By Sandeep Prasad Sira, Antonia Papanreou-Suppappola, Darryl Morrell
Contemporary advances in sensor expertise and knowledge processing manage to pay for a brand new flexibility within the layout of waveforms for agile sensing. Sensors are actually built having the ability to dynamically decide on their transmit or obtain waveforms with a view to optimize an target fee functionality. This has uncovered a brand new paradigm of important functionality advancements in lively sensing: dynamic waveform model to surroundings stipulations, objective constructions, or info positive aspects. The manuscript presents a evaluation of contemporary advances in waveform-agile sensing for objective monitoring purposes. A dynamic waveform choice and configuration scheme is constructed for 2 lively sensors that song one or a number of cellular pursuits. an in depth description of 2 sequential Monte Carlo algorithms for agile monitoring are provided, including proper Matlab code and simulation reports, to illustrate the advantages of dynamic waveform version. The paintings could be of curiosity not just to practitioners of radar and sonar, but additionally different purposes the place waveforms will be dynamically designed, corresponding to communications and biosensing. desk of Contents: Waveform-Agile goal monitoring software formula / Dynamic Waveform choice with software to Narrowband and Wideband Environments / Dynamic Waveform choice for monitoring in litter / Conclusions / CRLB overview for Gaussian Envelope GFM Chirp from the anomaly functionality / CRLB review from the advanced Envelope
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Extra info for Advances in Waveform-Agile Sensing for Tracking
1. SINGLE TARGET 37 probability. 3) as  −mik p(Zki |Xk , θ ik ) = (1 − Pdik )μ(mik )(Vki ) + −(mi −1) Pdik (Vki ) k μ(mik 1 − 1) · mik mik m=1 m p(zk,i |Xk , θ ik ) . 4 WAVEFORM SELECTION IN THE PRESENCE OF CLUTTER The algorithm for waveform selection presented in Chapter 3 assumes that there is only one measurement and that it is target-originated. While the algorithm structure remains the same when clutter and imperfect detection are considered, the computation of the predicted cost must reﬂect the uncertainty in the origin of the measurements.
Speciﬁcally, we considered tracking in narrowband and wideband environments with no clutter, as well as with clutter, both for single and multiple targets. Speciﬁcally, we considered the selection and conﬁguration of waveforms for waveform-agile sensors so as to minimize a tracking error. In contrast with past research on this problem, we considered a nonlinear observations model to track a target moving in two dimensions as well as the use of waveforms with nonlinear time-frequency signatures.
9) represent the state of S targets that move in a two-dimensional space. The state of target s, s = 1, . . , S is xks = [xks yks x˙ks y˙ks ]T , where xks and yks correspond to the position, and x˙ks and y˙ks to the velocity at time k in Cartesian coordinates. 2. 10) where wks is a white Gaussian noise sequence that models the process noise. 10). 2 MEASUREMENT MODEL At each sampling epoch, both sensors, Sensor A and Sensor B, obtain range (r) and range-rate (˙r ) measurements of each target.