Direction of Arrival Estimation for Coherent Signals’ Method Based on LSTM Neural Network

<div>The signal spectrum of the incoming source is not correlated based on the MUSIC method in the following cases: (a) 1 signal <span class="nowrap"><svg height="10.9836pt" id="M197" style="vertical-align:-0.2325001pt" version="1.1" viewbox="-0.0498162 -10.7511 76.6505 10.9836" width="76.6505pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M860 0V35C782 43 777 50 775 142L766 516C764 599 768 607 848 615V650H646L450 212L258 650H51V615C137 607 144 593 136 480L120 282C112 177 105 124 99 93C92 57 74 40 17 35V0H258V35C191 41 172 50 168 95C164 126 163 173 168 256L180 517H184L388 7H416L637 527H642L644 142C644 50 639 43 564 35V0H860Z"></path></g><g transform="matrix(.013,0,0,-0.013,11.544,0)"><path d="M442 495C434 557 427 602 423 639C405 645 348 665 284 665C142 665 55 579 55 469C55 373 127 319 229 267C304 227 348 190 348 131C348 67 309 25 248 25C152 25 93 138 73 202L38 190C45 130 58 46 68 21C81 15 100 7 128 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430 453 455 453 481C453 570 379 635 270 635ZM255 597C294 597 346 562 346 476C346 417 326 386 309 370C232 406 181 442 181 511C181 553 208 597 255 597ZM268 26C219 26 160 71 160 168C160 221 182 266 210 288C293 247 356 212 359 136C361 71 329 26 268 26Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,71.933,-5.741)"><path d="M383 256C383 343 310 414 221 414S59 343 59 256S132 98 221 98S383 169 383 256ZM318 256C318 200 275 155 221 155S124 200 124 256S167 357 221 357S318 312 318 256Z"></path></g></svg>,</span> and (c) 3 signals <span class="nowrap"><svg height="10.9836pt" id="M199" style="vertical-align:-0.2325001pt" version="1.1" viewbox="-0.0498162 -10.7511 76.2591 10.9836" width="76.2591pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M660 23C638 31 621 43 599 66C580 87 558 119 523 175C493 221 456 287 441 317C508 344 566 392 566 480C566 542 539 586 499 613C458 640 408 650 320 650H39V615C112 608 121 598 121 518V134C121 51 110 42 35 35V0H337V35C261 42 252 51 252 134V280H271C303 280 321 267 337 237C366 184 393 130 419 89C460 26 498 -2 624 -8C637 -8 648 -9 654 -9L660 23ZM252 320V563C252 584 254 594 261 600C267 606 281 610 306 610C362 610 424 572 424 465C424 414 410 374 376 346C354 327 320 320 278 320H252Z"></path></g><g transform="matrix(.013,0,0,-0.013,8.58,0)"><path d="M860 0V35C782 43 777 50 775 142L766 516C764 599 768 607 848 615V650H646L450 212L258 650H51V615C137 607 144 593 136 480L120 282C112 177 105 124 99 93C92 57 74 40 17 35V0H258V35C191 41 172 50 168 95C164 126 163 173 168 256L180 517H184L388 7H416L637 527H642L644 142C644 50 639 43 564 35V0H860Z"></path></g><g transform="matrix(.013,0,0,-0.013,20.124,0)"><path d="M442 495C434 557 427 602 423 639C405 645 348 665 284 665C142 665 55 579 55 469C55 373 127 319 229 267C304 227 348 190 348 131C348 67 309 25 248 25C152 25 93 138 73 202L38 190C45 130 58 46 68 21C81 15 100 7 128 -1C158 -10 193 -15 229 -15C375 -15 472 74 472 188C472 287 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Applied Computational Intelligence and Soft Computing

fig7

Figure 7

Figure 7: Direction of Arrival Estimation for Coherent Signals’ Method Based on LSTM Neural Network 