Radar angle-tracking system for multiple moving targets

Gu, Hongen

doi:10.1049/ip-rsn:20020223

Cited by 8 publications

(14 citation statements)

References 3 publications

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“…In general, the complexity of MUSIC is of the order P 3 , where P is the size of the array. Gu's algorithm has complexity of only N 3 +N 2 P (see [5]), where N is the number of the strongest multipath components. Thus, for the described measurements this algorithm is 512/40 = 12.8 times more efficient.…”

Section: Results For Mimo Measurementsmentioning

confidence: 99%

“…Therefore, in tracking the wireless channel we have to rely only on the current snapshot to estimate and update the DOA's. H. Gu [5] has developed a radar tracking algorithm for multiple moving targets, that does not require estimation and inversion of the covariance matrix R, which is a computationally heavy task. The algorithm computes Instantaneous Maximum Likelihood Estimates (IMLE) of the DOA's given an instantaneous data snapshot.…”

Section: Direction-of-arrival Estimationmentioning

confidence: 99%

“…This is hardly the case for SIMO wireless channels. Recently H. Gu [5] has proposed an algorithm to update the DOA estimates which does not require estimation of the sensor covariance matrix. However, this algorithm becomes unstable when several objects have the same or similar DOA's.…”

Section: Introductionmentioning

confidence: 99%

“…We show that for accurate tracking of DOA's the MUSIC-like methods based on inversion of the channel snapshot covariance matrix are suboptimal. In our approach we apply an alternative algorithm developed by H. Gu(2002) that uses the instant channel snapshot to update the DOA estimates directly. We improve on this algorithm by introducing online data association capabilities based on linear extrapolation that leads to better tracking performance and lower computational complexity.…”

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confidence: 99%

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Tracking direction-of-arrival for wireless communication with multiple antennas

Shutin

Kubin

2003

2003 4th IEEE Workshop on Signal Processing Advances in Wireless Communications - SPAWC 2003 (IEEE Cat. No.03EX689)

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The goal of this contribution is to bind together results available for radar systems for Direction-of-Arrival (DOA) tracking and wireless communications with multiple antennas. We show that for accurate tracking of DOA's the MUSIC-like methods based on inversion of the channel snapshot covariance matrix are suboptimal. In our approach we apply an alternative algorithm developed by H. Gu(2002) that uses the instant channel snapshot to update the DOA estimates directly. We improve on this algorithm by introducing online data association capabilities based on linear extrapolation that leads to better tracking performance and lower computational complexity. The performance is evaluated using simulated channels as well as real wideband Multiple-Input-Multiple-Output (MIMO) measurements at 2GHz. The obtained estimates confirm previous results obtained with alternative techniques but provide more accurate results at lower computational complexity.

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Section: Results For Mimo Measurementsmentioning

confidence: 99%

Section: Direction-of-arrival Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Tracking direction-of-arrival for wireless communication with multiple antennas

Shutin

Kubin

2003

2003 4th IEEE Workshop on Signal Processing Advances in Wireless Communications - SPAWC 2003 (IEEE Cat. No.03EX689)

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“…52 Iql direction 6 is "adaptively" located in inverting IV. ESTIMATED OPTIMAL ARRAY When K -*00, ) -*6 [4], hence Q (6) In practice, R is unknown and estimated by Q( 6) = Q and R 1K -* bs (0)Qs(b) = bqHq.…”

mentioning

confidence: 99%

Angle-Tracking Adaptive Array -- Adaptive-Adaptive Array Processing

2007

2007 IEEE Radar Conference

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The optimal array, which maximizes = S ( 6 )a(t) + w(t)(1.1) the signal to interference plus noise ratio (SINR), is beam-formed by the following weigh vector: jd,e jd2 e jdN;] t -~~~~~~~~where s (9 ) [eJd eJd ,.eJd Tis the =c[R_ ls(5)~m j = c[R s(s0)0 steering vector from an interference source such as jamming, clutter or target return at arriving where R is the covariance matrix of interference and receiver noise, and where s() is the steering angle 9 , 4 = (2m/2)sin 0 , X is the wavelength, vector from a desired direction 0. The optimal and dn is the distance between the n-th sensor array is well known and fundamental in the literature of adaptive array and signal processing and a reference poit i the array. In (2), S (6) but has not been actually applied to real radar [S( e )... s(o )] is a NxM matrix, formed by because of huge computation burden for inverting 1 M R, which is on the order of N3 / Ar. Here N is the steering vectors from 9 ...0 9 and 6 the number of sensors in an array and Al' is the [0 ]T a(t) =[a (t) a (t)] is the update interval. Typically, N = 1,000 in a planar 1 M a ) M( array and Al = 1 IS . In order to reduce the vector of complex amplitudes of interference at computational burden, Brookner and Howells time t. a(t) is assumed to be stationary in t and proposed 111 the technique of adaptive-adaptive satisfies array, which transforms the large array of N E a(t) = O E a(t) a (t) = O sensors to a small array of M+1 beams of pointing H t to M interference sources and the desired direction E a(t) a (t) = P. (1.2) 0. The optimal array based on M+1 beams Hereafter, E denotes the statistical expectation, requires a computation burden only on the order of (.) , (.) and (.)H denote the complex M3 / AT . The adaptive-adaptive array in [1] is an conjugate, transpose and conjugated transpose of ingenious conjecture rather than a solid technique, a complex number or a matrix, respectively. because no solid method for tracking the directions P d lt th . 2 of M interference sources is given. In this paper, S aiagonamarixwi ,the angle-tracking adaptive array (ATAA) in 121 is along its diagonal (so Pm the power of the m-th introduced to implement the adaptive-adaptive array processing. The ATAA offers an even higher interference source, m = 1,2... M). SINR than the well-known optimal array at a w (t) is the complex Gaussian receiver noise computational burden only on the order of vector at time t and is assumed to be white and N M2 / A 17. The ATAA is providing a solid basis stationary in t, satisfying for the adaptive-adaptive array and is superior E w (t) =O,E w (t) w (t) = 0, over the well known optimal array in both SINR H 2 and computation burden, suggesting a remarkable E w (t) w (t) = J7 I.(1.3) new direction to the adaptive array processing in Data snapshots x(t) are assumed to follow model the eve of digital beam-forming era.

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A Modified Data Association Algorithm Based on the Intermediary Method for Passive Location System

Huang

Wang

2011

2011 International Conference in Electrics, Communication and Automatic Control Proceedings

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Radar angle-tracking system for multiple moving targets

Cited by 8 publications

References 3 publications

Tracking direction-of-arrival for wireless communication with multiple antennas

Tracking direction-of-arrival for wireless communication with multiple antennas

Angle-Tracking Adaptive Array -- Adaptive-Adaptive Array Processing

A Modified Data Association Algorithm Based on the Intermediary Method for Passive Location System

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