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Multiple target identification using phased-MIMO radar.

INTRODUCTION

Phased array is an array of antennas in which the relative phases of the respective signals feeding the antennas are varied in such a way that the effective radiation pattern of the array is reinforced in a desired direction and suppressed in undesired directions. To improve target detection, multiple-input and multiple-output (MIMO) radar systems have received considerable attention in recent years. The MIMO radar transmits multiple independent waveforms from its antennas. Bi-static radar is the name given to a radar system which comprises a transmitter and receiver which are separated by a distance that is comparable to the expected target distance..

The target from multiple uncorrelated directions are detected benefiting from waveform diversity to form a long virtual array, much longer than phased array radar systems with the same number of TX and RX antennas, and get lower side lobes beam pattern (stoica et al 2007).

There are several techniques to further improve the detection performance of MIMO radars, significant gain in range resolution can be achieved by using a step-frequency approach during transmission (Gurbuz et al 2009, stoica et al 2007, yoon et al 2009, petropulu et al 2012). The detection performance is improved by using a measurement matrix that minimizes the signal-to-inference ratio (SIR) (petropulu et al 2011).

The goal of (Gogeneni et al 2011) is to maximize the target detection so that the probability of missing weak targets is reduced. MIMO radar is used to overcome performance degradations due to the radar cross section fluctuations (Nikolaus et al 2007).

Fishler et al (2004) proposed a general antenna configuration is considered where several well-separated sub arrays are used to form MIMO radar with each sub array operating in phased- array mode. The enabling concept for MIMO radar, the transmission of multiple orthogonal waveforms from different antennas, is usually referred to as the waveform diversity (chen et al 1986).

Phased mimo radar system model:

MIMO radar allows for beam forming at the transmit and receive arrays. The main idea behind is to partition the transmit array into K subarrays (1 [less than or equal to] K [less than or equal to] M) which are allowed to overlap. In general, each transmit subarray can be composed of any number of antennas ranging from 1 to M such that no sub array is exactly the same as another subarray. All antennas of the Kth subarray are used to coherently emit the [[phi].sub.k] signal (t) so that a beam is formed towards a certain direction in space, e.g., direction of the target. Then, the beam forming weight vector w is properly designed to maximize the coherent processing gain. At the same time, different waveforms are transmitted by different subarrays. The uplink steering vector is denoted as [a.sub.k]([theta]).

Let the K x 1 transmit coherent processing vector be

C([theta])[??][[[w.sup.H.sub.1][a.sub.1]([theta]) ... [w.sup.H.sub.k][a.sub.k]([theta])].sup.T] (1)

The waveform diversity is achieved by the phased- MIMO radar which significantly improves the direct applicability of adaptive arrays for target detection and much enhanced flexibility for transmit beam-pattern design.

The K x 1 waveform diversity vector is given as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)

Then, the reflected signal can be rewritten as

r(t, [theta]) = [square root of M/K] [beta]([theta])[(c([theta])[dot encircle]d([theta])).sup.T][[phi].sub.k](t) (3)

where [[phi].sub.K] (t)[??][[[phi].sub.1](t) ... [[phi].sub.k](t)] is the vector of K x 1 waveforms.

Target identification using transmit/receive beamforming:

Beamforming is a signal processing technique used in directional signal transmission or reception. This is achieved by combining elements in a phased array in such a way that signals at particular angles experience constructive interference while others experience destructive interference. Beamforming can be used at both the transmitting and receiving ends in order to achieve spatial selectivity. The improvement compared with omnidirectional reception/transmission is known as the receive/transmit gain. To change the directionality of the array when transmitting, a beamformer controls the phase and relative amplitude of the signal at each transmitter, in order to create a pattern of constructive and destructive interference in the wavefront. When receiving, information from different antennas is combined in a way where the expected pattern of radiation is preferentially observed.

The conventional transmit or receive beamforming technique is used to identify the target using phased-MIMO radar. The phased-MIMO radar, the phased-array radar and the MIMO radar are compared in terms of their transmit-receive beampatterns.

The non adaptive transmit/receive beamforming techniques are used for the phased-MIMO radar.

The transmit beamforming weight matrix is given as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)

where [w.sub.k,m] is the mth weight of the kth subarray beamforming weight vector.

The beamformer weight vectors for conventional uplink beam forming, are given by

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)

At the receive array, the conventional beamformer is applied to the virtual array and, therefore, the KN x 1 receive beamformer weight vector is given by

[w.sub.d] [??] ([[theta].sub.s]) = [c ([[theta].sub.s]) [dot encircle] d ([[theta].sub.s]) [cross product] ([[theta].sub.s])] (6)

Let [G.sub.k] ([theta]) be the normalized phased-MIMO radar beampattern, that is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (7)

The overall beam pattern of the phased-MIMO radar [G.sub.k] ([theta]) is proportional to the multiplication of the transmit beam pattern c([theta]) and the waveform diversity beam pattern d([theta]). From the target reflected beam pattern of overall beam pattern, the direction or angle of target is identified.

Simulation results and discussion:

A phased- MIMO radar system with the 20 transmit and 20 receive antennas are considered. If the number of sub array antenna decreases, the phased MIMO radar produces higher sidelobes. It reduces the accuracy of target detection. It is shown in figure 3 and figure 5. so only 20 transmit and receive antennas used here .

The transmitted waveforms are orthogonal Hadamard waveform and have unit power. The number of radar pulses for fast time is 400 and for slow time is 200. The transmitter spacing are assumed to be 0.5[lambda]. The search space for phased MIMO radar is in the range from -90[degrees] to 90[degrees], the size of search grid cell is 0.1000, the target is placed in any angle as a increment of 0.1000 with in search space, so target is assumed in angle position as 20[degrees] 10[degrees] 30[degrees].

Assume two interfering targets located at directions of -30[degrees] and - 10[degrees]. The target of interest is assumed to reflect a plane-wave that impinges on the array from [[theta].sub.s] = -20[degrees], 10[degrees] and 30[degrees] direction.

The uplink and downlink steering vectors are found using following equation

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (8)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (9)

The Orthogonal waveforms are simulated as shown in Figure 1 for two antennas. The transmit beampattern c([theta]) and waveform diversity beampattern d([theta]) are simulated. And compared with phased array radar and Bistatic MIMO radar (the distance between transmit antenna and target is higher than distance between transmit and receive antenna) .The simulated graph for the transmit beampattern is shown in Figure 2. It is found that the phased MIMO radar identifies the targets present at the three locations as that of phased-array radar but with minimum side lobe levels.

In figure 2 coherence processing gain C([theta]) for MIMO radar is zero but phased MIMO radar.

From the Figure 4, it can be concluded that the phased MIMO radar provides waveform diversity and accurate target identification. The phased- MIMO radar has a wider lobe compare than two radars in above figure which increases the accuracy of target estimation. In figure 3 diversity pattern d([theta]) of phased array radar is zero. The product of transmit beampattern c([theta]) and waveform diversity d([theta]) is called over all beampattern G ([theta]).

Conclusion:

The Phased-MIMO radar combines the advantages of the phased-array and MIMO radars, therefore, it has a superior performance. It can estimate target accurately. It is based on partitioning the transmit array to a number of subarrays which are allowed to overlap. Each subarray is used to coherently transmit a waveform which is orthogonal to the waveforms transmitted by other subarrays. Coherent processing gain is achieved by designing the weight vector for each transmit subarray to form a beam towards a certain direction in space. The subarrays are combined jointly to form a phased- MIMO radar resulting in higher angular resolution capabilities. Simulation is performed by using MATLAB, shows the effectiveness of the phased- MIMO radar technique.

ARTICLE INFO

Article history:

Received 3 September 2014

Received in revised form 30 October 2014

Accepted 4 November 2014

REFERENCES

Chen, K.M, D. Misra, H. Wang, H.R. Chuang, and E. Postow, 1986. An X-band microwave life-detection system, IEEE Trans. Biomed. Eng, 33: 697-701.

Fishler, E., A. Haimovich, R. Blum, D. Chizhik, L. Cimini and R. Valenzuela, 2004. MIMO radar: An idea whose time has come, in Proc. IEEE Radar Conf., Honolulu, HI, Apr. 2004, 2: 71- 78.

Gogineni, S. and A. Nehorai, 2011. Target estimation using sparse modeling for distributed MIMO radar. IEEE Transactions on Signal Processing, 59(11): 5315-5325.

Gurbuz, A.C., J.H. McClellan and W.R. Scott, 2009. A compressive sensing data acquisition and imaging method for stepped frequency GPRs. IEEE Transactions on Signal Processing, 57(7): 2640-2650.

Nikolaus, H., Lehmann, Eran Fishler, M. Alexander Haimovich, 2007. Evaluation of Transmit Diversity in MIMO-Radar Direction Finding IEEE TRANSACTIONS ON SIGNAL PROCESSING, 55(5).

Stoica, P. and J. Li, 2007. MIMO radar with colocated antennas. IEEE Signal Processing Magazine, 24(5): 106-114.

Yoon, Y.S. and M.G. Amin, 2009. Imaging of behind the wall targets using wideband beamforming with compressive sensing. In Proceedings of IEEE Workshop on Statistical Signal Processing (SSP2009), Cardiff, Wales, Aug, pp: 93-96.

Yu, Y., A.P. Petropulu and H.V. Poor, 2012. CSSF MIMO Radar: Low-complexity compressive sensing based MIMO radar that uses step frequency. IEEE Transactions on Aerospace and Electronic Systems, 48(2): 1490-1504.

Yu, Y., A.P. Petropulu and HV. Poor, 2011. The measurement matrix design for compressive sensing based MIMO radar. IEEE Transactions on Signal Processing, 59(11): 5338-5352.

(1) Dr. C. Geetha Priya and (2) J. Rijo

Professor of ECE Department, Kamaraj College of Engineering and Technology, Virudhunagar. PG Student, kamaraj college of Engineering Technology, Virudhunagar.

Corresponding Author: Dr. C. Geetha Priya, Professor of ECE Department, Kamaraj College of Engineering and Technology, Virudhunagar.
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Author:Priya, C. Geetha; Rijo, J.
Publication:Advances in Natural and Applied Sciences
Article Type:Report
Geographic Code:9INDI
Date:Nov 1, 2014
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