Automatic target recognition (ATR) is the ability for an

algorithm In mathematics and computer science, an algorithm () is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algorithms are used as specifications for performing ...

or device to recognize targets or other objects based on data obtained from

sensors A sensor is a device that produces an output signal for the purpose of sensing a physical phenomenon. In the broadest definition, a sensor is a device, module, machine, or subsystem that detects events or changes in its environment and sends ...

. Target recognition was initially done by using an audible representation of the received signal, where a trained operator who would decipher that sound to classify the target illuminated by the

radar Radar is a detection system that uses radio waves to determine the distance ('' ranging''), angle, and radial velocity of objects relative to the site. It can be used to detect aircraft, ships, spacecraft, guided missiles, motor vehicles, we ...

. While these trained operators had success, automated methods have been developed and continue to be developed that allow for more accuracy and speed in classification. ATR can be used to identify man made objects such as ground and air vehicles as well as for biological targets such as animals, humans, and vegetative clutter. This can be useful for everything from recognizing an object on a battlefield to filtering out interference caused by large flocks of birds on Doppler weather radar. Possible military applications include a simple identification system such as an

IFF transponder Identification, friend or foe (IFF) is an identification system designed for command and control. It uses a transponder that listens for an ''interrogation'' signal and then sends a ''response'' that identifies the broadcaster. IFF systems usual ...

, and is used in other applications such as

unmanned aerial vehicles An unmanned aerial vehicle (UAV), commonly known as a drone, is an aircraft without any human pilot, crew, or passengers on board. UAVs are a component of an unmanned aircraft system (UAS), which includes adding a ground-based controlle ...

and

cruise missiles A cruise missile is a guided missile used against terrestrial or naval targets that remains in the atmosphere and flies the major portion of its flight path at approximately constant speed. Cruise missiles are designed to deliver a large warhe ...

. There has been more and more interest shown in using ATR for domestic applications as well. Research has been done into using ATR for border security, safety systems to identify objects or people on a subway track, automated vehicles, and many others.

Concept

History

Target recognition has existed almost as long as

. Radar operators would identify enemy bombers and fighters through the audio representation that was received by the reflected signal (see Radar in World War II). Target recognition was done for years by playing the

baseband In telecommunications and signal processing, baseband is the range of frequencies occupied by a signal that has not been modulated to higher frequencies. Baseband signals typically originate from transducers, converting some other variable int ...

signal to the operator. Listening to this signal, trained radar operators can identify various pieces of information about the illuminated target, such as the type of vehicle it is, the size of the target, and can potentially even distinguish biological targets. However, there are many limitations to this approach. The operator must be trained for what each target will sound like, if the target is traveling at a high speed it may no longer be audible, and the human decision component makes the probability of error high. However, this idea of audibly representing the signal did provide a basis for automated classification of targets. Several classifications schemes that have been developed use features of the

signal that have been used in other audio applications such as

speech recognition Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers with the ...

Overview

Micro-Doppler Effect

Radar Radar is a detection system that uses radio waves to determine the distance ('' ranging''), angle, and radial velocity of objects relative to the site. It can be used to detect aircraft, ships, spacecraft, guided missiles, motor vehicles, we ...

determines the distance an object is away by timing how long it takes the transmitted signal to return from the target that is illuminated by this signal. When this object is not stationary, it causes a shift in frequency known as the Doppler effect. In addition to the translational motion of the entire object, an additional shift in frequency can be caused by the object vibrating or spinning. When this happens the Doppler shifted signal will become modulated. This additional Doppler effect causing the modulation of the signal is known as the micro-Doppler effect. This modulation can have a certain pattern, or signature, that will allow for algorithms to be developed for ATR. The micro-Doppler effect will change over time depending on the motion of the target, causing a time and frequency varying signal.

Time-frequency analysis

Fourier transform analysis of this signal is not sufficient since the Fourier transform cannot account for the time varying component. The simplest method to obtain a function of frequency and time is to use the

short-time Fourier transform The short-time Fourier transform (STFT), is a Fourier-related transform used to determine the sinusoidal frequency and phase content of local sections of a signal as it changes over time. In practice, the procedure for computing STFTs is to divi ...

(STFT). However, more robust methods such as the

Gabor transform The Gabor transform, named after Dennis Gabor, is a special case of the short-time Fourier transform. It is used to determine the sinusoidal frequency and phase content of local sections of a signal as it changes over time. The function to be tran ...

or the

Wigner distribution function The Wigner distribution function (WDF) is used in signal processing as a transform in time-frequency analysis. The WDF was first proposed in physics to account for quantum corrections to classical statistical mechanics in 1932 by Eugene Wigner, ...

(WVD) can be used to provide a simultaneous representation of the frequency and time domain. In all these methods, however, there will be a trade off between frequency resolution and time resolution.

Detection

Once this spectral information is extracted, it can be compared to an existing database containing information about the targets that the system will identify and a decision can be made as to what the illuminated target is. This is done by modeling the received signal then using a statistical estimation method such as

maximum likelihood In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed stat ...

(ML),

majority voting Majority rule is a principle that means the decision-making power belongs to the group that has the most members. In politics, majority rule requires the deciding vote to have majority, that is, more than half the votes. It is the binary deci ...

(MV) or

maximum a posteriori In Bayesian statistics, a maximum a posteriori probability (MAP) estimate is an estimate of an unknown quantity, that equals the mode of the posterior distribution. The MAP can be used to obtain a point estimate of an unobserved quantity on the ...

(MAP) to make a decision about which target in the library best fits the model built using the received signal.

Approach

Extraction of features

Studies have been done that take audio features used in

to build automated target recognition systems that will identify targets based on these audio inspired coefficients. These coefficients include the *

Linear predictive coding Linear predictive coding (LPC) is a method used mostly in audio signal processing and speech processing for representing the spectral envelope of a digital signal of speech in compressed form, using the information of a linear predictive mod ...

(LPC) coefficients * Cepstral linear predictive coding (LPCC) coefficients * Mel-frequency cepstral coefficients (MFCC). The

signal is processed to obtain these coefficients, then a statistical process is used to decide which target in the database is most similar to the coefficients obtained. The choice of which features and which decision scheme to use depends on the system and application. The features used to classify a target are not limited to speech inspired coefficients. A wide range of features and detection algorithms can be used to accomplish ATR.

Detection algorithms

In order for

detection {{Unreferenced, date=March 2018 In general, detection is the action of accessing information without specific cooperation from with the sender. In the history of radio communications, the term " detector" was first used for a device that detected ...

of targets to be automated, a training database needs to be created. This is usually done using experimental data collected when the target is known, and is then stored for use by the ATR algorithm. ATR flow

An example of a detection algorithm is shown in the flowchart. This method uses M blocks of data, extracts the desired features from each (i.e. LPC coefficients, MFCC) then models them using a Gaussian mixture model (GMM). After a model is obtained using the data collected, conditional probability is formed for each target contained in the training database. In this example, there are M blocks of data. This will result in a collection of M probabilities for each target in the database. These probabilities are used to determine what the target is using a

decision. This method has been shown to be able to distinguish between vehicle types (wheeled vs tracked vehicles for example), and even decide how many people are present up to three people with a high probability of success. CNN-Based Target Recognition

Convolutional neural network In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Netwo ...

(CNN)-based target recognition is able to outperform the conventional methods. It has been proved useful in recognizing targets (i.e. battle tanks) in infrared images of real scenes after training with synthetic images, since real images of those targets are scarce. Due to the limitation of the training set, how realistic the synthetic images are matters a lot when it comes to recognize the real scenes test set. The overall CNN networks structure contains 7 convolution layers, 3 max pooling layers and a Softmax layer as output. Max pooling layers are located after the second, the forth and the fifth convolution layer. A Global average pooling is also applied before the output. All convolution layers use Leaky ReLU nonlinearity activation function.{{Cite journal, last1=d’Acremont, first1=Antoine, last2=Fablet, first2=Ronan, last3=Baussard, first3=Alexandre, last4=Quin, first4=Guillaume, date=January 2019, title=CNN-Based Target Recognition and Identification for Infrared Imaging in Defense Systems, journal=Sensors, volume=19, issue=9, pages=2040, doi=10.3390/s19092040 , pmid=31052320 , pmc=6539764, doi-access=free

References

External links

Acoustic Experimental Data Analysis of Moving Targets Echoes Observed by Doppler Radars

Micro-Doppler radar signatures for intelligent target recognition
Targeting (warfare) Applications of computer vision Synthetic aperture radar Object recognition and categorization