Automatic target recognition (ATR) is the ability for an

algorithm In mathematics and computer science, an algorithm () is a finite sequence of Rigour#Mathematics, mathematically rigorous instructions, typically used to solve a class of specific Computational problem, problems or to perform a computation. Algo ...

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

sensors A sensor is often defined as a device that receives and responds to a signal or stimulus. The stimulus is the quantity, property, or condition that is sensed and converted into electrical signal. In the broadest definition, a sensor is a devi ...

. 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 system that uses radio waves to determine the distance ('' ranging''), direction ( azimuth and elevation angles), and radial velocity of objects relative to the site. It is a radiodetermination method used to detect and track ...

. 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, and is used in other applications such as

unmanned aerial vehicles An unmanned aerial vehicle (UAV) or unmanned aircraft system (UAS), commonly known as a drone, is an aircraft with no human pilot, crew, or passengers onboard, but rather is controlled remotely or is autonomous.De Gruyter Handbook of Dron ...

and cruise missiles. 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 into ...

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. It is also ...

Overview

Micro-Doppler Effect

Radar Radar is a system that uses radio waves to determine the distance ('' ranging''), direction ( azimuth and elevation angles), and radial velocity of objects relative to the site. It is a radiodetermination method used to detect and track ...

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 frequency shift known as the

Doppler effect The Doppler effect (also Doppler shift) is the change in the frequency of a wave in relation to an observer who is moving relative to the source of the wave. The ''Doppler effect'' is named after the physicist Christian Doppler, who described ...

. In addition to the translational motion of the entire object, an additional frequency shift 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 In mathematics, the Fourier transform (FT) is an integral transform that takes a function as input then outputs another function that describes the extent to which various frequencies are present in the original function. The output of the tr ...

analysis of this signal is not sufficient since the

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 (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 Sine wave, sinusoidal frequency and phase (waves), phase content of local sections of a signal as it changes over time ...

or the Wigner distribution function (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 stati ...

(ML), majority voting (MV) or

maximum a posteriori An estimation procedure that is often claimed to be part of Bayesian statistics is the maximum a posteriori (MAP) estimate of an unknown quantity, that equals the mode of the posterior density with respect to some reference measure, typically ...

(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 model ...

(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 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 A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different ty ...

(CNN)-based target recognition is able to outperform the conventional methods. CNNs have 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. The accuracy of the synthetic images is important to target recognition in operational mission conditions.

References

External links

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

Micro-Doppler radar signatures for intelligent target recognition
{{Webarchive, url=https://web.archive.org/web/20200504012112/https://apps.dtic.mil/dtic/tr/fulltext/u2/a427483.pdf , date=2020-05-04 Targeting (warfare) Applications of computer vision Synthetic aperture radar Object recognition and categorization