3,902 research outputs found
Towards Dual-functional Radar-Communication Systems: Optimal Waveform Design
We focus on a dual-functional multi-input-multi-output (MIMO)
radar-communication (RadCom) system, where a single transmitter communicates
with downlink cellular users and detects radar targets simultaneously. Several
design criteria are considered for minimizing the downlink multi-user
interference. First, we consider both the omnidirectional and directional
beampattern design problems, where the closed-form globally optimal solutions
are obtained. Based on these waveforms, we further consider a weighted
optimization to enable a flexible trade-off between radar and communications
performance and introduce a low-complexity algorithm. The computational costs
of the above three designs are shown to be similar to the conventional
zero-forcing (ZF) precoding. Moreover, to address the more practical constant
modulus waveform design problem, we propose a branch-and-bound algorithm that
obtains a globally optimal solution and derive its worst-case complexity as a
function of the maximum iteration number. Finally, we assess the effectiveness
of the proposed waveform design approaches by numerical results.Comment: 13 pages, 10 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
Photon-Efficient Computational 3D and Reflectivity Imaging with Single-Photon Detectors
Capturing depth and reflectivity images at low light levels from active
illumination of a scene has wide-ranging applications. Conventionally, even
with single-photon detectors, hundreds of photon detections are needed at each
pixel to mitigate Poisson noise. We develop a robust method for estimating
depth and reflectivity using on the order of 1 detected photon per pixel
averaged over the scene. Our computational imager combines physically accurate
single-photon counting statistics with exploitation of the spatial correlations
present in real-world reflectivity and 3D structure. Experiments conducted in
the presence of strong background light demonstrate that our computational
imager is able to accurately recover scene depth and reflectivity, while
traditional maximum-likelihood based imaging methods lead to estimates that are
highly noisy. Our framework increases photon efficiency 100-fold over
traditional processing and also improves, somewhat, upon first-photon imaging
under a total acquisition time constraint in raster-scanned operation. Thus our
new imager will be useful for rapid, low-power, and noise-tolerant active
optical imaging, and its fixed dwell time will facilitate parallelization
through use of a detector array.Comment: 11 pages, 8 figure
RADAR-EMBEDDED SATCOM WITH DEEP NEURAL NETWORK DEMODULATION
Approved for public release. Distribution is unlimited.In this work, the feasibility, design, and implementation of radar-embedded communications with
satellite applications are investigated. We design a deep neural network (DNN) machine learning detector to
demodulate SATCOM data. The performance result is compared with the detection method of using
maximum likelihood estimation (MLE) to estimate the amplitude and phase of the radar signal, which is
followed by a maximum likelihood detection (MLD) receiver. Pulsed radar and linear frequency modulation
(LFM) waveforms are chosen to embed communications symbols. Quaternary phase-shift keying (QPSK)
and eight phase-shift keying (8PSK) modulations are used for illustration. In this work, three DNN
demodulators for radar-embedded communications are developed. One of the DNN detectors actually
outperforms the MLD demodulator and is shown to be robust for pulsed radar-embedded communications.
One of our goals is to embed satellite communications into LFM waveform, which is used in synthetic
aperture radar (SAR). The DNN works well for LFM radar-embedded communications when the received
LFM phase offset is removed a priori. However, the DNN symbol error rate (SER) performance suffers
when the LFM phase offset is introduced for large RCR. Lastly, we perform laboratory transmission and
reception tests: a) shielded cable and b) over-the-air (OTA) tests. It is shown that pulsed radar-embedded
communication is feasible with both MLE-MLD and DNN detectors with reasonable SER performance.Lieutenant, United States Nav
Nonlinear Suppression of Range Ambiguity in Pulse Doppler Radar
Coherent pulse train processing is most commonly used in airborne pulse Doppler radar, achieving adequate transmitter/receiver isolation and excellent resolution properties while inherently inducing ambiguities in Doppler and range. First introduced by Palermo in 1962 using two conjugate LFM pulses, the primary nonlinear suppression objective involves reducing range ambiguity, given the waveform is nominally unambiguous in Doppler, by using interpulse and intrapulse coding (pulse compression) to discriminate received ambiguous pulse responses. By introducing a nonlinear operation on compressed (undesired) pulse responses within individual channels, ambiguous energy levels are reduced in channel outputs. This research expands the NLS concept using discrete coding and processing. A general theory is developed showing how NLS accomplishes ambiguity surface volume removal without requiring orthogonal coding. Useful NLS code sets are generated using combinatorial, simulated annealing optimization techniques - a general algorithm is developed to extended family size, code length, and number of phases (polyphase coding). An adaptive reserved code thresholding scheme is introduced to efficiently and effectively track the matched filter response of a target field over a wide dynamic range, such as normally experienced in airborne radar systems. An evaluation model for characterizing NLS clutter suppression performance is developed - NLS performance is characterized using measured clutter data with analysis indicating the proposed technique performs relatively well even when large clutter cells exist
Study to investigate and evaluate means of optimizing the Ku-band combined radar/communication functions for the space shuttle
The performance of the space shuttle orbiter's Ku-Band integrated radar and communications equipment is analyzed for the radar mode of operation. The block diagram of the rendezvous radar subsystem is described. Power budgets for passive target detection are calculated, based on the estimated values of system losses. Requirements for processing of radar signals in the search and track modes are examined. Time multiplexed, single-channel, angle tracking of passive scintillating targets is analyzed. Radar performance in the presence of main lobe ground clutter is considered and candidate techniques for clutter suppression are discussed. Principal system parameter drivers are examined for the case of stationkeeping at ranges comparable to target dimension. Candidate ranging waveforms for short range operation are analyzed and compared. The logarithmic error discriminant utilized for range, range rate and angle tracking is formulated and applied to the quantitative analysis of radar subsystem tracking loops
A Study of Adobe Wall Moisture Profiles and the Resulting Effects on Matched Illumination Waveforms in Through-The-Wall Radar Applications
In this dissertation, methods utilizing matched illumination theory to optimally design waveforms for enhanced target detection and identification in the context of through-the-wall radar (TWR) are explored. The accuracy of assumptions made in the waveform design process is evaluated through simulation. Additionally, the moisture profile of an adobe wall is investigated, and it is shown that the moisture profile of the wall will introduce significant variations in the matched illumination waveforms and subsequently, affect the resulting ability of the radar system to correctly identify and detect a target behind the wall. Experimental measurements of adobe wall moisture and corresponding dielectric properties confirms the need for accurate moisture profile information when designing radar waveforms which enhance signal-to-interference-plus-noise ratio (SINR) through use of matched illumination waveforms on the wall/target scenario. Furthermore, an evaluation of the ability to produce an optimal, matched illumination waveform for transmission using simple, common radar systems is undertaken and radar performance is evaluated
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
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