4,067 research outputs found
MIMO radar space–time adaptive processing using prolate spheroidal wave functions
In the traditional transmitting beamforming radar system, the transmitting antennas send coherent waveforms which form a highly focused beam. In the multiple-input multiple-output (MIMO) radar system, the transmitter sends noncoherent (possibly orthogonal) broad (possibly omnidirectional) waveforms. These waveforms can be extracted at the receiver by a matched filterbank. The extracted signals can be used to obtain more diversity or to improve the spatial resolution for clutter. This paper focuses on space–time adaptive processing (STAP) for MIMO radar systems which improves the spatial resolution for clutter. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (conventional radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this paper, the clutter space and its rank in the MIMO radar are explored. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). A new STAP algorithm is also proposed. It computes the clutter space using the PSWF and utilizes the block-diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity
Cramer-Rao Bound for Target Localization for Widely Separated MIMO Radar
In this paper, we derive the Cramer-Rao Bounds (CRBs) for the 2-dimensional (2D) target localization and velocity estimations for widely separated Multiple-Input Multiple-Output (MIMO) radar. The transmitters emit signals with different frequencies and the receivers receive these signals with amplitude fluctuations and with Doppler shifts due to the target motion. The received signal model is constructed using the Swerling target fluctuations to take into account the undesired effects of target amplitude and phase fluctuations. Moreover, the time delays and the Doppler frequencies are included in the signal model to get a more realistic model. Then, the Cramer-Rao Bounds are derived for the proposed signal model for the target position and velocity estimations. Contrary to known models of CRBs, we derived the CRBs jointly and using the Swerling target fluctuations
MIMO Radar Target Localization and Performance Evaluation under SIRP Clutter
Multiple-input multiple-output (MIMO) radar has become a thriving subject of
research during the past decades. In the MIMO radar context, it is sometimes
more accurate to model the radar clutter as a non-Gaussian process, more
specifically, by using the spherically invariant random process (SIRP) model.
In this paper, we focus on the estimation and performance analysis of the
angular spacing between two targets for the MIMO radar under the SIRP clutter.
First, we propose an iterative maximum likelihood as well as an iterative
maximum a posteriori estimator, for the target's spacing parameter estimation
in the SIRP clutter context. Then we derive and compare various
Cram\'er-Rao-like bounds (CRLBs) for performance assessment. Finally, we
address the problem of target resolvability by using the concept of angular
resolution limit (ARL), and derive an analytical, closed-form expression of the
ARL based on Smith's criterion, between two closely spaced targets in a MIMO
radar context under SIRP clutter. For this aim we also obtain the non-matrix,
closed-form expressions for each of the CRLBs. Finally, we provide numerical
simulations to assess the performance of the proposed algorithms, the validity
of the derived ARL expression, and to reveal the ARL's insightful properties.Comment: 34 pages, 12 figure
Target Localization Accuracy Gain in MIMO Radar Based Systems
This paper presents an analysis of target localization accuracy, attainable
by the use of MIMO (Multiple-Input Multiple-Output) radar systems, configured
with multiple transmit and receive sensors, widely distributed over a given
area. The Cramer-Rao lower bound (CRLB) for target localization accuracy is
developed for both coherent and non-coherent processing. Coherent processing
requires a common phase reference for all transmit and receive sensors. The
CRLB is shown to be inversely proportional to the signal effective bandwidth in
the non-coherent case, but is approximately inversely proportional to the
carrier frequency in the coherent case. We further prove that optimization over
the sensors' positions lowers the CRLB by a factor equal to the product of the
number of transmitting and receiving sensors. The best linear unbiased
estimator (BLUE) is derived for the MIMO target localization problem. The
BLUE's utility is in providing a closed form localization estimate that
facilitates the analysis of the relations between sensors locations, target
location, and localization accuracy. Geometric dilution of precision (GDOP)
contours are used to map the relative performance accuracy for a given layout
of radars over a given geographic area.Comment: 36 pages, 5 figures, submitted to IEEE Transaction on Information
Theor
Coexistence Analysis between Radar and Cellular System in LoS Channel
Sharing spectrum with incumbents such as radar systems is an attractive
solution for cellular operators in order to meet the ever growing bandwidth
requirements and ease the spectrum crunch problem. In order to realize
efficient spectrum sharing, interference mitigation techniques are required. In
this letter we address techniques to mitigate MIMO radar interference at MIMO
cellular base stations (BSs). We specifically look at the amount of power
received at BSs when radar uses null space projection (NSP)-based interference
mitigation method. NSP reduces the amount of projected power at targets that
are in-close vicinity to BSs. We study this issue and show that this can be
avoided if radar employs a larger transmit array. In addition, we compute the
coherence time of channel between radar and BSs and show that the coherence
time of channel is much larger than the pulse repetition interval of radars.
Therefore, NSP-based interference mitigation techniques which depends on
accurate channel state information (CSI) can be effective as the problem of CSI
being outdated does not occur for most practical scenarios.Comment: Corrected some typos and reference
Compressive Sensing for MIMO Radar
Multiple-input multiple-output (MIMO) radar systems have been shown to
achieve superior resolution as compared to traditional radar systems with the
same number of transmit and receive antennas. This paper considers a
distributed MIMO radar scenario, in which each transmit element is a node in a
wireless network, and investigates the use of compressive sampling for
direction-of-arrival (DOA) estimation. According to the theory of compressive
sampling, a signal that is sparse in some domain can be recovered based on far
fewer samples than required by the Nyquist sampling theorem. The DOA of targets
form a sparse vector in the angle space, and therefore, compressive sampling
can be applied for DOA estimation. The proposed approach achieves the superior
resolution of MIMO radar with far fewer samples than other approaches. This is
particularly useful in a distributed scenario, in which the results at each
receive node need to be transmitted to a fusion center for further processing
Recurrent Neural Networks For Accurate RSSI Indoor Localization
This paper proposes recurrent neuron networks (RNNs) for a fingerprinting
indoor localization using WiFi. Instead of locating user's position one at a
time as in the cases of conventional algorithms, our RNN solution aims at
trajectory positioning and takes into account the relation among the received
signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a
weighted average filter is proposed for both input RSSI data and sequential
output locations to enhance the accuracy among the temporal fluctuations of
RSSI. The results using different types of RNN including vanilla RNN, long
short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM
(BiLSTM) are presented. On-site experiments demonstrate that the proposed
structure achieves an average localization error of m with of the
errors under m, which outperforms the conventional KNN algorithms and
probabilistic algorithms by approximately under the same test
environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization,
recurrent neuron network (RNN), long shortterm memory (LSTM),
fingerprint-based localizatio
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