Fusion Plugin

The Fusion Plugin is a plugin which can be optionally used by the Orchestration Plugin. It is a factory that produces fusion engines. Currently, StandardFusionEngine is the only type of fusion engine specified by the API, although future versions could define more advanced types (like a sampled model).

The Fusion Engine

The fusion engine performs sensor fusion on behalf of the orchestration plugin. It is the glue between the State Modeling Plugin and the Fusion Strategy Plugin. It consumes state blocks, measurement processors, and virtual state blocks from the state model provider and dispatches to the fusion strategy for the actual estimation work.

For example, when the orchestration plugin adds a state block to the fusion engine, the fusion engine should tell the fusion strategy to add states equal to the number of states the new block provides. It will also need to maintain a mapping of the fusion strategy states to the states of the new block so that it can perform future operations on them.

State Interactions

The fusion engine allows the user to set and get state estimates and covariances via state block labels. Its bookkeeping enables it to translate between indices in the fusion strategy and the corresponding block label.

In the case where the user requests a state estimate or covariance using a virtual state block label, the fusion engine should convert the state estimate or covariance using the corresponding virtual state block.

The `StandardFusionEngine`

This type of fusion engine generally assumes either linear (or linearized) models for state propagation and updates.

Propagation

One of the major functions of the StandardFusionEngine is propagation (also known as prediction). During propagation, the filter time is advanced to produce new state estimates and covariances by leveraging the models provided by the state blocks.

The StandardFusionEngine propagates the filter by:

Querying the fusion strategy for the current estimate and covariance
Providing each state block with the estimate and covariance from 1. and querying each for a dynamics model
Combining each dynamics model into one large model
Passing the combined model to the fusion strategy

Update

The other major function of the fusion engine is to update the filter states. In Kalman filtering the update step incorporates a new sensor measurement, producing a new state estimate and shrinking the state covariance.

The StandardFusionEngine consumes sensor measurements by:

Querying the fusion strategy for the current estimate and covariance of the states which will be mapped to the measurement
Providing the corresponding measurement processor with the estimate and covariance from 1., based on its list of state block labels, in order to query it for a measurement model
Passing that model to the fusion strategy

Virtual state blocks

If the measurement processor references the label of a virtual state block in step 2., then the measurement processor will be producing a model for an aliased set of states. In this case, the StandardFusionEngine must post-process the update model by using the corresponding virtual state block to convert the representation to that of the states held by the fusion strategy.

The Cobra `StandardFusionEngine`

Most of the implementation of the Cobra StandardFusionEngine is fairly straightforward. However, there are a couple of design decisions which may be noteworthy.

The Fusion Strategy

The Cobra StandardFusionEngine does not provide its own fusion strategy. A fusion strategy must be supplied before it can be used.

Virtual State Block Support

The Cobra StandardFusionEngine does support the use of virtual state blocks. This support requires a significant amount of additional work to form chains of VirtualStateBlocks originating from a StandardStateBlock. Most of this work is delegated to an internal VirtualStateBlockManager.