Note

Go to the end to download the full example code or to run this example in your browser via Binder

# 3 - Optimised Sensor Management

This tutorial follows on from the Multi Sensor Management tutorial and explores the use of external optimisation libraries to overcome the limitations of the brute force optimisation method introduced in the previous tutorial.

The scenario in this example is the same as Tutorial 2, simulating 3
targets moving on nearly constant velocity trajectories and an adjustable number of sensors.
The sensors are
`RadarRotatingBearingRange`

with a defined field of view which can be pointed in a
particular direction in order
to make an observation.

The optimised sensor managers are built around the SciPy optimize library. Similar to the brute force method introduced previously the sensor manager considers all possible configurations of sensors and actions and here uses an optimising function to optimise over a given reward function, returning the optimal configuration.

The `UncertaintyRewardFunction`

is used for all sensor managers which chooses the configuration
for which the sum of estimated
uncertainties (as represented by the Frobenius norm of the covariance matrix) can be reduced the most by using
the chosen sensing configuration.

As in the previous tutorials the SIAP [1] and uncertainty metrics are used to assess the performance of the sensor managers.

## Sensor Management example

### Setup

First a simulation must be set up using components from Stone Soup. For this the following imports are required.

```
import numpy as np
import random
from ordered_set import OrderedSet
from datetime import datetime, timedelta
start_time = datetime.now().replace(microsecond=0)
from stonesoup.models.transition.linear import CombinedLinearGaussianTransitionModel, ConstantVelocity
from stonesoup.types.groundtruth import GroundTruthPath, GroundTruthState
```

### Generate ground truths

Generate transition model and ground truths as in Tutorials 1 & 2.

The number of targets in this simulation is defined by ntruths - here there are 3 targets travelling in different directions. The time the simulation is observed for is defined by time_max.

We can fix our random number generator in order to probe a particular example repeatedly. This can be undone by commenting out the first two lines in the next cell.

```
np.random.seed(1990)
random.seed(1990)
# Generate transition model
transition_model = CombinedLinearGaussianTransitionModel([ConstantVelocity(0.005),
ConstantVelocity(0.005)])
yps = range(0, 100, 10) # y value for prior state
truths = OrderedSet()
ntruths = 3 # number of ground truths in simulation
time_max = 50 # timestamps the simulation is observed over
timesteps = [start_time + timedelta(seconds=k) for k in range(time_max)]
xdirection = 1
ydirection = 1
# Generate ground truths
for j in range(0, ntruths):
truth = GroundTruthPath([GroundTruthState([0, xdirection, yps[j], ydirection], timestamp=timesteps[0])],
id=f"id{j}")
for k in range(1, time_max):
truth.append(
GroundTruthState(transition_model.function(truth[k - 1], noise=True, time_interval=timedelta(seconds=1)),
timestamp=timesteps[k]))
truths.add(truth)
xdirection *= -1
if j % 2 == 0:
ydirection *= -1
```

Plot the ground truths. This is done using the `Plotterly`

class from Stone Soup.

```
from stonesoup.plotter import AnimatedPlotterly
# Stonesoup plotter requires sets not lists
plotter = AnimatedPlotterly(timesteps, tail_length=1)
plotter.plot_ground_truths(truths, [0, 2])
plotter.fig
```

### Create sensors

Create a set of sensors for each sensor management algorithm. As in Tutorial 2 this tutorial uses the
`RadarRotatingBearingRange`

sensor with the
number of sensors initially set as 2 and each sensor positioned along the line \(x=10\), at distance
intervals of 50.

```
total_no_sensors = 2
from stonesoup.types.state import StateVector
from stonesoup.sensor.radar.radar import RadarRotatingBearingRange
from stonesoup.types.angle import Angle
sensor_setA = set()
for n in range(0, total_no_sensors):
sensor = RadarRotatingBearingRange(
position_mapping=(0, 2),
noise_covar=np.array([[np.radians(0.5) ** 2, 0],
[0, 1 ** 2]]),
ndim_state=4,
position=np.array([[10], [n * 50]]),
rpm=60,
fov_angle=np.radians(30),
dwell_centre=StateVector([0.0]),
max_range=np.inf,
resolutions={'dwell_centre': Angle(np.radians(30))}
)
sensor_setA.add(sensor)
for sensor in sensor_setA:
sensor.timestamp = start_time
sensor_setB = set()
for n in range(0, total_no_sensors):
sensor = RadarRotatingBearingRange(
position_mapping=(0, 2),
noise_covar=np.array([[np.radians(0.5) ** 2, 0],
[0, 1 ** 2]]),
ndim_state=4,
position=np.array([[10], [n * 50]]),
rpm=60,
fov_angle=np.radians(30),
dwell_centre=StateVector([0.0]),
max_range=np.inf,
resolutions={'dwell_centre': Angle(np.radians(30))}
)
sensor_setB.add(sensor)
for sensor in sensor_setB:
sensor.timestamp = start_time
sensor_setC = set()
for n in range(0, total_no_sensors):
sensor = RadarRotatingBearingRange(
position_mapping=(0, 2),
noise_covar=np.array([[np.radians(0.5) ** 2, 0],
[0, 1 ** 2]]),
ndim_state=4,
position=np.array([[10], [n * 50]]),
rpm=60,
fov_angle=np.radians(30),
dwell_centre=StateVector([0.0]),
max_range=np.inf,
resolutions={'dwell_centre': Angle(np.radians(30))}
)
sensor_setC.add(sensor)
for sensor in sensor_setC:
sensor.timestamp = start_time
```

### Create the Kalman predictor and updater

Construct a predictor and updater using the `KalmanPredictor`

and `ExtendedKalmanUpdater`

components from Stone Soup. The measurement model for the updater is None as it is an attribute of the sensor.

```
from stonesoup.predictor.kalman import KalmanPredictor
predictor = KalmanPredictor(transition_model)
from stonesoup.updater.kalman import ExtendedKalmanUpdater
updater = ExtendedKalmanUpdater(measurement_model=None)
# measurement model is added to detections by the sensor
```

### Run the Kalman filters

Create priors which estimate the targets’ initial states - these are the same as in the previous sensor management tutorials.

```
from stonesoup.types.state import GaussianState
priors = []
xdirection = 1.2
ydirection = 1.2
for j in range(0, ntruths):
priors.append(GaussianState([[0], [xdirection], [yps[j]+0.1], [ydirection]],
np.diag([0.5, 0.5, 0.5, 0.5]+np.random.normal(0,5e-4,4)),
timestamp=start_time))
xdirection *= -1
if j % 2 == 0:
ydirection *= -1
```

Initialise the tracks by creating an empty list and appending the priors generated. This needs to be done separately for each sensor manager method as they will generate different sets of tracks.

```
from stonesoup.types.track import Track
# Initialise tracks from the BruteForceSensorManager
tracksA = {Track([prior]) for prior in priors}
# Initialise tracks from the OptimizeBruteSensorManager
tracksB = {Track([prior]) for prior in priors}
# Initialise tracks from the OptimizeBasinHoppingSensorManager
tracksC = {Track([prior]) for prior in priors}
```

### Create sensor managers

The `UncertaintyRewardFunction`

will be used for each sensor manager as in Tutorials 1 & 2
and the `BruteForceSensorManager`

will be used as a comparison to the optimised methods.

```
from stonesoup.sensormanager.reward import UncertaintyRewardFunction
from stonesoup.sensormanager import BruteForceSensorManager
```

#### Optimised Brute Force Sensor Manager

The first optimised method, `OptimizeBruteSensorManager`

uses `brute()`

which minimizes a function over a given range using a brute force method. This can be tailored by setting
the number of grid points to search over or by adding the use of a polishing function.

```
from stonesoup.sensormanager import OptimizeBruteSensorManager
```

#### Optimised Basin Hopping Sensor Manager

The second optimised method, `OptimizeBasinHoppingSensorManager`

uses `basinhopping()`

which finds the global minimum of a function using the basin-hopping algorithm. This is a combination of a
global stepping algorithm and local minimization at each step. Parameters such as number of basin hopping
iterations or stepsize can be set to tailor the algorithm to requirements.

```
from stonesoup.sensormanager import OptimizeBasinHoppingSensorManager
```

### Initiate sensor managers

Create an instance of each sensor manager class. For the optimised sensor managers the default settings
will be used meaning only a sensor set and reward function is required. The `UncertaintyRewardFunction`

will be used for each sensor manager. For the `OptimizeBruteSensorManager`

a polishing function
is used by setting finish=True.

```
# initiate reward function
reward_function = UncertaintyRewardFunction(predictor, updater)
bruteforcesensormanager = BruteForceSensorManager(sensor_setA,
reward_function=reward_function)
optimizebrutesensormanager = OptimizeBruteSensorManager(sensor_setB,
reward_function=reward_function,
finish=True)
optimizebasinhoppingsensormanager = OptimizeBasinHoppingSensorManager(sensor_setC,
reward_function=reward_function)
```

### Run the sensor managers

Each sensor management method requires a timestamp and a list of tracks at each time step when calling
the function `choose_actions()`

. This returns a mapping of sensors and actions to be taken by each
sensor, decided by the sensor managers.

For each sensor management method, at each time step the chosen action is given to the sensors and then measurements taken. At each timestep the tracks are predicted and those with measurements associated are updated.

First a hypothesiser and data associator are required for use in each tracker.

```
from stonesoup.hypothesiser.distance import DistanceHypothesiser
from stonesoup.measures import Mahalanobis
hypothesiser = DistanceHypothesiser(predictor, updater, measure=Mahalanobis(), missed_distance=5)
from stonesoup.dataassociator.neighbour import GNNWith2DAssignment
data_associator = GNNWith2DAssignment(hypothesiser)
```

#### Run brute force sensor manager

Each sensor manager is run in the same way as in the previous tutorials.

```
from ordered_set import OrderedSet
import time
# Start timer for cell execution time
cell_start_time1 = time.time()
for timestep in timesteps[1:]:
# Generate chosen configuration
chosen_actions = bruteforcesensormanager.choose_actions(tracksA, timestep)
# Create empty dictionary for measurements
measurementsA = set()
for chosen_action in chosen_actions:
for sensor, actions in chosen_action.items():
sensor.add_actions(actions)
for sensor in sensor_setA:
sensor.act(timestep)
# Observe this ground truth
measurementsA |= sensor.measure(OrderedSet(truth[timestep] for truth in truths), noise=True)
hypotheses = data_associator.associate(tracksA,
measurementsA,
timestep)
for track in tracksA:
hypothesis = hypotheses[track]
if hypothesis.measurement:
post = updater.update(hypothesis)
track.append(post)
else: # When data associator says no detections are good enough, we'll keep the prediction
track.append(hypothesis.prediction)
cell_run_time1 = round(time.time() - cell_start_time1, 2)
```

Plot ground truths, tracks and uncertainty ellipses for each target. The positions of the sensors are indicated by black x markers.

```
plotterA = AnimatedPlotterly(timesteps, tail_length=1)
plotterA.plot_sensors(sensor_setA)
plotterA.plot_ground_truths(truths, [0, 2])
plotterA.plot_tracks(set(tracksA), [0, 2], uncertainty=True, plot_history=False)
plotterA.fig
```