Distance detection#
Introduction#
The purpose of the distance detector is to detect objects and estimate their distance from the sensor. The algorithm is built on top of the Sparse IQ service and has various configuration parameters available to tailor the detector to specific use cases. The detector utilizes the following key concepts:
1. Distance filtering: A matched filter is applied along the distance dimension to improve the signal quality and suppress noise.
2. Subsweeps: The measured range is split into multiple subsweeps, each configured to maintain SNR throughout the sweep while minimizing power consumption.
3. Comparing sweep to a threshold: Peaks in the filtered sweep are identified by comparison to one of three available threshold methods.
4. Estimate distance to object: Estimate the distance to the target by interpolation of the peak and neighboring amplitudes.
5. Sort found peaks: If multiple peaks are found in a sweep, three different sorting methods can be employed, each suitable for different use-cases.
Distance filter#
As the sensor produce coherent data, samples corresponding to the location of an object will have similar phase, while the phase of free-air measurements will be random. By applying a filter in the distance domain, the noise in the free-air regions will be suppressed, resulting in an improved SNR.
The filter is automatically configured based on the detector configuration as a second order Butterworth filter with a cutoff frequency corresponding to a matched filter.
Subsweeps#
The measurement range is split up into multiple subsweeps to allow for optimization of power consumption and signal quality. The profile, HWAAS and step length are automatically assigned per subsweep, based on the detector config.
A shorter profile is selected at the start of the measurement range to minimize the interference with direct leakage, followed by longer profiles to gain SNR. The longest profile used can be limited by setting the parameter
max_profile. If no profile is specified, the subsweeps will be configured to transfer to the longest profile(without interference from direct leakage) as quickly as possible to maximize SNR. Longer profiles yield a higher SNR at a given power consumtion level, while shorter profiles gives better depth resolution.The step length can also be limited by setting the parameter
max_step_length. If no value is supplied, the step length is automatically configured to appropriate size, maintaining good depth resolution while minimizing power consumption. Note, the algorithm interpolates between the measured points to maintain good resolution, even with a more coarse step length.HWAAS is assigned to each subsweep in order to maintain SNR throughout the measured range as the signal strength decrease with the distance between the sensor and the measured target. The target SNR level is adjusted using the parameter
signal_quality. Note, higher signal quality will increase power consumption and measurement time.
In the Exploration Tool GUI, the subsweeps can be seen as slightly overlapping lines. If the measured object is in the overlapping region, the result from the neighboring segments is averaged together.
Thresholds#
To determine if any objects are present, the sweep is compared to a threshold. Three different thresholds can be employed, each suitable for different use-cases.
- Fixed threshold
The simplest approach to setting the threshold is choosing a fixed threshold over the full range.
- Recorded threshold
In situations where stationary objects are present, the background signal is not flat. To isolate objects of interest, the threshold is based on measurements of the static environment. The first step is to collect multiple sweeps, from which the mean sweep and standard deviation is calculated. Secondly, the threshold is formed by adding a number of standard deviations (the number is determined by the parameter
threshold_sensitivity) to the mean sweep.- Constant False Alarm Rate (CFAR) threshold(default)
A final method to construct a threshold for a certain distance is to use the signal from neighbouring distances of the same sweep. This requires that the object gives rise to a single strong peak, such as a water surface and not, for example, the level in a large waste container. The main advantage is that the memory consumption is minimal.
Peak sorting#
Multiple objects in the scene will give rise to several peaks. Peak sorting allows selection of which peak is of highest importance. The following options are available.
- Closest
This method sorts the peaks according to distance from the sensor.
- Highest radar cross section(default)
This method sorts the peaks according to their radar cross section, estimated using the radar equation.
The radar cross section is a measure of how much energy a given object reflects. It is a combination of its material and shape. A higher radar cross section corresponds to a more reflective object.
It is employed to sort peaks in order of decreasing power and is used instead of amplitude as the overall gain varies across subsweeps, due to the optimized subsweep configurations.
Note, the calculated RCS is an approximation of the actual RCS and is known to be less accurate at close distances.
To learn more about radar cross section, see the radar principles section.
Detector calibration#
For optimal performance, the detector performs a number of calibration steps. The following section outlines the purpose and process of each step. Note, which of the following calibration procedures to perform is determined by the user provided detector config. For instance, the close range measurement is only performed when measuring close to the sensor.
To trigger the calibration process in the Exploration Tool gui, simply press the button labeled “Calibrate
detector”.
If you are running the detector from a script, the calibration is performed by calling
the method calibrate_detector.
- Noise level estimation
The noise level is estimated by disabling of the transmitting antenna and just sample the background noise with the receiving antenna.
- Offset compensation
The purpose of the offset compensation is to improve the distance trueness(average error) of the distance detector. The compensation utilize the loopback measurement, where the pulse is measured electronically on the chip, without transmitting it into the air. The location of the peak amplitude is correlated with the distance error and used to correct the distance raw estimate.
- Close range measurement calibration
Measuring the distance to objects close to the sensor is challenging due to the presence of strong direct leakage. One way to get around this is to characterize the leakage component and then subtract it from each measurement to isolate the signal component. This is exactly what the close range calibration does. While performing the calibration, it is important that the sensor is installed in its intended geometry and that there is no object in front of the sensor as this would interfer with the direct leakage.
Note, this calibration is only performed if close range measurement is active, given by the configured starting point.
- Recorded threshold
The recorded threshold is also recorded as a part of the detector calibration. Note, this calibration is only performed if the detector is configured to used recorded threshold or if close range measurement is active, where recorded threshold is used.
Temperature compensation#
The surrounding temperature impacts the amplitude of the measured signal and noise. To compensate for these effects, the recorded threshold has a built in compensation model, based on a temperature measurement, internal to the sensor. Note, the effectiveness of the compensation is limited when measuring in the close range region.
Configuration parameters#
- class acconeer.exptool.a121.algo.distance.DetectorConfig(*, start_m: float = 0.25, end_m: float = 3.0, max_step_length: Optional[int] = None, max_profile=Profile.PROFILE_5, signal_quality: float = 15.0, threshold_method=ThresholdMethod.CFAR, peaksorting_method=PeakSortingMethod.HIGHEST_RCS, num_frames_in_recorded_threshold: int = 100, fixed_threshold_value: float = 100.0, threshold_sensitivity: float = 0.5, update_rate: Optional[float] = 50.0)#
- start_m: float#
Start point of measurement interval in meters.
- end_m: float#
End point of measurement interval in meters.
- max_step_length: Optional[int]#
Used to limit step length. If no argument is provided, the step length is automatically calculated based on the profile.
- max_profile: Profile#
Specifies the longest allowed profile. If no argument is provided, the highest possible profile without interference of direct leakage is used to maximize SNR.
- signal_quality: float#
Signal quality. High quality equals higher HWAAS and better SNR but increase power consumption.
- threshold_method: ThresholdMethod#
Threshold method
- peaksorting_method: PeakSortingMethod#
Sorting method of estimated distances.
- num_frames_in_recorded_threshold: int#
Number of frames used when calibrating threshold.
- fixed_threshold_value: float#
Value of fixed threshold.
- threshold_sensitivity: float#
Sensitivity of threshold. High sensitivity equals low detection threshold, low sensitivity equals high detection threshold.
- update_rate: Optional[float]#
Sets the detector update rate.
Detector result#
- class acconeer.exptool.a121.algo.distance._detector.DetectorResult(*, distances: Optional[npt.NDArray[np.float_]] = None, rcs: Optional[npt.NDArray[np.float_]] = None, near_edge_status: Optional[bool] = None, processor_results: list[ProcessorResult], service_extended_result: list[dict[int, Result]])#
- distances: Optional[npt.NDArray[np.float_]]#
Estimated distances (m), sorted according to the selected peak sorting strategy.
- rcs: Optional[npt.NDArray[np.float_]]#
Estimated radar cross section (dBsm) corresponding to the peak amplitude of the estimated distances.
- near_edge_status: Optional[bool]#
Boolean indicating an object close to the start edge, located outside of the measurement range.
- processor_results: list[ProcessorResult]#
Processing result. Used for visualization in Exploration Tool.