Multisensor Fusion Model for Navigation Systems

a Kalman filter are used as the test statistic to monitor the navigation states. Hanlon and Maybeck[90] analyse the effects of mismodelled input matrix, output matrix and state transition matrix on the residuals resulting from a bank of Kalman filters, each using a different filter model to describe the same dynamic system. They also develop a hypothesis testing algorithm using these residuals to detect failure status of a flight control system and to estimate the true system model. Although this method has advantages in the design of a reliable flight control system, it cannot be used in distributed inertial sensor systems. However, this method may be suitable for the federated filter architecture or traditional multisensor-based navigation systems where the main purpose is to estimate the centralised navigation states.

All of the published sensor FDI algorithms that have been located are capable of detecting hard sensor failures in clustered inertial sensor system architectures. Although some of these FDI algorithms can enhance the performance of sensor FDI, they cannot improve the accuracy of an SRIMU system. Earlier NSIM methods were developed for special GPS/INS integrated navigation systems with a centralised filtering architecture. However, they are not amenable to expansion and cannot be used in distributed inertial network systems. In this thesis, several improved FDI and NSIM methods are presented to detect the drift sensor failures and the navigation state abnormalities in distributed inertial network systems. The compensation filters are developed for the correction of SRIMU measurements.

2.6 Multisensor Fusion Model for Navigation Systems

Multisensor data fusion covers fault-tolerant design and data fusion methods. As identified in Chapter 1, the JPL MSDF model and other models do not apply to the development of distributed multisensor navigation systems. From the definition of multisensor data fusion given in Chapter 1, a multisensor data fusion model for aircraft navigation systems is a conceptualised framework in which sensor network topology architecture, data communication mechanism, system functions and related operational modes are defined. The data fusion methodologies are then developed to

OVERVIEW & METHODOLOGY

2.6 Multisensor Fusion Model for Navigation Systems implement the required system functions and operational modes. This thesis presents a generalised MSDF model for the design, analysis, development and simulation of multisensor aircraft navigation systems, as illustrated in Figure 2.9.

Figure 2.9 Generalised MSDF Model for Aircraft Navigation Systems

2.6.1 Sensor Topology Network

The sensor topology network provides a hardware foundation for the design and development of multisensor navigation systems and describes distributions and allocations of various sensor systems in the network. The architecture of a sensor topology network is specified according to the system design requirements. A sensor network topology can be a serial, parallel or hybridised architecture; or a completely packaged, distributed network or combination of both. Parallel and distributed sensor network architectures are the most commonly used sensor topologies in modern aircraft.

Optimisation of the topological architectures of a sensor network determines the optimal sensor system configurations and allocations in an aircraft navigation system. The allocations of sensor systems depend on the requirements of both the aircraft navigation system (e.g. survivability and fault tolerance) and other avionics

System-Level Data Fusion

State Estimation Dynamic Alignment and

Correction

Navigation States Integrity

Monitoring

System-Level FDI

System Reconfiguration Sensor-Level Data Fusion

Sensor Data Filtering,

Correction and Compensation

Data Alignment (time and

space)

Sensor-Level FDI Sensor Reconfiguration

Sensor Topology Network

Architecture Optimisation Sensor System Allocations Data Communication Mechanism Technology Obsolescence Sensor 1 Sensor 2 Sensor 3 Sensor/System Management

Sensor Reconfiguration Strategy System Reconfiguration Strategy System Management Strategy System Interface Management

OVERVIEW & METHODOLOGY

2.6 Multisensor Fusion Model for Navigation Systems systems for the inertial and navigation states. For example, many avionics systems require highly reliable, continuous inertial data to implement individual functions, as stated in Section 1.1. Some inertial systems must be located close to specific avionics systems to provide the precise local motion states for stabilisation of specific avionics systems, such as weapon pointing systems and imaging radars.

The data communication specifies the architecture of a communication network and the requirements for data buses in order to exchange data among individual sensor systems and to transmit data to other avionics systems. The data protocol and transfer speed must be selected so that the data communication network can meet the requirements that data fusion algorithms require from sensor data.

The evaluation of technology obsolescence is a key to the mitigation of ageing technologies and to the application of emerging technologies to meet the long-term operational lifetime requirements for aircraft navigation systems.

Data fusion methodologies can then be developed so that the resultant data fusion algorithms, in combination with a data communication network, can fuse various sensor data to achieve the required performance for aircraft navigation and other airborne applications.

2.6.2 Sensor-Level Data Fusion

Sensor-level data fusion is preliminary data fusion. It analyses and qualifies all

sensor measurements to provide highly reliable sensor data for subsequent system- level data fusion. It can also transmit health status information of all sensor systems to the sensor management. At this level, the following functions are performed:

• Sensor corrections and compensations to obtain accurate sensor data; • Data alignment in time and space to ensure that associated measurements

of all sensor systems are time-synchronised and common-coordinated;

• Detection of sensor failures and isolation of failed sensors if necessary; • Reconfiguration of sensor systems based on certain sensor reconfiguration

strategies.

OVERVIEW & METHODOLOGY

2.6 Multisensor Fusion Model for Navigation Systems

2.6.3 System-Level Data Fusion

System-level data fusion is the kernel of a multisensor data fusion system. It

fuses data from sensors and subsystems in terms of optimised data fusion algorithms to estimate the required system states and to monitor the integrity of the estimated states by performing specific error covariance analysis and statistical tests. At this level, the following functions are undertaken:

• State estimation. This function covers the design and development of both

data fusion filter architectures on the basis of the topological architecture of sensor network and optimal data fusion algorithms suitable for the filter architectures;

• Navigation solution integrity monitoring and system FDI. These functions

are required in order to obtain the integrity of the navigation system. They are concerned with analysis and evaluation of the state error covariance and residual information of the data fusion filter;

• Alignment and correction of inertial systems in distributed sensor network.

This function is concerned with development of data fusion algorithms to dynamically align and correct distributed inertial systems.

• Reconfiguration of system models. This function implements fault-tolerant

design in a multisensor navigation system. It is provided to fulfil system reconfiguration strategies and operational modes.

2.6.4 Sensor/System Management

Sensor/system management performs three types of management functions:

sensor network system management, data communication management and human- machine interface management. According to the health status information from the sensor-level data fusion and system-level data fusion modules, and command inputs from the pilot, the sensor network system management determines the operational modes and reconfiguration strategies of the navigation system, and transmits the associated commands to the two data fusion modules. The sensor-level data fusion

OVERVIEW & METHODOLOGY

2.7 Summary