Multistatic adaptive pulse compression

Blunt, Shannon D.; Gerlach, K.

doi:10.1109/taes.2006.248196

Cited by 41 publications

(15 citation statements)

References 14 publications

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“…In [2][3][4][5], it is pointed out that strong scatter in the current range cell extends its energy to the adjacent range cells by autocorrelation sidelobes and cross-correlation residues when matched filtering is performed; however, the APC techniques can adaptively generate null at the range cell where strong scatter exists in order to suppress this energy diffusion. This idea of suppressing energy diffusion is very similar to that of interference suppression in ABF problem.…”

Section: The Derivation Of Apc-capon Filtermentioning

confidence: 99%

“…For the reiterative APC techniques based on single-pulse echoes [2][3][4], firstly, the required number of range cells is (2M − 1)(N − 1) + 1 which is almost proportional to the number M of iterations; secondly, the computation complexity at each iteration is basically equal to that of the APC-Capon filter; and finally, it is hard to determine the number of iterations, because the number of iterations is related to the contrast between strong scatter and weak scatter. b) In (2), the K b K t target echoes collected by K b receiver elements and K t coherent pulses are completely coherent, and these coherent echoes are formed into a target observation matrix Y t ( 0 ).…”

Section: The Features Of Apc-capon Filtermentioning

confidence: 99%

“…c) According the signal model in section 2 and the weight vector (8) in section 3, it can be found that, for APC-Capon filter, suppressing the autocorrelation sidelobes and cross-correlation residues of all the transmitted waveforms is equivalent to suppressing the autocorrelation sidelobes of the synthesized waveform f t at azimuth θ t . This method has remarkable advantage over the method proposed in [3,5] where the weight vector corresponding to each transmitted waveform is designed. The reasons are as follows.…”

Section: The Features Of Apc-capon Filtermentioning

confidence: 99%

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A CFAR detector for MIMO array radar based on adaptive pulse compression-Capon filter

Guan

Huang

2011

Sci. China Inf. Sci.

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Under the condition of strong clutter and finite training samples, a novel CFAR detector based on the proposed adaptive pulse compression-Capon filter (APC-Capon filter) is presented for MIMO array radar which transmits intrapulse coded waveforms. Both theoretical analysis and numerical simulations show that the proposed APC-Capon filter can utilize the non-reiterative adaptive pulse compression technique to alleviate both range sidelobes and mutual interference; at the same time it also can borrows ideas from the coherent signals cancellation of adaptive beamforming to perform clutter suppression. The detection performance simulations show that, with the same size parameters (including code length and the numbers of transmitter elements, receiver elements as well as pulses), the proposed CFAR detector is inferior to the adaptive matched filter (AMF) detector. But as the size parameters increase, the proposed CFAR detector will gradually outperform the AMF detector with smaller size parameters, and more importantly, unlike the AMF detector which has a lot of difficulties in performing due to the inordinate demands for the computational complexity and training sample size, this demands of the proposed CFAR detector will not significantly increase. The simulations also show that, the correlation properties of these intrapulse coded waveforms have certain effect on the APC-Capon filter and its corresponding CFAR detector. But fortunately, the effect will decrease as the number of intrapulse coded waveforms increases.Keywords MIMO array radar, non-reiterative adaptive pulse compression, Capon filter, complex Gaussian clutter CitationGuan J, Huang Y, He Y. A CFAR detector for MIMO array radar based on adaptive pulse compression-

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Section: The Derivation Of Apc-capon Filtermentioning

confidence: 99%

Section: The Features Of Apc-capon Filtermentioning

confidence: 99%

Section: The Features Of Apc-capon Filtermentioning

confidence: 99%

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A CFAR detector for MIMO array radar based on adaptive pulse compression-Capon filter

Guan

Huang

2011

Sci. China Inf. Sci.

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“…Additionally, several other methods in the time or frequency domain have been proposed to mitigate RF (radio frequency) interference in radar and radio systems, e.g., the time-frequency blanking [24], reconstructing and then subtracting [25] and range domain orthogonal projection filtering [16]. Particularly, a multistatic adaptive pulse compression algorithm was proposed in [26] to separate concurrently received radar signals within the same frequency band given the knowledge of the individual radar waveforms. This algorithm is based on a recursive implementation of a minimum mean-square error formulation.…”

Section: Introductionmentioning

confidence: 99%

Radar-to-Radar Interference Suppression for Distributed Radar Sensor Networks

Wang

Shao

2014

Remote Sensing

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Radar sensor networks, including bi-and multi-static radars, provide several operational advantages, like reduced vulnerability, good system flexibility and an increased radar cross-section. However, radar-to-radar interference suppression is a major problem in distributed radar sensor networks. In this paper, we present a cross-matched filtering-based radar-to-radar interference suppression algorithm. This algorithm first uses an iterative filtering algorithm to suppress the radar-to-radar interferences and, then, separately matched filtering for each radar. Besides the detailed algorithm derivation, extensive numerical simulation examples are performed with the down-chirp and up-chirp waveforms, partially overlapped or inverse chirp rate linearly frequency modulation (LFM) waveforms and orthogonal frequency division multiplexing (ODFM) chirp diverse waveforms. The effectiveness of the algorithm is verified by the simulation results.

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“…In [11,12], MAPC combined with an adaptive beamformer is adopted to obtain better performance, and in [13,14], a generalized framework is presented to take account of all the potential interferences, including transmit-mainbeam reflected into receive-mainbeam, transmit-mainbeam reflected into receive-sidelobe, transmit-sidelobe reflected into receive-mainbeam, transmit-sidelobe reflected into receive-sidelobe and noise. In general, the MAPC outperforms the matched filter and Least-Squares.…”

Section: Introductionmentioning

confidence: 99%

Co-Frequency Interference Suppression Algorithm via Maximum Signal Minus Interference Level

Kong¹,

Luo²

2010

PIER

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Abstract-Co-frequency interference problem is severe in sharedspectrum multistatic radar system, leading to detection problems in applications. In order to mitigate the co-frequency interferences, an adaptive pulse compression algorithm based on Maximum Signal Minus Interference Level criterion (MSMIL) is proposed in this paper, the main idea of which is to adaptively design an appropriate filter at each range cell, maximizing the result of signal power minus interference power, so that both range sidelobes and cross-correlation interference can be suppressed. Simulation results show that the algorithm outperforms the traditional multistatic adaptive pulse compression (MAPC) method in interference suppression. Compared with MAPC method, after one iteration step, the output SIR of the proposed algorithm is increased by about 10 dB. And after two iteration steps, it is increased by more than 40 dB. Moreover, it also outperforms the MAPC in convergence speed.

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Multistatic adaptive pulse compression

Abstract: Manuscript

Cited by 41 publications

References 14 publications

A CFAR detector for MIMO array radar based on adaptive pulse compression-Capon filter

A CFAR detector for MIMO array radar based on adaptive pulse compression-Capon filter

Radar-to-Radar Interference Suppression for Distributed Radar Sensor Networks

Co-Frequency Interference Suppression Algorithm via Maximum Signal Minus Interference Level

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