TOA Position Estimator

Estimated times of arrival of signals between anchors and single target, specified as a 1-by-L element real-valued vector. Units are in seconds.

Data Types: single | double

Variances of the estimated times of arrival at anchors, specified as a 1-by-L element real-valued vector.

When toavar is unknown, you can set it to be a 1-by-L element vector with identical finite values without affecting the solution tgtposest.

Data Types: single | double

Anchor positions, specified as 2-by-L real-valued matrix or 3-by-L real-valued matrix. A 2-by-L matrix represents the anchor positions in 2-D Cartesian space, while a 3-by-L matrix represents the anchor positions in 3-D Cartesian space.

Data Types: single | double

Estimated target positions, returned as a Q-by-1 vector representing the estimated target position obtained from TOA and TDOA measurements. Units are in meters.

Target position covariance matrix, returned as a real-valued positive semi-definite Q-by-Q matrix. The covariance matrix represents the estimated target position covariance calculated from the Cramer-Rao lower bound (CRLB) of the TOA position estimator. Calculating the covariance requires the knowledge of toavar. When toavar is inaccurate, tgtposcov is also inaccurate. When toavar is accurate and small, the tgtposcov value represents the TOA position estimate CRLB. Units are in ㎡eters-squared.

Data Types: single | double

Signal propagation speed, specified as a positive real scalar. Units are in meters per second.

Example: 3e8

Data Types: single | double

Select the Output covariance of TOA position estimates check box to enable the TgtPosCov output port containing the TOA position covariances.

Data Types: Boolean

Block simulation, specified as Interpreted Execution or Code Generation. If you want your block to use the MATLAB^® interpreter, choose Interpreted Execution. If you want your block to run as compiled code, choose Code Generation. Compiled code requires time to compile but usually runs faster.

Interpreted execution is useful when you are developing and tuning a model. The block runs the underlying System object™ in MATLAB. You can change and execute your model quickly. When you are satisfied with your results, you can then run the block using Code Generation. Long simulations run faster with generated code than in interpreted execution. You can run repeated executions without recompiling, but if you change any block parameters, then the block automatically recompiles before execution.

This table shows how the Simulate using parameter affects the overall simulation behavior.

When the Simulink model is in Accelerator mode, the block mode specified using Simulate using overrides the simulation mode.

For more information, see Choosing a Simulation Mode (Simulink).

Programmatic Use