In the past years, small unmanned aerial vehicles have increasingly become a hard to defend against threat to both military and civilian infrastructure. Both DIY and COTS UAVs are difficult to detect in a realistic environment, particularly in a cluttered and noisy one such as a port facility. In this context the use of active detection systems such as radar, and lidar is limited since it should not adversely interfere with the normal operation of the port, in particular the existing harbour sensors. This leads our interest towards passive multi-sensor detection systems such as electro-optic (EO) and acoustic monitoring. This work investigates the capability of passive acoustic systems to detect small commercial UAVs within the context of a harbour. We use a machine learning approach to detection using real-world data. We collected audio signatures of several different types of commercial off-the-shelf UAVs both in a quiet environment and in a variety of complex real environment. For this we used a directional 4-microphone array composed of readily available audio components. This setup limited our experiment to the audible spectrum, in which motor and propeller noise are the main characteristics used to distinguish the UAV from the background sounds. We studied machine learning algorithms typically applied to this category of problems, and implemented a Gaussian Mixture Model (GMM) classifier using the Mel-Frequency Cepstrum Coefficient (MFCC) as a feature representation of the audio data, and apply this to the data collected during our measurement campaigns.
Ship-based automatic detection of small floating objects on an agitated sea surface remains a hard problem. Our
main concern is the detection of floating mines, which proved a real threat to shipping in confined waterways
during the first Gulf War, but applications include salvaging,search-and-rescue and perimeter or harbour defense.
IR video was chosen for its day-and-night imaging capability, and its availability on military vessels.
Detection is difficult because a rough sea is seen as a dynamic background of moving objects with size order,
shape and temperature similar to those of the floating mine. We do find a determinant characteristic in the
target's periodic motion, which differs from that of the propagating surface waves composing the background.
The classical detection and tracking approaches give bad results when applied to this problem. While background
detection algorithms assume a quasi-static background, the sea surface is actually very dynamic, causing
this category of algorithms to fail. Kalman or particle filter algorithms on the other hand, which stress temporal
coherence, suffer from tracking loss due to occlusions and the great noise level of the image.
We propose an innovative approach. This approach uses the periodicity of the objects movement and thus its
temporal coherence. The principle is to consider the video data as a spacetime volume similar to a hyperspectral
data cube by replacing the spectral axis with a temporal axis. We can then apply algorithms developed for
hyperspectral detection problems to the detection of small floating objects.
We treat the detection problem using multilinear algebra, designing a number of finite impulse response
filters (FIR) maximizing the target response. The algorithm was applied to test footage of practice mines in the
Most of the current SAR systems aquire fully polarimetric data where the obtained scattering information can
be represented by various coherent and incoherent parameters. In previous contributions we reviewed these
parameters in terms of their "utility" for landcover classification, here, we investigate their impact on several
classification algoritms. Three classifiers: the minimum-distance classifier, a multi-layer perceptron (MLP) and
one based on logistic regression (LR) were applied on an L-Band scene acquired by the E-SAR sensor. MLP
and LR were chosen because they are robust w.r.t. the data statistics. An interesting result is that MLP gives
better results on the coherent parameters while LR gives better results on the incoherent parameters.
For locating maritime vessels longer than 45 meters, such vessels
are required to set up an Automatic Identification System (AIS) used
by vessel traffic services (VTS). However, when a boat is shutting
down its AIS, there are no means to detect it in open sea. In this
paper, we use Electro-Optical (EO) imagers for non-cooperative vessel
detection when the AIS is not operational. As compared to radar sensors,
EO sensors have less complex system (lower cost and lower payload)
and better computational processing load. EO sensors are mounted on
LEO micro-satellites. We propose a simulator providing an estimate
of sensor Receiver Operating Characteristics (ROC) curves in real-time
and without computing the entire image received at the sensor. This
simulator can help sensor manufacturers in optimizing the design of
In the first Gulf War, unmoored floating mines proved to be a real hazard for shipping traffic.
An automated system capable of detecting these and other free-floating small objects, using readily available sensors such as infra-red cameras, would prove to be a valuable mine-warfare asset, and could double as a collision avoidance mechanism, and a search-and-rescue aid. The noisy background provided by the sea surface, and occlusion by waves make it difficult to detect small floating objects using only algorithms based upon the intensity, size or shape of the target. This leads us to look at the sequence of images for temporal detection characteristics. The target's apparent motion is such a determinant, given the contrast between the bobbing motion of the floating object and the strong horizontal component present in the propagation of the wavefronts. We have applied the Proesmans optical flow algorithm to IR video footage of practice mines, in order to extract the motion characteristic and a threshold on the vertical motion characteristic is then imposed to detect the floating targets.