Radar based detection and tracking of a walking human

Radar based detection and tracking of a walking human

Author links open overlay panelJuhanaAhtiainen*SamiTerho*SampsaKoponen**Show more

Share

Cite

https://doi.org/10.3182/20100906-3-IT-2019.00076Get rights and content

Abstract

In this paper, a radar based approach for pedestrian detection and tracking is presented. The approach utilizes automotive radar for tracking the targets and a separate Doppler radar to provide extra information about the classes of the targets. The classification of objects as humans is based on analysis of the Doppler spectrum of targets as well as the speed of the targets gained through the tracking process. The presented method is able to make a difference between a non-human target and a human target if the human target is moving. At the moment the measurement platform needs to be stationary or move at very low speed.

Previous article in issue

Next article in issue

Keywords

Human detection

tracking

automotive radar

Doppler radar

spectrum analysis

filtering

Download full text in PDF

Radar based detection and tracking of a walking human

Juhana Ahtiainen*, Sami Terho*,

Sampsa Koponen**

*Department of Automation and Systems Technology, **Department of Radio Science and Engineering

Aalto University School of Science and Technology, Espoo, Finland, (e-mail: firstname.lastname@tkk.fi)

Abstract: In this paper, a radar based approach for pedestrian detection and tracking is presented. The

approach utilizes automotive radar for tracking the targets and a separate Doppler radar to provide extra

information about the classes of the targets. The classification of objects as humans is based on analysis

of the Doppler spectrum of targets as well as the speed of the targets gained through the tracking process.

The presented method is able to make a difference between a non-human target and a human target if the

human target is moving. At the moment the measurement platform needs to be stationary or move at very

low speed.

Keywords: Human detection, tracking, automotive radar, Doppler radar, spectrum analysis, filtering

1. INTRODUCTION

Human detection and tracking have been widely researched

topics in recent years. Radars are considered suitable sensors

for detecting and tracking objects. However, the classification

of the objects as humans with radar only, is a very

challenging problem. Therefore, most of the studies in the

field of human detection have been based on other sensors,

such as cameras and laser scanners.

Detecting humans with any kind of sensor system is a

challenging task due to the wide variety of positions and

appearances which humans can assume. A moving sensor

system and stationary people further complicate the task.

Also, the tracking of humans is especially hard since people’s

behaviors are often completely unpredictable. When dealing

with these problems outdoors, the adverse weather conditions

also present major limitations to the sensor system used.

Certain radars provide reliable measurements regardless of

the weather conditions, hence making radars a desirable

option when considering a human detection system.

In the following, a radar based measurement system for

detecting and tracking walking humans is presented. The

measurement system comprises of off-the-shelf automotive

radar and a continuous wave Doppler radar. The automotive

radar is used to track targets and measure their velocities.

Doppler radar is used to analyse whether or not there is a

human in the measurement sector. Information from both

radars is fused to achieve estimate whether a target is a

human or not. Two different test cases are analyzes with and

without the Doppler data revealing the advantages of using

the Doppler radar. The measurement system is able to track

and detect walking humans. Adverse weather conditions

deteriorate the classification of observed targets as humans

but do not affect the performance of the tracker. At the

moment, the sensor system needs to be stationary or move at

relatively slow speed. If the sensor system is moving, the

movements of the platform must be passed to the algorithm.

2. APPROACHES TO HUMAN DETECTION WITH

RADARS

Classifying targets as humans with radars is a challenging

task. There are, however, at least three different kind of

techniques applied to this problem. Yarovoy et al. (2008)

measured the reflectivity of a human in laboratory conditions

with ultra wide band (UWB) radar. They analyzed the

polarization of the reflected signal and discovered that there

are some frequencies where there is a maximum reflectivity

in one polarization and a minimum in the other. The

polarized signal, however, depends strongly on the shape,

posture and position of the person making it therefore very

unreliable method for human classification.

Nakane and Soshi (1998 & 2000) used dual band frequency

modulated continuous wave (FMCW) radar for

discriminating whether or not the detected object is an

animal, including a human. They compared the difference in

the power of the signal reflected form an object at different

frequencies (10 GHz and 60 GHz) and were able to establish

a threshold for the ratio of received intensity between two

frequencies, above which the detected objects can be

identified as animals. They received a patent for their method

in 2000.

Otero (2005) analyzed the Doppler spectrum of CW radar to

obtain a Doppler signature for a walking human. The

movement of a walking human’s legs, arms and torso cause

different components to the Doppler frequency. The motion

of the torso produces a steady Doppler shifted signal, which

is modulated by the motion of swinging arms and legs that

results in a Doppler signature that is very characteristic of 

humans. Otero developed a simple, binary classifier that

classifies detection as either a person present or not present.

Thayparan et al. (2004) used wavelet transform to extract the

micro signatures created by human walking. Gürbüz et al.

(2007) evaluated different methods used to detect walking

humans from the Doppler data with the conclusion that

spectrogram based methods are problematic if one needs to

distinguish human from other periodically moving targets

due to high signal-to-noise ratio.

Most of the mentioned studies concentrated on identifying

the human and distinguishing it from other periodically

moving targets, such as walking animals. In the presence of

other moving targets in the radar’s field of view, this may be

impossible because many different spectral signatures are

superimposed together, and the information from one signal

is not enough to distinguish them from each other. However,

there are situations where it is enough to detect any walking

object, but it is not necessary to distinguish animals from

humans. In such cases it is enough to detect just the periodic

motion caused by walking.

3. SYSTEM DESCRIPTION

The measurement system consists of a off-the-shelf

automotive FMCW radar used in adaptive cruise control

systems (ACC) and of a CW Doppler radar used by the police

to catch speeding violators. The characteristics of these radars

are explained in the following chapters.

3.1 Automotive Radar

The automotive radar in the measurement system, a long

range radar 77 GHz generation 2 produced by Bosch, is

described in this chapter. Originally it is manufactured for

ACC systems to measure the distance and relative radial

velocity of vehicles ahead. The radar has four antennas with

superimposed beams giving the radar system an angular

coverage of about 16 degrees. The signal is transmitted

through each of 4 antennas simultaneously.

The radar transmits a continuous wave signal which

frequency is modulated as a function of time with a periodic

wave form. The periodic wave consists of three linear

frequency ramps with different ramp times and bandwidth.

Using three ramps enables the radar system to detect the

range and relative radial velocity of multiple targets at every

time step. Angular position is determined by applying

comparisons of amplitude and phase between adjacent

antenna beams (Troppman and Höger, 2008).

The received signals are processed in the radar

microcontroller and a complete target list is passed to the

CAN-bus of the radar. Maximum of 32 strongest reflections

are considered as targets. The following information is

presented in the target list from every target: Distance, lateral

distance from center line and its variance, relative radial

speed and relative radial acceleration. Also, time stamps of

each measurement is provided as well as some radar

diagnostics.

3.2 Doppler Radar

The Doppler radar used in the system is Kustom Signals HR12 (Kustom Signals 2009). It is speed control radar used by

police in some countries. The radar operates on K-band. The

exact beam width is not known, but it is very narrow, just a

few degrees. The intermediate Doppler frequency signal can

be acquired from the radar with a special connection. This

raw signal is sent to a computer for further signal processing.

4. ALGORITHM FOR DETECTING A WALKING

HUMAN FROM DOPPLER SPECTRUM

Detection of the periodic motion can be done by analyzing

the Fourier spectrogram of the Doppler frequency. Fig. 1

shows a typical spectrogram caused by a walking human. A

typical feature of this type Doppler signature is that the

bandwidth of the signal varies periodically. A straightforward

way to measure this bandwidth is to calculate the variance of

the signal. However, there may be other frequency

components caused by other moving objects, such as cars or

machines. Therefore, it is necessary to analyze small bands of

the frequency spectrum individually.

In this research, three slices of the spectrogram are analyzed.

The slices are marked to the spectrogram in Fig. 1. Six onedimensional signals are obtained by calculating the variance

and mean of the frequencies as a function of time. The

signals are shown in Fig. 2. The cadence frequency caused by

the periodic movement is clearly visible in these signals. The

frequency can be determined with spectral or cepstral

analysis, autocorrelation, or auto-difference function, that is,

a sum of squared differences of the signal with itself with

different offsets. In this research, the latter method was used,

because it was found to be the most reliable.

The auto-difference function Dn of the function Fn at time t

and shift s is calculated with

( ) () ( ) ∑ ( ) +

−=

= −− 2

2

2 ,

s w

sd w n n n dtFtFstD , (1)

where w is the width of the analyzation window. The

Euclidean sum of squares

() () ∑=

= 6

1

2 , , n

n stDstD (2)

is calculated to form one function D(t,s).

D(t,s) needs to be low to indicate strong correlation with the

shift s. To distinguish low values caused by strong correlation

from just low signal strength, the values of D need to be

normalized. A typical characteristics of a periodic signal is

that where the value D(t,s) is low, the value D(t,s/2)

representing the half shift is high. Therefore the

normalization can be done with

( ) ( )

( ) 2 ,

, , s tD

stD Norm stD =

. (3) 

The cycle length of the periodic motion can now determined

by finding the local minima of the function DNorm(t,s) in s

direction. Because the pace of the walking is generally quite

slow, the function needs to be observed for at least two

seconds before a walking human can be detected reliably.

Fig. 3 shows the output of the DNorm(t,s) function.

Fig. 1. An excerpt of spectrogram containing measurement of

a walking human. The three numbered bands are analyzed

individually.

Fig. 2. Mean (blue curve) and standard deviation (red curve)

of frequencies as functions of time. The curves are

normalized to same scale.

Fig. 3. Function DNorm(t,s), with t as time and s as shift.

Darker values represent low values and of the function. The

yellow line indicates the local minima with low enough

values. This represents the detected periodicity in the signal.

5. TARGET TRACKING AND DATA FUSION

In this chapter a simple multitarget tracking method and data

fusion process used in the measurement system are presented.

Both, target tracking and data fusion, are research fields of

their own and are not addressed in this paper in more detail.

Interested reader should look up e.g. (Bar-Shalom and

Fortman 1998) and (Waltz and Llinas 1990).

5.1 Filtering the measurements of the automotive radar

Filtering out the clutter measurements is important in order to

reduce false detections. Human movements are modelled

with continuous Wiener process acceleration model (BarShalom et al. 2001). The process noise – changes in the

acceleration in this case – is modeled as continuous zeromean white noise. The measurement noise is also assumed to

be white. Since both dynamic model and measurement model

are linear, Kalman filtering (Kalman 1960) is an obvious

choice for the filtering problem.

Because there are possibly several targets in the field of view

at once, the data association problem needs to be solved. In

this case, a simple Nearest Neighbour (NN) method is used

(Blackman and Popoli 1999). This method associates the

nearest measurement within the validation region to the

target. If no target is associated to a measurement, a tentative

target is formed. If another measurement is associated to that

tentative target in the following time step, the target is

confirmed. Targets are deleted if there are no measurements

associated to them in several consecutive time steps. For the

validation region, the uncertainty ellipses of the

measurements and the targets are used. After the data

association is made, separate Kalman filters may be applied

to each target. The NN data association works fine since the

measurements are received with high enough frequency

(10 Hz) and they are not too close with one other. Therefore,

the usage of more complex data association algorithms is not

justified.

Each target is given an extra state – a human probability -

which is updated separately from the Kalman filter. The

human probability ranges from zero to one, one being that it

is absolutely certain that the target is human. If there is no

knowledge of the class of the target, a neutral value of 0.5 is

assigned to the human probability.

The velocity of each target is monitored and if it is close to

the average walking speed of a human, 1.35 m/s (Anderson

and Pandy 2001), the human probability is increased slightly.

If the velocity is not within the specified interval, the human

probability is decreased moderately. In this research, a

probability value 0.55 was used for targets moving with right

speed, and 0.45 for other targets. Probability value 0.9 was

used for Doppler radar detections. Targets with probabilities

over 0.9 were considered as humans. These values were

found experimentally.

These probability values are combined at every time step

with Bayes rule

))(1))(|(1()()|(

)()|( )|(

xpxypxpxyp

xpxyp yxp

−+ − = (4)

where y is the measurement and x is the state. 

If a target travelling close to the average human walking

speed is tracked for a while, the human probability increases

every time step eventually enabling the classification of that

target as human.

5.2 Data fusion

Fusing data from both radars is a simple procedure, since the

data from the Doppler radar only provides additional

information about the class of the target. It is known that if

there is an observation with the Doppler radar, there is also a

target which is moving roughly at the average walking speed

of a human. Therefore, the target velocity is taken into

consideration at this point as well.

The Doppler measurements are modelled as Gaussian

distributions roughly covering the measurement sector of the

radar. An example of this kind of distribution can be seen in

the Fig. 4 marked with blue dashed line. Data association

between the Doppler measurement and a tracked target is

made if the distribution of the Doppler measurement, at

certain time step, intersects with the estimated position

distribution of that target. If the velocity estimate of such

target lies on the specified human walking speed interval, the

human probability of the Doppler measurement is combined

to the human probability of the target with Bayes rule. This

way, there can be stationary targets in the area of that

relatively large distribution of the Doppler measurement

which human probabilities will not get falsely updated.

The fusion process can be clarified with the help of Fig. 4.

The green ellipses in the figure represent the 2-sigma

uncertainty ellipses of the positions of three targets tracked at

that time. The red dots represent positions of targets

classified as humans and the small black circle inside the

biggest green ellipse represents a clutter measurement which

is rather uncertain. The Doppler measurement, illustrated

with blue dashed line, is associated to all of these three

targets. However, only the red targets are moving at

appropriate velocity and therefore only their human

probabilities are updated.

Fig. 4. Visualization of the fused data. Red dots represent

targets classified as humans.

A photograph of the situation illustrated in the Fig 4 can be

seen in the Fig 5. The left hand red dot in Fig 4 represents the

human up front in Fig 5 and the red dot on the right hand side

represents the human at the back. As can be seen in the Fig 5,

there is no target between these humans, hence making the

third measurement a false detection.

Fig. 5. A photograph of the data set gathered from harbour.

Two human targets are walking in the field of view of the

sensor system.

6. EXPERIMENTS

This chapter explains where and how the data sets used to

analyze the method presented in this paper are gathered.

Also, the performance of the method is presented here in a

way that the advantages of the use of the Doppler radar can

be seen.

Four data sets were collected, one inside a mine, one in a

harbor, one with simulated rain, and one where the

measurement platform was moving. The latter two caused

problems to the Doppler detection. In case of rain, the

numerous moving rain drops caused strong signal that

masked the reflection from a human. In case of moving radar,

the similar effect was caused by the environment that was

moving relative to the radar. Strong radar targets again

masked the signal of the human, and the detection could not

be performed. Therefore, only static cases without rain were

considered.

6.1 Measurements

The two data sets analysed here are collected from a mine

and from a harbor with same measurement platform. Radars

were attached to an ATV enabling easy mobility of the

sensors. In addition, a stereo camera was attached to the

measurement platform to produce image information from

the tests. The radar measurements were validated based on

the stereo camera data. The movements of the measurement

platform were recorded as well. Fig 6 shows a still picture

from the data set recorded in the mine. Similar image from

the other data set is presented in Fig 5. 

Fig. 6. A photograph from the test sequence in the mine.

There is only one human target in the field of view. The walls

of the tunnel produce plenty of reflection back to the

automotive radar.

Both radars were attached to the same laptop computer,

automotive radar via USB-CAN adapter and the Doppler

radar via USB port attachable A/D converter. Since both

sensors were connected to the same computer, data was

automatically synchronized. The gathered data sets were

approximately 30 seconds in length.

In the data set from the mine, there was only one human

target walking in the field of view. The trajectory of that

human can be seen in Fig 7. The human started walking away

from the radar on the left hand side of the tunnel making a

turn back just after the 15 meter marker returning on the right

hand side of the tunnel.

The data set recorded at the harbor features two human

targets in the field of view at once. Trajectories of these

humans are illustrated in Fig 8. The other human walked

across the measurement sector once starting from the left in

the vicinity of the 7 meter marker ending up to the right on

the five meter marker. The other human started from the right

hand side on the 5 meter marker and walked away from field

of view on the left hand side just after the 20 meter marker.

The same human returned to the FOV on the left hand side

approximately on the 11 meter marker and walked out of the

measurement sector on the right around the 3 meter marker.

6.2 Results

Results of the measurement are summarized in Table 1. The

first row contains the combined number of true positive and

false negative observations. The second row contains the total

number of true positive observations. The third row contains

the number of observations made due to the clutter. The

fourth and fifth rows contain the total number observations

classified as humans with only the automotive radar and with

the combination of the automotive radar and the Doppler

radar respectively. The percentages are calculated with

respect to the combined value of true positive and false

negative observations. All the information in the Table 1 is

also included in the Fig. 7 and Fig. 8.

Fig. 7. The trajectory of the human target can be seen on the

figure. Black dots represent positions where the human was

observed but was not classified as human. The red dots are

observations which are classified as humans. The gray dots

represent positions where the human target was not observed

with the radar measurement system. Blue dots are radar

measurements originated from clutter or from some other

target.

Fig. 8. Trajectories of two humans in the harbour. The colour

codes follow the explanations in the Fig 7.

In the mine, there were a lot of reflections from the walls of

the tunnel. These measurements are not considered as clutter

since they are caused by real objects. Therefore these

measurements are excluded from the third line of the table.

However, the exclusion is somewhat subjective since there is

no exact method to tell which measurement originated from

the walls and which did not. In the harbor there was one

systematic false target which caused all the clutter

measurement.

The Doppler radar only contributes to the classification of the

targets. Hence, the advantages of the use of the Doppler radar

can be seen when comparing the fourth and the fifth line of

the table 1. In the mine, the effect of Doppler radar is that 20

more observations are classified as humans. At the harbor, 75

targets are classified as humans due to the Doppler radar. No

false human classifications were made in these data sets. 

Table 1. Results

 Mine Harbor

Targets present 89 95

Targets detected 80

(89.89 %)

79

(83.16 %)

Clutter targets 15

(16.85 %)

30

(31.58 %)

Targets classified

Bosch

29

(32.58 %)

0

(0 %)

Targets classified

Bosch + Doppler

49

(55.06 %)

75

(78.95 %)

7. CONCLUSIONS AND FUTURE WORK

The combination of automotive and Doppler radars is

promising. The inability of an automotive radar to distinguish

humans from other targets was compensated with Doppler

radar based detection. Since the beam width of the Doppler

radar was successfully approximated with Gaussian

distribution, the Kalman filter based multitarget tracking

method was suitable for the sensor fusion with minor

modifications.

Some aspects of the Doppler radar limit the applications of

the proposed system. Doppler radar cannot detect humans

who are stationary or walking across the radar’s field of view.

The radar can only detect the motion components that are

directed towards to or away from the radar. In addition,

Doppler radar is sensitive to rain and movement of the radar

itself. However, if the environment does not contain any

strong radar reflectors, the system can also be used when

moving. The relative speeds of walking humans change, but

because the proposed algorithm only observes the periodicity

of the motion, the walking can still be detected.

In presence of other moving targets, they can be falsely

classified as humans. This can be a result of walking-like

motion. In the test system, the measurement sectors were

very narrow for human detection application. A rotating

measurement system or multiple systems could be a solution.

Fundamentally FMCW and Doppler radars use the same

phenomenon, measuring the frequency shift between

transmitted and received signal. The automotive radar used

measures also the Doppler frequency. Therefore, if the raw

sensor data of the radar could be accessed, it might be

possible to implement the algorithms presented in this paper

using only the data from the automotive radar. The

combination of FMCW range and Doppler measurements

would also enable the system to distinguish Doppler

signatures caused by different people. The Doppler signatures

would be centered on the frequency respective to the distance

measured with FMCW principle.

ACKNOWLEDGEMENTS

This research has been carried out under funding from Tekes

(Finnish Funding Agency for Technology and Innovation),

and FIMA (Forum for Intelligent Machines ry).

REFERENCES

Anderson, F.C. and Pandy, M.G. (2001). Dynamic

optimization of human walking. Journal of

Biomechanical Engineering, 123:381-390.

Bar-Shalom, Y. and Fortman, T.E. (1988), Tracking and

Data Association, New York: Academic Press.

Bar-Shalom, Y., Li, X.R. and Kirubarajan, T. (2001).

Estimation with applications to tracking and navigation,

New York: John Wiley & Sons Inc.

Blackman S. and Popoli R. (1999). Design and Analysis of

Modern Tracking Systems, Norwood, MA: Artech

House, 1230 pp. ISBN- 978-1580530064.

Gürbüz, S.Z., Melvin, W.L. and Williams, D.B. (2007).

Detection and identification of human targets in radar

data. SPIE Defense and Security Symposium, Orlando,

FL, USA, April 2007

Kalman, R.E. (1960). A New approach to linear filtering and

prediction problem. Transaction of ASME, Journal of

Basic Engineering, 82. pp. 35–45.

Kustom Signals (2009).

 http://www.kustomsignals.com/product_body2.asp?prod

uct_id=34&cat_id=7, Accessed 30.12.2009

Nakane, T. and Soshi, K. (1998). A study on recognition of

pedestrians using dual band radar. ITS World

Conference, Seoul, South Korea, October 1998.

Nakane, T. and Soshi, K. (2000). Animal body detection

system utilizing electromagnetic waves. US Patent

6072173, Available:

 http://www.patentstorm.us/patents/6072173/description.

html

Otero, M. (2005). Application of a continuous wave radar for

human gait recognition. Technical paper, The MITRE

Corporation. Available:

 http://www.mitrecorporation.com/work/tech_papers/tech

_papers_05/05_0330/05_0330.pdf

Thayparan, T., Abrol, S., Riseborough, E. (2004), MicroDoppler radar signatures for intelligent target

recognition. Technical paper, Defence R&D Canada.

Available:

 http://www.ottawa.drdc-rddc.gc.ca/docs/e/TM-2004-

170-eng.pdf

Troppmann, R. and Höger, A. (2008). An introduction to

adabtive cruise control (ACC). Driver assistance

systems, Embedded design India. 4 pp.

Waltz, E. and Llinas, J. (1990). Multisensor data fusion,

Boston, MA: Artech house.

Yarovoy, A.G., Ligthart, L.P., Matuzas, J. and Levitas, B.

(2008). UWB radar for human being detection. IEEE

A&E Systems Magazine, volume (28), pp. 36-40. 

ReferencesAnderson and Pandy, 2001

F.C. Anderson, M.G. PandyDynamic optimization of human walking

Journal of Biomechanical Engineering, 123 (2001), pp. 381-390

View Record in ScopusGoogle ScholarBar-Shalom and Fortman, 1988

Y. Bar-Shalom, T.E. Fortman

Tracking and Data Association, Academic Press, New York (1988)

Google ScholarBar-Shalom et al., 2001

Y. Bar-Shalom, X.R. Li, T. Kirubarajan

Estimation with applications to tracking and navigation, John Wiley & Sons Inc, New York (2001)

Google ScholarBlackman and Popoli, 1999

S. Blackman, R. Popoli

Design and Analysis of Modern Tracking Systems, 978-1580530064, Artech House, Norwood, MA (1999), p. 1230

Google ScholarGürbüz et al., 2007Gürbüz, S.Z., Melvin, W.L. and Williams, D.B. (2007). Detection and identification of human targets in radar data. SPIE Defense and Security Symposium, Orlando, FL, USA, April 2007.

Google ScholarKalman, 1960

R.E. KalmanA New approach to linear filtering and prediction problem

Transaction of ASME, Journal of Basic Engineering, 82 (1960), pp. 35-45

CrossRefView Record in ScopusGoogle ScholarKustom Signals, 2009

Kustom Signals

http://www.kustomsignals.com/product_body2.asp?product_id=34&cat_id=7 (2009)

Accessed 30.12.2009.

Nakane and Soshi, 1998Nakane, T. and Soshi, K. (1998). A study on recognition of pedestrians using dual band radar. ITS World Conference, Seoul, South Korea, October 1998.

Google ScholarNakane and Soshi, 2000

T. Nakane, K. SoshiAnimal body detection system utilizing electromagnetic waves. US Patent 6072173

Available: http://www.patentstorm.us/patents/6072173/description.html (2000)

Google ScholarOtero, 2005

M. Otero

Application of a continuous wave radar for human gait recognition. Technical paper, The MITRE Corporation (2005)

Available: http://www.mitrecorporation.com/work/tech_papers/tech_papers_05/05_0330/05_0330.pdf

Google ScholarThayparan et al., 2004

T. Thayparan, S. Abrol, E. RiseboroughMicro-Doppler radar signatures for intelligent target recognition. Technical paper, Defence R&D Canada

Available: http://www.ottawa.drdc-rddc.gc.ca/docs/e/TM-2004-170-eng.pdf (2004)

Google ScholarTroppmann and Höger, 2008Troppmann, R. and Höger, A. (2008). An introduction to adabtive cruise control (ACC). Driver assistance systems, Embedded design India. 4 pp.

Google ScholarWaltz and Llinas, 1990

E. Waltz, J. Llinas

Multisensor data fusion, Artech house, Boston, MA (1990)

Google ScholarYarovoy et al., 2008

A.G. Yarovoy, L.P. Ligthart, J. Matuzas, B. LevitasUWB radar for human being detection

IEEE A&E Systems Magazine (28) (2008), pp. 36-40

CrossRefView Record in ScopusGoogle Scholar

View Abstract