Introduction

ADS-B is the

abbreviation for broadcast-related automatic surveillance. It mainly implements

air-to-air surveillance. Generally, it only needs on-board electronic devices

(GPS receiver, data link transceiver and antenna, Cockpit Conflict Information

Display CDTI ) That does not require any ground support equipment and that an

ADS-B equipped aircraft can broadcast its own precise location and other data

(such as speed, altitude and whether the aircraft turns, climbs or descents,

etc.) through the data link. The ADS-B receiver, combined with the ATC system

and other aircraft’s on-board ADS-B, provides accurate and real-time collision

information in the open space. ADS-B is a completely new technology that

redefines the three elements of today’s air traffic control communications,

navigation and surveillance.

Automatic

– automatic, “all-weather operation”, without duty.

Dependent

– it only needs to rely on accurate global positioning data of the satellite

navigation.

Surveillance

– Surveillance, surveillance (gain) aircraft position, altitude, speed,

heading, identification number and other information.

Broadcast

– Broadcast, unanswered, airplanes or ground stations broadcasting each other’s

own data messages.

ADS-B system consists

of multi-ground stations and airborne stations to form a network, multi-point

to multi-point data to complete the two-way communication. Airborne ADS-B

communication equipment broadcasts navigation information collected by airborne

information processing units, receives broadcast information from other

aircrafts and terrestrials, and processes the same to give a comprehensive

information display to the cabin. Based on ADS-B information collected from

other aircraft and ground, airborne radar information and navigation

information, the integrated information display provides pilots with

situational information and other additional information about the aircraft

(eg, collision warning information, collision avoidance strategies, weather

information ).

ADS-B system is a

collection of communications and surveillance in one of the information

systems, information sources, information transmission channels and information

processing and display of three parts. ADS-B’s main information is the

aircraft’s 4-dimensional location information (longitude, latitude, altitude

and time) and other possible additional information (collision warning

information, pilot input, track angle, inflection point and other information)

and aircraft identification information And category information. In addition,

it may include some additional information such as heading, airspeed, wind

speed, wind direction and temperature outside the aircraft.

Disadvantage of ADS-B system

1)

Related surveillance relies entirely on airborne navigation sources

ADS-B

itself does not have the verification function of the information source, the

ground station equipment (system) cannot be discerned if the position

information given by the aircraft is wrong; ADS-B cannot work normally in the

case of GNSS failure;

2)

Information processing time is long, communication lags behind

Therefore, there is

necessary to find a new system such that ADS-B will not fail in the case of

GNSS failure. In this case, combine two navigation system together is a better

approach.

Strapdown inertial

navigation systems and global navigation satellite systems have their own

distinct advantages and disadvantages, SINS has the advantages of anti-jamming,

but there is a fatal flaw in positioning accuracy over time: satellite navigation

systems is with high positioning accuracy, and positioning accuracy does not

diverge with time but weak navigation satellite signals vulnerable to

interference. The combination of these two satellite Inertial Navigation System

can overcome the shortcomings of both to play the strengths of both to achieve

complementary advantages. According to the level of information fusion,

satellite inertial integrated navigation system can be divided into loose

combination, tight combination, ultra tight combination and deep integrated

navigation system. Among them, the loosely combined and tight integrated

navigation system does not improve the loop performance of the satellite

receiver. The ultra-tight integrated navigation and deep integrated navigation

use the inertial information aided tracking loop to improve the performance of

satellite navigation receiver tracking loop. One of the key technologies of

deep integrated navigation system based on vector tracking is high performance

vector tracking loop. Some domestic and foreign researchers have done a lot of

researches on deep inertial combination of satellite based on vector tracking

loop. Based on Matlab and Sigmaplot Software platform to build a

vector-tracking software receiver and details of the implementation details and

parameter setting details, and based on this platform to build a vector

tracking deep combination of guidance air system, and tested it. Wang Xinlong

et al at Beijing University of Aeronautics and Astronautics researched a SINS /

GPS integrated deep navigation method based on vector tracking. The simulation

proves the excellent performance of the vector tracking deep integrated

navigation system, which can guarantee the performance of the integrated

navigation system Navigation accuracy and reliability. Draper Laboratory,

Aerospace Corporation and Raython Corporation, MIT overseas institutes put

forward their own deep integrated navigation system, which proves that the

vector tracking deep integrated navigation system has stronger anti-jamming

performance. At present, the research of vector tracking deep integrated

navigation system mainly focuses on reducing the amount of computation and

improving system fault tolerance. At present, there are few researches on

fault-tolerant of deep tracking navigation system based on vector tracking. In

this paper, we propose a new channel subfilter and state detection function to

detect the possible influence of abnormal channel on normal channel in deep

naval navigation status.

In

this paper, the basic principle of vector tracking deep integrated navigation

system is firstly analyzed. Aiming at the problem of channel status detection

of deep integrated navigation system under the condition of frequent occlusion

of some satellite signals, a subfilter and its corresponding subfilter state

detection function are designed. Used to detect the channel running status, and

finally verify and analyze the performance of the algorithm through simulation

experiments.

1 Fault-tolerant deep integrated

navigation system

1.1 Deep combination navigation

system

Figure

1 is a fault-tolerant vector tracking deep composite navigation system

structure, fault-tolerant deep integrated navigation system is mainly composed

of vector receiver module, inertial navigation module and integrated navigation

module. This program retains the vector tracking receiver navigation filter,

mainly for two reasons, the first point, so you can reduce the operating

frequency of the combined navigation filter, the navigation signal is not used

in the calculation of navigation information Tracking loop parameters to update

the vector tracking loop; the second point, this design for the entire

integrated navigation system in terms of retaining the entire vector receiver

system for measuring the channel state of operation, the sub-filter model and

fault detection function in detail 1.2 and 1.3

1.2 sub-filter model

State

equation of sub-filter model:

??k+1 0 T ??k v?

??’k+1 =

T 0 ·

??’k

+ v?’

In the formula, ??k+1, ??’k+1 are K +1 moment pseudorange,

pseudo-range error, T is the filter period of 1ms, ??k, ??’k are k moment pseudorange, pseudo-range

error, v?, v?’ are pseudo-range, pseudo-range system

noise, respectively.

Sub-filter model of the measurement equation:

zncode 1

0 ??k ??

zncarrier = 0

1 · ??’k + ??’

In the formula, zncode, zncarrier are for the channel n pseudo-range,

pseudo-range measurement, respectively; ??k, ??’k are k moment pseudorange, pseudo-range

error, respectively; ??, ??’ are pseudo-range, pseudo-range system noise,

respectively.

1.3 state detection function

Navigation filter for

the Kalman filter is calculated as follows:

Consider a linear

discrete system:

xk = ?k,k-1 xk-1 + wk-1

zk =

HK xk + vk

among them, xk is defined to be the moment k state

vector, ?k,k-1 is defined to be the state transition matrix, zk is defined to be the measurement vector, HK is

defined to be the measurement matrix, and wk-1, vk

are defined to be the system and measurement noise, and satisfy the

following:

E{wk} = 0, E{ wk wTj}

= Qk

E{vk} = 0, E{ vk vTj}

= Rk

E{ vk wTj} =

0

In the above equations, Qk >= 0 is the system noise variance matrix; Rk > 0 is the

measuring noise variance matrix.

x’k,k-1 =?k,k-1 x’k-1

x’k = x’k,k-1 + Kk(zk

– HK x’k,k-1)

Kk = Pk,k-1 HTk(HK

Pk,k-1 HTk + Rk)-1

Pk,k-1 =?k,k-1 Pk-1 ?Tk,k-1 + Qk-1

Pk = (1 – Kk HK) Pk,k-1

among them, x’k,k-1 is the state prediction, Kk is the filter gain matrix, Pk

is the covariance matrix.

Residual is defined to be:

rk = zk – HK x’k,k-1

It can be proved that the residual rk is

zero-mean Gaussian white noise when the filter is fault-free, the variance is:

Ak = HK Pk,k-1 HTk

+ Rk

When the system fails, the mean value of the

residual rk will not be zero, the fault detection function is as

follows:

?k = rTk A-1k

rk

In the above formula, ?k is in chi-squared distribution with degree freedom of m, where m is the

measurement dimension of zk. Judgment criteria are as follows:

?2m (k) > TD, it is abnormal

?2m (k) 0

, so Kk(:, j) -> 0 .

2) According to equation x’k = x’k,k-1 + Kk(zk – HK x’k,k-1), we can see the j-th component of zk measure contributes

little or nothing to the state estimate effect x’k.

1.4

combined navigation filter

In the integrated navigation system, the Kalman

filter used by both the vector receiver and the combined navigation filter, and

the system state variables of the Kalman filter in the integrated navigation

system take the error quantities of the navigation output parameters, including

SINS output error and GNSS receiver output error . The system’s state variables

are:

X = XI XGT

among them, XI is SINS error variable,

the concrete form is:

XI =

?E ?N ?U ?VE ?VN ?VU ?L ?? ?h ?x ?y ?z ?x ?y ?z

In the above formula, ?E ?N ?U are the

attitude error angle in east, north and upward direction; ?VE ?VN ?VU are the velocity error in the east, north and sky direction; ?L ?? ?h are the latitude, longitude and height error; ?x ?y ?z are the random drift of the

three gyroscopes in the carrier system; ?x ?y ?z are the

common bias of the accelerometer in the three axial directions of the carrier

system,

XG is GNSS error variables of

clock drift

XG = ?tu ?tru T

System Measurement Inputs Pseudo-range and

Pseudo-range Estimation for Sub-Filter is:

Z = zp = Hp X +

Vp = HX + V

zp’

Hp’

Vp’

2

Simulation and Result

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Conclusion

Aiming at the robustness of vector tracking

deep integrated navigation system, a GNSS / SINS fault tolerant deep integrated

navigation system is proposed. First, a simple subfilter is designed for each

channel, and the state of the subfilter is detected by the detection function

Judging the operation of the channel, when the channel satellite signal is

briefly blocked, it can be timely judged and isolated, thus avoiding the

influence of the blocked channel on the navigation and positioning accuracy of

the deep integrated navigation, thereby improving the stability of the system.

When the signal appear again, due to the mutual assistance between the vector

tracking channels, when the signal reappears, it can immediately be tracked again

and incorporated into the navigation filter, thus avoiding the traditional

fault diagnosis method of removing the satellite directly, and when satellite

signals can take full advantage when they reappear all satellite signals.