CN104075715A - Underwater navigation and positioning method capable of combining terrain and environment characteristics - Google Patents

Abstract

The invention discloses an underwater navigation and positioning method capable of combining terrain and environment characteristics. The method comprises the steps of judging whether all sub-areas in a planned track of an underwater vehicle are matched or not according to terrain information; if so, positioning by a terrain-aided inertial navigation system; and if not, positioning by a simultaneous localization and composition algorithm auxiliary master inertial navigation system. The method can be used for calculating the terrain information of a navigation area, and the underwater terrain is divided into a matched terrain area and an unmatchable terrain area. Different navigation algorithms are adopted aiming at the different areas, so that the position error of a master inertial navigation system can be corrected, and the method is relatively high in autonomy.

Description

The underwater navigation localization method of a kind of combination landform and environmental characteristic

Technical field

The present invention relates to underwater navigation technical field, specifically design a kind of method that can meet the requirement of the long-time high-precision independent navigator fix of underwater hiding-machine.

Background technology

Inertial navigation system is without any need for external information, can be to any information of external radiation yet, only depend on the inertial navigation system itself just can be under all weather conditions, carry out continuous three-dimensional localization and navigation in the world with in any media environment, this possess independence, disguise simultaneously and can obtain other navigational system such as distinct advantages such as radio navigation, satellite navigation and celestial navigation of the complete movable information of carrier incomparable.But inertial navigation system greatest weakness is that its systematic error accumulates in time, the time is longer, and error is larger.Accurate transmitting in order to ensure safe navigation and the weapon of underwater hiding-machine, must utilize extraneous positional information inertial navigation system to be carried out to adjustment and the correction in cycle.

Models in Terrain Aided Navigation (Terrain-Aided Navigation, TAN), essence is the integrated navigation system consisting of inertial navigation system (providing real-time figure) and sensor (figure and reference map contacts and tie in real time) and numerical map (providing reference map), it is as a kind of high precision navigator fix technology, the capacity that only needs terrain information content enough to enrich and increase storer just can improve navigation accuracy nearly order of magnitude, reaches the positioning precision of tens meters.Terrain-aided Navigation has autonomous, hidden, continuous, all weather operations, the advantage that navigation positioning error does not accumulate in time, is the desirable assisting navigation positioning means of underwater hiding-machine.

The typical Models in Terrain Aided Navigation based on ICCP algorithm can change obvious marine site having priori topomap and landform, revises the site error of main inertial navigation system.Yet, Terrain-aided Navigation need to have the topomap of priori, and require to have obvious landform to change, for those, not yet survey and draw or landform changes too mild, the unconspicuous marine site of terrain feature, it is very difficult with terrain auxiliary navigation method, reducing the error that main inertial navigation system accumulates in time.

Now, can utilize thering is other observation of feature such as submerged structure, shipwreck etc. under water of time stability, synchronous location and map structuring (the Simultaneous Localization and Mapping of employing based on marine environment feature, SLAM) algorithm is used as the alternative method of Terrain-aided Navigation, to revise in real time the position of underwater hiding-machine, reduce the accumulation of navigation error, improve the positioning precision of navigational system.

Summary of the invention

The technical matters solving: for the deficiencies in the prior art, the present invention proposes the underwater navigation localization method of a kind of combination landform and environmental characteristic, for seabed priori topomap non-availability or the not abundant marine site of terrain information amount, cannot utilize Models in Terrain Aided Navigation to reduce the technical matters of the site error that main inertial navigation system accumulates in time.

Technical scheme: for solving the problems of the technologies described above, the present invention by the following technical solutions:

The underwater navigation localization method of a kind of combination landform and environmental characteristic, according to landform quantity of information, judge in the planning flight path of underwater hiding-machine, whether each sub regions can mate, if can mate, adopt terrain aided inertial navigation system to realize location, if can not mate, adopt the auxiliary main inertial navigation system of synchronous location and composition algorithm to realize location.

Terrain auxiliary navigation method has the advantages such as autonomous, hidden, in abundant the mated marine site of terrain information amount, generally based on ICCP algorithm, according to priori topomap, revise the error of inertial navigation system, modification method and makeover process are prior art, can well assist main inertial navigation system to realize the navigator fix of underwater hiding-machine; And can not matching area in landform, synchronous location and composition algorithm, bring into play self and observed realizing location to thering is the environmental characteristic of time stability, and revised the feature of main inertial navigation system site error, effectively made up the deficiency of Models in Terrain Aided Navigation; Two kinds of methods are used in conjunction with, and make the navigator fix of underwater hiding-machine have better independence and accuracy.

Further, in the present invention, judge that the method whether each sub regions in the planning flight path of underwater hiding-machine can mate is as follows:

Step 1.1: adopt the mode of grid matrix to be divided into polylith landform candidate region submarine topography elevation, on the planning flight path of underwater hiding-machine,, through L piece landform candidate region wherein, the longitude and latitude span of setting a certain landform candidate region is M * N grid, and net point coordinate is that the landform altitude value that (i, j) locates is height (i, j), here i=1,2 ..., M, j=1,2 ..., N;

Step 1.2: utilize mobile computing window technique to calculate the parameter of landform candidate region, concrete grammar is:

Define the local mobile computing window that a size is m * n, and calculate the Terrain Elevation mean value in local mobile computing window when the center of local mobile computing window after mobile one time, can obtain the landform standard deviation sigma (l) of each landform candidate region, the landform coefficient R in longitudinal on the total-grid point of each landform candidate region _longitude(l) the landform coefficient R and in latitude direction _latitude(l), l=1 wherein, 2 ..., L, subscript longitude represents longitude, and latitude represents latitude, and the parameter specific formula for calculation of landform candidate region is as follows:

$\mathrm{\σ}\left(l\right)=\sqrt{\frac{1}{m(n-1)}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{[\mathrm{height}(i,j)-\mathrm{height}\_\mathrm{ave}]}^2}$

$R_{\mathrm{longitude}}\left(l\right)=\frac{1}{n(m-1)\mathrm{\σ}{\left(l\right)}^2}\underset{i=1}{\overset{m-1}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}[\mathrm{height}(i,j)-\mathrm{height}\_\mathrm{ave}][\mathrm{height}(i+1,j)-\mathrm{height}\_\mathrm{ave}]$

$R_{\mathrm{latitude}}\left(l\right)=\frac{1}{n(m-1)\mathrm{\σ}{\left(l\right)}^2}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n-1}{\mathrm{\Σ}}}[\mathrm{height}(i,j)-\mathrm{height}\_\mathrm{ave}][\mathrm{height}(i,j+1)-\mathrm{height}\_\mathrm{ave}]$

Step 1.3: the L piece landform candidate region on planning flight path, judges respectively whether the landform standard deviation of each piece landform candidate region and longitude and latitude direction landform related coefficient meet σ (l) > 4 σ simultaneously _cand R _longitude(l) < 0.7 and R _latitude(l) < 0.7, if meet, corresponding landform candidate region is can matching area, otherwise is can not matching area, wherein, and σ _cstandard deviation for depth sensor measuring error.

Judge whether that the method that can mate is prior art, for different artificially generated terrains, calculate the various parameters relevant to landform, determine that the threshold value of parameter is determined decision criteria after statistical study, concrete numerical value is mainly obtained by Computer Simulation.

Further, in the present invention, can not matching area for landform, described synchronous location and composition algorithm comprise the following steps:

Step 3.1: the foundation of system nonlinear process model

Step 3.1.1: the foundation of underwater hiding-machine state equation

Choosing sky, northeast is navigation coordinate system, and carrier coordinate system x axle points to starboard along underwater hiding-machine transverse axis, and before carrier coordinate system y axle points to along the underwater hiding-machine longitudinal axis, carrier coordinate system z axle forms right-handed coordinate system perpendicular to x axle and the determined plane of y axle; Underwater hiding-machine state equation is as follows constantly for k:

Wherein:

Pos _vehicle(k) represent the k position of underwater hiding-machine constantly,

Vel _vehicle(k) represent the k speed of underwater hiding-machine constantly,

Qua _vehicle(k) represent the k attitude quaternion of underwater hiding-machine constantly,

Subscript G represents navigation coordinate system,

Subscript B represents carrier coordinate system,

Δ t represents the discrete sampling time interval,

C _b2Gexpression transforms to the direction cosine matrix of navigation coordinate system from carrier coordinate system,

F ^b(k) represent k accelerometer output constantly,

G represents acceleration of gravity,

the hypercomplex number that the angular speed that expression is recorded by gyroscope forms,

represent hypercomplex number multiplication;

Step 3.1.2: the foundation of map state

Set k and constantly observed N _mindividual new environmental characteristic, k moment map state is:

$x_{\mathrm{feature}}\left(k\right)={\left[\begin{array}{cccc}{x}_{\mathrm{feature}\_m1}^T\left(k\right)& x_{\mathrm{feature}\_m2}^T\left(k\right)& ...& x_{\mathrm{feature}\_m{N}_m}^T\left(k\right)\end{array}\right]}^T$

Wherein:

represent the k state of m1 environmental characteristic constantly,

represent the k state of m2 environmental characteristic constantly,

represent k mN constantly _mthe state of individual environmental characteristic,

Subscript T represents transposition;

By underwater environment feature modeling, be some feature, the position of environmental characteristic under navigation coordinate system is as follows:

$x_{\mathrm{feature}\_\mathrm{mi}}^G\left(k\right)=x_{\mathrm{feature}\_\mathrm{mi}}^G(k-1)={\mathrm{pos}}_{\mathrm{vehicle}}^G\left(k\right)+C_{B2G}\left(k\right)r_{\mathrm{BS}}^B+C_{B2G}\left(k\right)C_{S2B}{r}_{\mathrm{Sfeature}\_\mathrm{mi}}^S\left(k\right)$

Wherein:

be illustrated in mi environmental characteristic that k obtains the constantly position in navigation coordinate system,

be illustrated in mi environmental characteristic that k-1 obtains the constantly position in navigation coordinate system,

represent orientation/range sensor on underwater hiding-machine to the lever arm effect compensation rate at underwater hiding-machine center the component in carrier coordinate system,

Subscript S represents orientation/range sensor coordinate system, and orientation/range sensor is all fixed on underwater hiding-machine, while setting up generally by orientation/range sensor coordinate system with respect to carrier coordinate system x axle translation one segment distance, y axle and z axle are constant,

C _s2Brepresent that orientation/range sensor coordinate system transformation is to the direction cosine matrix of carrier coordinate system,

$r_{\mathrm{Sfeature}\_\mathrm{mi}}^S\left(k\right)={\left[\begin{array}{ccc}d\cos \left(\mathrm{phi}\right)\cos \left(\mathrm{theta}\right)& d\sin \left(\mathrm{phi}\right)\cos \left(\mathrm{theta}\right)& d\cos \left(\mathrm{theta}\right)\end{array}\right]}^T$ Mi the environmental characteristic that represents that sensor records and the relative position between underwater hiding-machine, wherein, d represents that sensor is to the distance between environmental characteristic, phi represents the position angle between environmental characteristic and orientation/range sensor, and theta represents the elevation angle between environmental characteristic and orientation/range sensor;

Step 3.2: the foundation of the non-linear observation model of system

Step 3.2.1: obtain the relative position observation of environmental characteristic and underwater hiding-machine according to orientation/range sensor, thereby the non-linear observation model of the system of setting up is as follows:

z(k)＝h(x(k))+ν(k)

Wherein:

Z (k) represents the observed reading to environmental characteristic,

H () is non-linear observation function,

X (k) is system state vector, comprises the position of position, speed, attitude quaternion and the environmental characteristic of underwater hiding-machine,

ν (k) is systematic observation noise;

K is constantly only relevant with underwater hiding-machine state to this environmental characteristic to the observation of mi environmental characteristic, therefore has:

z _mi(k)＝h(x _vehicle(k),x _{feature_mi}(k))+ν _mi(k)

Wherein:

Z _mi(k) represent the k observation to mi environmental characteristic constantly,

X _vehicle(k) represent that underwater hiding-machine is at k state constantly,

X _{feature_mi}(k) represent that mi environmental characteristic is at k state constantly,

ν _mi(k) be the observation noise to mi environmental characteristic;

Step 3.2.2: set $r_{\mathrm{Sfeatrue}\_\mathrm{mi}}^S\left(k\right)={\left[\begin{array}{ccc}x& y& z\end{array}\right]}^T,$ K being observed mi environmental characteristic constantly:

$z_{\mathrm{mi}}\left(k\right)=\left[\begin{array}{c}d\\ \mathrm{phi}\\ \mathrm{theta}\end{array}\right]=\left[\begin{array}{c}\sqrt{x^2+y^2+z^2}\\ \arctan \left(\frac{y}{x}\right)\\ \arctan \left(\frac{z}{\sqrt{x^2+y^2}}\right)\end{array}\right]$

Wherein:

D represents that orientation/range sensor is to the distance between environmental characteristic,

Phi represents the position angle between environmental characteristic and orientation/range sensor,

Theta represents the elevation angle between environmental characteristic and orientation/range sensor;

Step 3.3: system state augmentation process

In underwater hiding-machine navigation process, orientation/range sensor observes environmental characteristic, if the feature of this environmental characteristic for having observed directly performs step 3.5; If this environmental characteristic is emerging feature, system state is carried out to augmentation and order performs step 3.4 and step 3.5, the system state after augmentation becomes:

$x\left(k\right)=\left[\begin{array}{c}{x}_{\mathrm{vehicle}}\left(k\right)\\ x_{\mathrm{feature}}\left(k\right)\end{array}\right]=\left[\begin{array}{c}{f}_{\mathrm{vehicle}}(x_{\mathrm{vehicle}}(k-1),u\left(k\right),w\left(k\right))\\ x_{\mathrm{feature}}(k-1)\end{array}\right]$

Wherein:

X _vehicle(k) represent k underwater hiding-machine state constantly, comprise underwater hiding-machine position, speed and attitude quaternion,

X _feature(k) represent the k state of environmental characteristic constantly,

F _vehicle() is nonlinear state transfer function;

Step 3.4: synchronous location and composition algorithm predicts process

The non-linear observation model of system that the system nonlinear process model of setting up by step 3.1 and step 3.2 are set up predicts have to k system state and state covariance matrix constantly:

x(k|k-1)＝f(x(k-1|k-1))

$P_{\mathrm{cov}}\left(k\right|k-1)=\stackrel{\&OverBar;}{F}\left(k\right)P_{\mathrm{cov}}(k-1|k-1)\stackrel{\&OverBar;}{F}{\left(k\right)}^T+Q\left(k\right)$

Wherein:

X (k|k-1) be according to k-1 system state constantly to k one-step prediction quantity of state constantly,

X (k-1|k-1) is k-1 system state estimation value constantly,

P _cov(k|k-1) be according to k-1 system state constantly to k prediction covariance constantly,

P _cov(k-1|k-1) be k-1 state covariance estimated value constantly,

for the Jacobi matrix of system state equation,

T represents transposition,

Q (k) is the covariance of noise ω (k).

Step 3.5: according to expanded Kalman filtration algorithm to synchronous location and composition algorithm renewal process

The environmental characteristic obtaining for observation, integrating step 3.2 and step 3.4 pair system state value is upgraded, and concrete formula is as follows:

New breath: v _innov(k)=z (k)-h (x (k|k-1))

Filter gain: $K_{\mathrm{gain}}\left(k\right)=P_{\mathrm{cov}}\left(k\right|k-1)\stackrel{\&OverBar;}{H}{\left(k\right)}^T{(\stackrel{\&OverBar;}{H}\left(k\right)P_{\mathrm{cov}}\left(k\right|k-1)\stackrel{\&OverBar;}{H}{\left(k\right)}^T+R\left(k\right))}^{-1}$

State estimation value: x (k|k)=x (k|k-1)+K _gain(k) v _innov(k)

State covariance: $P_{\mathrm{cov}}\left(k\right|k)=P_{\mathrm{cov}}\left(k\right|k-1)-K_{\mathrm{gain}}\left(k\right)\stackrel{\&OverBar;}{H}\left(k\right)P_{\mathrm{cov}}\left(k\right|k-1)$

Wherein:

V _innov(k) be new breath,

Z (k) is k observed reading constantly,

H (x (k|k-1)) observes predicted value constantly for k,

K _gain(k) be k filter gain constantly,

for the Jacobi matrix of systematic observation equation,

R (k) is the covariance of ν (k),

X (k|k) is k system state estimation value constantly,

P _cov(k|k) be k system state covariance constantly;

Step 3.6: the pos obtaining from x (k|k) _vehiclebe revised main inertial navigation positional information.

Above-mentioned steps 3.1-3.6 has described the main process of synchronous location and composition algorithm, synchronous location receives the raw data from inertial sensor and orientation/range sensor with composition algorithm, set up the underwater hiding-machine state equation based on inertial navigation mechanical equation, and the state that is obtained environmental characteristic by orientation/range sensor; Then carry out respectively forecast period, observation stage and new stage more, forecast period is with k-1 system state estimation value x (k-1|k-1) and covariance matrix P constantly _cov(k-1|k-1) be basis, k system state and covariance constantly predicted; The observation stage, for the environmental characteristic having existed, is used for system state to upgrade, and to new environmental characteristic, carries out system augmentation; More the new stage, utilize the information between observed reading and predicted value to upgrade state value; Whole process adopts extended Kalman filter state is predicted and upgraded, and does not need system nonlinear process model and the non-linear observation model of system are done to linearization process, and calculated amount is less and be easy to realize.

Beneficial effect:

The present invention is by the calculating to navigation area terrain information amount, by landform be divided into landform can matching area and landform can not matching area.For different navigation areas, adopt respectively different navigation algorithms to assist main inertial navigation system, revise the site error that main inertial navigation system accumulates in time, there is higher independence.

The present invention can matching area in landform, adopts site error that the terrain auxiliary navigation method based on ICCP algorithm revises main inertial navigation system to obtain the device position of diving more accurately.When underwater hiding-machine enters landform can not matching area time, can provide the initial position of synchronous location and composition algorithm, to improve precision and the robustness of SLAM algorithm.

The present invention can not matching area in landform, synchronous location and the composition algorithm of employing based on marine environment feature assisted main inertial navigation system, and in computation process, adopt extended Kalman filter state is predicted and upgraded, and not by system non-linearization process model and non-linearization observation model being done to the method that adopts after linearization process Kalman filter to realize status predication and renewal, make calculated amount little and be more easy to realize.

Simulation result shows, the flight path that the underwater hiding-machine flight path that this algorithm obtains obtains than pure-inertial guidance system is closer to Desired Track, can overcome main inertial navigation error with between accumulation cause the problem that navigation and positioning accuracy is not high.

Accompanying drawing explanation

Fig. 1 is the underwater navigation positioning system schematic diagram of combination landform described in the invention and environmental characteristic;

Fig. 2 is synchronous location and composition algorithm aided inertial navigation schematic diagram;

Fig. 3 is the topomap 1 adopting in embodiment;

Fig. 4 is the bathymetric chart of embodiment mesorelief 1;

Fig. 5 be in embodiment underwater hiding-machine through the navigator fix result figure of landform auxiliary main inertial navigation system of ICCP can matching area time;

Fig. 6 be in embodiment underwater hiding-machine through the navigator fix result figure of landform auxiliary main inertial navigation system of SLAM can not matching area time;

Embodiment

Below in conjunction with accompanying drawing, the present invention is further described.

A underwater navigation localization method for combination landform and environmental characteristic,

The first step: according to landform quantity of information, judge whether landform can mate

Step 1.1: submarine topography elevation adopts the mode of graticule mesh matrix to divide and be stored as polylith landform candidate region, on the planning flight path of underwater hiding-machine, through L piece landform candidate region wherein, the longitude and latitude span of supposing a certain landform candidate region is M * N grid, and net point coordinate is that the landform altitude value that (i, j) locates is height (i, j), here i=1,2 ..., M, j=1,2 ..., N;

Step 1.2: utilize mobile computing window technique to calculate the parameter of landform candidate region, concrete grammar is:

Define the local mobile computing window that a size is m * n, and calculate the Terrain Elevation mean value in local mobile computing window when the center of local mobile computing window after mobile one time, can obtain the landform standard deviation sigma (l) of each landform candidate region, the landform coefficient R in longitudinal on the total-grid point of each landform candidate region _longitude(l) the landform coefficient R and in latitude direction _latitude(l), l=1 wherein, 2 ..., L, subscript longitude represents longitude, and latitude represents latitude, and the parameter specific formula for calculation of landform candidate region is as follows:

$\mathrm{\&sigma;}\left(l\right)=\sqrt{\frac{1}{m(n-1)}\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{[\mathrm{height}(i,j)-\mathrm{height}\_\mathrm{ave}]}^2}$

$R_{\mathrm{longitude}}\left(l\right)=\frac{1}{n(m-1)\mathrm{\&sigma;}{\left(l\right)}^2}\underset{i=1}{\overset{m-1}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}[\mathrm{height}(i,j)-\mathrm{height}\_\mathrm{ave}][\mathrm{height}(i+1,j)-\mathrm{height}\_\mathrm{ave}]$

$R_{\mathrm{latitude}}\left(l\right)=\frac{1}{n(m-1)\mathrm{\&sigma;}{\left(l\right)}^2}\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{n-1}{\mathrm{\&Sigma;}}}[\mathrm{height}(i,j)-\mathrm{height}\_\mathrm{ave}][\mathrm{height}(i,j+1)-\mathrm{height}\_\mathrm{ave}]$

Step 1.3: the L piece landform candidate region on planning flight path, judges respectively whether the landform standard deviation of each piece landform candidate region and longitude and latitude direction landform related coefficient meet σ (l) > 4 σ simultaneously _cand R _longitude(l) < 0.7 and R _latitude(l) < 0.7, if meet, corresponding landform candidate region is can matching area, otherwise is can not matching area, wherein, and σ _cstandard deviation for depth sensor measuring error.

Second step: when underwater hiding-machine enters landform can matching area time, the site error that adopts the Models in Terrain Aided Navigation based on isoline closest approach iteration (ICCP) algorithm assist the main inertial navigation system of correction to accumulate in time, specifically comprises the following steps:

Step 2.1: the N that provides flight path to measure by inertial navigation system _pindividual positional value sequence, gathers and is $\mathrm{Pos}=\left\{{{\mathrm{pos}}_N}_i\right\},$ And ${{\mathrm{pos}}_N}_i={\left[\begin{array}{cc}{{\mathrm{latitude}}_N}_i& {{\mathrm{longitude}}_N}_i\end{array}\right]}^T,$ N _i=1,2 ..., N _p, represent N _iindividual sequence of points latitude, represent N _iindividual sequence of points longitude; Sounding gear provides sequence of points in real time corresponding actual measurement water depth value , N wherein _i=1,2 ..., N _p, and from known reference map, extract corresponding isoline , N wherein _i=1,2 ..., N _p, and hypothesis closest approach on corresponding isoline is , all form arrangement set ;

Step 2.2: find and comprise rotation matrix $\left[\begin{array}{cc}\cos \mathrm{\&theta;}& -\sin \mathrm{\&theta;}\\ \sin \mathrm{\&theta;}& \cos \mathrm{\&theta;}\end{array}\right]$ And translation vector $\left[\begin{array}{c}{t}_{\mathrm{latitude}}\\ t_{\mathrm{longitude}}\end{array}\right]$ Rigid transformation T, wherein, θ is Random-Rotation angle, t _latitudeand t _longitudebe respectively the translational movement of latitude and longitudinal, make arrangement set with set between distance minimum, even if objective function also is below minimum

$d(Y,\mathrm{TPos})=\underset{N_i=1}{\overset{N_p}{\mathrm{\&Sigma;}}}{{\mathrm{\&omega;}}_N}_i{\left|\right|{y_N}_i-{{\mathrm{Tpos}}_N}_i\left|\right|}^2$

${{\mathrm{\&omega;}}_N}_i=1-\frac{D({{\mathrm{pos}}_N}_i,{y_N}_i)}{D_{\max }}$

Wherein:

D is objective function,

the weights that represent sequence of points,

for with between distance,

D _maxfor with between the maximal value of distance;

The process that solves T solves θ, t exactly _latitudeand t _longitudeprocess, can and construct a Hamiltonian matrix by Quaternion Method and obtain;

Step 2.3: will gather transform to set , have e supposes to obtain after the iteration_k time iteration after the iteration_k+1 time iteration, obtain ${\mathrm{Pos}}_{N_i}^{\mathrm{iteration}\_k+}{=}^1\left\{{\mathrm{pos}}_{N_i}^{\mathrm{iteration\; k}+}\right\},$ Calculate $d(\mathrm{iteration}\_k+1)=d(Y,\mathrm{TPo}{s}_{N_i}^{\mathrm{iteration}\_k+1})$ With if d (iteration_k+1)-d (iteration_k) > is τ, the value of τ is 10 ^-6, return to execution step 2.2, if d (iteration_k+1)-d (iteration_k)≤τ, and iterations is less than maximum iteration time, and judgement meets final stopping criterion for iteration, exits iteration, determines that final matching results is [latitude _iCCPlongitude _iCCP] ^t;

Step 2.4: by the positional information latitude of main inertial navigation system output _iNS, longitute _iNSmate the positional information latitude obtaining with ICCP _iCCP, longitute _iCCPdifference latitude _iNS-latitude _iCCPas observed quantity, carry out Kalman filtering, and utilize site error amount that filtering obtains to feed back in main inertial navigation system the position of main inertial navigation output is proofreaied and correct, the position after being proofreaied and correct is

The 3rd step: when underwater hiding-machine enters landform can not matching area time, the synchronous location of employing based on marine environment feature and composition (SLAM) algorithm are assisted and are revised the site error that main inertial navigation system accumulates in time, its basic thought as shown in Figure 2, comprises the following steps:

Step 3.1: the foundation of system nonlinear process model

Step 3.1.1: the foundation of underwater hiding-machine state equation

Choosing sky, northeast is navigation coordinate system, and carrier coordinate system x axle points to starboard along underwater hiding-machine transverse axis, and before carrier coordinate system y axle points to along the underwater hiding-machine longitudinal axis, carrier coordinate system z axle forms right-handed coordinate system perpendicular to x axle and the determined plane of y axle; K moment underwater hiding-machine state equation can be provided by main inertial navigation mechanical equation:

Wherein:

Pos _vehicle(k) represent the k position of underwater hiding-machine constantly,

Vel _vehicle(k) represent the k speed of underwater hiding-machine constantly,

Qua _vehicle(k) represent the k attitude quaternion of underwater hiding-machine constantly,

Subscript G represents navigation coordinate system,

Subscript B represents carrier coordinate system,

Δ t represents the discrete sampling time interval,

C _b2Gexpression transforms to the direction cosine matrix of navigation coordinate system from carrier coordinate system,

F ^b(k) represent k accelerometer output constantly,

G represents acceleration of gravity,

the hypercomplex number that the angular speed that expression is recorded by gyroscope forms,

represent hypercomplex number multiplication;

Step 3.1.2: the foundation of map state

Set k and constantly observed N _mindividual new environmental characteristic, k moment map state is:

$x_{\mathrm{feature}}\left(k\right)={\left[\begin{array}{cccc}{x}_{\mathrm{feature}\_m1}^T\left(k\right)& x_{\mathrm{feature}\_m2}^T\left(k\right)& ...& x_{\mathrm{feature}\_m{N}_m}^T\left(k\right)\end{array}\right]}^T$

Wherein:

represent the k state of m1 environmental characteristic constantly,

represent the k state of m2 environmental characteristic constantly,

represent k mN constantly _mthe state of individual environmental characteristic,

Subscript T represents transposition;

By underwater environment feature modeling, be some feature, the position of environmental characteristic under navigation coordinate system is as follows:

$x_{\mathrm{feature}\_\mathrm{mi}}^G\left(k\right)=x_{\mathrm{feature}\_\mathrm{mi}}^G(k-1)={\mathrm{pos}}_{\mathrm{vehicle}}^G\left(k\right)+C_{B2G}\left(k\right)r_{\mathrm{BS}}^B+C_{B2G}\left(k\right)C_{S2B}{r}_{\mathrm{Sfeature}\_\mathrm{mi}}^S\left(k\right)$

Wherein:

be illustrated in mi environmental characteristic that k obtains the constantly position in navigation coordinate system,

be illustrated in mi environmental characteristic that k-1 obtains the constantly position in navigation coordinate system,

represent orientation/range sensor on underwater hiding-machine to the lever arm effect compensation rate at underwater hiding-machine center the component in carrier coordinate system,

Subscript S represents orientation/range sensor coordinate system,

C _s2Brepresent that orientation/range sensor coordinate system transformation is to the direction cosine matrix of carrier coordinate system,

$r_{\mathrm{Sfeature}\_\mathrm{mi}}^S\left(k\right)={\left[\begin{array}{ccc}d\cos \left(\mathrm{phi}\right)\cos \left(\mathrm{theta}\right)& d\sin \left(\mathrm{phi}\right)\cos \left(\mathrm{theta}\right)& d\cos \left(\mathrm{theta}\right)\end{array}\right]}^T$ Mi the environmental characteristic that represents that orientation/range sensor records and the relative position between underwater hiding-machine, wherein, d represents that orientation/range sensor is to the distance between environmental characteristic, and phi and theta represent respectively position angle and the elevation angle between environmental characteristic and orientation/range sensor;

Step 3.2: the foundation of the non-linear observation model of system

Step 3.2.1: the orientation/range sensor on underwater hiding-machine can obtain the relative position observation of environmental characteristic and underwater hiding-machine, and the non-linear observation model of system can be expressed as follows:

z(k)＝h(x(k))+ν(k)

Wherein:

Z (k) represents the observed reading to environmental characteristic,

H () is non-linear observation function,

X (k) is system state vector, comprises the position of position, speed, attitude quaternion and the environmental characteristic of underwater hiding-machine,

ν (k) is systematic observation noise;

K is constantly only relevant with underwater hiding-machine state to this feature to the observation of mi environmental characteristic, therefore has:

z _mi(k)＝h(x _vehicle(k),x _{feature_mi}(k))+ν _mi(k)

Wherein:

Z _mi(k) represent the k observation to mi environmental characteristic constantly,

X _vehicle(k) represent that underwater hiding-machine is at k state constantly,

X _{feature_mi}(k) represent that mi environmental characteristic is at k state constantly,

ν _mi(k) be the observation noise to mi environmental characteristic;

Step 3.2.2: set $r_{\mathrm{Sfeature}\_\mathrm{mi}}^S\left(k\right)={\left[\begin{array}{ccc}x& y& z\end{array}\right]}^T,$ K being observed mi environmental characteristic constantly:

$z_{\mathrm{mi}}\left(k\right)=\left[\begin{array}{c}d\\ \mathrm{phi}\\ \mathrm{theta}\end{array}\right]=\left[\begin{array}{c}\sqrt{x^2+y^2+z^2}\\ \arctan \left(\frac{y}{x}\right)\\ \arctan \left(\frac{z}{\sqrt{x^2+y^2}}\right)\end{array}\right]$

Wherein:

D represents that orientation/range sensor is to the distance between environmental characteristic,

Phi represents the position angle between environmental characteristic and orientation/range sensor,

Theta represents the elevation angle between environmental characteristic and orientation/range sensor;

Step 3.3: system state augmentation process

In underwater hiding-machine navigation process, orientation/range sensor observes environmental characteristic, if the feature of this environmental characteristic for having observed directly performs step 3.5; If this environmental characteristic is emerging feature, system state is carried out to augmentation and order performs step 3.4 and step 3.5, the system state after augmentation becomes:

$x\left(k\right)=\left[\begin{array}{c}{x}_{\mathrm{vehicle}}\left(k\right)\\ x_{\mathrm{feature}}\left(k\right)\end{array}\right]=\left[\begin{array}{c}{f}_{\mathrm{vehicle}}(x_{\mathrm{vehicle}}(k-1),u\left(k\right),w\left(k\right))\\ x_{\mathrm{feature}}(k-1)\end{array}\right]$

Wherein:

X _vehicle(k) represent k underwater hiding-machine state constantly, comprise underwater hiding-machine position, speed and attitude,

X _feature(k) represent the state of environmental characteristic, because the state of environmental characteristic only comprises the positional information of environmental characteristic, Gu x _feature(k) the actual position that represents environmental characteristic,

F _vehicle() is nonlinear state transfer function;

Step 3.4:SLAM algorithm predicts process

By process model and observation model, k system state and state covariance matrix constantly predicted to have:

x(k|k-1)＝f(x(k-1|k-1))

$P_{\mathrm{cov}}\left(k\right|k-1)=\stackrel{\&OverBar;}{F}\left(k\right)P_{\mathrm{cov}}(k-1|k-1)\stackrel{\&OverBar;}{F}{\left(k\right)}^T+Q\left(k\right)$

Wherein:

X (k|k-1) be according to k-1 system state constantly to k one-step prediction quantity of state constantly,

X (k-1|k-1) is k-1 system state estimation value constantly,

P _cov(k|k-1) be according to k-1 system state constantly to k prediction covariance constantly,

P _cov(k-1|k-1) be k-1 state covariance estimated value constantly,

for the Jacobi matrix of system state equation,

T represents transposition,

Q (k) is the covariance of noise ω (k);

Step 3.5:SLAM algorithm renewal process

The environmental characteristic obtaining for observation, integrating step 3.2 and step 3.4 pair system state value is upgraded, and concrete formula is as follows:

New breath: v _innov(k)=z (k)-h (x (k|k-1))

Filter gain: $K_{\mathrm{gain}}\left(k\right)=P_{\mathrm{cov}}\left(k\right|k-1)\stackrel{\&OverBar;}{H}{\left(k\right)}^T{(\stackrel{\&OverBar;}{H}\left(k\right)P_{\mathrm{cov}}\left(k\right|k-1)\stackrel{\&OverBar;}{H}{\left(k\right)}^T+R\left(k\right))}^{-1}$

State estimation value: x (k|k)=x (k|k-1)+K _gain(k) v _innov(k)

State covariance: $P_{\mathrm{cov}}\left(k\right|k)=P_{\mathrm{cov}}\left(k\right|k-1)-K_{\mathrm{gain}}\left(k\right)\stackrel{\&OverBar;}{H}\left(k\right)P_{\mathrm{cov}}\left(k\right|k-1)$

Wherein:

V _innov(k) be new breath,

Z (k) is k observed reading constantly,

H (x (k|k-1)) observes predicted value constantly for k,

K _gain(k) be k filter gain constantly,

for the Jacobi matrix of systematic observation equation,

R (k) is the covariance of ν (k),

X (k|k) is k system state estimation value constantly,

P _cov(k|k) be k system state covariance constantly;

The pos obtaining from x (k|k) in step 3.6:SLAM algorithm _vehiclebe revised main inertial navigation positional information.

Embodiment:

Emulation experiment is chosen landform region as shown in Figure 3, its scope is (38.0 ° of north latitude, 120.0 ° of east longitudes) to (38.04 ° of north latitude, 120.05 ° of east longitudes) rectangular area, according to mobile computing window technique, by simulation analysis obtain as the blocked areas on the left side in Fig. 4 be terrain match region, the blocked areas on the right is not matching area of landform.

In landform, can in matching area, a desirable guidance path that contains 10 points be set, as 1# line in Fig. 5, inertial navigation indication flight path is as 2# line in Fig. 5, adopts ICCP algorithm mate coupling flight path after locating as shown in 3# line in Fig. 5, can see, coupling flight path almost overlaps with Desired Track.

Can not matching area in landform, adopt SLAM algorithm to carry out navigator fix.In Fig. 6, in environment, provide arbitrarily 3 environmental characteristics and a desirable guidance path of take 10 points that ICCP aided inertial navigation system gained final position is starting point is set, as shown in 1# line in Fig. 6.Inertial navigation indication flight path, as shown in 2# line in Fig. 6, adopts the estimated path of SLAM algorithm acquisition as shown in 3# line in Fig. 6.Can see SLAM estimate flight path than pure-inertial guidance system indication flight path closer to Desired Track.

From simulation result, algorithm is independently selected suitable assisting navigation means after can having judged whether landform can mate, can matching area in landform, adopt the Models in Terrain Aided Navigation based on ICCP algorithm, and carry out aided inertial navigation system; Can not matching area in landform, adopt SLAM algorithm to carry out aided inertial navigation system, thereby realize the long independent navigation of submarine navigation device.

The above is only the preferred embodiment of the present invention; be noted that for those skilled in the art; under the premise without departing from the principles of the invention, can also make some improvements and modifications, these improvements and modifications are also considered as protection scope of the present invention.

Claims (3)

1. the underwater navigation localization method in conjunction with landform and environmental characteristic, it is characterized in that: according to landform quantity of information, judge in the planning flight path of underwater hiding-machine, whether each sub regions can mate, if can mate, adopt terrain aided inertial navigation system to realize location, if can not mate, adopt the auxiliary main inertial navigation system of synchronous location and composition algorithm to realize location.

2. the underwater navigation localization method of combination landform according to claim 1 and environmental characteristic, is characterized in that: judge that the method whether each sub regions in the planning flight path of underwater hiding-machine can mate is as follows:

Step 1.1: adopt the mode of grid matrix to be divided into polylith landform candidate region submarine topography elevation, on the planning flight path of underwater hiding-machine,, through L piece landform candidate region wherein, the longitude and latitude span of setting a certain landform candidate region is M * N grid, and net point coordinate is that the landform altitude value that (i, j) locates is height (i, j), here i=1,2 ..., M, j=1,2 ..., N;

Step 1.2: utilize mobile computing window technique to calculate the parameter of landform candidate region, concrete grammar is:

Define the local mobile computing window that a size is m * n, and calculate the Terrain Elevation mean value in local mobile computing window when the center of local mobile computing window after mobile one time, can obtain the landform standard deviation sigma (l) of each landform candidate region, the landform coefficient R in longitudinal on the total-grid point of each landform candidate region _longitude(l) the landform coefficient R and in latitude direction _latitude(l), l=1 wherein, 2 ..., L, subscript longitude represents longitude, and latitude represents latitude, and the parameter specific formula for calculation of landform candidate region is as follows:

$\mathrm{\&sigma;}\left(l\right)=\sqrt{\frac{1}{m(n-1)}\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{[\mathrm{height}(i,j)-\mathrm{height}\_\mathrm{ave}]}^2}$

$R_{\mathrm{longitude}}\left(l\right)=\frac{1}{n(m-1)\mathrm{\&sigma;}{\left(l\right)}^2}\underset{i=1}{\overset{m-1}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}[\mathrm{height}(i,j)-\mathrm{height}\_\mathrm{ave}][\mathrm{height}(i+1,j)-\mathrm{height}\_\mathrm{ave}]$

$R_{\mathrm{latitude}}\left(l\right)=\frac{1}{n(m-1)\mathrm{\&sigma;}{\left(l\right)}^2}\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{n-1}{\mathrm{\&Sigma;}}}[\mathrm{height}(i,j)-\mathrm{height}\_\mathrm{ave}][\mathrm{height}(i,j+1)-\mathrm{height}\_\mathrm{ave}]$

Step 1.3: the L piece landform candidate region on planning flight path, judges respectively whether the landform standard deviation of each piece landform candidate region and longitude and latitude direction landform related coefficient meet σ (l) > 4 σ simultaneously _cand R _longitude(l) < 0.7 and R _latitude(l) < 0.7, if meet, corresponding landform candidate region is can matching area, otherwise is can not matching area, wherein, and σ _cstandard deviation for depth sensor measuring error.

3. the underwater navigation localization method of combination landform according to claim 1 and environmental characteristic, is characterized in that: described synchronous location and composition algorithm comprise the following steps:

Step 3.1: the foundation of system nonlinear process model

Step 3.1.1: the foundation of underwater hiding-machine state equation

Choosing sky, northeast is navigation coordinate system, and carrier coordinate system x axle points to starboard along underwater hiding-machine transverse axis, and before carrier coordinate system y axle points to along the underwater hiding-machine longitudinal axis, carrier coordinate system z axle forms right-handed coordinate system perpendicular to x axle and the determined plane of y axle; Underwater hiding-machine state equation is as follows constantly for k:

Wherein:

Pos _vehicle(k) represent the k position of underwater hiding-machine constantly,

Vel _vehicle(k) represent the k speed of underwater hiding-machine constantly,

Qua _vehicle(k) represent the k attitude quaternion of underwater hiding-machine constantly,

Subscript G represents navigation coordinate system,

Subscript B represents carrier coordinate system,

Δ t represents the discrete sampling time interval,

C _b2Gexpression transforms to the direction cosine matrix of navigation coordinate system from carrier coordinate system,

F ^b(k) represent k accelerometer output constantly,

G represents acceleration of gravity,

the hypercomplex number that the angular speed that expression is recorded by gyroscope forms,

represent hypercomplex number multiplication;

Step 3.1.2: the foundation of map state

Set k and constantly observed N _mindividual new environmental characteristic, k moment map state is:

$x_{\mathrm{feature}}\left(k\right)={\left[\begin{array}{cccc}{x}_{\mathrm{feature}\_m1}^T\left(k\right)& x_{\mathrm{feature}\_m2}^T\left(k\right)& ...& x_{\mathrm{feature}\_m{N}_m}^T\left(k\right)\end{array}\right]}^T$

Wherein:

represent the k state of m1 environmental characteristic constantly,

represent the k state of m2 environmental characteristic constantly,

represent k mN constantly _mthe state of individual environmental characteristic,

Subscript T represents transposition;

By underwater environment feature modeling, be some feature, the position of environmental characteristic under navigation coordinate system is as follows:

$x_{\mathrm{feature}\_\mathrm{mi}}^G\left(k\right)=x_{\mathrm{feature}\_\mathrm{mi}}^G(k-1)={\mathrm{pos}}_{\mathrm{vehicle}}^G\left(k\right)+C_{B2G}\left(k\right)r_{\mathrm{BS}}^B+C_{B2G}\left(k\right)C_{S2B}{r}_{\mathrm{Sfeature}\_\mathrm{mi}}^S\left(k\right)$

Wherein:

be illustrated in mi environmental characteristic that k obtains the constantly position in navigation coordinate system,

be illustrated in mi environmental characteristic that k-1 obtains the constantly position in navigation coordinate system,

represent orientation/range sensor on underwater hiding-machine to the lever arm effect compensation rate at underwater hiding-machine center the component in carrier coordinate system,

Subscript S represents orientation/range sensor coordinate system,

C _s2Brepresent that orientation/range sensor coordinate system transformation is to the direction cosine matrix of carrier coordinate system,

$r_{\mathrm{Sfeature}\_\mathrm{mi}}^S\left(k\right)={\left[\begin{array}{ccc}d\cos \left(\mathrm{phi}\right)\cos \left(\mathrm{theta}\right)& d\sin \left(\mathrm{phi}\right)\cos \left(\mathrm{theta}\right)& d\cos \left(\mathrm{theta}\right)\end{array}\right]}^T$ Mi the environmental characteristic that represents that orientation/range sensor records and the relative position between underwater hiding-machine, wherein, d represents that orientation/range sensor is to the distance between environmental characteristic, phi represents the position angle between environmental characteristic and orientation/range sensor, and theta represents the elevation angle between environmental characteristic and orientation/range sensor;

Step 3.2: the foundation of the non-linear observation model of system

Step 3.2.1: obtain the relative position observation of environmental characteristic and underwater hiding-machine according to orientation/range sensor, thereby the non-linear observation model of the system of setting up is as follows:

z(k)＝h(x(k))+ν(k)

Wherein:

Z (k) represents the observed reading to environmental characteristic,

H () is non-linear observation function,

X (k) is system state vector, comprises the position of position, speed, attitude quaternion and the environmental characteristic of underwater hiding-machine,

ν (k) is systematic observation noise;

K is constantly only relevant with underwater hiding-machine state to this environmental characteristic to the observation of mi environmental characteristic, therefore has:

z _mi(k)＝h(x _vehicle(k),x _{feature_mi}(k))+ν _mi(k)

Wherein:

Z _mi(k) represent the k observation to mi environmental characteristic constantly,

X _vehicle(k) represent that underwater hiding-machine is at k state constantly,

X _{feature_mi}(k) represent that mi environmental characteristic is at k state constantly,

ν _mi(k) be the observation noise to mi environmental characteristic;

Step 3.2.2: set $r_{\mathrm{Sfeature}\_\mathrm{mi}}^S\left(k\right)={\left[\begin{array}{ccc}x& y& z\end{array}\right]}^T,$ K being observed mi environmental characteristic constantly:

$z_{\mathrm{mi}}\left(k\right)=\left[\begin{array}{c}d\\ \mathrm{phi}\\ \mathrm{theta}\end{array}\right]=\left[\begin{array}{c}\sqrt{x^2+y^2+z^2}\\ \arctan \left(\frac{y}{x}\right)\\ \arctan \left(\frac{z}{\sqrt{x^2+y^2}}\right)\end{array}\right]$

Wherein:

D represents that orientation/range sensor is to the distance between environmental characteristic,

Phi represents the position angle between environmental characteristic and orientation/range sensor,

Theta represents the elevation angle between environmental characteristic and orientation/range sensor;

Step 3.3: system state augmentation process

In underwater hiding-machine navigation process, orientation/range sensor observes environmental characteristic, if the feature of this environmental characteristic for having observed directly performs step 3.5; If this environmental characteristic is emerging feature, system state is carried out to augmentation and order performs step 3.4 and step 3.5, the system state after augmentation becomes:

$x\left(k\right)=\left[\begin{array}{c}{x}_{\mathrm{vehicle}}\left(k\right)\\ x_{\mathrm{feature}}\left(k\right)\end{array}\right]=\left[\begin{array}{c}{f}_{\mathrm{vehicle}}(x_{\mathrm{vehicle}}(k-1),u\left(k\right),w\left(k\right))\\ x_{\mathrm{feature}}(k-1)\end{array}\right]$

Wherein:

X _vehicle(k) represent k underwater hiding-machine state constantly, comprise underwater hiding-machine position, speed and attitude quaternion,

X _feature(k) represent the k state of environmental characteristic constantly,

F _vehicle() is nonlinear state transfer function;

Step 3.4: synchronous location and composition algorithm predicts process

The non-linear observation model of system that the system nonlinear process model of setting up by step 3.1 and step 3.2 are set up predicts have to k system state and state covariance matrix constantly:

x(k|k-1)＝f(x(k-1|k-1))

$P_{\mathrm{cov}}\left(k\right|k-1)=\stackrel{\&OverBar;}{F}\left(k\right)P_{\mathrm{cov}}(k-1|k-1)\stackrel{\&OverBar;}{F}{\left(k\right)}^T+Q\left(k\right)$

Wherein:

X (k|k-1) be according to k-1 system state constantly to k one-step prediction quantity of state constantly,

X (k-1|k-1) is k-1 system state estimation value constantly,

P _cov(k|k-1) be according to k-1 system state constantly to k prediction covariance constantly,

P _cov(k-1|k-1) be k-1 state covariance estimated value constantly,

for the Jacobi matrix of system state equation,

T represents transposition,

Q (k) is the covariance of noise ω (k);

Step 3.5: synchronous location and composition algorithm renewal process

The environmental characteristic obtaining for observation, integrating step 3.2 and step 3.4 pair system state value is upgraded, and concrete formula is as follows:

New breath: v _innov(k)=z (k)-h (x (k|k-1))

Filter gain: $K_{\mathrm{gain}}\left(k\right)=P_{\mathrm{cov}}\left(k\right|k-1)\stackrel{\&OverBar;}{H}{\left(k\right)}^T{(\stackrel{\&OverBar;}{H}\left(k\right)P_{\mathrm{cov}}\left(k\right|k-1)\stackrel{\&OverBar;}{H}{\left(k\right)}^T+R\left(k\right))}^{-1}$

State estimation value: x (k|k)=x (k|k-1)+K _gain(k) v _innov(k)

State covariance: $P_{\mathrm{cov}}\left(k\right|k)=P_{\mathrm{cov}}\left(k\right|k-1)-K_{\mathrm{gain}}\left(k\right)\stackrel{\&OverBar;}{H}\left(k\right)P_{\mathrm{cov}}\left(k\right|k-1)$

Wherein:

V _innov(k) be new breath,

Z (k) is k observed reading constantly,

H (x (k|k-1)) observes predicted value constantly for k,

K _gain(k) be k filter gain constantly,

for the Jacobi matrix of systematic observation equation,

R (k) is the covariance of ν (k),

X (k|k) is k system state estimation value constantly,

P _cov(k|k) be k system state covariance constantly;

Step 3.6: the pos obtaining from x (k|k) _vehiclebe revised main inertial navigation positional information.