IMPLEMENTATION OF THROUGH-WALL MOTION DETECTION USING BEAMFORMING 1

ABHISHEK BISHT, 2R.ADITYAN, 3ARJUN MOHANDAS, 4KUNAL VERMA, 5PRAKASH.V, 6 GANDHIRAJ R, 7SOMAN K P

1-6

Department of Electronics and Communication Engineering,7Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham

Abstract-- This paper focuses on through-wall motion detection of objects using 2.4 GHz frequency and a bandwidth of 20 MHZ. In this paper, we use delay and sum beam-forming technique to make the receiving antenna directive such that we can neglect the disturbances from different directions. OFDM technique is used at the transmitter and channel estimation is performed using MMSE technique. This paper uses MIMO interference nulling to eliminate reflections off static objects and focus the receiver on a moving target. We demonstrate the result by simulating the same channel parameters to see the received signal getting nulled at the receiver.

I.

antenna to negate the effect of wall using nulling algorithm which subtracts the signals from all static objects in the surrounding. The concept of beamforming is of special interest to this project as its application requires the antenna setup to be directional in order to increase the antenna gain for better reception of signal. The TTW motion detection using narrow-band can have various applications in hostage situations and further advancement in TTW technique can increase the resolution of detection of motion which may lead to search and rescue operations of trapped victims in situations like earthquakes. This technique can also be used by security personnel to track down miscreants in face of emergency.

INTRODUCTION

For many years, cost-effective see-through wall system remains one of the most challenging and coveted areas of research. Till recent times through wall motion detection was done using UWB (Ultra Wide Band) which utilizes a bandwidth of approximately 2 GHz. This type of TTW (through the wall) motion detection setup requires bandwidth which can only be used by military entities. This state-of-the-art system requires 2 GHz of bandwidth, a large power source, and an 8-foot long antenna array (2.4 meters) [10],[13]. The UWB radar used for TTW motion detection [2] has various advantages over narrow-band TTW system such as high resolution and good range. The concept of TTW motion detection uses the same concept as sonar or radar. When the transmitted signal, an RF signal, is incident on a non-metallic wall, some part of the signal is reflected back while some part of the signal travels through the wall and falls on the objects present on the other side of the wall. These objects will reflect the signal again for to be detected by the receiver. This gives us information about the objects present in the room. The received signal from the wall is much stronger as compared to the signals received from the objects behind the wall; this phenomenon is called ‘Flash Effect’. Recent advances in the field of MISO nulling technique [4] let us neglect these stronger reflected signals from the wall which overwhelm the signals reflected from the object present behind the wall. Recent breakthrough in through-wall motion detection using narrowband systems [4], using passive bi-static radar [1] has already been implemented. Beamforming technique enables us to give directivity to the receiver antenna system to concentrate on receiving signals only from the desired direction and subdues any other stray signals from other directions. We make use of OFDM technique using 2.4 GHz(ISM band) to transmit signals using two transmit antennas, and one receive

II.

ELIMINATING WALL REFLECTIONS

In this section, we discuss how we resolve the issue of flash effect which was earlier discussed. The stronger reflections off the wall are to be negated because the wall is typically much larger than the objects of interest, and has a higher reflection coefficient [11]. Recent advances show that MIMO systems can pre-code their transmissions such that the signal received at a particular antenna is cancelled [13]. Here, we use MIMO nulling algorithm [4] with basic modifications. In this algorithm, the system performs standard nulling. Initially the first transmitting antenna transmits while the other transmit antenna is switched off. The channel h1, between first transmit antenna and receiver, is estimated at the receiver as ĥ1. Similarly, the second antenna transmits while the first is switched off. The channel h2, between the second transmit antenna and the receiver, is estimated at the receiver as ĥ2. Now, a ratio p = -ĥ1/ ĥ2 is calculated [4]. Finally, both the transmit antennas simultaneously transmit only with the constraint that first transmit antenna transmits signals that are amplified by factor p.

Proceedings of 7th IRF International Conference, 27th April-2014, Pune, India, ISBN: 978-93-84209-09-4 139

Implementation of Through-Wall Motion Detection Using Beamforming

Hence the resultant channel perceived at the receiver will be: hres = h1 + h2(p) (1) hres = h1 + h2(-ĥ1/ ĥ2) ≈ 0 (2) Ideally, when the estimates are perfect the received signal would be equal to zero. This is how the static reflections are nulled. When there is object movement, the estimated channels won’t equal i.e. ĥ1 ≠ h1 and ĥ2 ≠ h2. Hence, hres ≠ 0. This denotes that there is a change in channel and movement exists. This, basically, signifies motion detection. The sections III-VI show how the system is implemented, to perform motion detection. III.

transmission. On reception all processes are repeated in the inverted order for effective demodulation of transmitted signals. The channel is added in the intermediate as shown in the block diagram. This is discussed in the next section. IV.

CHANNEL MODELING

The channel that is to be added is modeled. The channel is assumed to have zero line-of-sight paths because the antennas are going to be beamformed to provide better directivity. Channel 1, between the first transmit antenna and the receiver is modeled with three multipaths whose components are respectively P01, P11, P21. Two delays are specified (τ11, τ21) for multipath components P11, P21. The delay taken for P01 to arrive is considered zero and other delays are considered with reference to this. The above design is replicated for channel 2, which is between the second transmit antenna and the receiver as well. The replicated parameters are P02, P12, P22and τ12, τ22.[9]

IMPLEMENTATION OF OFDM

OFDM (Orthogonal Frequency Division Multiplexing) is basically specialized FDM. It is a parallel transmission scheme where a high-rate serial data stream is split up into a set of low-rate sub streams, each of which is modulated on a separate subcarrier (FDM). Each of the subcarriers, how much ever the number be, are mutually orthogonal to each other. By selecting a special set of (orthogonal) carrier frequencies, high spectral efficiency is obtained because the spectra of the subcarriers overlap. Hence, the spectrum is efficiently used by allowing overlap. Moreover, OFDM is more resistant to frequency selectivity because of division of channels into narrowband flat fading sub-channels. The mobile radio channel is characterized by multipath reception: the signal received contains no direct line-of-sight (LOS) radio wave, but a large number of reflected radio waves that arrive at the receiver at different times. Delayed signals are the result of reflections from features such as surrounding clutter etc. These reflected, delayed waves interfere with the other multipath components to cause Inter Symbol Interference (ISI), which in turn cause significant degradation of network performance. This necessitates a wireless network that should be designed to minimize adverse effects which is OFDM, which effectively reduces the influence of multipath fading.

Fig 1. Theoretical setup

The three multipaths considered, are diagrammatically shown in Figure-1 in general for any transmitting antenna and the receiver. The first multipath, represented by P0, signifies the signals that are directly reflected off the wall. The second and third multipath, represented by P1 and P2, signify signal powers that are reflected off the object. These parameters are assigned values based on an arbitrary office setup involving distances between transmitter/receiver and the wall, the type of wall, the wall thickness, the distance between the transmitter/receiver and the wall. The arbitrary setup specifies a distance of 1meter between the transmitter/receiver and the wall. The wall is assumed to be dry plywood as it has moderate reflectance and very less attenuation. The thickness is 0.75 inches. The distance between the transmitter/receiver and the object is 1 meter. Based on the log-distance path loss model, for an office setup with path loss exponent of 2.6 and standard deviation of 14.1 dB [15], the values of P0, P1 and P2 are calculated. Finally, the channel is given as:

Initially the serial data source is converted to a parallel stream of data. The number of streams is 64 which equals the number of subcarriers [8]. Then all the data streams are modulated using 16 Quadrature Amplitude Modulation (16-QAM). Since the data is encoded into subcarriers all representations are in frequency domain. The modulated data streams are Inverse Fast Fourier Transformed (IFFT) from Frequency domain to time domain. Cyclic extension is performed to ensure the orthogonal characteristics at all conditions considering our setup [5]. Cyclic extension of 25% (16 bits) is implemented. Then parallel streams are converted to a serial stream for real time

Proceedings of 7th IRF International Conference, 27th April-2014, Pune, India, ISBN: 978-93-84209-09-4 140

Implementation of Through-Wall Motion Detection Using Beamforming

Now, this channel is added to complete the OFDM setup. Following this, the antennas are to be beamformed which is discussed in the next section. V.

VI.

CHANNEL ESTIMATION

Channel Estimation deals with estimation of various channel parameters which are responsible for the changes in the characteristics of the received signals from that of transmitted. In a typical communication system, Channel State Information (CSI) is obtained to formulate an equalizer at the receiving end, to undo the effects on the transmitted signal due to the presence of channel. In this project we make use of the CSIby operating on it, such that, the reflections by all static objects can be negated. Channel estimation techniques for OFDM systems can be grouped into two categories: blind and non-blind. [3]The blind channel estimation method exploits the statistical behavior of the received signals, while the non-blind channel estimation method utilizes some or all portions of the transmitted signals, that is, pilot tones or training sequences, which are available to the receiver to be used for the channel estimation.

BEAMFORMING

Beam-forming is the process of performing spatial filtering, that is, the response of sensors is made sensitive to signals coming from a specific direction while signals from other directions are attenuated. The directivity (D) for a given frequency is directly proportional to equivalent aperture area, equal to the product of the physical aperture area and the antenna efficiency. One way to increase the aperture area is by placing several individual antenna elements in an electrical and geometrical configuration which can be referred to as an array [1]. Such configuration provides flexibility to shape the resulting radiation pattern to have maxima and minima in different ‘custom’ directions. There are various methods of implementation, in this paper delay and sum beam-forming has been implemented in time domain [11]. The underlying idea of delay and sum beamforming is that when an electromagnetic signal impinges upon the aperture of the antenna array, the element outputs, added together with appropriate amounts of delays, reinforce signals with respect to noise or signals arriving at different directions. The delays required depend on the physical spacing between the elements in the array.

Block type pilot channel estimation has been developed under the assumption of slow fading channels.

and q is always an integer. To create a beam in the transmitter antenna we specify a term beam index ‘b’ where,

The pilot assisted channel estimation process consi sts of two steps; first statistical estimation of the cha nnel at OFDM tones consisting of reference symbols is determined using statistical methods including Least Squares (LS) and Minimum Mean Squares (MM SE) estimates. Without using any knowledge of the statistics of the channels, the LS estimators can be implemented with very low complexity, but they suffer from a high mean-square error.Whereas, the MMSE estimator employs the second-order statistics of the channel conditions to minimize the meansquare error.The MMSE estimator yields much better performance than LS estimators, especially under the low SNR scenarios.The objective here is to estimate the channel(H or g) given the pilot signals (specified by vector X) and received signals (specified by Y), without using certain knowledge of the channel. We denote Rgg,RHH, and RYYas the auto-covariance matrix of g, H, and Y, respectively, andRgY, as the cross covariance matrix between g and Y. Also denote N2 the noise variance E{|N|2}. Assume the channel vector g and the noise N are uncorrelated, it is derived that

Following beam-forming, the functionality of the setup is administered which is to implement nulling to track motion detection. The next section discusses the channel estimation methodology.

Where Rgg, (thus RHH) and 2N are known at the receiver in advance, the MMSE estimator of g is given by gMMSE=RgYRYYYHH.[12] To find the estimated channel,

Where, ‘t’ is the delay for mth antenna in the array, ‘m’ is the antenna for which delay is being calculated , ‘d’ is the distance between the two antennas in an array, ‘ψb’ is the direction from which signal is arriving, ‘c’ is the speed at which signal travels, which is taken to be 3*108 m/s. The received signal is

Proceedings of 7th IRF International Conference, 27th April-2014, Pune, India, ISBN: 978-93-84209-09-4 141

Implementation of Through-Wall Motion Detection Using Beamforming

Fig 2(a) shows the result when there is no change in the channel, in this figure it can be noticed that the values start from 100 and the variations are because of the AWG noise added to the system. Fig 2(b) was obtained when there was change in channel, we have considered that only P1 changes with corresponding delay whereas P2 remains same with same delay.

where HLS is the channel estimated by Least Square(LS) method,

VII.

The result shown in fig 2(a) and fig 2(b) were carried out without the implementation of beamforming technique. The results with beamforming were carried outwith the same channel parameters as mentioned above.

IMPLEMENTATION AND RESULTS

After performing various trials with different values of P0, P1 and P2 which were calculated based on distance between the transmitter and the wall, material of the wall and, the distance between wall and the objects. For cardboard wall which gives a loss of 1dB and distance between the transmitter and the wall assumed to be 1metre, gives a loss of 28dB by making use of the path loss formula, given by

Fig 3 shows the beamforming output the beam has been centered at 0o with a null to null bandwidth of 60o. this beam is used to receive the signals which are transmitted using omnidirectional antenna.

Simulated results for the given values in Table1 were computed in MatLab 2013b and the graphs were plotted. Fig 3 Beamforming output.

Fig 2(a) Static channel without beamforming.

Fig 4(a). Static channel withbeamforming

Fig 2(b).Non-static channel without beamforming.

Fig 4(b).Non-static channel with beamforming.

Proceedings of 7th IRF International Conference, 27th April-2014, Pune, India, ISBN: 978-93-84209-09-4 142

Implementation of Through-Wall Motion Detection Using Beamforming

Fig 4(a) and 4(b) show the output for the same set of values as given in table 1, where fig 4(a) shows the result when there is no change in the channel and fig 4(b) shows result when P1 is changed with respect to the delay and P2 is kept unchanged as it is considered that only one multipath changes. It can be seen that the result obtained with beamforming has much higher output value due to gain because of implementation of beamforming technique.

Channels with Impulsive Noise ” IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 61, NO. 11, NOVEMBER 2013. [6] [7]

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[9]

Ramjee Prasad, “OFDM for Wireless Communication Systems”, Artech House, London.

[10] G.Charvat, L. Kempel, E.Rothwell, C.Coleman, and E.Mokole. “An ultrawideband (UWB) switched-antennaaray radar imaging system.,“In IEEE ARRAY, 2010. [11] Anderson, J.B., Rappaport, T.S., and Yoshida, S., “Propagation Measurements and Models for Wireless Communications Channels,” IEEE Communications [12] Manwinder Singh,Maninder Singh and AnudeepGoraya.”Block based Channel Estimation Algorithms for OFDM-IEEE 802.16e(Mobile Wimax) System.”International Journal of Computer Applications,Vol. 13,January 2011. [13] T.Ralston, G.Charvat, and J.Peabody. “Real-time throughwall imaging using an ultrawideband multiple-input multiple-output (MIMO) phased array radar system.,” IEEE ARRAY, 2010.

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Proceedings of 7th IRF International Conference, 27th April-2014, Pune, India, ISBN: 978-93-84209-09-4 143