ONE-BIT SPARSE ARRAY DOA ESTIMATION

被引:0
作者
Liu, Chun-Lin [1 ]
Vaidyanathan, P. P. [1 ]
机构
[1] 136 93 Calif Inst Technol, Dept Elect Engn, Pasadena, CA 91125 USA
来源
2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2017年
关键词
One-bit quantization; sparse arrays; nested arrays; coprime arrays; DOA estimation; GAUSSIAN STATIONARY-PROCESSES; MASSIVE MIMO; AUTOCORRELATION FUNCTION; SIGNALS;
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
One-bit quantization has become an important topic in massive MIMO systems, as it offers low cost and low complexity in the implementation. Techniques to achieve high performance in spite of the coarse quantizers have recently been advanced. In the context of array processing and direction-of-arrival (DOA) estimation also, one bit quantizers have been studied in the past, although not as extensively. This paper shows that sparse arrays such as nested and coprime arrays are more robust to the deleterious effects of one-bit quantization, compared to uniform linear arrays (ULAs); in fact, sparse arrays with one-bit quantizers are often found to be as good as ULAs with unquantized data. Nested and coprime arrays without quanitzers are known to be able to resolve more DOAs than the number of sensors, when sources are uncorrelated. It will be demonstrated that this continues to be true even with one-bit quantization.
引用
收藏
页码:3126 / 3130
页数:5
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