Radar Parameter

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Simon Haykin - One of the best experts on this subject based on the ideXlab platform.

  • syntactic modeling and signal processing of multifunction Radars a stochastic context free grammar approach
    Proceedings of the IEEE, 2007
    Co-Authors: N Visnevski, Vikram Krishnamurthy, Alex Wang, Simon Haykin
    Abstract:

    Multifunction Radars (MFRs) are sophisticated sensors with complex dynamical modes that are widely used in surveillance and tracking. This paper demonstrates that stochastic context-free grammars (SCFGs) are adequate models for capturing the essential features of the MFR dynamics. Specifically, MFRs are modeled as systems that ldquospeakrdquo a language that is characterized by an SCFG. The paper shows that such a grammar is modulated by a Markov chain representing Radar's policy of operation. The paper also demonstrates how some well-known statistical signal processing techniques can be applied to MFR signal processing using these stochstic syntactic models. We derive two statistical estimation approaches for MFR signal processing-a maximum likelihood sequence estimator to estimate Radar's policies of operation, and a maximum likelihood Parameter estimator to infer the Radar Parameter values. Two layers of signal processing are introduced in this paper. The first layer is concerned with the estimation of MFR's policies of operation. It involves signal processing in the CFG domain. The second layer is concerned with identification of tasks the Radar is engaged in. It involves signal processing in the finite-state domain. Both of these signal processing techniques are important elements of a bigger Radar signal processing problem that is often encountered in electronic warfare applications-the problem of the estimation of the level of threat that a Radar poses to each individual target at any point in time.

  • syntactic modeling and signal processing of multifunction Radars a stochastic context free grammar approach using improved pattern recognition techniques the target of a Radar system can model and identify that system and estimate how much of a threat it poses
    Proceedings of the IEEE, 2007
    Co-Authors: N Visnevski, Vikram Krishnamurthy, Alex Wang, Simon Haykin
    Abstract:

    Multifunction Radars (MFRs) are sophisticated sensors with complex dynamical modes that are widely used in surveillance and tracking. This paper demonstrates that sto- chastic context-free grammars (SCFGs) are adequate models for capturing the essential features of the MFR dynamics. Specifically, MFRs are modeled as systems that Bspeak( a language that is characterized by an SCFG. The paper shows that such a grammar is modulated by a Markov chain re- presenting Radar's policy of operation. The paper also demon- strates how some well-known statistical signal processing techniques can be applied to MFR signal processing using these stochstic syntactic models. We derive two statistical estimation approaches for MFR signal processingVa maximum likelihood sequence estimator to estimate Radar's policies of operation, and a maximum likelihood Parameter estimator to infer the Radar Parameter values. Two layers of signal processing are introduced in this paper. The first layer is concerned with the estimation of MFR's policies of operation. It involves signal processing in the CFG domain. The second layer is concerned with identification of tasks the Radar is engaged in. It involves signal processing in the finite-state domain. Both of these signal processing techniques are important elements of a bigger Radar signal processing problem that is often encoun- tered in electronic warfare applicationsVthe problem of the estimation of the level of threat that a Radar poses to each individual target at any point in time.

N Visnevski - One of the best experts on this subject based on the ideXlab platform.

  • syntactic modeling and signal processing of multifunction Radars a stochastic context free grammar approach
    Proceedings of the IEEE, 2007
    Co-Authors: N Visnevski, Vikram Krishnamurthy, Alex Wang, Simon Haykin
    Abstract:

    Multifunction Radars (MFRs) are sophisticated sensors with complex dynamical modes that are widely used in surveillance and tracking. This paper demonstrates that stochastic context-free grammars (SCFGs) are adequate models for capturing the essential features of the MFR dynamics. Specifically, MFRs are modeled as systems that ldquospeakrdquo a language that is characterized by an SCFG. The paper shows that such a grammar is modulated by a Markov chain representing Radar's policy of operation. The paper also demonstrates how some well-known statistical signal processing techniques can be applied to MFR signal processing using these stochstic syntactic models. We derive two statistical estimation approaches for MFR signal processing-a maximum likelihood sequence estimator to estimate Radar's policies of operation, and a maximum likelihood Parameter estimator to infer the Radar Parameter values. Two layers of signal processing are introduced in this paper. The first layer is concerned with the estimation of MFR's policies of operation. It involves signal processing in the CFG domain. The second layer is concerned with identification of tasks the Radar is engaged in. It involves signal processing in the finite-state domain. Both of these signal processing techniques are important elements of a bigger Radar signal processing problem that is often encountered in electronic warfare applications-the problem of the estimation of the level of threat that a Radar poses to each individual target at any point in time.

  • syntactic modeling and signal processing of multifunction Radars a stochastic context free grammar approach using improved pattern recognition techniques the target of a Radar system can model and identify that system and estimate how much of a threat it poses
    Proceedings of the IEEE, 2007
    Co-Authors: N Visnevski, Vikram Krishnamurthy, Alex Wang, Simon Haykin
    Abstract:

    Multifunction Radars (MFRs) are sophisticated sensors with complex dynamical modes that are widely used in surveillance and tracking. This paper demonstrates that sto- chastic context-free grammars (SCFGs) are adequate models for capturing the essential features of the MFR dynamics. Specifically, MFRs are modeled as systems that Bspeak( a language that is characterized by an SCFG. The paper shows that such a grammar is modulated by a Markov chain re- presenting Radar's policy of operation. The paper also demon- strates how some well-known statistical signal processing techniques can be applied to MFR signal processing using these stochstic syntactic models. We derive two statistical estimation approaches for MFR signal processingVa maximum likelihood sequence estimator to estimate Radar's policies of operation, and a maximum likelihood Parameter estimator to infer the Radar Parameter values. Two layers of signal processing are introduced in this paper. The first layer is concerned with the estimation of MFR's policies of operation. It involves signal processing in the CFG domain. The second layer is concerned with identification of tasks the Radar is engaged in. It involves signal processing in the finite-state domain. Both of these signal processing techniques are important elements of a bigger Radar signal processing problem that is often encoun- tered in electronic warfare applicationsVthe problem of the estimation of the level of threat that a Radar poses to each individual target at any point in time.

Alex Wang - One of the best experts on this subject based on the ideXlab platform.

  • syntactic modeling and signal processing of multifunction Radars a stochastic context free grammar approach
    Proceedings of the IEEE, 2007
    Co-Authors: N Visnevski, Vikram Krishnamurthy, Alex Wang, Simon Haykin
    Abstract:

    Multifunction Radars (MFRs) are sophisticated sensors with complex dynamical modes that are widely used in surveillance and tracking. This paper demonstrates that stochastic context-free grammars (SCFGs) are adequate models for capturing the essential features of the MFR dynamics. Specifically, MFRs are modeled as systems that ldquospeakrdquo a language that is characterized by an SCFG. The paper shows that such a grammar is modulated by a Markov chain representing Radar's policy of operation. The paper also demonstrates how some well-known statistical signal processing techniques can be applied to MFR signal processing using these stochstic syntactic models. We derive two statistical estimation approaches for MFR signal processing-a maximum likelihood sequence estimator to estimate Radar's policies of operation, and a maximum likelihood Parameter estimator to infer the Radar Parameter values. Two layers of signal processing are introduced in this paper. The first layer is concerned with the estimation of MFR's policies of operation. It involves signal processing in the CFG domain. The second layer is concerned with identification of tasks the Radar is engaged in. It involves signal processing in the finite-state domain. Both of these signal processing techniques are important elements of a bigger Radar signal processing problem that is often encountered in electronic warfare applications-the problem of the estimation of the level of threat that a Radar poses to each individual target at any point in time.

  • syntactic modeling and signal processing of multifunction Radars a stochastic context free grammar approach using improved pattern recognition techniques the target of a Radar system can model and identify that system and estimate how much of a threat it poses
    Proceedings of the IEEE, 2007
    Co-Authors: N Visnevski, Vikram Krishnamurthy, Alex Wang, Simon Haykin
    Abstract:

    Multifunction Radars (MFRs) are sophisticated sensors with complex dynamical modes that are widely used in surveillance and tracking. This paper demonstrates that sto- chastic context-free grammars (SCFGs) are adequate models for capturing the essential features of the MFR dynamics. Specifically, MFRs are modeled as systems that Bspeak( a language that is characterized by an SCFG. The paper shows that such a grammar is modulated by a Markov chain re- presenting Radar's policy of operation. The paper also demon- strates how some well-known statistical signal processing techniques can be applied to MFR signal processing using these stochstic syntactic models. We derive two statistical estimation approaches for MFR signal processingVa maximum likelihood sequence estimator to estimate Radar's policies of operation, and a maximum likelihood Parameter estimator to infer the Radar Parameter values. Two layers of signal processing are introduced in this paper. The first layer is concerned with the estimation of MFR's policies of operation. It involves signal processing in the CFG domain. The second layer is concerned with identification of tasks the Radar is engaged in. It involves signal processing in the finite-state domain. Both of these signal processing techniques are important elements of a bigger Radar signal processing problem that is often encoun- tered in electronic warfare applicationsVthe problem of the estimation of the level of threat that a Radar poses to each individual target at any point in time.

Vikram Krishnamurthy - One of the best experts on this subject based on the ideXlab platform.

  • syntactic modeling and signal processing of multifunction Radars a stochastic context free grammar approach
    Proceedings of the IEEE, 2007
    Co-Authors: N Visnevski, Vikram Krishnamurthy, Alex Wang, Simon Haykin
    Abstract:

    Multifunction Radars (MFRs) are sophisticated sensors with complex dynamical modes that are widely used in surveillance and tracking. This paper demonstrates that stochastic context-free grammars (SCFGs) are adequate models for capturing the essential features of the MFR dynamics. Specifically, MFRs are modeled as systems that ldquospeakrdquo a language that is characterized by an SCFG. The paper shows that such a grammar is modulated by a Markov chain representing Radar's policy of operation. The paper also demonstrates how some well-known statistical signal processing techniques can be applied to MFR signal processing using these stochstic syntactic models. We derive two statistical estimation approaches for MFR signal processing-a maximum likelihood sequence estimator to estimate Radar's policies of operation, and a maximum likelihood Parameter estimator to infer the Radar Parameter values. Two layers of signal processing are introduced in this paper. The first layer is concerned with the estimation of MFR's policies of operation. It involves signal processing in the CFG domain. The second layer is concerned with identification of tasks the Radar is engaged in. It involves signal processing in the finite-state domain. Both of these signal processing techniques are important elements of a bigger Radar signal processing problem that is often encountered in electronic warfare applications-the problem of the estimation of the level of threat that a Radar poses to each individual target at any point in time.

  • syntactic modeling and signal processing of multifunction Radars a stochastic context free grammar approach using improved pattern recognition techniques the target of a Radar system can model and identify that system and estimate how much of a threat it poses
    Proceedings of the IEEE, 2007
    Co-Authors: N Visnevski, Vikram Krishnamurthy, Alex Wang, Simon Haykin
    Abstract:

    Multifunction Radars (MFRs) are sophisticated sensors with complex dynamical modes that are widely used in surveillance and tracking. This paper demonstrates that sto- chastic context-free grammars (SCFGs) are adequate models for capturing the essential features of the MFR dynamics. Specifically, MFRs are modeled as systems that Bspeak( a language that is characterized by an SCFG. The paper shows that such a grammar is modulated by a Markov chain re- presenting Radar's policy of operation. The paper also demon- strates how some well-known statistical signal processing techniques can be applied to MFR signal processing using these stochstic syntactic models. We derive two statistical estimation approaches for MFR signal processingVa maximum likelihood sequence estimator to estimate Radar's policies of operation, and a maximum likelihood Parameter estimator to infer the Radar Parameter values. Two layers of signal processing are introduced in this paper. The first layer is concerned with the estimation of MFR's policies of operation. It involves signal processing in the CFG domain. The second layer is concerned with identification of tasks the Radar is engaged in. It involves signal processing in the finite-state domain. Both of these signal processing techniques are important elements of a bigger Radar signal processing problem that is often encoun- tered in electronic warfare applicationsVthe problem of the estimation of the level of threat that a Radar poses to each individual target at any point in time.

Robert W Heath - One of the best experts on this subject based on the ideXlab platform.

  • Improved CRB for Millimeter-Wave Radar With 1-Bit ADCs
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Khurram Usman Mazher, Amine Mezghani, Robert W Heath
    Abstract:

    Millimeter-wave is widely used for consumer Radar applications like driver assistance systems in automated vehicles and gesture recognition in touch-free interfaces. To cope with the increased hardware complexity, higher costs and power consumption of wideband systems at millimeter-wave frequencies, we propose a fully digital architecture with low-resolution analog-to-digital converters (ADCs) on each radio-frequency chain. The effect of the low-resolution ADCs on Radar Parameter estimation is characterized by the Cramér-Rao bound (CRB) under the proposed hardware constraints. Prior work has shown that at low signal-to-noise ratio, a Radar system with 1-bit ADCs suffers a performance loss of 2 dB in Parameter estimation compared to a system with ideal infinite resolution ADCs. In this paper, we design an analog preprocessing unit that beamforms in a particular direction and improves the system performance in terms of the achievable CRB. We optimize the proposed preprocessing architecture and show that the optimized network is realizable through low-cost low-resolution phase-shifters. With the optimized preprocessor network in the system, we reduce the gap to 1.16 dB compared to a system with ideal ADCs. We demonstrate the potential of the proposed architecture to meet the requirements of high-resolution sensing through analytical derivation and numerical computation of an improved CRB and show its achievability through a correlation-based estimator

  • investigating the ieee 802 11ad standard for millimeter wave automotive Radar
    Vehicular Technology Conference, 2015
    Co-Authors: Preeti Kumari, Nuria Gonzalezprelcic, Robert W Heath
    Abstract:

    Millimeter wave (mmWave) technology is widely used for automotive Radar applications, like adaptive cruise control and obstacle detection. Unlike conventional Radar waveforms which are usually propriety, this paper explores the use of a consumer wireless local area network (WLAN) waveform in the 60GHz unlicensed mmWave band for automotive Radar applications. In particular, this paper develops a joint framework of long range automotive Radar (LRR) and vehicle-to-vehicle communication (V2V) at 60 GHz by exploiting the special data-aided structure (repeated Golay complimentary sequences) of an IEEE 802.11ad single carrier physical layer (SCPHY) frame. This framework leverages the signal processing algorithms used in the typical WLAN receiver for time and frequency synchronization to perform Radar Parameter estimation. The initial simulation results show that it is possible to achieve the desired range accuracy of 0.1 m with a very high probability of detection (above 99%) using the preamble of a SCPHY frame. Furthermore, the velocity estimation algorithm achieves the desired accuracy of 0.1 m/s at high SNR using the preamble and pilot words of only a single frame.