9 research outputs found
Time-Frequency Distribution for Undersampled Non-stationary Signals using Chirp-based Kernel
Missing samples and randomly sampled nonstationary signals give rise to artifacts that spread over both the time-frequency and the ambiguity domains. These two domains are related by a two-dimensional Fourier transform. As these artifacts resemble noise, the traditional reduced interference signal-independent kernels, which belong to Cohen’s class, cannot mitigate them efficiently. In this paper, a novel signal-independent kernel in the ambiguity domain is proposed. The proposed method is based on three important facts. Firstly, any windowed non-stationary signal can be approximated as a sum of chirps. Secondly, in the ambiguity domain, any chirp resides inside certain regions, which just occupy half of the ambiguity plane. Thirdly, the missing data artifacts always appear along the Doppler axis where the chirps auto-terms do not appear. Therefore, we propose using a chirp-based fixed kernel on windowed non-stationary signals in order to remove half of the noise-like artifacts in the ambiguity domain and compensate for the missing data effect located along the Doppler axis. It is shown that our method outperforms other reduced interference time-frequency distributions
Adaptive Interference Removal for Un-coordinated Radar/Communication Co-existence
Most existing approaches to co-existing communication/radar systems assume
that the radar and communication systems are coordinated, i.e., they share
information, such as relative position, transmitted waveforms and channel
state. In this paper, we consider an un-coordinated scenario where a
communication receiver is to operate in the presence of a number of radars, of
which only a sub-set may be active, which poses the problem of estimating the
active waveforms and the relevant parameters thereof, so as to cancel them
prior to demodulation. Two algorithms are proposed for such a joint waveform
estimation/data demodulation problem, both exploiting sparsity of a proper
representation of the interference and of the vector containing the errors of
the data block, so as to implement an iterative joint interference removal/data
demodulation process. The former algorithm is based on classical on-grid
compressed sensing (CS), while the latter forces an atomic norm (AN)
constraint: in both cases the radar parameters and the communication
demodulation errors can be estimated by solving a convex problem. We also
propose a way to improve the efficiency of the AN-based algorithm. The
performance of these algorithms are demonstrated through extensive simulations,
taking into account a variety of conditions concerning both the interferers and
the respective channel states
Identification of Ghost Targets for Automotive Radar in the Presence of Multipath
Colocated multiple-input multiple-output (MIMO) technology has been widely
used in automotive radars as it provides accurate angular estimation of the
objects with relatively small number of transmitting and receiving antennas.
Since the Direction Of Departure (DOD) and the Direction Of Arrival (DOA) of
line-of-sight targets coincide, MIMO signal processing allows forming a larger
virtual array for angle finding. However, multiple paths impinging the receiver
is a major limiting factor, in that radar signals may bounce off obstacles,
creating echoes for which the DOD does not equal the DOA. Thus, in complex
scenarios with multiple scatterers, the direct paths of the intended targets
may be corrupted by indirect paths from other objects, which leads to
inaccurate angle estimation or ghost targets. In this paper, we focus on
detecting the presence of ghosts due to multipath by regarding it as the
problem of deciding between a composite hypothesis, say, that the
observations only contain an unknown number of direct paths sharing the same
(unknown) DOD's and DOA's, and a composite alternative, say, that
the observations also contain an unknown number of indirect paths, for which
DOD's and DOA's do not coincide. We exploit the Generalized Likelihood Ratio
Test (GLRT) philosophy to determine the detector structure, wherein the unknown
parameters are replaced by carefully designed estimators. The angles of both
the active direct paths and of the multi-paths are indeed estimated through a
sparsity-enforced Compressed Sensing (CS) approach with Levenberg-Marquardt
(LM) optimization to estimate the angular parameters in the continuous domain.
An extensive performance analysis is finally offered in order to validate the
proposed solution.Comment: 13 pages, 10 figure
Interference Removal for Radar/Communication Co-existence: the Random Scattering Case
In this paper we consider an un-cooperative spectrum sharing scenario,
wherein a radar system is to be overlaid to a pre-existing wireless
communication system. Given the order of magnitude of the transmitted powers in
play, we focus on the issue of interference mitigation at the communication
receiver. We explicitly account for the reverberation produced by the
(typically high-power) radar transmitter whose signal hits scattering centers
(whether targets or clutter) producing interference onto the communication
receiver, which is assumed to operate in an un-synchronized and un-coordinated
scenario. We first show that receiver design amounts to solving a non-convex
problem of joint interference removal and data demodulation: next, we introduce
two algorithms, both exploiting sparsity of a proper representation of the
interference and of the vector containing the errors of the data block. The
first algorithm is basically a relaxed constrained Atomic Norm minimization,
while the latter relies on a two-stage processing structure and is based on
alternating minimization. The merits of these algorithms are demonstrated
through extensive simulations: interestingly, the two-stage alternating
minimization algorithm turns out to achieve satisfactory performance with
moderate computational complexity