Marginalized Point Mass Filter with Estimating Tidal Depth Bias for Underwater Terrain-Aided Navigation

<div>Horizontal position and tidal depth bias estimate RMS errors with different measurement noise covariances (<svg height="12.0314pt" id="M162" style="vertical-align:-3.2911pt" version="1.1" viewbox="-0.0498162 -8.7403 35.0329 12.0314" width="35.0329pt" 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(.0091,0,0,-0.0091,8.567,3.132)"><path d="M485 418C485 434 469 451 441 451C389 451 312 387 255 335C221 304 194 278 159 242H157L259 678C264 698 264 710 255 710C242 710 183 681 96 673L90 643L128 642C165 641 171 640 162 602L24 -5L31 -12C51 -4 81 4 110 9L145 181C154 195 180 220 205 242C233 170 263 106 291 53C318 1 335 -12 360 -12C383 -12 425 0 483 82L463 105C437 79 411 57 400 57C388 57 378 68 355 110C329 156 288 244 268 298C299 333 355 376 405 376C426 376 440 373 448 368C453 365 465 368 468 373C477 386 485 405 485 418Z"></path></g><g transform="matrix(.013,0,0,-0.013,17.346,0)"><path d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z"></path></g><g transform="matrix(.013,0,0,-0.013,28.609,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"></path></g></svg> m<sup>2</sup>, 3 m<sup>2</sup>, and 5 m<sup>2</sup>) from the marginalized PMF for 50 Monte Carlo runs with <svg height="12.5794pt" id="M163" style="vertical-align:-3.29107pt" version="1.1" viewbox="-0.0498162 -9.28833 39.352 12.5794" width="39.352pt" 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="M494 514C482 587 419 712 303 712C238 712 174 667 174 603C174 561 205 514 249 449C219 438 187 422 162 407C93 366 23 283 23 177C23 69 87 -12 190 -12C244 -12 288 5 328 33C406 87 444 170 444 249C444 329 404 391 331 475C265 550 222 605 222 627C222 647 238 657 267 657C355 657 421 585 484 499L494 514ZM359 234C359 143 319 30 219 30C172 30 114 75 114 178C114 275 163 343 195 378C212 397 241 415 269 425C305 382 359 313 359 234Z"></path></g><g transform="matrix(.013,0,0,-0.013,6.722,0)"><path d="M475 445L452 468C423 436 401 426 385 426S352 427 305 436C268 443 216 448 185 448C156 448 128 425 102 382C89 360 77 338 64 304L89 291C117 340 143 380 190 380C214 380 268 376 295 373C320 370 344 371 362 374L70 101C32 65 23 36 23 18C23 5 27 -3 31 -8C36 -8 39 -6 42 -1C52 15 67 31 80 40S110 49 143 33C195 8 257 -20 290 -20C339 -20 382 11 439 129L416 148C366 74 334 55 305 55C278 55 236 68 203 81C169 94 141 100 122 96C205 170 348 308 411 376L475 445Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,12.871,3.132)"><path d="M485 418C485 434 469 451 441 451C389 451 312 387 255 335C221 304 194 278 159 242H157L259 678C264 698 264 710 255 710C242 710 183 681 96 673L90 643L128 642C165 641 171 640 162 602L24 -5L31 -12C51 -4 81 4 110 9L145 181C154 195 180 220 205 242C233 170 263 106 291 53C318 1 335 -12 360 -12C383 -12 425 0 483 82L463 105C437 79 411 57 400 57C388 57 378 68 355 110C329 156 288 244 268 298C299 333 355 376 405 376C426 376 440 373 448 368C453 365 465 368 468 373C477 386 485 405 485 418Z"></path></g><g transform="matrix(.013,0,0,-0.013,21.65,0)"><path d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z"></path></g><g transform="matrix(.013,0,0,-0.013,32.913,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"></path></g></svg> m in area A: (a) horizontal position estimate RMS error and (b) tidal depth bias estimate RMS error.</div>

Journal of Sensors

fig12

Figure 12

Figure 12: Marginalized Point Mass Filter with Estimating Tidal Depth Bias for Underwater Terrain-Aided Navigation 