changed by a certain degree or more is selected. This is to eliminate the case of a straight line. In step 420, it is determined that a bias error has occurred in one direction. This is because, when the direction is not one-way, for example, a phenomenon caused by operating the steering wheel of the vehicle may be recognized as an error. Further, in step 422, it is determined that the variation width of Δω is small. This is because if this is large, there is a possibility that the steering wheel is operated. Where Δω₀, Ω₀, Δθ₀, Θ₀Are predetermined reference values for the comparison.
[0064]
If the above determination is satisfied, a value obtained by dividing the amount of change in the gyro angle difference Δθ by the elapsed time NT is regarded as the bias error b (step 424), and the interrupt processing ends.
[0065]
A process of calculating a gyro bias error occurring over a relatively long time (several tens of minutes) is shown in a flowchart of FIG. Here, this process is a timer interrupt process that is started every time the predetermined time T elapses.
[0066]
Steps 502 to 514 are the same processing as steps 402 to 414 in FIG. 9, respectively. However, here, the angular velocity is not used, and in step 510, only the gyro angle difference Δθ based on the road angle θm output from the map sensor 607 is calculated.
[0067]
In step 516, the long-term change of Δθ (calculated in step 510) is statistically processed to calculate the slope of the regression line, which is regarded as the n-th bias error b (n).
[0068]
Where Sn²Is the variance of n, S_{n Δθ}Is the covariance of n and Δθ, and T is the sampling period of the road direction or gyro direction.
[0069]
In step 518, when the fluctuation range of the bias error b (n) is smaller than the predetermined fluctuation value δb, the average of the maximum and the minimum is set as the final bias error b as in step 520.
[0070]
Here, the procedure after step 518 was determined for the following reasons. Since the slope b (see FIG. 12) representing the gyro bias is obtained, for example, from statistical calculation using a regression line, it cannot be calculated accurately unless a certain amount of information is accumulated. Therefore, information is accumulated until n = N−k, and the calculation of the bias error b is started from N−k. Thereafter, b (n) is calculated each time by adding the increased information every time until n = N. Then, it is determined in step 518 whether or not b (n) that fluctuates with time converges to a certain fixed width. If Yes, the maximum value and the minimum value of b (n) are determined in step 520. Are taken as the gyro bias error b.
[0071]
This is because if these conditions are not performed when the fluctuation is small in calculating b, there is a possibility that an adverse effect may occur.
[0072]
A second embodiment of the integral sensing device to which the present invention is applied will be described. In the present embodiment, an integral sensing device that detects a traveling direction using a Kalman filter is used. The integrating sensing device of the present embodiment is used for a navigation device having a configuration as shown in FIG.
[0073]
In the integration type sensing device of the present embodiment, the Kalman filter gain is calculated in a method of performing the Kalman filtering on the output signals of the first means 1 and the second means 2 for outputting the traveling direction to calculate the traveling direction. The present invention is applied as a means for measuring an error (noise) component necessary for performing the operation.
[0074]
That is, as shown in FIG. 3, the integral type sensing device of the present embodiment detects the angular velocity which is a differential amount of the traveling azimuth and integrates the angular velocity with the first means 1 and the traveling azimuth directly or indirectly. , A subtractor 3 that calculates the difference between the values of the traveling azimuths obtained from the first and second means, and a time change rate of the output from the subtractor 3, It has a bias error measuring means 45 for calculating an estimation error, and a Kalman filter 13 for estimating the running direction 44 using the estimated error.
[0075]
The Kalman filter 13 receives the measurement error from the bias error measurement unit 45, uses the filter gain calculation unit 11 that calculates the gain of the Kalman filter, uses the calculated gain, and the output signals from the first and second units. And a required physical quantity estimating means 12 for estimating a required physical quantity, in this case, the traveling direction 44.
[0076]
The first means 1 has a gyro sensor 603 for measuring an angular velocity which is a differential amount of the traveling azimuth, and a time integrating means 8 for calculating the traveling azimuth of the vehicle by integrating the corrected angular velocity with time.
[0077]
The second means 2 includes, as sensors, a map sensor 607 for calculating a running direction from a vehicle position determined previously, a geomagnetic sensor 602, and a GPS for detecting an absolute position and an absolute direction on the earth based on a GPS satellite signal. It has three of the devices 606. Here, these sensors 607, 602, and 606 need not be dedicated to this sensing device. The output signal of the sensor can also be used for a physical quantity determined by the sensing device, that is, for determining other than the traveling direction.
[0078]
The second means further selects the sensor with the highest accuracy according to the situation according to the selector control signal 27 input from the outside, as in the first embodiment, among the combinations of these sensors, It has a selector device 23 that outputs the azimuth from the sensor.
[0079]
The bias error measuring means 45 has a time change rate calculating means 43 for calculating a time change of the azimuth by the output of the gyro sensor 603 based on the azimuth output of the second means 2.
[0080]
In this embodiment, the entire operation of the navigation device is the same as that of the first embodiment (see FIG. 6), but the operation of the sensing device used for detecting the traveling azimuth is different. Hereinafter, the operation of the integral sensing device according to the present embodiment will be described.
[0081]
In this embodiment, as in the above embodiment, the gyro sensor 603 is applied to the first means 1 to measure the angular velocity. Further, the second means 2 combines three of the map sensor 607, the geomagnetic sensor 602, and the GPS device 606, selects the sensor with the highest accuracy according to the situation by the selector device 23, and selects the sensor. Is output as the azimuth of the second means 2.
[0082]
The bias error measuring means 45 measures an error (noise) component in the first means based on the change rate calculated by the time change rate calculating means 43. Here, as the processing in the change rate calculating means 43 of the bias error measuring means 45, the same processing as the bias error calculation interrupt processing described with reference to FIGS. 9 and 10 in the above embodiment can be applied.
[0083]
The filter gain calculating means 11 calculates the Kalman filter gain, and using the calculated Kalman filter gain, the required physical quantity estimating means 12 calculates the required azimuth based on the calculated Kalman filter gain for each running direction obtained from the first and second means. The running direction 44 can be estimated by performing the weighting process.
[0084]
Next, an example of the Kalman filter processing in the sensing device will be described.
[0085]
First, the state equation, which was created by modeling the target system, is as follows.
[0086]
(Equation 1)

[0087]
Here, X (k) is a state vector, which is an estimated amount at time k including a required physical quantity. A is a state transition matrix, B is a driving matrix, U (k) is brand noise, and V (k) is system noise.
[0088]
In addition, an observation equation indicating the relationship between the amount of observation from each sensor and the state quantity is as follows.
[0089]
(Equation 2)

[0090]
Here, Y (k) is an observation vector, f is a functional expression indicating a relationship with a state quantity, and W (k) is observation noise.
[0091]
When the Kalman filter processing is applied to the above model, the optimum estimated value X (k | k) is obtained by the following recurrence formula. If the observation equation is nonlinear, an algorithm of the extended Kalman filter is used.
[0092]
(Equation 3)

[0093]
Here, the subscript (k | k) is the optimum value obtained by the Kalman filter based on the observation value at time k, and (k | k-1) is the value at time k-1 from the value at time k-1 using the state equation. It is a prediction. V (k) and W (k) are covariance matrices of system noise and observation noise, respectively.
[0094]
P is a covariance matrix of the estimation error, which is a measure of the estimation accuracy by the Kalman filter. K is a Kalman filter gain, which is calculated when the amount of observation is obtained, is a quantity that compares the statistic of the system noise and the observation noise, and determines which of the state equation and the observation is weighted. is there.
[0095]
The second equation from the top of Equation 3 is an equation for calculating the optimum estimated value. The value at time k is predicted according to the state equation using the optimal estimated value at time k-1. This predicted value and the internal dividing point of the observed value at the time k by the Kalman filter gain K become the optimum estimated value. The matrix C (k) is a Jacobian of f (X (k | k-1)) used for linearizing the observation equation.
[0096]
In the Kalman filter processing as described above, based on the magnitude of the noise component obtained from the bias error measuring means 45, V (k), W (k), or the corresponding component of P is appropriately changed. , Kalman filter gain K for performing optimum weighting under the conditions can be calculated.
[0097]
A third embodiment of the integral sensing device for vehicle navigation to which the present invention is applied will be described.
[0098]
As shown in FIG. 4, the integrating sensing device of the present embodiment detects a speed, which is a differential amount of the traveling distance, and integrates the first means 1 for integrating the traveling distance and the first means for directly or indirectly acquiring the traveling distance. (2), a subtractor (3) for calculating the difference between the mileage values obtained from the first and second means, and a time change rate of the output from the subtractor (3) to calculate an estimation error. And a scale error estimating means 52.
[0099]
The first means 1 includes a vehicle speed sensor 50 for measuring a speed which is a differential amount of the traveling distance, a scale error correction means 51 for performing a scale error correction for the measured speed, which will be described below, And time integration means 8 for calculating the travel distance of the vehicle by performing time integration.
[0100]
The second means 2 includes, as sensors, a map sensor 607 for calculating a travel distance from a vehicle position which has been determined, and a GPS device 606 for detecting an absolute position on the earth and a travel distance based on a GPS satellite signal. Having one. Here, these sensors 607 and 606 need not be dedicated to this sensing device. The output signal of the sensor can also be used to determine a physical quantity determined by the sensing device, that is, other than the travel distance.
[0101]
The second means further selects the sensor with the highest accuracy according to the situation according to the command of the selector control signal 27 input from the outside, out of the combination of these sensors, as in the first embodiment. And a selector device 23 for outputting the direction.
[0102]
Next, the operation of the present embodiment will be described.
[0103]
In the first means 1, for example, a vehicle speed sensor 50 that periodically counts the number of rotations of the wheels measures the vehicle speed, and the time integration means 8 integrates the time to calculate the traveling distance. On the other hand, the second means 2 outputs the traveling distance by using data from the high-precision sensor selected by the selector device 23 among the map sensor 607 and the GPS device 606.
[0104]
On the basis of the distance output of the second means 2, the distance change rate calculating means 53 calculates a change in the distance output by the first means 1 with an increase in the traveling distance. The scale error estimating means 52 calculates the distance change rate α at this time by statistical processing such as regression analysis to be described later, and estimates the scale factor error of the first means 1. Further, the scale error correcting means 51 corrects the scale error for calculating the vehicle speed by the following equation.
[0105]
(Equation 4)

[0106]
Therefore, the accuracy of the finally output traveling distance 54 can be improved by an amount corresponding to the correction of the scale error.
[0107]
Fourth Embodiment A fourth embodiment of the integral sensing device to which the present invention is applied will be described with reference to FIG.
[0108]
The integrated sensing device of the present embodiment detects the traveling speed of a vehicle. As shown in FIG. 5, a first means 1 for detecting and integrating acceleration, which is a differential amount of the traveling speed, A second means 2 for obtaining directly or indirectly, a subtractor 3 for calculating the difference between the values of the traveling speeds obtained from the first and second means, and a time change rate of the output from the subtractor 3 And a bias error estimating means 42 for calculating an estimation error.
[0109]
The first means 1 includes an acceleration sensor 60 for measuring an acceleration that is a differential amount of the traveling speed, a bias error correction means 41 for correcting an error of the measured acceleration, and a time integration of the corrected acceleration. Time integration means 8 for calculating the running speed of the vehicle.
[0110]
The second means 2 includes, as sensors, a vehicle speed sensor 50 that counts the number of rotations of wheels to calculate a vehicle speed, and a GPS device 606 that detects an absolute position on the earth and a traveling speed based on a GPS satellite signal. Having one. Here, these sensors 50 and 606 need not be dedicated to this sensing device. The output signal of the sensor can also be used to determine a physical quantity determined by the sensing device, that is, other than the traveling speed.
[0111]
The second means further includes a selector device 23 that selects the sensor with the highest accuracy according to the situation and outputs the azimuth according to a selector control signal 27 input from the outside, out of the combination of these sensors.
[0112]
Next, the operation of the present embodiment will be described.
[0113]
In this embodiment, in the first means 1, the acceleration sensor 60 measures the acceleration of the vehicle. The second means 2 combines a vehicle speed sensor 50 and a GPS device 606 for detecting the speed of a moving object on the earth based on a satellite signal, and the selector device 23 determines the sensor with the highest accuracy in the situation. The output from the selected sensor is output as the vehicle speed output of the second means 2.
[0114]
The time change rate calculating means 43 calculates the time change of the vehicle speed by the output of the acceleration sensor 60 based on the vehicle speed output of the second means 2. The bias error estimating means 42 calculates the rate of change over time at that time by statistical processing such as the above-described regression analysis, and outputs it as a bias error of the acceleration sensor 60. Finally, the bias error correction means 41 corrects the bias error of the measured acceleration.
[0115]
Therefore, the accuracy of the finally output traveling speed 61 can be improved by an amount corresponding to the correction of the bias error.
[0116]
In the present embodiment, the bias error of the acceleration of the vehicle is estimated and corrected. However, for example, as in the estimation and correction of the vehicle running speed in the third embodiment (see FIG. 4), the scale in the detected acceleration is corrected. Even when the error is estimated and corrected, the accuracy can be improved as in the present embodiment.
[0117]
A fifth embodiment of the integral sensing device to which the present invention is applied will be described with reference to FIG. This embodiment is a combination of the third embodiment (see FIG. 4) and the fourth embodiment (see FIG. 5). The traveling speed 61, which is the output of the fourth embodiment, is This is configured to be input as the vehicle speed measurement value of the third embodiment.
[0118]
That is, in this embodiment, as shown in FIG. 13, the first means 1 for detecting the acceleration of the vehicle and outputting the traveling speed, the second means 2 for detecting the traveling speed similarly, and the first means A bias error estimating means for estimating a bias error from a difference between the values detected by the first and second means.
[0119]
Further, in this embodiment, the first means 1 'for receiving the traveling speed 61 output from the first means and integrating the time to detect the traveling distance, and the second means 2 for similarly detecting the traveling distance. And a scale error estimating means 52 for estimating a scale error from a difference between the values detected by the first means 1 'and the second means 2'.
[0120]
The operation of this embodiment is the same as that of the third and fourth embodiments, and the description is omitted here.
[0121]
According to this embodiment, when the travel distance 54 is calculated by integrating the acceleration amount detected using the acceleration sensor 60 of the first means 2 twice, the errors generated at each integration stage are calculated as follows: Since the correction can be made based on the sensor data of the second means 2 and the second means 2 ', the accuracy of the finally obtained travel distance 54 can be improved as a result.
[0122]
In the present embodiment, error correction is performed in each integration stage by comparison with the second means. However, the error correction may be performed in only one of the stages. At this time, as the error correction performed, one of a scale error and a bias error is performed. Further, a configuration may be adopted in which the scale error is corrected in the first integration stage (output of the first means 1) and the bias error is corrected in the next integration step (first means 1 ').
[0123]
Although several embodiments have been described above, the present invention can be applied commonly to the integral type sensing device related to vehicle navigation, and is not limited to the configuration shown in the above example.
[0124]
Therefore, according to the present invention, in a method of measuring a differential amount of a required physical quantity related to navigation of a vehicle, and then calculating the required quantity by integrating the physical quantity, an error included in the measured differential quantity is calculated. For example, a bias error or the like can be estimated and corrected. Therefore, the error that should have accumulated originally can be reduced by time integration.
[0125]
That is, since the error of the measurement signal itself by the sensor can be reduced, for example, unlike the correction of the final stage such as the error correction of the map matching method in the conventional navigation device, after the correction, a similar error is immediately generated again. It does not happen. Therefore, the present invention is equivalent to using a highly accurate sensor, and the system reliability can be improved.
[0126]
【The invention's effect】
According to the integral sensing device of the present invention, the error of the measurement signal itself by the sensor can be reduced, and the same error does not occur immediately after the error is corrected. Therefore, the present invention is equivalent to using a highly accurate sensor, and the system reliability can be improved.
[0127]
[Brief description of the drawings]
FIG. 1 is a block diagram showing a configuration of an embodiment of an integral sensing device for detecting a traveling direction to which the present invention is applied.
FIG. 2 is a block diagram illustrating an example of a conventional integral sensing device in a vehicle navigation device.
FIG. 3 is a block diagram showing a configuration of another embodiment of the integral type sensing device for detecting a running direction to which the present invention is applied.
FIG. 4 is a block diagram showing the configuration of another embodiment of the integral type sensing device for detecting a traveling distance to which the present invention is applied.
FIG. 5 is a block diagram showing a configuration of another embodiment of the integral type sensing device for detecting a traveling speed to which the present invention is applied.
FIG. 6 is a flowchart showing the overall operation of the vehicle navigation device provided with the integrating sensing device of the embodiment of FIG. 1;
FIG. 7 is a flowchart of a running vector integration interruption process in FIG. 6;
FIG. 8 is a flowchart of a direction calculation interrupt process in FIG. 6;
FIG. 9 is a flowchart of a bias error calculation interruption process (short term) in FIG. 6;
FIG. 10 is a flowchart of a bias error calculation interruption process (long term) in FIG. 6;
11 is a block diagram illustrating an example of a hardware configuration of a navigation device including the integral sensing device according to the embodiment of FIG.
FIG. 12 is an explanatory diagram showing an example of a method for obtaining a bias error according to the present invention.
FIG. 13 is a block diagram showing a configuration of another embodiment of the integral type sensing device for detecting a traveling distance to which the present invention is applied.
[Explanation of symbols]
DESCRIPTION OF SYMBOLS 1 ... 1st means, 2 ... 2nd means, 3 ... Adder (subtractor), 8 ... Time integration means, 11 ... Filter gain calculation means, 12 ... Required physical quantity estimation means, 13 ... Kalman filter, 21 ... Map Sensor 22, 22 geomagnetic sensor, 23 selector device, 24 filter, 27 selector control signal, 41 bias error correction means, 42 bias error estimation means, 43 time change rate calculation means, 44 running direction, 45 ... bias error measuring means, 50 ... vehicle speed sensor, 51 ... scale error correcting means, 52 ... scale error estimating means, 53 ... distance change rate calculating means, 54 ... running distance, 60 ... acceleration sensor, 61 ... running speed, 601 ... Distance sensor, 602 geomagnetic sensor, 603 gyro sensor, 604 controller, 605 display device, 606 GPS device, 607 map Capacitors, 608 ... input device, 610 ... map data storage, 611 ... map operation means.

Claims (9)

In an integrated sensing device mounted on a vehicle,
First means and second means for independently obtaining one physical quantity related to vehicle navigation,
Error estimating means for estimating an error generated by the first means,
The error estimating means includes a short-time error estimating means for calculating an error at every predetermined time, and a long-time error estimating means for calculating an error at a time longer than an interval at which the short-time error estimating means calculates the error. Have
The first means is
A first sensor unit including a sensor that detects a differential amount that is a time change rate of the physical quantity;
Error correction means for correcting the detected differential amount using the error estimated by the short-time error estimation means and the long-time error estimation means,
Integrating means for obtaining the physical quantity by integrating the corrected differential quantity with time,
The short-time error estimating means,
A subtraction unit for calculating a difference between each value of the physical quantity obtained by the first and second means;
A change rate calculating means for obtaining a change rate of the calculated difference by calculation,
From the calculated change rate, an estimated error calculating means for obtaining an error used in the error correcting means ,
The long-time error estimating means,
If the variation width of the error determined by the long-time error estimation means is smaller than a predetermined variation width, an average of the maximum value and the minimum value of the error is determined as the error used in the error correction means,
The integrating sensing device further comprises:
A determination unit that determines whether the error of the differential amount has occurred in one direction,
The integral type sensing device, wherein the short-time error estimating means calculates the error only when the determining means determines that the error of the differential amount occurs in one direction. In claim 1,
The determining means includes:
An integral sensing device, wherein it is determined whether or not the error of the differential amount occurs in one direction based on whether the change rates calculated by the change rate calculating means are continuously the same sign. In claim 1 or 2,
The second means is a sensor that detects the physical quantity, and one or more sensors that detect one or more physical quantities that are different from the physical quantity and can be converted to the physical quantity. An integral type sensing device comprising a second sensor unit including one or both of them. In claim 1 or 2,
The rate-of-change calculating means calculates a rate of change with time. In claim 4,
The error estimated by the error estimating means is a differential error bias error of the differential amount detected by the first means. In claim 5,
The estimation error calculation means obtains, using a regression line, a change with respect to time of the difference between the physical quantities obtained by the change rate calculation means, and detects a constant slope of the regression line by the first means. An integrated sensing device, wherein a bias error of the differential amount is used. In claim 6,
The physical quantity is a running azimuth of the vehicle,
The sensor of the first sensor unit is a gyro sensor that detects a traveling direction,
A sensor of the second sensor unit, an azimuth detection map sensor that detects a road azimuth corresponding to the vehicle position as a traveling azimuth, an azimuth detection GPS receiving unit that detects an absolute azimuth on the earth based on a GPS satellite signal, And an integrated sensing device, which is at least one of a geomagnetic sensor for detecting a geomagnetic field. In claim 1 or 2,
The physical quantity is a traveling distance of the vehicle,
The sensor of the first sensor unit is travel distance detection means for measuring the travel distance of the vehicle, and the sensor of the second sensor unit is a distance detection map in which the travel distance is a road length corresponding to the vehicle locus. At least one of a sensor and a distance detection GPS receiving means for detecting a moving distance of the vehicle based on a GPS satellite signal;
The change rate calculating means calculates a change rate of the difference between the physical quantities according to a traveling distance, and the estimation error calculating means calculates a scale factor error of the differential amount detected by the first means,
The error sensing means corrects the differential amount detected by the first means based on the calculated scale factor error. In claim 1 or 2,
The physical quantity is a traveling speed of the vehicle,
The sensor of the first sensor unit is acceleration detection means for detecting acceleration of the vehicle,
The sensor of the second sensor unit includes at least one of a traveling speed detecting unit that detects a traveling speed of the vehicle, and a speed detecting GPS receiving unit that detects a speed of the vehicle based on a GPS satellite signal. An integrated sensing device, characterized in that:

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